Cannon Air Force Base (Cannon AFB) is located in the High Plains physiographic region of east-central New Mexico, about 5 miles west of Clovis, New Mexico. The area surrounding Cannon AFB is primarily used for agriculture, including irrigated cropland and dairies. The Southern High Plains aquifer is the principal source of water for Cannon AFB, for the nearby town of Clovis, and for local agriculture and dairies. The Southern High Plains aquifer in the vicinity of Cannon AFB consists of three subsurface geological formations: the Chinle Formation of Triassic age, the Ogallala Formation of Tertiary age, and the Blackwater Draw Formation of Quaternary age. The Ogallala Formation is the main water-yielding formation of the Southern High Plains aquifer. Groundwater-supplied, center-pivot irrigation dominates pumping from the Southern High Plains aquifer in the area surrounding Cannon AFB, where the irrigation season typically extends from early March through October. The U.S. Geological Survey has been monitoring groundwater levels in the vicinity of Cannon AFB since 1954 and has developed general potentiometric-surface maps that show groundwater flow from northwest to southeast in the study area. While previous potentiometric-surface maps show the general direction of groundwater flow, a denser well network is needed to show details of groundwater flow at a local scale. Groundwater levels were measured in 93 wells during summer 2013 and 100 wells during winter 2015.
The summer and winter potentiometric-surface maps display the presence of what is interpreted to be a groundwater trough trending from the northwest to the southeast through the study area. This groundwater trough may be the hydraulic expression of a Tertiary-age paleochannel. Groundwater north of the trough flows in a southerly direction into the trough, and groundwater south of the trough flows in an easterly direction into the trough.
During the 18-month period between summer 2013 and winter 2015, changes in groundwater levels ranged from a rise of 10.0 to a decline of 3.8 feet. The regions to the north and south of the groundwater trough contained the majority of the rises in groundwater levels, whereas the regions within the trough contained the majority of the declines in groundwater levels. In contrast, the long-term groundwater-level trend in wells with 20 to 60 years of record is a steady decline in average annual water levels, with declines ranging from 0.41 to 2.81 feet per year. Overall, the northwestern part of the study area exhibits the smallest average annual declines, while the southeastern part of the study area exhibits the largest average annual declines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3352","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center, San Antonio, Texas","usgsCitation":"Collison, Jake, 2016, Potentiometric surfaces, summer 2013 and winter 2015, and select hydrographs for the Southern High Plains aquifer, Cannon Air Force Base, Curry County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3352, https://dx.doi.org/10.3133/sim3352.","productDescription":"2 Sheets: 22 x 30 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069490","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":319842,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3352/sim3352.pdf","text":"Map","size":"3.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3352"},{"id":319796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3352/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Curry County","otherGeospatial":"Southern High Plains Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.1997299194336,\n 34.30799463462751\n ],\n [\n -103.20213317871094,\n 34.32472485669068\n ],\n [\n -103.20144653320312,\n 34.349105308603086\n ],\n [\n -103.20144653320312,\n 34.36922801491184\n ],\n [\n -103.20144653320312,\n 34.387079327815805\n ],\n [\n -103.20247650146484,\n 34.415406930259294\n ],\n [\n -103.20281982421875,\n 34.43239888779366\n ],\n [\n -103.20625305175781,\n 34.453350878522286\n ],\n [\n -103.2117462158203,\n 34.45844651413059\n ],\n [\n -103.22410583496094,\n 34.46552326993406\n ],\n [\n -103.23715209960938,\n 34.468353804284924\n ],\n [\n -103.25809478759764,\n 34.469769035464836\n ],\n [\n -103.27869415283203,\n 34.47203335543746\n ],\n [\n -103.29689025878905,\n 34.472599425831355\n ],\n [\n -103.32572937011719,\n 34.47458064197167\n ],\n [\n -103.34152221679688,\n 34.47797690306098\n ],\n [\n -103.36658477783203,\n 34.47882594673332\n ],\n [\n -103.38512420654297,\n 34.477693886583516\n ],\n [\n -103.4036636352539,\n 34.474014585017066\n ],\n [\n -103.40572357177734,\n 34.46920294587255\n ],\n [\n -103.41053009033203,\n 34.45788034775209\n ],\n [\n -103.41053009033203,\n 34.44259240448441\n ],\n [\n -103.41190338134766,\n 34.43466422118617\n ],\n [\n -103.41293334960938,\n 34.41795594404557\n ],\n [\n -103.41361999511719,\n 34.41059191445944\n ],\n [\n -103.41293334960938,\n 34.40237742424137\n ],\n [\n -103.41121673583984,\n 34.39387882699731\n ],\n [\n -103.40744018554688,\n 34.38311269824024\n ],\n [\n -103.4084701538086,\n 34.37206181069581\n ],\n [\n -103.40572357177734,\n 34.361292876752564\n ],\n [\n -103.39508056640625,\n 34.35193978491414\n ],\n [\n -103.3868408203125,\n 34.34627073645813\n ],\n [\n -103.38306427001952,\n 34.336348979311985\n ],\n [\n -103.38272094726561,\n 34.326426048404265\n ],\n [\n -103.37448120117188,\n 34.3207552752374\n ],\n [\n -103.3480453491211,\n 34.31678550602219\n ],\n [\n -103.31817626953125,\n 34.313382697259456\n ],\n [\n -103.2879638671875,\n 34.311397661782244\n ],\n [\n -103.26221466064453,\n 34.30969616544571\n ],\n [\n -103.23646545410156,\n 34.30997975056304\n ],\n [\n -103.21792602539062,\n 34.30714385628804\n ],\n [\n -103.1997299194336,\n 34.30799463462751\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montgomery Blvd. NE, Suite 400
Albuquerque, NM 87109–1311
Landsat missions have always been an important component of U.S. foreign policy, as well as science and technology policy. The Landsat program’s longstanding network of International Cooperators (ICs), which operates numerous International Ground Stations (IGS) around the world, embodies the United States’ policy of peaceful use of outer space and the worldwide dissemination of civil space technology for public benefit. Thus, the ICs provide an essential dimension to the Landsat mission.
In 2010, the Landsat Global Archive Consolidation (LGAC) initiative began, with goals to consolidate the Landsat data archives of all IGSs, make the data more accessible to the global Landsat user community, and significantly increase the frequency of observations over a given area of interest to contribute to the understanding of a changing Earth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163018","usgsCitation":"U.S. Geological Survey, 2016, Landsat International Cooperators and Global Archive Consolidation (ver. 1.3, April 2023): U.S. Geological Survey Fact Sheet 2016–3018, 2 p., https://doi.org/10.3133/fs20163018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072463","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":416367,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3018/versionHist.txt","text":"Version History","size":"3.57 kB","linkFileType":{"id":2,"text":"txt"}},{"id":416366,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3018/fs20163018.pdf","text":"Report","size":"572 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2016–3018"},{"id":319664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3018/coverthb4.jpg"}],"edition":"Version 1.0: April 7, 2016; Version 1.1: December 8, 2016; Version 1.2: June 10, 2019; Version 1.3: April 26, 2023","contact":"Landsat User Services
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
custserv@usgs.gov
https://www.usgs.gov/land-resources/national-land-imaging-program
","tableOfContents":"This study combined a radar-based time series of Hurricane Sandy surge and estimated persistence with optical sensor-based marsh condition change to assess potential causal linkages of surge persistence and marsh condition change along the New Jersey Atlantic Ocean coast. Results based on processed TerraSAR-X and COSMO-SkyMed synthetic aperture radar (SAR) images indicated that surge flooding persisted for 12 h past landfall in marshes from Great Bay to Great Egg Harbor Bay and up to 59 h after landfall in many back-barrier lagoon marshes. Marsh condition change (i.e. loss of green marsh vegetation) was assessed from optical satellite images (Satellite Pour l’Observation de la Terre and Moderate Resolution Imaging Spectroradiometer) collected before and after Hurricane Sandy. High change in condition often showed spatial correspondence, with high surge persistence in marsh surrounding the lagoon portion of Great Bay, while in contrast, low change and high persistence spatial correspondence dominated the interior marshes of the Great Bay and Great Egg Harbor Bay estuaries. Salinity measurements suggest that these areas were influenced by freshwater discharges after landfall possibly mitigating damage. Back-barrier marshes outside these regions exhibited mixed correspondences. In some cases, topographic features supporting longer surge persistence suggested that non-correspondence between radar and optical data-based results may be due to differential resilience; however, in many cases, reference information was lacking to determine a reason for non-correspondence.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/01431161.2016.1163748","usgsCitation":"Rangoonwala, A., Enwright, N.M., Ramsey, E.W., and Spruce, J.P., 2016, Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy: International Journal of Remote Sensing, v. 37, no. 7, p. 1692-1713, https://doi.org/10.1080/01431161.2016.1163748.","productDescription":"22 p.","startPage":"1692","endPage":"1713","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064429","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471086,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2016.1163748","text":"Publisher Index Page"},{"id":438621,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEFCOR","text":"USGS data release","linkHelpText":"Radar and optical mapping of surge persistence and marsh dieback along the New Jersey Mid-Atlantic coast after Hurricane Sandy"},{"id":319878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75,\n 39\n ],\n [\n -75,\n 40.5\n ],\n [\n -74,\n 40.5\n ],\n [\n -74,\n 39\n ],\n [\n -75,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"7","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-04","publicationStatus":"PW","scienceBaseUri":"572477b0e4b0b13d3914e11a","contributors":{"authors":[{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":626153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enwright, Nicholas M. 0000-0002-7887-3261 enwrightn@usgs.gov","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":4880,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","email":"enwrightn@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":626154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":2883,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":626155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spruce, Joseph P.","contributorId":168501,"corporation":false,"usgs":false,"family":"Spruce","given":"Joseph","email":"","middleInitial":"P.","affiliations":[{"id":25312,"text":"Computer Sciences Corporation, John C. Stennis Space Centre, MS","active":true,"usgs":false}],"preferred":false,"id":626156,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170089,"text":"70170089 - 2016 - High rates of detection of Clade 2.3.4.4 Highly Pathogenic Avian Influenza H5 viruses in wild birds in the Pacific Northwest during the winter of 2014-2015","interactions":[],"lastModifiedDate":"2018-01-03T13:03:50","indexId":"70170089","displayToPublicDate":"2016-04-06T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"High rates of detection of Clade 2.3.4.4 Highly Pathogenic Avian Influenza H5 viruses in wild birds in the Pacific Northwest during the winter of 2014-2015","docAbstract":"In 2014, Clade 2.3.4.4 H5N8 highly pathogenic avian influenza (HPAI) viruses spread across the Republic of Korea and ultimately were reported in China, Japan, Russia and Europe. Mortality associated with a reassortant HPAI H5N2 virus was detected in poultry farms in Western Canada at the end of November. The same strain (with identical genetic structure) was then detected in free-living wild birds that had died prior to December 8 of unrelated causes in Whatcom County, Washington, USA in an area contiguous with the index Canadian location. A gyrfalcon (Falco rusticolus) that had hunted and fed on an American wigeon (Anas americana) on December 6 in the same area and died two days later, tested positive for the Eurasian origin HPAI H5N8. Subsequently, an Active Surveillance Program using hunter-harvest waterfowl in Washington and Oregon detected ten HPAI H5 viruses, of three different subtypes (four H5N2, three H5N8 and three H5N1) with 4 segments in common (HA, PB2, NP and MA). In addition, a mortality-based Passive Surveillance Program detected 18 HPAI (14 H5N2 and four H5N8) cases from Idaho, Kansas, Oregon, Minnesota, Montana, Washington and Wisconsin. Comparatively, mortality-based passive surveillance appears to be detecting these HPAI infections at a higher rate than active surveillance during the period following initial introduction into the US.
","language":"English","publisher":"American Association of Avian Pathologists","publisherLocation":"Ithaca, NY","doi":"10.1637/11137-050815-Reg","usgsCitation":"Ip, S., Dusek, R.J., Bodenstein, B., Kim Torchetti, M., DeBruyn, P., Mansfield, K.G., DeLiberto, T., and Sleeman, J.M., 2016, High rates of detection of Clade 2.3.4.4 Highly Pathogenic Avian Influenza H5 viruses in wild birds in the Pacific Northwest during the winter of 2014-2015: Avian Diseases, v. 60, no. 1s, p. 354-358, https://doi.org/10.1637/11137-050815-Reg.","productDescription":"5 p.","startPage":"354","endPage":"358","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065496","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":319830,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"1s","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5719f9b9e4b071321fe22bd1","contributors":{"authors":[{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":626102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":152316,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":626113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":139354,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara L.","email":"bbodenstein@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":626114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim Torchetti, Mia","contributorId":139355,"corporation":false,"usgs":false,"family":"Kim Torchetti","given":"Mia","email":"","affiliations":[{"id":12747,"text":"USDA APHIS VS National Veterinary Services Laboratories, Ames, IA","active":true,"usgs":false}],"preferred":false,"id":626103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeBruyn, Paul","contributorId":139364,"corporation":false,"usgs":false,"family":"DeBruyn","given":"Paul","email":"","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":626111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mansfield, Kristin G.","contributorId":139359,"corporation":false,"usgs":false,"family":"Mansfield","given":"Kristin","email":"","middleInitial":"G.","affiliations":[{"id":12748,"text":"State of Washington","active":true,"usgs":false}],"preferred":false,"id":626112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeLiberto, Thomas J.","contributorId":139362,"corporation":false,"usgs":false,"family":"DeLiberto","given":"Thomas J.","affiliations":[{"id":12749,"text":"USDA APHIS National Wildlife Research Center, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":626117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":626118,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70170072,"text":"70170072 - 2016 - Differentiating sex and species of Western Grebes (Aechmophorus occidentalis) and Clark's Grebes (Aechmophorus clarkii) and their eggs using external morphometrics and discriminant function analysis","interactions":[],"lastModifiedDate":"2018-08-07T11:53:57","indexId":"70170072","displayToPublicDate":"2016-04-06T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Differentiating sex and species of Western Grebes (Aechmophorus occidentalis) and Clark's Grebes (Aechmophorus clarkii) and their eggs using external morphometrics and discriminant function analysis","docAbstract":"In birds where males and females are similar in size and plumage, sex determination by alternative means is necessary. Discriminant function analysis based on external morphometrics was used to distinguish males from females in two closely related species: Western Grebe (Aechmophorus occidentalis) and Clark's Grebe (A. clarkii). Additionally, discriminant function analysis was used to evaluate morphometric divergence between Western and Clark's grebe adults and eggs. Aechmophorus grebe adults (n = 576) and eggs (n = 130) were sampled across 29 lakes and reservoirs throughout California, USA, and adult sex was determined using molecular analysis. Both Western and Clark's grebes exhibited considerable sexual size dimorphism. Males averaged 6–26% larger than females among seven morphological measurements, with the greatest sexual size dimorphism occurring for bill morphometrics. Discriminant functions based on bill length, bill depth, and short tarsus length correctly assigned sex to 98% of Western Grebes, and a function based on bill length and bill depth correctly assigned sex to 99% of Clark's Grebes. Further, a simplified discriminant function based only on bill depth correctly assigned sex to 96% of Western Grebes and 98% of Clark's Grebes. In contrast, external morphometrics were not suitable for differentiating between Western and Clark's grebe adults or their eggs, with correct classification rates of discriminant functions of only 60%, 63%, and 61% for adult males, adult females, and eggs, respectively. Our results indicate little divergence in external morphology between species of Aechmophorus grebes, and instead separation is much greater between males and females.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.039.0103","usgsCitation":"Hartman, C.A., Ackerman, J., Eagles-Smith, C.A., and Herzog, M.P., 2016, Differentiating sex and species of Western Grebes (Aechmophorus occidentalis) and Clark's Grebes (Aechmophorus clarkii) and their eggs using external morphometrics and discriminant function analysis: Waterbirds, v. 39, no. 1, p. 13-26, https://doi.org/10.1675/063.039.0103.","productDescription":"14 p.","startPage":"13","endPage":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068816","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":319822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477a4e4b0b13d3914e046","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131109,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":626025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":626024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":626026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":626027,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170073,"text":"70170073 - 2016 - Hawksbill satellite-tracking case study: Implications for remigration interval and population estimates","interactions":[],"lastModifiedDate":"2016-07-17T23:37:45","indexId":"70170073","displayToPublicDate":"2016-04-06T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2679,"text":"Marine Turtle Newsletter","active":true,"publicationSubtype":{"id":10}},"title":"Hawksbill satellite-tracking case study: Implications for remigration interval and population estimates","docAbstract":"Hawksbill sea turtles (Eretmochelys imbricata) are circumtropically distributed and listed as Critically Endangered by the IUCN (Meylan & Donnelly 1999; NMFS & USFWS 1993). To aid in population recovery and protection, the Hawksbill Recovery Plan identified the need to determine demographic information for hawksbills, such as distribution, abundance, seasonal movements, foraging areas (sections 121 and 2211), growth rates, and survivorship (section 2213, NMFS & USFWS 1993). Mark-recapture analyses are helpful in estimating demographic parameters and have been used for hawksbills throughout the Caribbean (e.g., Richardson et al. 1999; Velez-Zuazo et al. 2008); integral to these studies are recaptures at the nesting site as well as remigration interval estimates (Hays 2000). Estimates of remigration intervals (the duration between nesting seasons) are critical to marine turtle population estimates and measures of nesting success (Hays 2000; Richardson et al. 1999). Although hawksbills in the Caribbean generally show natal philopatry and nesting-site fidelity (Bass et al. 1996; Bowen et al. 2007), exceptions to this have been observed for hawksbills and other marine turtles (Bowen & Karl 2007; Diamond 1976; Esteban et al. 2015; Hart et al. 2013). This flexibility in choosing a nesting beach could therefore affect the apparent remigration interval and subsequently, region-wide population counts.
","language":"English","publisher":"Marine Turtle Newsletter : seaturtle.org","usgsCitation":"Sartain-Iverson, A.R., Hart, K.M., Fujisaki, I., Cherkiss, M.S., Pollock, C., Lundgren, I., and Hillis-Starr, Z., 2016, Hawksbill satellite-tracking case study: Implications for remigration interval and population estimates: Marine Turtle Newsletter, v. 148, p. 2-7.","productDescription":"6 p.","startPage":"2","endPage":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068663","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":319823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":319784,"type":{"id":15,"text":"Index Page"},"url":"https://www.seaturtle.org/mtn/archives/mtn148/mtn148-2.shtml"}],"volume":"148","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477a8e4b0b13d3914e091","contributors":{"authors":[{"text":"Sartain-Iverson, Autumn R. 0000-0002-8353-6745 asartain@usgs.gov","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":5477,"corporation":false,"usgs":true,"family":"Sartain-Iverson","given":"Autumn","email":"asartain@usgs.gov","middleInitial":"R.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":626121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":626028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":626122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cherkiss, Michael S. 0000-0002-7802-6791 mcherkiss@usgs.gov","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":4571,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","email":"mcherkiss@usgs.gov","middleInitial":"S.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":626123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pollock, Clayton","contributorId":168497,"corporation":false,"usgs":false,"family":"Pollock","given":"Clayton","affiliations":[],"preferred":false,"id":626124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lundgren, Ian","contributorId":29727,"corporation":false,"usgs":true,"family":"Lundgren","given":"Ian","affiliations":[],"preferred":false,"id":626125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hillis-Starr, Zandy","contributorId":56142,"corporation":false,"usgs":true,"family":"Hillis-Starr","given":"Zandy","affiliations":[],"preferred":false,"id":626126,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70170071,"text":"70170071 - 2016 - The high cost of motherhood: End-lactation syndrome in southern sea otters (Enhydra lutris nereis) on the central California, USA, coast","interactions":[],"lastModifiedDate":"2016-05-17T16:18:45","indexId":"70170071","displayToPublicDate":"2016-04-06T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"The high cost of motherhood: End-lactation syndrome in southern sea otters (Enhydra lutris nereis) on the central California, USA, coast","docAbstract":"Sea otters (Enhydra lutris) have exceptionally high energetic requirements, which nearly double during lactation and pup care. Thus, females are extremely vulnerable to caloric insufficiency. Despite a number of compensatory strategies, the metabolic challenge of reproduction culminates in numerous maternal deaths annually. Massive depletion of energy reserves results in a case presentation that we define as end-lactation syndrome (ELS), characterized by moderate to severe emaciation not attributable to a concurrent, independent disease process in females dying during late pup care or postweaning. We compiled detailed data for 108 adult female southern sea otters (Enhydra lutris nereis) examined postmortem that stranded in California, US, 2005–12, and assessed pathology, reproductive status, and the location and timing of stranding. We introduce simple, grossly apparent, standardized physical criteria to assess reproductive stage for female sea otters. We also describe ELS, examine associated risk factors, and highlight female life history strategies that likely optimize reproduction and survival. Our data suggest that females can reset both the timing and energetic demands of reproduction through fetal loss, pup abandonment, or early weaning as part of specific physiologic checkpoints during each reproductive cycle. Females appear to preload nutritionally during delayed implantation and gestation to increase fitness and reproductive success. We found that ELS was a major cause of death, affecting 56% of enrolled adult females. Peak ELS prevalence occurred in late spring, possibly reflecting the population trend toward fall/winter pupping. Increasing age and number of pregnancies were associated with a higher risk of ELS. Although the proportion of ELS females was highest in areas with dense sea otter populations, cases were recovered throughout the range, suggesting that death from ELS is associated with, but not solely caused by, population resource limitation.
","language":"English","publisher":"Wildlife Diseases Association","doi":"10.7589/2015-06-158","usgsCitation":"Chinn, S.S., Miller, M.A., Tinker, M.T., Staedler, M., Batac, F.I., Dodd, E.M., and Henkel, L.A., 2016, The high cost of motherhood: End-lactation syndrome in southern sea otters (Enhydra lutris nereis) on the central California, USA, coast: Journal of Wildlife Diseases, v. 52, no. 2, p. 307-318, https://doi.org/10.7589/2015-06-158.","productDescription":"12 p.","startPage":"307","endPage":"318","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070046","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":319824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"52","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477b3e4b0b13d3914e15d","contributors":{"authors":[{"text":"Chinn, Sarah S","contributorId":168466,"corporation":false,"usgs":false,"family":"Chinn","given":"Sarah","email":"","middleInitial":"S","affiliations":[{"id":25299,"text":"CA DFW","active":true,"usgs":false}],"preferred":false,"id":626018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Melissa A.","contributorId":57701,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":626019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":626017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staedler, Michelle M.","contributorId":40087,"corporation":false,"usgs":true,"family":"Staedler","given":"Michelle M.","affiliations":[],"preferred":false,"id":626020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Batac, Francesca I.","contributorId":168467,"corporation":false,"usgs":false,"family":"Batac","given":"Francesca","email":"","middleInitial":"I.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":626021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dodd, Erin M.","contributorId":168468,"corporation":false,"usgs":false,"family":"Dodd","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":626022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Henkel, Laird A.","contributorId":84288,"corporation":false,"usgs":true,"family":"Henkel","given":"Laird","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":626023,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70170085,"text":"70170085 - 2016 - Semi-arid vegetation response to antecedent climate and water balance windows","interactions":[],"lastModifiedDate":"2016-06-15T16:24:21","indexId":"70170085","displayToPublicDate":"2016-04-06T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Semi-arid vegetation response to antecedent climate and water balance windows","docAbstract":"Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation?
\nSemi-arid grasslands in Capitol Reef National Park, Utah, USA.
\nWe built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years.
\nWater balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology.
\nWe determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.
\nIn August 2008, Kasatochi volcano erupted and buried a small island in pyroclastic deposits and fine ash; since then, microbes, plants and birds have begun to re-colonize the initially sterile surface. Five years post-eruption, bacterial 16S rRNA gene and fungal internal transcribed spacer (ITS) copy numbers and extracellular enzyme activity (EEA) potentials were one to two orders of magnitude greater in pyroclastic materials with organic matter (OM) inputs relative to those without, despite minimal accumulation of OM (< 0.2%C). When normalized by OM levels, post-eruptive surfaces with OM inputs had the highest β-glucosidase, phosphatase, NAGase and cellobiohydrolase activities, and had microbial population sizes approaching those in reference soils. In contrast, the strongest factor determining bacterial community composition was the dominance of plants versus birds as OM input vectors. Although soil pH ranged from 3.9 to 7.0, and %C ranged 100×, differentiation between plant- and bird-associated microbial communities suggested that cell dispersal or nutrient availability are more likely drivers of assembly than pH or OM content. This study exemplifies the complex relationship between microbial cell dispersal, soil geochemistry, and microbial structure and function; and illustrates the potential for soil microbiota to be resilient to disturbance.
","language":"English","publisher":"Wiley","doi":"10.1111/1462-2920.12924","usgsCitation":"Zeglin, L.H., Wang, B., Waythomas, C.F., Rainey, F., and Talbot, S.L., 2016, Organic matter quantity and source affects microbial community structure and function following volcanic eruption on Kasatochi Island, Alaska: Environmental Microbiology, v. 18, no. 1, p. 146-158, https://doi.org/10.1111/1462-2920.12924.","productDescription":"13 p.","startPage":"146","endPage":"158","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061616","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":319820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kasatochi Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.52959442138672,\n 52.1600333315075\n ],\n [\n -175.52959442138672,\n 52.18587870630056\n ],\n [\n -175.4922580718994,\n 52.18587870630056\n ],\n [\n -175.4922580718994,\n 52.1600333315075\n ],\n [\n -175.52959442138672,\n 52.1600333315075\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-22","publicationStatus":"PW","scienceBaseUri":"572477aee4b0b13d3914e0cd","chorus":{"doi":"10.1111/1462-2920.12924","url":"http://dx.doi.org/10.1111/1462-2920.12924","publisher":"Wiley-Blackwell","authors":"Zeglin Lydia H., Wang Bronwen, Waythomas Christopher, Rainey Frederick, Talbot Sandra L.","journalName":"Environmental Microbiology","publicationDate":"7/22/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Zeglin, Lydia H. lzeglin@usgs.gov","contributorId":5355,"corporation":false,"usgs":true,"family":"Zeglin","given":"Lydia","email":"lzeglin@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":626076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Bronwen 0000-0003-1044-2227 bwang@usgs.gov","orcid":"https://orcid.org/0000-0003-1044-2227","contributorId":2351,"corporation":false,"usgs":true,"family":"Wang","given":"Bronwen","email":"bwang@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":626077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":626119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rainey, Frederick","contributorId":168495,"corporation":false,"usgs":false,"family":"Rainey","given":"Frederick","email":"","affiliations":[],"preferred":false,"id":626120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":626078,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170082,"text":"70170082 - 2016 - Recommended reporting standards for test accuracy studies of infectious diseases of finfish, amphibians, molluscs and crustaceans: the STRADAS-aquatic checklist","interactions":[],"lastModifiedDate":"2016-04-06T11:08:59","indexId":"70170082","displayToPublicDate":"2016-04-06T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"Recommended reporting standards for test accuracy studies of infectious diseases of finfish, amphibians, molluscs and crustaceans: the STRADAS-aquatic checklist","docAbstract":"Complete and transparent reporting of key elements of diagnostic accuracy studies for infectious diseases in cultured and wild aquatic animals benefits end-users of these tests, enabling the rational design of surveillance programs, the assessment of test results from clinical cases and comparisons of diagnostic test performance. Based on deficiencies in the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines identified in a prior finfish study (Gardner et al. 2014), we adapted the Standards for Reporting of Animal Diagnostic Accuracy Studies—paratuberculosis (STRADAS-paraTB) checklist of 25 reporting items to increase their relevance to finfish, amphibians, molluscs, and crustaceans and provided examples and explanations for each item. The checklist, known as STRADAS-aquatic, was developed and refined by an expert group of 14 transdisciplinary scientists with experience in test evaluation studies using field and experimental samples, in operation of reference laboratories for aquatic animal pathogens, and in development of international aquatic animal health policy. The main changes to the STRADAS-paraTB checklist were to nomenclature related to the species, the addition of guidelines for experimental challenge studies, and the designation of some items as relevant only to experimental studies and ante-mortem tests. We believe that adoption of these guidelines will improve reporting of primary studies of test accuracy for aquatic animal diseases and facilitate assessment of their fitness-for-purpose. Given the importance of diagnostic tests to underpin the Sanitary and Phytosanitary agreement of the World Trade Organization, the principles outlined in this paper should be applied to other World Organisation for Animal Health (OIE)-relevant species.
","language":"English","publisher":"Inter-Research","doi":"10.3354/dao02947","usgsCitation":"Gardner, I.A., Whittington, R.J., Caraguel, C.G., Hick, P., Moody, N.J., Corbeil, S., Garver, K.A., Warg, J.V., Arzul, I., Purcell, M.K., St. J. Crane, M., Waltzek, T., Olesen, N.J., and Lagno, A.G., 2016, Recommended reporting standards for test accuracy studies of infectious diseases of finfish, amphibians, molluscs and crustaceans: the STRADAS-aquatic checklist: Diseases of Aquatic Organisms, v. 118, no. 2, p. 91-111, https://doi.org/10.3354/dao02947.","productDescription":"21 p.","startPage":"91","endPage":"111","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068746","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":471087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao02947","text":"Publisher Index Page"},{"id":319821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477b0e4b0b13d3914e12d","contributors":{"authors":[{"text":"Gardner, Ian A","contributorId":168476,"corporation":false,"usgs":false,"family":"Gardner","given":"Ian","email":"","middleInitial":"A","affiliations":[{"id":25301,"text":"Atlantic Veterinary College, University of Prince Edward Island, 550 University Avenue, 9 Charlottetown, Prince Edward Island C1A 4P3, Canada","active":true,"usgs":false}],"preferred":false,"id":626060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whittington, Richard J","contributorId":168477,"corporation":false,"usgs":false,"family":"Whittington","given":"Richard","email":"","middleInitial":"J","affiliations":[{"id":25302,"text":"Faculty of Veterinary Science, University of Sydney, 425 Werombi Rd, Camden, New South Wales, 2570 Australia","active":true,"usgs":false}],"preferred":false,"id":626061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caraguel, Charles G B","contributorId":168478,"corporation":false,"usgs":false,"family":"Caraguel","given":"Charles","email":"","middleInitial":"G B","affiliations":[{"id":25303,"text":"School of Animal & Veterinary Sciences, Roseworthy Campus, University of Adelaide, Roseworthy, South Australia 5371, Australia","active":true,"usgs":false}],"preferred":false,"id":626062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hick, Paul","contributorId":168496,"corporation":false,"usgs":false,"family":"Hick","given":"Paul","email":"","affiliations":[],"preferred":false,"id":626063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moody, Nicholas J G","contributorId":168479,"corporation":false,"usgs":false,"family":"Moody","given":"Nicholas","email":"","middleInitial":"J G","affiliations":[{"id":25304,"text":"CSIRO Australian Animal Health Laboratory, Geelong Victoria, 3220, Australia","active":true,"usgs":false}],"preferred":false,"id":626064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Corbeil, Serge","contributorId":168480,"corporation":false,"usgs":false,"family":"Corbeil","given":"Serge","email":"","affiliations":[{"id":25304,"text":"CSIRO Australian Animal Health Laboratory, Geelong Victoria, 3220, Australia","active":true,"usgs":false}],"preferred":false,"id":626065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garver, Kyle A.","contributorId":149992,"corporation":false,"usgs":false,"family":"Garver","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":17880,"text":"Fisheries and Oceans, Canada, Pacific Biological Station, Nanaimo, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":626066,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Warg, Janet V.","contributorId":168481,"corporation":false,"usgs":false,"family":"Warg","given":"Janet","email":"","middleInitial":"V.","affiliations":[{"id":25305,"text":"Diagnostic Virology Laboratory, National Veterinary Services Laboratories, VS, APHIS, USDA, Ames, Iowa 50010, USA","active":true,"usgs":false}],"preferred":false,"id":626067,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Arzul, Isabelle","contributorId":168482,"corporation":false,"usgs":false,"family":"Arzul","given":"Isabelle","email":"","affiliations":[{"id":25306,"text":"IFREMER SG2M-LGPMM, Laboratory of Genetics and Pathology of Marine Molluscs, 17390 La Tremblade, France","active":true,"usgs":false}],"preferred":false,"id":626068,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626059,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"St. J. Crane, Mark","contributorId":168483,"corporation":false,"usgs":false,"family":"St. J. Crane","given":"Mark","email":"","affiliations":[{"id":25304,"text":"CSIRO Australian Animal Health Laboratory, Geelong Victoria, 3220, Australia","active":true,"usgs":false}],"preferred":false,"id":626069,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Waltzek, Thomas B.","contributorId":9574,"corporation":false,"usgs":true,"family":"Waltzek","given":"Thomas B.","affiliations":[],"preferred":false,"id":626070,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Olesen, Niels J","contributorId":168484,"corporation":false,"usgs":false,"family":"Olesen","given":"Niels","email":"","middleInitial":"J","affiliations":[{"id":25307,"text":"National Veterinary Institute, Technical University of Denmark, Frederiksberg C, Denmark","active":true,"usgs":false}],"preferred":false,"id":626071,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lagno, Alicia Gallardo","contributorId":168485,"corporation":false,"usgs":false,"family":"Lagno","given":"Alicia","email":"","middleInitial":"Gallardo","affiliations":[{"id":25308,"text":"Jefa Unidad de Salud Animal Servicio Nacional de Pesca y Acuicultura Calle Victoria 2832, Chile","active":true,"usgs":false}],"preferred":false,"id":626072,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70170086,"text":"70170086 - 2016 - Effect of variable annual precipitation and nutrient input on nitrogen and phosphorus transport from two Midwestern agricultural watersheds","interactions":[],"lastModifiedDate":"2016-08-17T11:01:21","indexId":"70170086","displayToPublicDate":"2016-04-06T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Effect of variable annual precipitation and nutrient input on nitrogen and phosphorus transport from two Midwestern agricultural watersheds","docAbstract":"Precipitation patterns and nutrient inputs affect transport of nitrate (NO3-N) and phosphorus (TP) from Midwest watersheds. Nutrient concentrations and yields from two subsurface-drained watersheds, the Little Cobb River (LCR) in southern Minnesota and the South Fork Iowa River (SFIR) in northern Iowa, were evaluated during 1996–2007 to document relative differences in timings and amounts of nutrients transported. Both watersheds are located in the prairie pothole region, but the SFIR exhibits a longer growing season and more livestock production. The SFIR yielded significantly more NO3-N than the LCR watershed (31.2 versus 21.3 kg NO3-N ha− 1 y− 1). The SFIR watershed also yielded more TP than the LCR watershed (1.13 versus 0.51 kg TP ha− 1 yr− 1), despite greater TP concentrations in the LCR. About 65% of NO3-N and 50% of TP loads were transported during April–June, and < 20% of the annual loads were transported later in the growing season from July–September. Monthly NO3-N and TP loads peaked in April from the LCR but peaked in June from the SFIR; this difference was attributed to greater snowmelt runoff in the LCR. The annual NO3-N yield increased with increasing annual runoff at a similar rate in both watersheds, but the LCR watershed yielded less annual NO3-N than the SFIR for a similar annual runoff. These two watersheds are within 150 km of one another and have similar dominant agricultural systems, but differences in climate and cropping inputs affected amounts and timing of nutrient transport.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.03.127","usgsCitation":"Kalkhoff, S.J., Hubbard, L.E., Tomer, M.D., and James, D., 2016, Effect of variable annual precipitation and nutrient input on nitrogen and phosphorus transport from two Midwestern agricultural watersheds: Science of the Total Environment, v. 559, p. 53-62, https://doi.org/10.1016/j.scitotenv.2016.03.127.","productDescription":"10 p.","startPage":"53","endPage":"62","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066764","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":471088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.127","text":"Publisher Index Page"},{"id":319818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"559","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477a5e4b0b13d3914e070","chorus":{"doi":"10.1016/j.scitotenv.2016.03.127","url":"http://dx.doi.org/10.1016/j.scitotenv.2016.03.127","publisher":"Elsevier BV","authors":"Kalkhoff S.J., Hubbard L.E., Tomer M.D., James D.E.","journalName":"Science of The Total Environment","publicationDate":"7/2016","publiclyAccessibleDate":"4/5/2017"},"contributors":{"authors":[{"text":"Kalkhoff, Stephen J. 0000-0003-4110-1716 sjkalkho@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-1716","contributorId":1731,"corporation":false,"usgs":true,"family":"Kalkhoff","given":"Stephen","email":"sjkalkho@usgs.gov","middleInitial":"J.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Laura E. 0000-0003-3813-1500 lhubbard@usgs.gov","orcid":"https://orcid.org/0000-0003-3813-1500","contributorId":4221,"corporation":false,"usgs":true,"family":"Hubbard","given":"Laura","email":"lhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tomer, Mark D.","contributorId":168489,"corporation":false,"usgs":false,"family":"Tomer","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":25309,"text":"U.S. Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":626086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"James, D.E.","contributorId":22927,"corporation":false,"usgs":true,"family":"James","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":626087,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170088,"text":"70170088 - 2016 - Angler harvest, hatchery return, and tributary stray rates of recycled adult summer steelhead Oncorhynchus mykiss in the Cowlitz River, Washington","interactions":[],"lastModifiedDate":"2016-10-11T16:12:42","indexId":"70170088","displayToPublicDate":"2016-04-06T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Angler harvest, hatchery return, and tributary stray rates of recycled adult summer steelhead Oncorhynchus mykiss in the Cowlitz River, Washington","docAbstract":"Hatchery ‘recycling’ programs have been used to increase angling opportunities by re-releasing fish into a river after they returned to a hatchery or fish trap. Recycling is intended to increase opportunities for fishermen, but this strategy could affect wild fish populations if some recycled fish remain in the river and interact with wild fish populations. To quantify hatchery return and angler harvest rates of recycled steelhead, we conducted a 2-year study on the Cowlitz River, Washington. A total of 1051 steelhead were recycled, including 218 fish that were radio-tagged. Fates of recycled steelhead were similar between years: 48.4% returned to the hatchery, 19.2% were reported captured by anglers, and 32.4% remained in the river. A multistate model quantified the effects of covariates on hatchery return and angler harvest rates, which were positively affected by river discharge and negatively affected by time since release. However, hatchery return rates increased and angler harvest rates decreased during periods of increasing discharge. A total of 21.1% (46 fish) of the radio-tagged steelhead failed to return to the hatchery or be reported by anglers, but nearly half of those fish (20 fish) appeared to be harvested and not reported. The remaining tagged fish (11.9% of the radio-tagged population) were monitored into the spawning period, but only five fish (2.3% of the radio-tagged population) entered tributaries where wild steelhead spawning occurs. Future research focused on straying behaviour, and spawning success of recycled steelhead may further advance the understanding of the effects of recycling as a management strategy.
","language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1002/rra.3023","usgsCitation":"Kock, T.J., Perry, R.W., Gleizes, C., Dammers, W., and Liedtke, T.L., 2016, Angler harvest, hatchery return, and tributary stray rates of recycled adult summer steelhead Oncorhynchus mykiss in the Cowlitz River, Washington: River Research and Applications, v. 32, no. 8, p. 1790-1799, https://doi.org/10.1002/rra.3023.","productDescription":"10 p.","startPage":"1790","endPage":"1799","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070184","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":319817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.13751220703125,\n 46.07132518308108\n ],\n [\n -123.13751220703125,\n 46.645665192584936\n ],\n [\n -122.54150390625,\n 46.645665192584936\n ],\n [\n -122.54150390625,\n 46.07132518308108\n ],\n [\n -123.13751220703125,\n 46.07132518308108\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-23","publicationStatus":"PW","scienceBaseUri":"572477a0e4b0b13d3914dfec","contributors":{"authors":[{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gleizes, Chris","contributorId":37233,"corporation":false,"usgs":true,"family":"Gleizes","given":"Chris","email":"","affiliations":[],"preferred":false,"id":626099,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dammers, Wolf","contributorId":79385,"corporation":false,"usgs":true,"family":"Dammers","given":"Wolf","email":"","affiliations":[],"preferred":false,"id":626100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626101,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170132,"text":"70170132 - 2016 - Salmon and steelhead in the White Salmon River after the removal of Condit Dam–Planning efforts and recolonization results","interactions":[],"lastModifiedDate":"2018-02-28T14:38:16","indexId":"70170132","displayToPublicDate":"2016-04-06T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Salmon and steelhead in the White Salmon River after the removal of Condit Dam–Planning efforts and recolonization results","docAbstract":"Condit Dam, at river kilometer 5.3 on the White Salmon River, Washington, was breached in 2011 and completely removed in 2012. This action opened habitat to migratory fish for the first time in 100 years. The White Salmon Working Group was formed to create plans for fish salvage in preparation for fish recolonization and to prescribe the actions necessary to restore anadromous salmonid populations in the White Salmon River after Condit Dam removal. Studies conducted by work group members and others served to inform management decisions. Management options for individual species were considered, including natural recolonization, introduction of a neighboring stock, hatchery supplementation, and monitoring natural recolonization for some time period to assess the need for hatchery supplementation. Monitoring to date indicates that multiple species and stocks of anadromous salmonids are finding and spawning in the now accessible and recovering habitat.
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98628","active":true,"usgs":false}],"preferred":false,"id":626247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shrier, Frank C","contributorId":168517,"corporation":false,"usgs":false,"family":"Shrier","given":"Frank","email":"","middleInitial":"C","affiliations":[{"id":25318,"text":"PacifiCorp, Portland, OR 97232","active":true,"usgs":false}],"preferred":false,"id":626248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Jeremy T","contributorId":168518,"corporation":false,"usgs":false,"family":"Wilson","given":"Jeremy","email":"","middleInitial":"T","affiliations":[{"id":25319,"text":"Washington Department of Fish and Wildlife, Vancouver, WA 98661","active":true,"usgs":false}],"preferred":false,"id":626249,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Connolly, Patrick J. 0000-0001-7365-7618 pconnolly@usgs.gov","orcid":"https://orcid.org/0000-0001-7365-7618","contributorId":2920,"corporation":false,"usgs":true,"family":"Connolly","given":"Patrick J.","email":"pconnolly@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":626250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70159959,"text":"ofr20151231 - 2016 - Report of the River Master of the Delaware River for the period December 1, 2008–November 30, 2009","interactions":[],"lastModifiedDate":"2016-04-06T10:42:33","indexId":"ofr20151231","displayToPublicDate":"2016-04-06T10:30:00","publicationYear":"2016","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":"2015-1231","title":"Report of the River Master of the Delaware River for the period December 1, 2008–November 30, 2009","docAbstract":"A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey (USGS). In addition, the Decree authorizes diversions of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 56th Annual Report of the River Master of the Delaware River. It covers the 2009 River Master report year, the period from December 1, 2008, to November 30, 2009.
During the report year, precipitation in the upper Delaware River Basin was 50.89 inches (in.) or 116 percent of the long-term average. Combined storage in Pepacton, Cannonsville, and Neversink Reservoirs remained high throughout the year and did not decline below 80 percent of combined capacity at any time. Delaware River operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program (FFMP).
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 25 days during the report year. Releases were made at conservation rates—rates designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs—on all other days.
During the report year, New York City and New Jersey complied fully with the terms of the Decree, and directives and requests of the River Master.
As part of a long-term program, the quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites. In addition, selected water-quality data were collected at 22 sites on a monthly basis.
The Delaware River Basin Commission (DRBC) collects monthly samples from March through October at 22 sites between Biles Channel and South Brown Shoal. Samples were collected and analyzed by the State of Delaware for the DRBC. At each site, water samples were collected at a single point near the center of the channel near the surface and analyzed for selected physical properties, and chemical and biological constituents including routine chemical substances, nutrients and bacteria. These consist of analyses of field measurements and laboratory determinations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151231","usgsCitation":"Krejmas, B.E., Paulachok, G.N., Mason, R.R., Jr., and Owens, Marie, 2016, Report of the River Master of the Delaware River for the period December 1, 2008–November 30, 2009: U.S. Geological Survey Open-File Report 2015–1231, 76 p., https://dx.doi.org/10.3133/ofr20151231.","productDescription":"vi, 76 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2008-12-01","ipdsId":"IP-070813","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":319219,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1231/ofr20151231.pdf","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1231"},{"id":319218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1231/coverthb.jpg"}],"country":"United States","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4815673828125,\n 39.70296052957233\n ],\n [\n -74.498291015625,\n 39.8465036024177\n ],\n [\n -74.4927978515625,\n 40.26695230509781\n ],\n [\n -74.970703125,\n 40.75974059207392\n ],\n [\n -74.6685791015625,\n 40.979898069620155\n ],\n [\n -74.5806884765625,\n 41.335575973123895\n ],\n [\n -74.11376953125,\n 42.13082130188811\n ],\n [\n -74.9432373046875,\n 42.44372793752476\n ],\n [\n -75.574951171875,\n 42.00848901572399\n ],\n [\n -75.8880615234375,\n 41.244772343082104\n ],\n [\n -76.343994140625,\n 40.329795743702064\n ],\n [\n -76.04736328125,\n 39.73253798438173\n ],\n [\n -75.4815673828125,\n 39.70296052957233\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
U.S. Geological Survey
10 Buist Road, Bldg. 3, Suite 304
Milford Professional Park
Milford, PA 18337
Fax: 570–296–9414
http://water.usgs.gov/osw/odrm/
The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Source-water supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial events, and indicating changes in water-quality conditions that may affect drinking-water treatment processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161040","collaboration":"Prepared in cooperation with the Kansas Water Office, the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One","usgsCitation":"Foster, G.M., and Graham, J.L., 2016, Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015: U.S. Geological Survey Open-File Report 2016–1040, 27 p., https://dx.doi.org/10.3133/ofr20161040. ","productDescription":"Report: iv, 27 p.; 31 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071163","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":319781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1040/coverthb.jpg"},{"id":319791,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1040/downloads/","text":"Appendixes 1 through 31","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1040 Appendixes"},{"id":319782,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1040/ofr20161040.pdf","text":"Report","size":"830 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1040"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6142578125,\n 38.81403111409755\n ],\n [\n -94.921875,\n 39.14710270770074\n ],\n [\n -95.185546875,\n 39.45316112807394\n ],\n [\n -95.5810546875,\n 39.90130858574735\n ],\n [\n -95.635986328125,\n 39.95185892663005\n ],\n [\n -95.82275390625,\n 39.707186656826565\n ],\n [\n -96.207275390625,\n 39.690280594818034\n ],\n [\n -96.39404296875,\n 39.487084981687495\n ],\n [\n -96.50390625,\n 39.18117526158749\n ],\n [\n -96.866455078125,\n 39.26628442213066\n ],\n [\n -96.88842773437499,\n 38.976492485539424\n ],\n [\n -96.910400390625,\n 38.60828592850559\n ],\n [\n -96.94335937499999,\n 38.42777351132905\n ],\n [\n -96.624755859375,\n 38.53097889440026\n ],\n [\n -96.075439453125,\n 38.61687046392973\n ],\n [\n -95.020751953125,\n 38.65119833229951\n ],\n [\n -94.68017578125,\n 38.659777730712534\n ],\n [\n -94.6142578125,\n 38.81403111409755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Watershed restoration is the focus of many resource managers and can include a multitude of restoration actions each with specific restoration objectives. For the White River flowing through the cities of Pacific and Sumner, Washington, a levee setback has been proposed to reconnect the river with its historical floodplain to help reduce flood risks, as well as provide increased habitat for federally listed species of salmonids. The study presented here documents the use of a modeling framework that integrates two-dimensional hydraulic modeling with process-based bioenergetics modeling for predicting how changes in flow from reconnecting the river with its floodplain affects invertebrate drift density and the net rate of energy intake of juvenile salmonids. Modeling results were calculated for flows of 25.9 and 49.3 cubic meters per second during the spring, summer, and fall. Predicted hypothetical future mean velocities and depths were significantly lower and more variable when compared to current conditions. The abundance of low energetic cost and positive growth locations for salmonids were predicted to increase significantly in the study reach following floodplain reconnection, particularly during the summer. This modeling framework presents a viable approach for evaluating the potential fisheries benefits of reconnecting a river to its historical floodplain that integrates our understanding of hydraulic, geomorphology, and organismal biology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165025","collaboration":"Prepared in cooperation with the King County Water and Land Resources Division","usgsCitation":"Black, R.W., Czuba, C.R., Magirl, C.S., McCarthy, Sarah, Berge, Hans, and Comanor, Kyle, 2016, Effect of a levee setback on aquatic resources using two-dimensional flow and bioenergetics models: U.S. Geological Survey Scientific Investigations Report 2016–5025, 26 p., https://dx.doi.org/10.3133/sir20165025.","productDescription":"v, 26 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066982","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":319584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5025/coverthb.jpg"},{"id":319585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5025/sir20165025.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5025"}],"country":"United States","state":"Washington","city":"Pacific","otherGeospatial":"White River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25688934326172,\n 47.22866141531796\n ],\n [\n -122.25688934326172,\n 47.28633255817871\n ],\n [\n -122.18358993530273,\n 47.28633255817871\n ],\n [\n -122.18358993530273,\n 47.22866141531796\n ],\n [\n -122.25688934326172,\n 47.22866141531796\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
The Chicago Department of Transportation’s Sustainable Streetscapes Program is an innovative program that strives to convert Chicago’s neighborhood commercial areas, riverwalks, and bicycle facilities into active, attractive places for Chicagoans to live, work, and play. The objective of each project is to create flourishing public places while improving the ability of infrastructure to support dense urban living. The U.S. Geological Survey (USGS), in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT), is monitoring the pre- and postconstruction hydrologic characteristics of an urban corridor on the south side of Chicago that is being renovated using sustainable streetscapes technology.
The CDOT Sustainable Streetscapes Program utilizes urban stormwater best-management practices (BMPs) to reduce the storm runoff to the local combined sewer system. The urban stormwater BMPs include permeable pavement, bioswales, infiltration basins, and planters. The urban stormwater BMPs are designed to capture the first flush of storm runoff through features that enhance the infiltration of stormwater runoff to shallow groundwater.
The hydrology of the Sustainable Streetscapes Program area is being monitored to evaluate the impacts and effectiveness of the urban stormwater BMP’s. Continuous monitoring of rainfall, sewer flows, stormwater runoff, soil moisture, and groundwater levels will give engineers and scientists measured data to define baseline pre- and postconstruction conditions for the evaluation of the BMPs.
Three tipping-bucket rain gages are located along the project corridor. The data provide information on the intensity and volume of rainfall. Rainfall can be highly variable even over a small area like the project corridor.
Continuous recording meters are located at specific locations in the combined sewers to record water level and flow during both dry weather (mostly sanitary flow) and wet weather conditions (stormwater runoff in addition to the sanitary flow). Sanitary flow is the largest source of flow in the combined sewers during dry weather, and stormwater runoff and sanitary flow combine during wet weather. The sewer flow data allow engineers and scientists to calculate total runoff volume for selected storm events.
Wells are located within the project corridor to record water levels and help determine the direction of movement of groundwater in response to rainfall and snowmelt. In urban settings with aging sewer systems, groundwater can seep into the sewers or combined sewage can seep from the sewers into the local groundwater system. The groundwater data are also important in evaluating the overall impacts of increased infiltration resulting from BMPs.
Data from wells show the relative water levels of shallow groundwater, water levels in the combined sewer system, and nearby surface-water channels within the project corridor. In some aging urban sewer systems, the local combined sewer system lies below the water table and receives substantial amounts of groundwater inflow, which can significantly reduce the amount of additional water the sewer system can accept.
The bioswale along the south side of West Cermak Road near South Throop Street functions to infiltrate stormwater runoff from the road. Stormwater on the road surface initially drains to the curb and then flows along the curb until it reaches a curb cut-out. Materials within the bioswale allow stormwater to infiltrate and reduce the load to the combined sewer.
A common feature in urban areas are curbside catch basins that collect stormwater runoff from paved streets. Stormwater drains first to the curb and then flows along the curb to the catch basin. Lateral sewer pipe connects the catch basin to the combined sewer beneath the street. The use of permeable pavers along the curbs in the project study reach let stormwater infiltrate before it reaches the curb, thus reducing the amount of stormwater draining to the combined sewers.
Water-level data from catch basins in the project study area show the effects of permeable pavers in reducing the stormwater drainage to the combined sewers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163014","collaboration":"Prepared in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT)","usgsCitation":"Duncker, J.J., and Morrow, W.S., 2016, Hydrologic monitoring for Chicago’s Sustainable Streetscapes Program. U.S. Geological Survey Fact Sheet 2016–3014, 6 p., https://dx.doi.org/10.3133/fs20163014.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054040","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":319331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3014/coverthb.jpg"},{"id":319332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3014/fs20163014.pdf","text":"Report","size":"3.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3014"}],"country":"United States","state":"Illinois","city":"Chicago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.68531799316406,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.8384904853889\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave
Urbana, IL 61801
http://il.water.usgs.gov/
As part of the Great Lakes and Mississippi River Interbasin Study, the U.S. Army Corps of Engineers (USACE) is conducting an assessment of the vulnerability of the Chicago Area Waterway System and Des Plaines River to Asian carp (specifically, Hypophthalmichthys nobilis (bighead carp) and Hypophthalmichthys molitrix (silver carp)) spawning and recruitment. As part of this assessment, the USACE requested the help of the U.S. Geological Survey in predicting the fate and transport of Asian carp eggs hypothetically spawned at the electric dispersal barrier on the Chicago Sanitary and Ship Canal and downstream of the Brandon Road Lock and Dam on the Des Plaines River under dry weather flow and high water temperature conditions. The Fluvial Egg Drift Simulator (FluEgg) model predicted that approximately 80 percent of silver carp eggs spawned near the electric dispersal barrier would hatch within the Lockport and Brandon Road pools (as close as 3.6 miles downstream of the barrier) and approximately 82 percent of the silver carp eggs spawned near the Brandon Road Dam would hatch in the Des Plaines River (as close as 1.6 miles downstream from the gates of Brandon Road Lock). Extension of the FluEgg model to include the fate and transport of larvae until gas bladder inflation—the point at which the larvae begin to leave the drift—suggests that eggs spawned at the electric dispersal barrier would reach the gas bladder inflation stage primarily within the Dresden Island Pool, and those spawned at the Brandon Road Dam would reach this stage primarily within the Marseilles and Starved Rock Pools.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161011","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Murphy, E.A., Garcia, Tatiana, Jackson, P.R., and Duncker J.J., 2016, Simulation of hypothetical Asian carp egg and larvae development and transport in the Lockport, Brandon Road, Dresden Island, and Marseilles Pools of the Illinois Waterway by use of the fluvial egg drift simulator (FluEgg) model: U.S. Geological Survey Open File Report 2016–1011, 19 p., https://dx.doi.org/10.3133/ofr20161011.","productDescription":"vii, 19 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070545","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":319192,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1011/ofr20161011.pdf","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1011"},{"id":319191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1011/coverthb.jpg"}],"country":"United States","state":"Illinois","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.96728515624999,\n 40.95501133048621\n ],\n [\n -88.96728515624999,\n 41.85319643776675\n ],\n [\n -87.9345703125,\n 41.85319643776675\n ],\n [\n -87.9345703125,\n 40.95501133048621\n ],\n [\n -88.96728515624999,\n 40.95501133048621\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N Goodwin
Urbana, IL 61801
http://il.water.usgs.gov/
The lower Genesee River and Rochester Embayment of Lake Ontario are a designated Area of Concern (AOC) under the binational Great Lakes Water Quality Agreement. The “degradation of phytoplankton and zooplankton populations” or plankton Beneficial Use Impairment (BUI) was classified as unknown and in need of further assessment in this AOC because water quality data suggested plankton communities could be effected and community data were either unavailable or indicated impacts. The plankton BUI may now be obsolete because local contaminant sources have been largely eliminated. The present study was conducted between July 2013 and August 2014 to assess the BUI-removal criteria: “AOC plankton bioassays confirm that toxicity in ambient waters (i.e., no growth inhibition) is not significantly higher than comparable non-AOC controls”. Acute and chronic toxicity of waters from 13 sites were quantified seasonally using standardized bioassays with the green alga Pseudokirchneriella subcapitata and water flea Ceriodaphnia dubia to test the hypothesis that toxicity of waters from AOC sites was not higher than that of waters from comparable non-AOC reference sites. Survival and reproduction of C. dubia did not differ significantly between site types, systems, or months. The growth of P. subcapitata did not differ between site types, but differed among systems and months. All results indicate that waters from AOC sites were no more toxic to both plankton species than waters from reference sites. Assuming test species represent natural plankton assemblages, water quality should not negatively affect survival and growth of resident plankton populations in the Rochester Embayment AOC.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2015.12.007","usgsCitation":"Baldigo, B.P., Duffy, B.T., Smith, A., and George, S.D., 2016, Toxicity of waters from the Rochester Embayment Area of Concern to the plankton species Pseudokirchneriella subcapitata and Ceriodaphnia dubia: Journal of Great Lakes Research, v. 42, no. 2, p. 464-471, https://doi.org/10.1016/j.jglr.2015.12.007.","productDescription":"8 p.","startPage":"464","endPage":"471","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062670","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2015.12.007","text":"Publisher Index Page"},{"id":325100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.7005615234375,\n 42.944360446966314\n ],\n [\n -78.7005615234375,\n 43.44893105587766\n ],\n [\n -76.80541992187499,\n 43.44893105587766\n ],\n [\n -76.80541992187499,\n 42.944360446966314\n ],\n [\n -78.7005615234375,\n 42.944360446966314\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"2","edition":"2016","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"579dd04ee4b0589fa1cbdf45","chorus":{"doi":"10.1016/j.jglr.2015.12.007","url":"http://dx.doi.org/10.1016/j.jglr.2015.12.007","publisher":"Elsevier BV","authors":"Baldigo Barry P., Duffy Brian T., Smith Alexander J., George Scott D.","journalName":"Journal of Great Lakes Research","publicationDate":"4/2016"},"contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, Brian T.","contributorId":6352,"corporation":false,"usgs":true,"family":"Duffy","given":"Brian","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":642215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Alexander J.","contributorId":140345,"corporation":false,"usgs":false,"family":"Smith","given":"Alexander J.","affiliations":[{"id":13464,"text":"Environmental Analyst, NY State Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":642216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642214,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168419,"text":"ofr20161016 - 2016 - Estimating the economic impacts of ecosystem restoration—Methods and case studies","interactions":[],"lastModifiedDate":"2016-04-05T09:49:35","indexId":"ofr20161016","displayToPublicDate":"2016-04-05T09:00:00","publicationYear":"2016","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":"2016-1016","title":"Estimating the economic impacts of ecosystem restoration—Methods and case studies","docAbstract":"Federal investments in ecosystem restoration projects protect Federal trusts, ensure public health and safety, and preserve and enhance essential ecosystem services. These investments also generate business activity and create jobs. It is important for restoration practitioners to be able to quantify the economic impacts of individual restoration projects in order to communicate the contribution of these activities to local and national stakeholders. This report provides a detailed description of the methods used to estimate economic impacts of case study projects and also provides suggestions, lessons learned, and trade-offs between potential analysis methods.
\nThis analysis estimates the economic impacts of a wide variety of ecosystem restoration projects associated with U.S. Department of the Interior (DOI) lands and programs. Specifically, the report provides estimated economic impacts for 21 DOI restoration projects associated with Natural Resource Damage Assessment and Restoration cases and Bureau of Land Management lands. The study indicates that ecosystem restoration projects provide meaningful economic contributions to local economies and to broader regional and national economies, and, based on the case studies, we estimate that between 13 and 32 job-years4 and between $2.2 and $3.4 million in total economic output5 are contributed to the U.S. economy for every $1 million invested in ecosystem restoration. These results highlight the magnitude and variability in the economic impacts associated with ecosystem restoration projects and demonstrate how investments in ecosystem restoration support jobs and livelihoods, small businesses, and rural economies. In addition to providing improved information on the economic impacts of restoration, the case studies included with this report highlight DOI restoration efforts and tell personalized stories about each project and the communities that are positively affected by restoration activities. Individual case studies are provided in appendix 1 of this report and are available from an online database at https://www.fort.usgs.gov/economic-impacts-restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161016","collaboration":"Prepared in cooperation with the U.S. Department of the Interior Natural Resource Damage Assessment and Restoration Program and Office of Policy Analysis and the Bureau of Land Management Socioeconomics Program","usgsCitation":"Cullinane Thomas, Catherine; Huber, Christopher; Skrabis, Kristin; and Sidon, Joshua, 2016, Estimating the economic impacts of ecosystem restoration—Methods and case studies: U.S. Geological Survey Open-File Report 2016–1016, 98 p., https://dx.doi.org/10.3133/ofr20161016.","productDescription":"v, 98 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068960","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":318716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1016/ofr20161016.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1016"},{"id":318715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1016/coverthb2.jpg"}],"contact":"Director, Fort Collins Science Center
2150 Centre Ave. Building C
Fort Collins, CO 80526-8118
The U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, developed regional regression equations based on basin and streamflow characteristics for streamflow-gaging stations through water year 2009 that can be used to estimate streamflow characteristics for ungaged sites in western Montana. The regression equations allow estimation of low-flow frequencies; mean annual and mean monthly streamflows; and the 20-, 50-, and 80-percent durations for annual and monthly duration streamflows for ungaged sites in western Montana that are unaffected by regulation.
\nRegression equations were developed for 54 streamflow characteristics in each of the 4 hydrologic regions generally located in western Montana. Exploratory data analyses determined that of the 40 basin characteristics used as explanatory variables, only 3 were significant in the final equations. These three basin characteristics are contributing drainage area, mean annual precipitation of the drainage basin, and percentage of basin with slopes greater than 50 percent. Low-flow frequency data and daily mean streamflow data from 152 streamflow-gaging stations were used to develop the regression equations. These 152 streamflow-gaging stations were considered to be unregulated (or only the unregulated period of record for regulated streamflow-gaging stations were used). For this study, a streamflow-gaging station is considered to be unregulated if cumulative drainage area upstream from all upstream dams is less than 20 percent of the drainage basin for the streamflow-gaging station, and if no large diversion canals are upstream from the streamflow-gaging station.
\nIn addition to the regional regression equations, two methods for estimating streamflow characteristics based on drainage areas of a nearby streamflow-gaging station are provided. Also, spreadsheet tools are provided for calculating streamflow characteristics based on regression equations or the drainage-area ratio method.
\nAll of the data used to calculate basin characteristics were derived from publicly available data sources and are available through the U.S. Geological Survey Streamstats program (http://water.usgs.gov/osw/streamstats/) for Montana. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report have been loaded to the Montana StreamStats application and can be used to derive streamflow characteristics for ungaged sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019G","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Sando, Roy, Sando, S.K., and Dutton, D.M., 2016, Methods for estimating streamflow characteristics at ungaged sites in western Montana based on data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5019–G, 19 p., https://dx.doi.org/10.3133/sir20155019G.","productDescription":"Report: vii, 19 p.; 10 Tables","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067085","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":319659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5019G/coverthb.jpg"},{"id":319661,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G_tables.xlsx","text":"Tables 1 through 4, 1–1 through 1–4, and 2–1 through 2–2","size":"483 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015–5019–G Tables"},{"id":319660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G.pdf","text":"Report","size":"9.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5019–G"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.03759765625,\n 48.99463598353408\n ],\n [\n -116.08154296875001,\n 47.97521412341618\n ],\n [\n -115.6201171875,\n 47.39834920035926\n ],\n [\n -114.64233398437499,\n 46.66451741754235\n ],\n [\n -114.43359375,\n 46.52863469527167\n ],\n [\n -114.53247070312499,\n 46.14178273759234\n ],\n [\n -114.43359375,\n 45.87471224890479\n ],\n [\n -114.53247070312499,\n 45.79050946752472\n ],\n [\n -114.576416015625,\n 45.590978249451936\n ],\n [\n -114.334716796875,\n 45.4524242413431\n ],\n [\n -113.9501953125,\n 45.69850658738848\n ],\n [\n -113.7744140625,\n 45.57560020947799\n ],\n [\n -113.7744140625,\n 45.390735154248894\n ],\n [\n -113.433837890625,\n 45.0502402697946\n ],\n [\n -113.51074218749999,\n 44.933696389694674\n ],\n [\n -113.26904296874999,\n 44.78573392716592\n ],\n [\n -113.08227539062499,\n 44.66865287227321\n ],\n [\n -112.9833984375,\n 44.43377984606825\n ],\n [\n -112.8515625,\n 44.33956524809713\n ],\n [\n -112.796630859375,\n 44.457309801319305\n ],\n [\n -112.47802734375,\n 44.4808302785626\n ],\n [\n -112.379150390625,\n 44.457309801319305\n ],\n [\n -112.291259765625,\n 44.574817404670306\n ],\n [\n -111.8408203125,\n 44.55133484083592\n ],\n [\n -111.52221679687499,\n 44.535674532413196\n ],\n [\n -111.42333984375,\n 44.69989765840318\n ],\n [\n -111.346435546875,\n 44.72332018895825\n ],\n [\n -111.060791015625,\n 44.465151013519616\n ],\n [\n -111.060791015625,\n 44.99588261816546\n ],\n [\n -107.22656249999999,\n 45.02695045318546\n ],\n [\n -107.57812499999999,\n 45.99696161820381\n ],\n [\n -108.12744140625,\n 47.234489635299184\n ],\n [\n -111.42333984375,\n 47.2195681123155\n ],\n [\n -112.7197265625,\n 48.06339653776211\n ],\n [\n -113.35693359375,\n 49.009050809382046\n ],\n [\n -116.03759765625,\n 48.99463598353408\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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3162 Bozeman Ave
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Chapter C of this Scientific Investigations Report documents results from a study by the U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources, to provide an update of statewide peak-flow frequency analyses and results for Montana. The purpose of this report chapter is to present peak-flow frequency analyses and results for 725 streamflow-gaging stations in or near Montana based on data through water year 2011. The 725 streamflow-gaging stations included in this study represent nearly all streamflowgaging stations in Montana (plus some from adjacent states or Canadian Provinces) that have at least 10 years of peak-flow records through water year 2011. For 29 of the 725 streamflow-gaging stations, peak-flow frequency analyses and results are reported for both unregulated and regulated conditions. Thus, peak-flow frequency analyses and results are reported for a total of 754 analyses. Estimates of peak-flow magnitudes for 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities are reported. These annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019C","collaboration":"Prepared in cooperation with the Montana Department of Transportation and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., McCarthy, P.M., and Dutton, D.M., 2016, Peak-flow frequency analyses and results based on data through water year 2011 for selected streamflow-gaging stations in or near Montana: U.S. Geological Survey Scientific Investigations Report 2015–5019–C, 27 p., https://dx.doi.org/10.3133/sir20155019C.","productDescription":"Report: v, 27 p.; 6 Tables","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061583","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey (USGS), in cooperation with the Montana Department of Natural Resources and Conservation, completed a study to update methods for estimating peak-flow frequencies at ungaged sites in Montana based on peak-flow data at streamflow-gaging stations through water year 2011. The methods allow estimation of peak-flow frequencies (that is, peak-flow magnitudes, in cubic feet per second, associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites. The annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Regional regression analysis is a primary focus of Chapter F of this Scientific Investigations Report, and regression equations for estimating peak-flow frequencies at ungaged sites in eight hydrologic regions in Montana are presented. The regression equations are based on analysis of peak-flow frequencies and basin characteristics at 537 streamflow-gaging stations in or near Montana and were developed using generalized least squares regression or weighted least squares regression.
All of the data used in calculating basin characteristics that were included as explanatory variables in the regression equations were developed for and are available through the USGS StreamStats application (http://water.usgs.gov/osw/streamstats/) for Montana. StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report chapter can be conveniently solved using the Montana StreamStats application.
Selected results from this study were compared with results of previous studies. For most hydrologic regions, the regression equations reported for this study had lower mean standard errors of prediction (in percent) than the previously reported regression equations for Montana. The equations presented for this study are considered to be an improvement on the previously reported equations primarily because this study (1) included 13 more years of peak-flow data; (2) included 35 more streamflow-gaging stations than previous studies; (3) used a detailed geographic information system (GIS)-based definition of the regulation status of streamflow-gaging stations, which allowed better determination of the unregulated peak-flow records that are appropriate for use in the regional regression analysis; (4) included advancements in GIS and remote-sensing technologies, which allowed more convenient calculation of basin characteristics and investigation of many more candidate basin characteristics; and (5) included advancements in computational and analytical methods, which allowed more thorough and consistent data analysis.
This report chapter also presents other methods for estimating peak-flow frequencies at ungaged sites. Two methods for estimating peak-flow frequencies at ungaged sites located on the same streams as streamflow-gaging stations are described. Additionally, envelope curves relating maximum recorded annual peak flows to contributing drainage area for each of the eight hydrologic regions in Montana are presented and compared to a national envelope curve. In addition to providing general information on characteristics of large peak flows, the regional envelope curves can be used to assess the reasonableness of peak-flow frequency estimates determined using the regression equations.
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\"}}]}","edition":"Version 1.1: February 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey (USGS) provides streamflow characteristics and other related information needed by water-resource managers to protect people and property from floods, plan and manage water-resource activities, and protect water quality. Streamflow characteristics provided by the USGS, such as peak-flow and low-flow frequencies for streamflow-gaging stations, are frequently used by engineers, flood forecasters, land managers, biologists, and others to guide their everyday decisions. In addition to providing streamflow characteristics at streamflow-gaging stations, the USGS also develops regional regression equations and drainage area-adjustment methods for estimating streamflow characteristics at locations on ungaged streams. Regional regression equations can be complex and often require users to determine several basin characteristics, which are physical and climatic characteristics of the stream and its drainage basin. Obtaining these basin characteristics for streamflow-gaging stations and ungaged sites traditionally has been time consuming and subjective, and led to inconsistent results.
StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. StreamStats allows users to easily obtain streamflow and basin characteristics for USGS streamflow-gaging stations and user-selected locations on ungaged streams. The USGS, in cooperation with Montana Department of Transportation, Montana Department of Environmental Quality, and Montana Department of Natural Resources and Conservation, completed a study to develop a StreamStats application for Montana, compute streamflow characteristics at streamflow-gaging stations, and develop regional regression equations to estimate streamflow characteristics at ungaged sites. Chapter A of this Scientific Investigations Report describes the Montana StreamStats application and the datasets, streamflow-gaging stations, streamflow characteristics, and regression equations (as described fully in Chapters B through G of this report) that are used for development of the StreamStats application for Montana.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019A","collaboration":"Prepared in cooperation with the Montana Department of Transportation, Montana Department of Environmental Quality, and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Dutton, D.M., Sando, S.K., and Sando, Roy, 2016, Montana StreamStats—A method for retrieving basin and streamflow characteristics in Montana: U.S. Geological Survey Scientific Investigations Report 2015–\n5019–A, 16 p., https://dx.doi.org/10.3133/sir20155019A.","productDescription":"Report: v, 16 p.; 4 Tables","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069671","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Chapter E of this Scientific Investigations Report documents results from a study by the U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, to provide an update of statewide streamflow characteristics based on data through water year 2009 for streamflow-gaging stations in or near Montana. Streamflow characteristics are presented for 408 streamflow-gaging stations in Montana and adjacent areas having 10 or more years of record. Data include the magnitude and probability of annual low and high streamflow, the magnitude and probability of low streamflow for three seasons (March–June, July–October, and November–February), streamflow duration statistics for monthly and annual periods, and mean streamflows for monthly and annual periods. Streamflow is considered to be regulated at streamflow-gaging stations where dams or other large-scale human modifications affect 20 percent or more of the contributing drainage basin. Separate streamflow characteristics are presented for the unregulated and regulated periods of record for streamflow-gaging stations with sufficient data.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019E","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., 2016, Streamflow characteristics based on data through water year 2009 for selected streamflow-gaging stations in or near Montana: U.S. Geological Survey Scientific Investigations Report 2015–5019–E, 10 p., https://dx.doi.org/10.3133/sir20155019E.","productDescription":"Report: v, 10 p.; Table","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061125","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601