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,{"id":70199845,"text":"sim3415 - 2018 - Altitude of the potentiometric surface, 2000–15, and historical water-level changes in the Memphis aquifer in the Memphis area, Tennessee","interactions":[],"lastModifiedDate":"2018-10-03T12:35:51","indexId":"sim3415","displayToPublicDate":"2018-10-02T15:04:09","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3415","title":"Altitude of the potentiometric surface, 2000–15, and historical water-level changes in the Memphis aquifer in the Memphis area, Tennessee","docAbstract":"The Memphis and Fort Pillow aquifers are the principal sources of water for municipal, industrial, and commercial uses in the Memphis area. About 207 million gallons per day of groundwater were withdrawn in Shelby County, Tennessee, from both aquifers in 2010 for these uses, with most of the water coming from the Memphis aquifer. The U.S. Geological Survey, in cooperation with the City of Memphis, Memphis Light, Gas and Water Division, collects groundwater-level data in the Memphis area and periodically prepares potentiometric-surface maps for the Memphis aquifer to assess conditions in this regionally important water supply aquifer. This report presents the altitudes of the potentiometric surface of water in wells screened in the Memphis aquifer based on water-level measurements made in the fall of 2000, 2005, 2010, and 2015 and describes historical water-level changes in the Memphis aquifer at key observation wells in the Memphis area. The Memphis area is about 1,500 square miles and includes all of Shelby County and parts of Tipton and Fayette Counties in Tennessee, parts of DeSoto and Marshall Counties in Mississippi, and part of Crittenden County in Arkansas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3415","collaboration":"Prepared in cooperation with the City of Memphis, Memphis Light, Gas and Water Division","usgsCitation":"Kingsbury, J.A., 2018, Altitude of the potentiometric surface, 2000–15, and historical water-level changes in the Memphis aquifer in the Memphis area, Tennessee: U.S. Geological Survey Scientific Investigations Map 3415, 1 sheet, https://doi.org/10.3133/sim3415.","productDescription":"Sheet: 41.5 x 37.0 inches; Figures: 5","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084162","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":357999,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sim/3415/sim3415_fig03.pdf","text":"Figure 3.","size":"320 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415 Figure 3","linkHelpText":"Altitude of the potentiometric surface of the Memphis aquifer in the Memphis area, Tennessee, October and November 2010."},{"id":358000,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sim/3415/sim3415_fig04.pdf","text":"Figure 4.","size":"510 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415 Figure 4","linkHelpText":"Altitude of the potentiometric surface of the Memphis aquifer in the Memphis area, Tennessee, October and November 2015."},{"id":358023,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sim/3415/sim3415_fig05.pdf","text":"Figure 5.","size":"216 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415 Figure 5","linkHelpText":"Historical water-level changes in selected Memphis aquifer wells located away from well fields."},{"id":357995,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3415/coverthb2.jpg"},{"id":357996,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3415/sim3415.pdf","text":"Map","size":"1.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415"},{"id":357997,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sim/3415/sim3415_fig01.pdf","text":"Figure 1.","size":"324 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415 Figure 1","linkHelpText":"Altitude of the potentiometric surface of the Memphis aquifer in the Memphis area, Tennessee, October 2000."},{"id":357998,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sim/3415/sim3415_fig02.pdf","text":"Figure 2.","size":"321 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3415 Figure 2","linkHelpText":"Altitude of the potentiometric surface of the Memphis aquifer in the Memphis area, Tennessee, September and October 2005."}],"country":"United States","state":"Tennessee","city":"Memphis","otherGeospatial":"Memphis Aquifer","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.25,\n              35\n            ],\n            [\n              -89.5,\n              35\n            ],\n            [\n              -89.5,\n              35.5\n            ],\n            [\n              -90.25,\n              35.5\n            ],\n            [\n              -90.25,\n              35\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_tn@usgs.gov\" href=\"mailto:%20dc_tn@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/lmg-water/\" href=\"https://www.usgs.gov/centers/lmg-water/\">Lower Mississippi-Gulf Water Science Center—Tennessee</a><br>U.S. Geological Survey<br>640 Grassmere&nbsp;Park, Suite 100<br>Nashville, TN 37211</p>","tableOfContents":"<ul><li>Introduction</li><li>Memphis Aquifer</li><li>Potentiometric-Surface Maps</li><li>Historical Water-Level Changes</li><li>References</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2018-10-02","noUsgsAuthors":false,"publicationDate":"2018-10-02","publicationStatus":"PW","scienceBaseUri":"5bc02f7ee4b0fc368eb53861","contributors":{"authors":[{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science 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,{"id":70199684,"text":"ofr20181155 - 2018 - Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States","interactions":[{"subject":{"id":70104194,"text":"ofr20141097 - 2014 - Technical implementation plan for the ShakeAlert production system: an Earthquake Early Warning system for the West Coast of the United States","indexId":"ofr20141097","publicationYear":"2014","noYear":false,"title":"Technical implementation plan for the ShakeAlert production system: an Earthquake Early Warning system for the West Coast of the United States"},"predicate":"SUPERSEDED_BY","object":{"id":70199684,"text":"ofr20181155 - 2018 - Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States","indexId":"ofr20181155","publicationYear":"2018","noYear":false,"title":"Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States"},"id":1}],"lastModifiedDate":"2021-08-17T20:08:32.173062","indexId":"ofr20181155","displayToPublicDate":"2018-10-02T14:56:59","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1155","title":"Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States","docAbstract":"<p>The U.S. Geological Survey (USGS), along with partner organizations, has developed an earthquake early warning (EEW) system called ShakeAlert for the highest risk areas of the United States: namely, California, Oregon, and Washington. The purpose of the system is to reduce the impact of earthquakes and save lives and property by providing alerts to institutional users and the public. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Alerts can then be sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking.</p><p>ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System. However, these networks were not originally designed for EEW; old equip­ment needs to be updated and new stations must be added to construct EEW-capable networks. The ShakeAlert data-processing infrastructure includes redundant servers that are geographically distributed at monitoring centers in Seattle, Washington, as well as Menlo Park, Berkeley, and Pasadena in California. Three data-processing layers collect raw ground-motion data from field stations (data layer), analyze these data to estimate the area and intensity of the resulting shaking (production layer), and publish alert products as appropriate for end users (alert layer). The alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission, infrastructure, or expertise to perform public notifications and is therefore recruiting technology enablers from the private sector. Additionally, ShakeAlert will coordinate with both public and private partners to accomplish consistent and ongoing public communication, education, and outreach.</p><p>The estimated cost of completing the ShakeAlert infrastructure and sensor networks is \\$39.4 million and has an estimated annual operation and maintenance cost of \\$28.6 million per year. Building a highly reliable data telemetry infrastructure would cost another \\$20.5 million and operating this telemetry system would add \\$49.8 million per year; however, these costs could be reduced if project partners provide bandwidth on existing systems.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181155","usgsCitation":"Given, D.D., Allen, R.M., Baltay, A.S., Bodin, P., Cochran, E.S., Creager, K., de Groot, R.M., Gee, L.S., Hauksson, E., Heaton, T.H., Hellweg, M., Murray, J.R., Thomas, V.I., Toomey, D., and Yelin, T.S., 2018, Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States: U.S. Geological Survey Open-File Report 2018–1155, 42 p., https://doi.org/10.3133/ofr20181155. 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 \"}}]}","contact":"<p><a href=\"https://earthquake.usgs.gov/contactus/pasadena/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://earthquake.usgs.gov/contactus/pasadena/\">Earthquake Science Center-Pasadena Field Office</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>525 South Wilson Ave.<br>Pasadena, CA 91106-3212</p>","tableOfContents":"<div><ul><li>Executive Summary</li><li>Introduction</li><li>Vision</li><li>Mission</li><li>Goal</li><li>Authorities</li><li>The Physics of the Problem</li><li>Benefits and Uses of ShakeAlert</li><li>A Brief History of Early Earthquake Warning</li><li>The ShakeAlert System Strategy</li><li>Major System Components</li><li>Alert Distribution and Use</li><li>Alerts to the Public</li><li>Communications, Education, and Outreach</li><li>ShakeAlert Partners and Users</li><li>System Roles and Governance</li><li>Expanding to Other Regions</li><li>Continuing Research and Development</li><li>Budget Estimate</li><li>Timeline</li><li>Conclusion</li><li>References</li><li>Appendix</li></ul></div>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2018-10-02","noUsgsAuthors":false,"publicationDate":"2018-10-02","publicationStatus":"PW","scienceBaseUri":"5bc02f7ee4b0fc368eb53863","contributors":{"authors":[{"text":"Given, Douglas D. 0000-0002-3277-5121 doug@usgs.gov","orcid":"https://orcid.org/0000-0002-3277-5121","contributorId":201870,"corporation":false,"usgs":true,"family":"Given","given":"Douglas","email":"doug@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":746178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Richard M.","contributorId":195244,"corporation":false,"usgs":false,"family":"Allen","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":746179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":746180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bodin, Paul","contributorId":206932,"corporation":false,"usgs":false,"family":"Bodin","given":"Paul","email":"","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":746181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":747138,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creager, Kenneth","contributorId":194763,"corporation":false,"usgs":false,"family":"Creager","given":"Kenneth","affiliations":[],"preferred":false,"id":746182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"de Groot, Robert M. 0000-0001-9995-4207","orcid":"https://orcid.org/0000-0001-9995-4207","contributorId":210451,"corporation":false,"usgs":false,"family":"de Groot","given":"Robert M.","affiliations":[],"preferred":false,"id":750748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gee, Lind S. lgee@usgs.gov","contributorId":2247,"corporation":false,"usgs":true,"family":"Gee","given":"Lind S.","email":"lgee@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":746183,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hauksson, Egill","contributorId":198159,"corporation":false,"usgs":false,"family":"Hauksson","given":"Egill","email":"","affiliations":[],"preferred":false,"id":746184,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heaton, Thomas H.","contributorId":187505,"corporation":false,"usgs":false,"family":"Heaton","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":746185,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hellweg, Margaret","contributorId":49823,"corporation":false,"usgs":true,"family":"Hellweg","given":"Margaret","email":"","affiliations":[],"preferred":false,"id":746186,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":746187,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas, Valerie I. vthomas@usgs.gov","contributorId":5829,"corporation":false,"usgs":true,"family":"Thomas","given":"Valerie","email":"vthomas@usgs.gov","middleInitial":"I.","affiliations":[],"preferred":true,"id":746188,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Toomey, Douglas 0000-0003-2873-4084","orcid":"https://orcid.org/0000-0003-2873-4084","contributorId":206934,"corporation":false,"usgs":false,"family":"Toomey","given":"Douglas","affiliations":[{"id":27574,"text":"UO","active":true,"usgs":false}],"preferred":false,"id":746189,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yelin, Thomas S. yelin@usgs.gov","contributorId":4155,"corporation":false,"usgs":true,"family":"Yelin","given":"Thomas","email":"yelin@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":746190,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70198896,"text":"ofr20181125 - 2018 - Hydrologic characteristics and water quality of headwater streams and wetlands at the Allegheny Portage Railroad National Historic Site, Summit area,   Blair and Cambria Counties, Pennsylvania, 2014–16","interactions":[],"lastModifiedDate":"2018-12-17T13:17:44","indexId":"ofr20181125","displayToPublicDate":"2018-10-02T14:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1125","displayTitle":"Hydrologic Characteristics and Water Quality of Headwater Streams and Wetlands at the Allegheny Portage Railroad National Historic Site, Summit Area,  Blair and Cambria Counties, Pennsylvania, <br> 2014–16","title":"Hydrologic characteristics and water quality of headwater streams and wetlands at the Allegheny Portage Railroad National Historic Site, Summit area,   Blair and Cambria Counties, Pennsylvania, 2014–16","docAbstract":"<p>The Allegheny Portage Railroad National Historic Site (ALPO) in Blair and Cambria Counties, Pennsylvania, protects historic features of the first railroad portage over the Allegheny Front and the first railroad tunnel in the United States. This report, which was completed by the U.S. Geological Survey in cooperation with the National Park Service, summarizes water resources in the headwaters of the Blair Gap Run and Bradley Run watersheds at the ALPO Summit area during 2014–16. These new baseline data fill an existing gap in knowledge and may be helpful to evaluate potential changes in the hydrologic characteristics of streams and associated wetlands at the Summit area.</p><p>Results of synoptic water-quality surveys and continuous stage records at two streamgages near the headwaters of Blair Gap Run and Bradley Run indicate that the headwater streams of the ALPO Summit area are perennial but have different water-quality characteristics. The water sampled in the headwaters of Blair Gap Run had pH that ranged from acidic to near neutral, combined with elevated concentrations of dissolved solids, mainly sulfate, chloride, and sodium. These characteristics can be attributed to drainage from legacy coal mines and runoff from nearby roads treated with deicing salt. More than once during the study, the chloride and associated contaminant concentrations in tributaries of Blair Gap Run exceeded chronic thresholds for protection of freshwater aquatic organisms. In contrast, the water quality at tributaries of Bradley Run in the Summit area was characterized by near-neutral pH and relatively low concentrations of dissolved constituents, which met criteria for protection of freshwater aquatic life. By comparison, the deep groundwater discharged as abandoned mine drainage to Sugar Run from the Argyle Stone Bridge Mine, which underlies the Summit area, had acidic pH and elevated concentrations of sulfate and metals, which exceeded chronic and acute thresholds for aquatic life.</p><p>Data on shallow groundwater levels in piezometers at two wetlands in the Summit area, which were monitored during spring through fall of 2016, indicate downward hydraulic gradients (higher water level in shallow piezometer than in deeper piezometer) and potential for local groundwater recharge during rainfall events, particularly in the summer and fall seasons. The wetlands in the upland area (wetland 3, at altitude 2,370 feet NAVD 88) near the divide between Blair Gap Run and Bradley Run between the Lemon House and Picnic Area, exhibited a consistent downward gradient from spring through fall of 2016. The associated surface seepage at wetland 3 dried up in the summer of 2016. In contrast, the wetlands in the adjoining valley (wetland 6, at altitude 2,198 feet NAVD 88) in the northwestern Summit area exhibited upward hydraulic gradients in the spring and produced continuous seepage. Despite downward gradients during summer and fall, the seepage associated with wetland 6 sustained perennial conditions in the Bradley Run drainage through the summer of 2016.</p><p>Differences in groundwater altitudes and associated water quality among the surface water, shallow groundwater, and deep groundwater in the Summit area imply that the surface water and shallow groundwater in the Summit area could recharge the groundwater of the underlying coal mines. Seasonally upward and downward vertical gradients in the near-surface soil and bedrock at wetland 6, and unimpaired water quality in the Bradley Run headwaters, are consistent with a perched water table and local hydrology that is influenced by local recharge. Persistent downward gradients and impaired water quality at wetland 3 and the adjacent headwaters seeps and tributaries of Blair Gap Run could be attributed to subsidence and drainage from shallow coalbeds (Upper Freeport, seam E) and associated mine workings in that area; however, the underlying deep coal mine pool (Lower Kittanning, seam B), which is hundreds of feet below the surface, does not appear to affect the hydrologic characteristics of the headwater streams and wetlands in the Summit area.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181125","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Cravotta, C.A., III, Galeone, D.G., and Penrod, K.A., 2018, Hydrologic characteristics and water quality of headwater streams and wetlands at the Allegheny Portage Railroad National Historic Site, Summit area, Blair and Cambria Counties, Pennsylvania, 2014–16 (ver. 1.1, December 2018): U.S. Geological Survey Open-File Report 2018–1125, 21 p., https://doi.org/10.3133/ofr20181125.","productDescription":"Report: vi, 21 p.; Table; Appendix; Data release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098767","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":357980,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1125/coverthb2.jpg"},{"id":357981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1125"},{"id":357982,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125_appendixes.xlsx","size":"1.65 MB","linkFileType":{"id":3,"text":"xlsx"}},{"id":357984,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YWMMHG","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrologic data collected by the U.S. Geological Survey and National Park Service at the Allegheny Portage Railroad National Historic Site, Summit Area, Blair and Cambria Counties, Pennsylvania, April 2014-December 2016"},{"id":357983,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2018/1125/ofr20181125_tables.xlsx","size":"1.12 MB","linkFileType":{"id":3,"text":"xlsx"}},{"id":360331,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2018/1125/versionHist.txt","text":"Version History","size":"1.27 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Pennsylvania","county":"Blair County, Cambria County","otherGeospatial":"Allegheny Portage Railroad National Historic Site","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.56268,\n              40.45209\n            ],\n            [\n              -78.5237,\n              40.45209\n            ],\n            [\n              -78.5237,\n              40.47688\n            ],\n            [\n              -78.56268,\n              40.47688\n            ],\n            [\n              -78.56268,\n              40.45209\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.1: December 2018; Version 1.0: October 2018","contact":"<p><a href=\"mailto:dc_pa@usgs.gov\" data-mce-href=\"mailto:dc_pa@usgs.gov\">Director</a>, <a href=\"https://pa.water.usgs.gov/\" data-mce-href=\"https://pa.water.usgs.gov/\">Pennsylvania Water Science Center</a><br>U.S. Geological Survey <br>215 Limekiln Road <br>New Cumberland, PA 17070</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction and Background</li><li>Data Collection and Evaluation</li><li>Hydrologic Characteristics of Headwater Streams and Wetlands</li><li>Water Quality</li><li>Summary and Conclusions</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Descriptions of water-quality parameters for samples collected by the U.S. Geological Survey at the ALPO Summit area, Pennsylvania, 2014–16</li><li>Appendix 2. Synoptic water-quality data for environmental samples collected by the U.S. Geological Survey at the ALPO Summit area, Pennsylvania, 2014–16</li><li>Appendix 3. Quality assurance quality control (QAQC) data for deionized-water blanks and U.S. Geological Survey standard reference water samples (SRWS) analyzed with environmental samples from the ALPO Summit area, Pennsylvania, 2014–16</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2018-10-02","revisedDate":"2018-12-17","noUsgsAuthors":false,"publicationDate":"2018-10-02","publicationStatus":"PW","scienceBaseUri":"5bc02f7fe4b0fc368eb53865","contributors":{"authors":[{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galeone, Daniel G. 0000-0002-8007-9278","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":207250,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Penrod, Kathy A.","contributorId":207251,"corporation":false,"usgs":false,"family":"Penrod","given":"Kathy","email":"","middleInitial":"A.","affiliations":[{"id":37500,"text":"National Park Service, Allegheny Portage Railroad National Historic Site","active":true,"usgs":false}],"preferred":false,"id":746981,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70201036,"text":"70201036 - 2018 - Observations of rotational motions from local earthquakes using two temporary portable sensors in Waynoka, Oklahoma","interactions":[],"lastModifiedDate":"2018-11-26T12:02:49","indexId":"70201036","displayToPublicDate":"2018-10-02T12:02:42","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Observations of rotational motions from local earthquakes using two temporary portable sensors in Waynoka, Oklahoma","docAbstract":"<p>Characterizing rotational motions from earthquakes at local distances has the potential to improve earthquake engineering and seismic gradiometry by better characterizing the complete seismic wavefield. Applied Technology Associates (ATA) has developed a proto‐seismic magnetohydrodynamic (SMHD) three‐component rotational rate sensor. We deploy two ATA rotational rate sensors at a temporary aftershock station in Waynoka, Oklahoma. From 27 April to 6 June 2017, we recorded the translational and rotational motions of 155 earthquakes of ML≥2.0 within 220 km of the station. Using the recorded events, we compare peak ground rotation rate (⁠ PGω˙ ⁠) with peak ground velocity (PGV) and with peak ground acceleration (PGA). Our results support previously identified potential relationships between the two quantities. We also compare peak ground rotations (⁠ PGω ⁠) as a function of seismic moment and distance. We found that PGω˙ decays with an exponent of approximately −4.0km−1 for both horizontal and vertical components. On the other hand, PGA decays with an exponent of approximately −1.8km−1 for all components. We compute apparent phase velocity directly from the rotational data for both horizontally polarized shear waves (SH; 379m/s with a standard deviation of 114m/s ⁠) and vertically polarized compression and shear waves (P‐SV; 387m/s with a standard deviation of 121m/s ⁠). Finally, by comparing various rotational and translational components, we look at potential implications for estimating local event source parameters. We found that the absolute correlation of nearby earthquakes decays at a rate of approximately 0.39/km for rotational sensors. This decay rate of absolute correlation is faster on translational sensors with a decay rate of 0.44/km. The latter may help in identifying phenomena such as repeating earthquakes by using differences in correlations as a function of distance and how these differences compare with translational correlations.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120170347","usgsCitation":"Ringler, A.T., Anthony, R.E., Wilson, D.C., Holland, A., and Lin, C., 2018, Observations of rotational motions from local earthquakes using two temporary portable sensors in Waynoka, Oklahoma: Bulletin of the Seismological Society of America, v. 108, no. 6, p. 3562-3575, https://doi.org/10.1785/0120170347.","productDescription":"14 p.","startPage":"3562","endPage":"3575","ipdsId":"IP-092094","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":359659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Waynoka","volume":"108","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-02","publicationStatus":"PW","scienceBaseUri":"5bfd146fe4b0815414ca38fa","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":751954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":751955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":751974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holland, A.A.","contributorId":140381,"corporation":false,"usgs":false,"family":"Holland","given":"A.A.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":751972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, C.-J.","contributorId":198564,"corporation":false,"usgs":false,"family":"Lin","given":"C.-J.","affiliations":[],"preferred":false,"id":751973,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70202620,"text":"70202620 - 2018 - Climate Assessments and Scenario Planning (CLASP)","interactions":[],"lastModifiedDate":"2025-04-25T16:34:59.593138","indexId":"70202620","displayToPublicDate":"2018-10-01T16:03:35","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"Climate Assessments and Scenario Planning (CLASP)","docAbstract":"<div class=\"hidden-xs\"><div class=\"sb-section-body\"><div class=\"sb-expander\" data-collapsed-height=\"300\" data-min-height-to-collapse=\"400\"><div class=\"sb-expander-content\"><span>The NE CASC boasts an interdisciplinary array of scientists, from ecologists to biologists, hydrologists to climatologists, each contributing new, original academic research to advance our understanding of the impacts of climate change on wildlife and other natural resources in the Northeast. Needed was an outreach specialist who would interface directly with the management agencies who benefited from this research to aid the integration of this research into their management planning as part of adapting to climate change. A climatologist was preferred to address queries about climate modeling, climate change uncertainties, and other areas of climate science outside the expertise of NE CASC ecologists, biologists, and hydrologists. This person’s activities would include responding to inquiries, presenting climate science concepts at management workshops, gathering managers’ climate science needs, contributing to the climate portion of synthesis reports and other resources for managers, and collaborating with ecologists on interdisciplinary studies of climate change by providing climate data and guidance on interpretation. The objective was to relay and interpret the scientific findings most relevant to managers’ resources of concern and management activities around those resources to guide adjustments to their activities toward better protecting their resources of concern. As an outcome, managers often relied on synthesized climate science to be able to consider how climate trends affect their work, especially those thinking about climate change for the first time in the context of their work. This lays the groundwork for more detailed management-driven science delivery that can aid eventual implementation. Management groups reached by this project include land managers with the National Park Service, state fish and game departments and DNRs, and tribal environmental coordinators.</span></div></div></div></div>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70202620","usgsCitation":"Bryan, A., 2018, Climate Assessments and Scenario Planning (CLASP), 13 p., https://doi.org/10.3133/70202620.","productDescription":"13 p.","ipdsId":"IP-103466","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":362038,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencebase.gov/catalog/item/5c7589fde4b0fe48cb4a397b"},{"id":362047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/usgs_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bryan, Alexander 0000-0003-2040-7636 abryan@usgs.gov","orcid":"https://orcid.org/0000-0003-2040-7636","contributorId":168822,"corporation":false,"usgs":true,"family":"Bryan","given":"Alexander","email":"abryan@usgs.gov","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":759232,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70199847,"text":"70199847 - 2018 - Current research in land, water, and agroecosystems: ASABE journals 2017 year in review","interactions":[],"lastModifiedDate":"2018-11-16T11:23:21","indexId":"70199847","displayToPublicDate":"2018-10-01T15:52:29","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3619,"text":"Transactions of the ASABE","active":true,"publicationSubtype":{"id":10}},"title":"Current research in land, water, and agroecosystems: ASABE journals 2017 year in review","docAbstract":"<p><span>This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals (Transactions of the ASABE and Applied Engineering in Agriculture) in 2017. This article reviews the context, scope, and key results of the published articles and perhaps more importantly recommends areas for increased research attention. Experimental and modeling advances were described in hydrology, agroecosystems, climate-change effects, soil erosion, irrigation, drainage, forest resources, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling to Optimize Water Use, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land and water resources, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to grow its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. In this vane, recommendations for future research direction are discussed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results.</span></p>","language":"English","publisher":"American Society of Agricultural and Biological Engineers (ASABE)","doi":"10.13031/trans.12821","usgsCitation":"Douglas-Mankin, K.R., 2018, Current research in land, water, and agroecosystems: ASABE journals 2017 year in review: Transactions of the ASABE, v. 61, no. 5, p. 1639-1651, https://doi.org/10.13031/trans.12821.","productDescription":"13 p.","startPage":"1639","endPage":"1651","ipdsId":"IP-095123","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":468343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.13031/trans.12821","text":"Publisher Index Page"},{"id":357993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f80e4b0fc368eb53867","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746880,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70201892,"text":"70201892 - 2018 - Examining the relationship between portable luminescence reader measurements and depositional ages of paleowetland sediments, Las Vegas Valley, Nevada","interactions":[],"lastModifiedDate":"2019-02-01T15:23:47","indexId":"70201892","displayToPublicDate":"2018-10-01T15:23:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3216,"text":"Quaternary Geochronology","active":true,"publicationSubtype":{"id":10}},"title":"Examining the relationship between portable luminescence reader measurements and depositional ages of paleowetland sediments, Las Vegas Valley, Nevada","docAbstract":"<p><span>Portable&nbsp;luminescence&nbsp;readers are exciting new tools that have the potential to rapidly determine the&nbsp;age structure&nbsp;of late&nbsp;</span>Quaternary<span>&nbsp;stratigraphic columns. This is important because high-resolution age profiling can reveal details about the temporal dynamics of climate cause and ecosystem effect, often while researchers are still in the field. In this paper, we compare new portable luminescence reader measurements of total photon counts with a suite of robust, highly resolved ages from middle to late Pleistocene-age paleowetland deposits in the Las Vegas Valley of southern Nevada. Our results show that total photon counts correlate with age, with a&nbsp;quadratic equation&nbsp;providing the best fit to the data. Significant scatter is present in the data, which is likely the result of dose rate variations, multiple sediment sources, and transport mechanisms that include both eolian and&nbsp;fluvial processes. The observed scatter can be reduced significantly using a simple pretreatment procedure involving a 250 μm sieve and&nbsp;neodymium&nbsp;hand magnet to normalize particle sizes and remove magnetic grains. Following this treatment, age estimates based on the reader measurements have an average error of 30 ± 18% when compared against known ages. These findings confirm that portable reader measurements scale with age in paleowetland deposits, allowing its use in establishing rapid, albeit approximate, chronologies for these deposits throughout the American Southwest.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2018.07.006","usgsCitation":"Gray, H., Mahan, S.A., Springer, K.B., and Pigati, J.S., 2018, Examining the relationship between portable luminescence reader measurements and depositional ages of paleowetland sediments, Las Vegas Valley, Nevada: Quaternary Geochronology, v. 48, p. 80-90, https://doi.org/10.1016/j.quageo.2018.07.006.","productDescription":"11 p.","startPage":"80","endPage":"90","ipdsId":"IP-096744","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468344,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2018.07.006","text":"Publisher Index Page"},{"id":360933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Las Vegas Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.42,\n              36.25\n            ],\n            [\n              -115.14,\n              36.25\n            ],\n            [\n              -115.14,\n              36.45\n            ],\n            [\n              -115.42,\n              36.45\n            ],\n            [\n              -115.42,\n              36.25\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"48","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pigati, Jeffrey S. 0000-0002-1615-2928 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0002-1615-2928","contributorId":212247,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755855,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70199838,"text":"70199838 - 2018 - Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes","interactions":[],"lastModifiedDate":"2018-10-01T14:36:32","indexId":"70199838","displayToPublicDate":"2018-10-01T14:36:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes","docAbstract":"<p><span>Long-term&nbsp;ecological monitoring&nbsp;programs often use linear mixed models to estimate trend in an&nbsp;ecological indicator&nbsp;sampled across large landscapes. A linear mixed model is versatile for estimating a linear trend in time as well as components of spatial and&nbsp;temporal variationin the case of unbalanced&nbsp;</span>data structures<span>, which are common in complex monitoring designs where limited sampling effort must be optimized over time and space. A power analysis was used to inform a lake chemistry monitoring design, including selecting the most appropriate temporal revisit design. Pilot data from surveys of lakes across large wilderness national parks (Sequoia, Kings Canyon, and Yosemite national parks) were used to obtain variance components for a Monte Carlo power simulation. Using a linear mixed model for a range of temporal revisit designs, sample sizes, and trend magnitudes, we evaluated the power to detect trend, the trend test size, and the relative bias of trend coefficient estimates for four continuous and normally distributed indicators. Contrary to prior research based on large-sample approximations that identified a single panel of sites visited annually as the revisit design generating the highest power, we found that the power to detect a 12-year trend based on the Wald t-test from a linear mixed model may be optimized by obtaining unbalanced data sets with limited to no annual replication. We emphasize the importance of examining variance composition, sample size, and the power and size of the trend test with Monte Carlo simulation when allocating sampling effort over time and space.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2018.05.087","usgsCitation":"Starcevich, L., Irvine, K.M., and Heard, A.M., 2018, Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes: Ecological Indicators, v. 93, p. 847-855, https://doi.org/10.1016/j.ecolind.2018.05.087.","productDescription":"9 p.","startPage":"847","endPage":"855","ipdsId":"IP-066434","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":357970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f80e4b0fc368eb53869","contributors":{"authors":[{"text":"Starcevich, Leigh Ann H.","contributorId":208351,"corporation":false,"usgs":false,"family":"Starcevich","given":"Leigh Ann H.","affiliations":[{"id":37787,"text":"WEST, Inc., 456 SW Monroe Ave. Suite 106, Corvallis, OR 97333","active":true,"usgs":false}],"preferred":false,"id":746849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":746848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heard, Andrea M.","contributorId":208352,"corporation":false,"usgs":false,"family":"Heard","given":"Andrea","email":"","middleInitial":"M.","affiliations":[{"id":37788,"text":"Sierra Nevada Network, National Park Service, 47050 Generals Hwy, Three Rivers, CA 93271","active":true,"usgs":false}],"preferred":false,"id":746850,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70199834,"text":"70199834 - 2018 - Multidirectional abundance shifts among North American birds and the relative influence of multifaceted climate factors","interactions":[],"lastModifiedDate":"2018-10-01T14:24:15","indexId":"70199834","displayToPublicDate":"2018-10-01T14:24:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Multidirectional abundance shifts among North American birds and the relative influence of multifaceted climate factors","docAbstract":"<p><span>Shifts in species distributions are major fingerprint of climate change. Examining changes in species abundance structures at a continental scale enables robust evaluation of climate change influences, but few studies have conducted these evaluations due to limited data and methodological constraints. In this study, we estimate temporal changes in abundance from North American Breeding Bird Survey data at the scale of physiographic strata to examine the relative influence of different components of climatic factors and evaluate the hypothesis that shifting species distributions are multidirectional in resident bird species in North America. We quantify the direction and velocity of the abundance shifts of 57 permanent resident birds over 44&nbsp;years using a centroid analysis. For species with significant abundance shifts in the centroid analysis, we conduct a more intensive correlative analysis to identify climate components most strongly associated with composite change of abundance within strata. Our analysis focus on two contrasts: the relative importance of climate extremes vs. averages, and of temperature vs. precipitation in strength of association with abundance change. Our study shows that 36 species had significant abundance shifts over the study period. The average velocity of the centroid is 5.89&nbsp;km·yr</span><sup>−1</sup><span>. The shifted distance on average covers 259&nbsp;km, 9% of range extent. Our results strongly suggest that the climate change fingerprint in studied avian distributions is multidirectional. Among 6 directions with significant abundance shifts, the northwestward shift was observed in the largest number of species (</span><i>n</i><span>&nbsp;=&nbsp;13). The temperature/average climate model consistently has greater predictive ability than the precipitation/extreme climate model in explaining strata‐level abundance change. Our study shows heterogeneous avian responses to recent environmental changes. It highlights needs for more species‐specific approaches to examine contributing factors to recent distributional changes and for comprehensive conservation planning for climate change adaptation.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13683","usgsCitation":"Huang, Q., Sauer, J.R., and Dubayah, R.O., 2018, Multidirectional abundance shifts among North American birds and the relative influence of multifaceted climate factors: Global Change Biology, v. 23, no. 9, p. 3610-3622, https://doi.org/10.1111/gcb.13683.","productDescription":"13 p.","startPage":"3610","endPage":"3622","ipdsId":"IP-083977","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":357968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-11","publicationStatus":"PW","scienceBaseUri":"5bc02f81e4b0fc368eb5386b","contributors":{"authors":[{"text":"Huang, Qiongyu","contributorId":208347,"corporation":false,"usgs":false,"family":"Huang","given":"Qiongyu","email":"","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":746837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dubayah, Ralph O.","contributorId":208348,"corporation":false,"usgs":false,"family":"Dubayah","given":"Ralph","email":"","middleInitial":"O.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":746838,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70200430,"text":"70200430 - 2018 - Informing research priorities for immature sea turtles through expert elicitation","interactions":[],"lastModifiedDate":"2018-10-18T14:22:32","indexId":"70200430","displayToPublicDate":"2018-10-01T14:22:26","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Informing research priorities for immature sea turtles through expert elicitation","docAbstract":"<p class=\"abstract_block\">Although sea turtles have received substantial focus worldwide, research on the immature life stages is still relatively limited. The latter is of particular importance, given that a large proportion of sea turtle populations comprises immature individuals. We set out to identify knowledge gaps and identify the main barriers hindering research in this field. We analyzed the perceptions of sea turtle experts through an online survey which gathered their opinions on the current state of affairs on immature sea turtle research, including species and regions in need of further study, priority research questions, and barriers that have interfered with the advancement of research. Our gap analysis indicates that studies on immature leatherback<span>&nbsp;</span><i>Dermochelys coriacea</i><span>&nbsp;</span>and hawksbill<span>&nbsp;</span><i>Eretmochelys imbricata</i><span>&nbsp;</span>turtles are lacking, as are studies on all species based in the Indian, South Pacific, and South Atlantic Oceans. Experts also perceived that studies in population ecology, namely on survivorship and demography, and habitat use/behavior, are needed to advance the state of knowledge on immature sea turtles. Our survey findings indicate the need for more inter-disciplinary research, collaborative efforts (e.g. data-sharing, joint field activities), and improved communication among researchers, funding bodies, stakeholders, and decision-makers.</p>","language":"English","publisher":"Inter-Research","doi":"10.3354/esr00916","usgsCitation":"Wildermann, N.E., Gredzens, C., Avens, L., Barrios-Garrido, H.A., Bell, I., Blumenthal, J., Bolten, A.B., McNeill, J.B., Casale, P., Di Domenico, M., Domit, C.A., Epperly, S.P., Godfrey, M.H., Godley, B.J., Gonzalez-Carman, V., Hamann, M., Hart, K., Ishihara, T., Mansfield, K., Metz, T.L., Miller, J.D., Pilcher, N.J., Read, M.A., Sasso, C., Seminoff, J.A., Seney, E.E., Southwood Williard, A., Tomas, J., Velez-Rubio, G.M., Ware, M., Williams, J.L., Wyneken, J., and Fuentes, M.M., 2018, Informing research priorities for immature sea turtles through expert elicitation: Endangered Species Research, v. 37, p. 55-76, https://doi.org/10.3354/esr00916.","productDescription":"22 p.","startPage":"55","endPage":"76","ipdsId":"IP-097968","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00916","text":"Publisher Index Page"},{"id":358545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10a930e4b034bf6a7e506c","contributors":{"authors":[{"text":"Wildermann, Natalie E.","contributorId":209783,"corporation":false,"usgs":false,"family":"Wildermann","given":"Natalie","email":"","middleInitial":"E.","affiliations":[{"id":37980,"text":"Marine Turtle Research, Ecology and Conservation Group, Florida State University, Tallahassee, FL, USA 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,{"id":70201693,"text":"70201693 - 2018 - A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","interactions":[],"lastModifiedDate":"2018-12-21T13:38:22","indexId":"70201693","displayToPublicDate":"2018-10-01T13:38:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","docAbstract":"<p><span>Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands&nbsp;</span><i>versus</i><span>&nbsp;non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R</span><sup>2</sup><span>&nbsp;value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at&nbsp;</span><a rel=\"noreferrer noopener\" href=\"http://www.croplands.org/\" target=\"_blank\" data-mce-href=\"http://www.croplands.org/\">www.croplands.org</a><span>&nbsp;and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC)&nbsp;</span><a rel=\"noreferrer noopener\" href=\"https://www.lpdaac.usgs.gov/node/1282\" target=\"_blank\" data-mce-href=\"https://www.lpdaac.usgs.gov/node/1282\">https://www.lpdaac.usgs.gov/node/1282</a><span>.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2018.07.017","usgsCitation":"Teluguntla, P., Thenkabail, P.S., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., and Huete, A., 2018, A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform: ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 325-340, 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,{"id":70198509,"text":"sir20185106 - 2018 - Simulation of groundwater flow, 1895–2010, and effects of additional groundwater withdrawals on future stream base flow in the Elkhorn and Loup River Basins, central Nebraska—Phase three","interactions":[],"lastModifiedDate":"2018-10-02T10:59:41","indexId":"sir20185106","displayToPublicDate":"2018-10-01T11:33:36","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5106","title":"Simulation of groundwater flow, 1895–2010, and effects of additional groundwater withdrawals on future stream base flow in the Elkhorn and Loup River Basins, central Nebraska—Phase three","docAbstract":"<p>The U.S.&nbsp;Geological Survey, in cooperation with the Lewis and Clark, Lower Elkhorn, Lower Loup, Lower Platte North, Lower Niobrara, Middle Niobrara, Upper Elkhorn, and the Upper Loup Natural Resources Districts, designed a study to refine the spatial and temporal discretization of a previously modeled area. This updated study focused on a 30,000-square-mile area of the High Plains aquifer and constructed regional groundwater-flow models to evaluate the effects of groundwater withdrawal on stream base flow in the Elkhorn and Loup River Basins, Nebraska. The model was calibrated to match groundwater-level and base-flow data from the stream-aquifer system from pre-1940 through 2010 (including predevelopment [pre-1895], early development [1895–1940], and historical development [1940 through 2010] conditions) using an automated parameter-estimation method. The calibrated model then was used to simulate hypothetical development conditions (2011 through 2060). Predicted changes to stream base flow based on simulated changes to groundwater withdrawal will aid in developing strategies for management of hydrologically connected water supplies.<br></p><p>Additional wells were simulated throughout the model domain and pumped for 50&nbsp;years to assess the effect of wells on aquifer depletions, including stream base flow. The percentage of withdrawal for each well after 50&nbsp;years, which was compensated by aquifer reductions to stream base flow, storage, or evapotranspiration, was computed and mapped. These depletions are influenced by aquifer properties, time, and distance from the well. Stream base-flow depletion results showed that the closer the added well was to a stream, the greatest the effect on the stream base flow. Areas of stream base-flow depletion percentages greater than 80&nbsp;percent were generally within 1&nbsp;mile (mi) from the stream. The distance increased to 6&nbsp;mi near the confluence of the Dismal and Middle Loup Rivers, and the North Loup and Calamus Rivers. The percentage of stream base-flow depletion decreased as the distance from the stream increased. Areas more than 10&nbsp;mi from the stream generally had a stream base-flow depletion of 10&nbsp;percent or less. Evapotranspiration depletion was largest in areas closest to streams, specifically in the Elkhorn River watershed. It was also larger in areas of interdunal wetlands within the Sand Hills. Evapotranspiration depletion was negligible in areas greater than 5&nbsp;mi from a stream, with the exception of interdunal areas in Cherry, Grant, and Arthur Counties. The storage depletion percentage increased as the distance from a stream increased. Storage depletion was largest in areas between streams. Areas experiencing the smallest amount of storage depletion were adjacent to streams. Calibrated model outputs and streamflow depletion analysis are publicly available online.<br></p><p>Accuracy of the simulations is affected by input data limitations, system simplifications, assumptions, and resources available at the time of the simulation construction and calibration. Most of the important limitations relate either to data used as simulation inputs or to data used to estimate simulation inputs. Development of the regional simulations focused on generalized hydrogeologic characteristics within the study area and did not attempt to describe variations important to local-scale conditions. These simulations are most appropriate for analyzing groundwater-management scenarios for large areas and during long periods and are not suitable for analysis of small areas or short periods.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185106","collaboration":"Prepared in cooperation with the Lewis and Clark, Lower Elkhorn, Lower Loup, Lower Platte North, Lower Niobrara, Middle Niobrara, Upper Elkhorn, and  Upper Loup Natural Resources Districts","usgsCitation":"Flynn, A.T., and Stanton, J.S., 2018, Simulation of groundwater flow, 1895–2010, and effects of additional groundwater withdrawals on future stream base flow in the Elkhorn and Loup River Basins, central Nebraska—Phase three: U.S. Geological Survey Scientific Investigations Report 2018–5106, 65 p., https://doi.org/10.3133/sir20185106.","productDescription":"Report: ix, 65 p.; Data Releases","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-085070","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":357262,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RITFNL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT groundwater flow model used to evaluate groundwater flow in the Elkhorn and Loup River Basins, Central Nebraska, Phase Three: U.S. Geological Survey data release"},{"id":356818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5106/coverthb3.jpg"},{"id":357263,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UA3UUD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-level and digital data for the Elkhorn and Loup River Basins groundwater flow model, Phase Three"},{"id":357261,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5106/sir20185106.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5106"}],"country":"United States","state":"Nebraska","otherGeospatial":"Elkhorn and Loup River Basins","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -102.14216807317632,\n              40.58107734258557\n            ],\n            [\n              -97.251923792,\n              40.58107734258557\n            ],\n            [\n              -97.251923792,\n              42.959577151422394\n            ],\n            [\n              -102.14216807317632,\n              42.959577151422394\n            ],\n            [\n              -102.14216807317632,\n              40.58107734258557\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_ne@usgs.gov\" href=\"mailto:%20dc_ne@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/ne-water\" href=\"https://www.usgs.gov/centers/ne-water\">Nebraska Water Science Center</a><br>U.S. Geological Survey<br>5231 South 19th Street<br>Lincoln, NE 68512</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Simulation of Groundwater Flow</li><li>Simulation of Effect of Additional Groundwater Withdrawals on Future Stream Base-Flow, Evapotranspiration, and Storage Depletion</li><li>Model Assumptions</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li><li>Appendix Figures</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-10-01","noUsgsAuthors":false,"publicationDate":"2018-10-01","publicationStatus":"PW","scienceBaseUri":"5bc02f82e4b0fc368eb5386f","contributors":{"authors":[{"text":"Flynn, Amanda T. 0000-0001-9768-2076 aflynn@usgs.gov","orcid":"https://orcid.org/0000-0001-9768-2076","contributorId":176644,"corporation":false,"usgs":true,"family":"Flynn","given":"Amanda","email":"aflynn@usgs.gov","middleInitial":"T.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanton, Jennifer S. 0000-0002-2520-753X jstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-2520-753X","contributorId":830,"corporation":false,"usgs":true,"family":"Stanton","given":"Jennifer","email":"jstanton@usgs.gov","middleInitial":"S.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741805,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70199840,"text":"70199840 - 2018 - Occupancy modeling species–environment relationships with non‐ignorable survey designs","interactions":[],"lastModifiedDate":"2018-10-02T11:16:16","indexId":"70199840","displayToPublicDate":"2018-10-01T11:16:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Occupancy modeling species–environment relationships with non‐ignorable survey designs","docAbstract":"<p><span>Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non‐probability surveys are combined or unequal selection probabilities are used, the design may be non‐ignorable. We outline the use of pseudo‐maximum likelihood estimation for site‐occupancy models to account for such non‐ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo‐likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non‐detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo‐maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non‐ignorable sampling designs was biased, whereas the pseudo‐likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non‐probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model‐based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence‐based conservation decision‐making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1754","usgsCitation":"Irvine, K.M., Rodhouse, T., Wright, W.J., and Olsen, A.R., 2018, Occupancy modeling species–environment relationships with non‐ignorable survey designs: Ecological Applications, v. 28, no. 6, p. 1616-1625, https://doi.org/10.1002/eap.1754.","productDescription":"10 p.","startPage":"1616","endPage":"1625","ipdsId":"IP-088406","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468351,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://europepmc.org/articles/pmc6457115","text":"External Repository"},{"id":437730,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55MPV","text":"USGS data release","linkHelpText":"Software Supplement to accompany 'Estimating Species-Environment Relationships with Non-ignorable Sampling Designs'"},{"id":358015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-19","publicationStatus":"PW","scienceBaseUri":"5bc02f83e4b0fc368eb53871","contributors":{"authors":[{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":746859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":746860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Wilson J. 0000-0003-4276-3850 wjwright@usgs.gov","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":198317,"corporation":false,"usgs":true,"family":"Wright","given":"Wilson","email":"wjwright@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":746862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olsen, Anthony R.","contributorId":208362,"corporation":false,"usgs":false,"family":"Olsen","given":"Anthony","email":"","middleInitial":"R.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":747118,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70203155,"text":"70203155 - 2018 - Great Lakes coastal fish habitat classification and assessment","interactions":[],"lastModifiedDate":"2019-06-27T08:04:17","indexId":"70203155","displayToPublicDate":"2018-10-01T09:49:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Great Lakes coastal fish habitat classification and assessment","docAbstract":"<p><span>Basin-scale assessment of fish habitat in Great Lakes&nbsp;coastal ecosystems&nbsp;would increase our ability to prioritize fish&nbsp;habitat management and restoration&nbsp;actions. As a first step in this direction, we identified key habitat factors associated with highest probability of occurrence for several societally and ecologically important coastal fish species as well as community metrics, using data from the Great Lakes Aquatic Habitat Framework (GLAHF), Great Lakes&nbsp;Environmental Indicators&nbsp;(GLEI) and&nbsp;Coastal Wetland&nbsp;Monitoring Program (CWMP). Secondly, we assessed whether species-specific habitat was threatened by watershed-level anthropogenic stressors. In the southern Great Lakes, key habitat factors for determining presence/absence of several species of coastal fish were&nbsp;chlorophyll&nbsp;concentrations,&nbsp;turbidity, and wave height, whereas in the northern ecoprovince temperature was the major habitat driver for most of the species modeled. Habitat factors best explaining fish richness and diversity were bottom slope and chlorophyll&nbsp;</span><i>a</i><span>. These models could likely be further improved with addition of high-resolution&nbsp;submerged macrophytecomplexity data which are currently unavailable at the basin-wide scale. Proportion of&nbsp;invasive species&nbsp;was correlated primarily with increasing maximum observed inorganic turbidity and chlorophyll&nbsp;</span><i>a</i><span>. We also demonstrate that preferred habitat for several coastal species and high-diversity areas overlap with areas of high watershed stress. Great Lakes coastal wetland fish are a large contributor to ecosystem services as well as commercial and recreational&nbsp;fishery&nbsp;harvest, and scalable basin-wide habitat models developed in this study may be useful for informing management actions targeting specific species or overall coastal fish biodiversity.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2018.07.007","usgsCitation":"Kovalenko, K.E., L.B. Johnson, Riseng, C.M., Cooper, M.J., Johnson, K., L. A. Mason, McKenna, J.E., Sparks-Jackson, B.L., and D.G. Uzarski, 2018, Great Lakes coastal fish habitat classification and assessment: Journal of Great Lakes Research, v. 44, no. 5, p. 1100-1109, https://doi.org/10.1016/j.jglr.2018.07.007.","productDescription":"10 p.","startPage":"1100","endPage":"1109","ipdsId":"IP-099461","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":363174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92,\n              40\n            ],\n            [\n              -74,\n              40\n            ],\n            [\n              -74,\n              49.5\n            ],\n            [\n              -92,\n              49.5\n            ],\n            [\n              -92,\n              40\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"44","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kovalenko, K. E.","contributorId":215010,"corporation":false,"usgs":false,"family":"Kovalenko","given":"K.","email":"","middleInitial":"E.","affiliations":[{"id":32419,"text":"U. of Minnesota","active":true,"usgs":false}],"preferred":false,"id":761416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"L.B. Johnson","contributorId":215011,"corporation":false,"usgs":false,"family":"L.B. Johnson","affiliations":[{"id":32419,"text":"U. of Minnesota","active":true,"usgs":false}],"preferred":false,"id":761417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riseng, C. M.","contributorId":215012,"corporation":false,"usgs":false,"family":"Riseng","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":39155,"text":"U. of Michigan","active":true,"usgs":false}],"preferred":false,"id":761418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cooper, M. J.","contributorId":215013,"corporation":false,"usgs":false,"family":"Cooper","given":"M.","email":"","middleInitial":"J.","affiliations":[{"id":18886,"text":"Northland College","active":true,"usgs":false}],"preferred":false,"id":761419,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, K.","contributorId":215014,"corporation":false,"usgs":false,"family":"Johnson","given":"K.","email":"","affiliations":[{"id":32419,"text":"U. of Minnesota","active":true,"usgs":false}],"preferred":false,"id":761420,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"L. A. Mason","contributorId":215015,"corporation":false,"usgs":false,"family":"L. A. Mason","affiliations":[{"id":39155,"text":"U. of Michigan","active":true,"usgs":false}],"preferred":false,"id":761421,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":761415,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sparks-Jackson, B. L.","contributorId":215016,"corporation":false,"usgs":false,"family":"Sparks-Jackson","given":"B.","email":"","middleInitial":"L.","affiliations":[{"id":39155,"text":"U. of Michigan","active":true,"usgs":false}],"preferred":false,"id":761422,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"D.G. Uzarski","contributorId":215017,"corporation":false,"usgs":false,"family":"D.G. Uzarski","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":761423,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70227750,"text":"70227750 - 2018 - Persistence-based area prioritization for conservation: Applying occupancy and habitat threats and risks analyses","interactions":[],"lastModifiedDate":"2022-01-28T15:30:42.063646","indexId":"70227750","displayToPublicDate":"2018-10-01T09:24:24","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Persistence-based area prioritization for conservation: Applying occupancy and habitat threats and risks analyses","docAbstract":"<p><span>Effective habitat conservation is predicated on maintaining high levels or increasing local persistence probability of the species it purports to protect. Thus, methodological approaches that improve the inferential value of local persistence are of utmost value to guide conservation planning as they inform area selection processes. Herein we used the painted bunting&nbsp;</span><i>Passerina ciris,</i><span>&nbsp;a species of conservation interest in North Carolina, as an illustrative case that combined single-season, single-species occupancy analyses and a threats and risk decision support tool to rank five areas of conservation interest in terms of local persistence probability. We used survey data from two seasons (2008–2009) grouped into 21 natal dispersal sampling units and land-cover data from 12 habitat classes to establish the relationship between local occupancy probability and habitat. Occupancy increased most strongly with increasing amount of maritime forest. Projections to year 2050, relative to year 2000, indicated that a potential loss of maritime forest of 200–1,300 ha, depending on the area of interest. Projected loss was lowest at Bald Head Island–Wilmington (2%) and highest at Camp Lejune (27%). Bald Head Island–Wilmington ranked highest in projected local persistence probability (0.91; 95% confidence interval [CI] = 0.53–0.99), whereas Top Sail–Hammocks Beach Park ranked lowest (0.28; 95% CI = 0.03–0.82). Estimates of local persistence offer decision-makers another criterion to prioritize areas for conservation and help guide efforts aimed at maintaining or enhancing local persistence. These include in situ habitat management, expanding or connecting existing areas of interest. In the future, we recommend the use of multiseason occupancy models, coupled with measures of uncertainty of land-cover projections, to strengthen inferences about local persistence, particularly useful in nonstationary landscapes driven by human activities.</span></p>","language":"English","publisher":"Allen Press","doi":"10.3996/112017-JFWM-089","usgsCitation":"Yirka, L., Collazo, J.A., Williams, S.G., and Cobb, D.T., 2018, Persistence-based area prioritization for conservation: Applying occupancy and habitat threats and risks analyses: Journal of Fish and Wildlife Management, v. 9, no. 2, p. 554-564, https://doi.org/10.3996/112017-JFWM-089.","productDescription":"11 p.","startPage":"554","endPage":"564","ipdsId":"IP-091420","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468355,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/112017-jfwm-089","text":"Publisher Index Page"},{"id":395051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.6126708984375,\n              33.80197351806589\n            ],\n            [\n              -76.53350830078125,\n              33.80197351806589\n            ],\n            [\n              -76.53350830078125,\n              34.87015842600913\n            ],\n            [\n              -78.6126708984375,\n              34.87015842600913\n            ],\n            [\n              -78.6126708984375,\n              33.80197351806589\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Yirka, L. M.","contributorId":272521,"corporation":false,"usgs":false,"family":"Yirka","given":"L. M.","affiliations":[{"id":48918,"text":"North Carolina Museum of Natural Sciences","active":true,"usgs":false}],"preferred":false,"id":832034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, S. G.","contributorId":272522,"corporation":false,"usgs":false,"family":"Williams","given":"S.","email":"","middleInitial":"G.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":832036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cobb, D. T.","contributorId":272523,"corporation":false,"usgs":false,"family":"Cobb","given":"D.","email":"","middleInitial":"T.","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":832037,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210081,"text":"70210081 - 2018 - The San Andreas Fault System--Complexities along a major transform fault system and relation to earthquake hazards","interactions":[],"lastModifiedDate":"2020-05-13T14:33:48.440524","indexId":"70210081","displayToPublicDate":"2018-09-28T09:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"The San Andreas Fault System--Complexities along a major transform fault system and relation to earthquake hazards","docAbstract":"The San Andreas Fault System is a 1300-km-long transform boundary that accommodates motion between the North American and Pacific Plates.  New technologies and data reveal rich details about the present configuration of faults, distribution of strain and associated seismic hazard on this complex network of faults.  This contribution provides a brief summary of the geologic history of the San Andreas Fault System, followed by an introduction to recent research that has changed understanding of the hazards along the main faults.  Organized by region, we highlight a selection of recent research using new geodetic techniques, improved topographic data, advanced geochronologic methods, and high-resolution geophysics.  The contribution ends with a review of the historic earthquakes on the San Andreas and San Jacinto Faults, comparing these to past ruptures interpreted from paleoseismic studies.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Transform plate boundaries and fracture zones","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-812064-4.00010-4","usgsCitation":"Scharer, K., and Streig, A., 2018, The San Andreas Fault System--Complexities along a major transform fault system and relation to earthquake hazards, chap. 10 <i>of</i> Transform plate boundaries and fracture zones, p. 249-269, https://doi.org/10.1016/B978-0-12-812064-4.00010-4.","productDescription":"21 p.","startPage":"249","endPage":"269","ipdsId":"IP-093013","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.14672851562499,\n              34.34343606848294\n            ],\n            [\n              -115.9442138671875,\n              34.34343606848294\n            ],\n            [\n              -115.9442138671875,\n              35.43381992014202\n            ],\n            [\n              -119.14672851562499,\n              35.43381992014202\n            ],\n            [\n              -119.14672851562499,\n              34.34343606848294\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":789032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Streig, Ashley","contributorId":189716,"corporation":false,"usgs":false,"family":"Streig","given":"Ashley","affiliations":[],"preferred":false,"id":789033,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70199767,"text":"70199767 - 2018 - A new modeling approach to prioritize riparian restoration to reduce sediment loading in two Virginia river basins","interactions":[],"lastModifiedDate":"2018-09-27T14:26:04","indexId":"70199767","displayToPublicDate":"2018-09-27T14:26:01","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"A new modeling approach to prioritize riparian restoration to reduce sediment loading in two Virginia river basins","docAbstract":"<p><span>Human impact, particularly land cover changes (e.g., agriculture, construction) increase erosion and sediment loading into streams. Benthic species are negatively affected by silt deposition that coats and embeds stream substrate. Given that riparian buffers are effective sediment filters, riparian restoration is increasingly implemented by conservation groups to protect stream habitats. Limited funding and a multitude of impaired streams warrant the need for cost-effective prioritization of potential restoration actions. We created a decision-support framework for conservation agencies and aquatic resource managers to prioritize riparian restoration efforts. Our framework integrates GIS data and field surveys into a statistical model to predict instream silt from estimates of upland soil loss and riparian filtration capacity. We focus specifically on prioritizing sites in upper sections of the Roanoke and Nottoway river basins (Virginia, US) based on observed records of Roanoke logperch (</span><i class=\"EmphasisTypeItalic \">Percina rex</i><span>), an imperiled sediment-sensitive species. Our statistical approach examines soil characteristics, land cover, precipitation, topography, and annual soil loss estimates from the empirically derived Revised Universal Soil Loss Equation, combined with land cover-based riparian filtration capacity as potential stream habitat predictors. We found riparian filtration capacity to be a significant predictor of silt cover, while precipitation was a significant predictor of embeddedness. Spatial scale was also a factor, in that spatial variance in silt cover and embeddedness was more accurately predicted at smaller spatial extents. Ultimately, our model can be used as a prioritization tool for mitigating high siltation areas, or for protecting low soil erosion areas.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00267-018-1078-6","usgsCitation":"Scott, L.N., Villamagna, A.M., and Angermeier, P., 2018, A new modeling approach to prioritize riparian restoration to reduce sediment loading in two Virginia river basins: Environmental Management, v. 62, no. 4, p. 721-739, https://doi.org/10.1007/s00267-018-1078-6.","productDescription":"19 p.","startPage":"721","endPage":"739","ipdsId":"IP-090172","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/99269","text":"External Repository"},{"id":357850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia","volume":"62","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-16","publicationStatus":"PW","scienceBaseUri":"5bc02f87e4b0fc368eb5388b","contributors":{"authors":[{"text":"Scott, Lisa N.","contributorId":208250,"corporation":false,"usgs":false,"family":"Scott","given":"Lisa","email":"","middleInitial":"N.","affiliations":[{"id":35056,"text":"Plymouth State University","active":true,"usgs":false}],"preferred":false,"id":746534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villamagna, Amy M.","contributorId":201421,"corporation":false,"usgs":false,"family":"Villamagna","given":"Amy","email":"","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":746535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angermeier, Paul L. 0000-0003-2864-170X","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":204519,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":746533,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70199768,"text":"70199768 - 2018 - Diel fledging patterns among grassland passerines: Relative impacts of energetics and predation risk","interactions":[],"lastModifiedDate":"2018-09-27T14:23:05","indexId":"70199768","displayToPublicDate":"2018-09-27T14:22:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3544,"text":"The Auk","onlineIssn":"1938-4254","printIssn":"0004-8038","active":true,"publicationSubtype":{"id":10}},"title":"Diel fledging patterns among grassland passerines: Relative impacts of energetics and predation risk","docAbstract":"<p><span>The time of day that nestlings fledge from a nest is thought to be shaped by predation risk and energetics. To minimize predation risk, fledging is predicted to start as early in the day as possible so that nestlings can maximize time outside the nest to find a safe place to stay before nightfall. Fledging times are predicted to be tightly grouped and to not be affected by the number of nestlings, given that all nestlings are responding to the same relative risk of predation. Conversely, energetic considerations predict that fledging time of day should vary so that nestlings can maximize energy intake before having to forage for themselves. However, data to evaluate the relative importance of these drivers in grassland birds are scarce because of the difficulty of observing nestlings as they fledge. We used nest surveillance video from 178 nests to evaluate how the initiation and duration of fledging varied among 7 grassland passerine species, as well as by the number of nestlings in the nest and fledging date. Fledging initiation varied most strongly by species, with some effects of date. Across species, the median start time of fledging was 4.55 hr after sunrise. Fledging before the solstice started ∼30 min earlier compared to fledging at or after the solstice. Fledging duration increased with number of nestlings in the nest and was spread over &gt;1 day in 21% of nests. While our results primarily supported the hypothesis that fledging is motivated by energetic considerations, additional data on basic life history traits and behavior will be needed to fully understand how fledging grassland birds balance energetics against predation risk.</span></p>","language":"English","publisher":"American Ornithological Society","doi":"10.1642/AUK-17-213.1","usgsCitation":"Ribic, C., Ng, C., Koper, N., Ellison, K., Pietz, P., and Rugg, D.J., 2018, Diel fledging patterns among grassland passerines: Relative impacts of energetics and predation risk: The Auk, v. 135, no. 4, p. 1100-1112, https://doi.org/10.1642/AUK-17-213.1.","productDescription":"13 p.","startPage":"1100","endPage":"1112","ipdsId":"IP-090183","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468363,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1642/AUK-17-213.1","text":"External Repository"},{"id":357849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"135","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f87e4b0fc368eb5388d","contributors":{"authors":[{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":746536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ng, Christoph","contributorId":208253,"corporation":false,"usgs":false,"family":"Ng","given":"Christoph","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":746538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koper, Nicola","contributorId":208255,"corporation":false,"usgs":false,"family":"Koper","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":746541,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellison, Kevin","contributorId":208254,"corporation":false,"usgs":false,"family":"Ellison","given":"Kevin","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":746539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pietz, Pamela J. ppietz@usgs.gov","contributorId":2382,"corporation":false,"usgs":true,"family":"Pietz","given":"Pamela J.","email":"ppietz@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":746537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rugg, David J.","contributorId":171931,"corporation":false,"usgs":false,"family":"Rugg","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":746540,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70199765,"text":"70199765 - 2018 - Sensor suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite","interactions":[],"lastModifiedDate":"2018-11-14T09:11:54","indexId":"70199765","displayToPublicDate":"2018-09-27T14:20:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Sensor suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite","docAbstract":"<p><span>To standardize parameters used in seismometer testing and calibration and to make these algorithms accessible to the seismological community, we have developed a new seismometer testing software package called Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. This software is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. Our goal is not to be all‐inclusive but instead to focus on a few of the instrumentation tests we view as critical when verifying a sensor’s performance. The tests include self‐noise, relative azimuth, relative gain, and estimation of the poles and zeros. For the self‐noise and the relative azimuth, we also include three‐component versions of these tests to allow for the case of sensors with potentially different orientations (e.g., boreholes). The software has been made available on GitHub with the hope that it will be useful for other seismologists who need to quickly verify various sensor parameters without having to write their own versions of the algorithms. Furthermore, by using a common platform and processing algorithms, it becomes possible to compare results among different tests with similar processing methods being used for both.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220180174","usgsCitation":"Kearns, A., Ringler, A.T., Holland, J., Storm, T., Wilson, D.C., and Anthony, R.E., 2018, Sensor suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite: Seismological Research Letters, v. 89, no. 6, p. 2374-2385, https://doi.org/10.1785/0220180174.","productDescription":"12 p.","startPage":"2374","endPage":"2385","ipdsId":"IP-100486","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437738,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XXBOVR","text":"USGS data release","linkHelpText":"ASL Sensor Test Suite"},{"id":357847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-12","publicationStatus":"PW","scienceBaseUri":"5bc02f87e4b0fc368eb5388f","contributors":{"authors":[{"text":"Kearns, A.","contributorId":208247,"corporation":false,"usgs":false,"family":"Kearns","given":"A.","email":"","affiliations":[{"id":37766,"text":"KBRwyle Technology Solutions Incorporated","active":true,"usgs":false}],"preferred":false,"id":746524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":746525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holland, James 0000-0002-6973-9722 jholland@usgs.gov","orcid":"https://orcid.org/0000-0002-6973-9722","contributorId":208248,"corporation":false,"usgs":true,"family":"Holland","given":"James","email":"jholland@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":746526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Tyler 0000-0002-6787-9545 tstorm@usgs.gov","orcid":"https://orcid.org/0000-0002-6787-9545","contributorId":152165,"corporation":false,"usgs":true,"family":"Storm","given":"Tyler","email":"tstorm@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":746527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":746528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":746529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70199755,"text":"70199755 - 2018 - Practical approaches to maximizing the resolution of sparker seismic reflection data","interactions":[],"lastModifiedDate":"2019-09-16T11:44:51","indexId":"70199755","displayToPublicDate":"2018-09-27T14:09:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2668,"text":"Marine Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Practical approaches to maximizing the resolution of sparker seismic reflection data","docAbstract":"<p><span>Sparkers are a type of sound source widely used by the marine seismic community to provide high-resolution imagery of the shallow sub-bottom (i.e., &lt;&nbsp;1000&nbsp;m). Although sparkers are relatively simple, inexpensive, and high-frequency (100–2500&nbsp;Hz) sources, they have several potential pitfalls due to their complicated and unpredictable signature. In this study we quantify the source characteristics of several sparker systems and develop a suite of simple processing approaches for both single channel and multi-channel sparker data. In all cases, the results show improved vertical resolution and reflection coherency. Correcting for small static variations in multi-channel seismic (MCS) data is a critical first step to preserve the broad frequency content during stacking, and to reduce the shot-to-shot variability of outgoing and incoming signals. Application of predictive deconvolution to static-corrected, post-stack traces suppresses short-path multiples and restores the latent high-resolution reflection patterns. However, if shot-to-shot source signatures are recorded directly, pre-stack deterministic deconvolution followed by post-stack predictive deconvolution produces the most robust results. Processing sparker data without broadband techniques results in less confident or completely missed interpretations when compared to the broadband equivalent. If processed correctly, marine sparker data can provide exceptional sub-bottom imagery that rivals other more repeatable marine seismic sources (e.g., high-frequency air-guns).</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11001-018-9367-2","usgsCitation":"Kluesner, J.W., Brothers, D.S., Hart, P.E., Miller, N.C., and Hatcher, G.A., 2018, Practical approaches to maximizing the resolution of sparker seismic reflection data: Marine Geophysical Research, v. 40, no. 3, p. 279-301, https://doi.org/10.1007/s11001-018-9367-2.","productDescription":"12 p.","startPage":"279","endPage":"301","ipdsId":"IP-097450","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437739,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CV4FW6","text":"USGS data release","linkHelpText":"Minisparker and chirp seismic-reflection data of field activity 2014-645-FA collected in the outer Santa Barbara Channel, California, between 2014-11-12 to 2014-11-25 (ver. 2.0, March 2020)"},{"id":357841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-20","publicationStatus":"PW","scienceBaseUri":"5bc02f88e4b0fc368eb53895","contributors":{"authors":[{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatcher, Gerry A. 0000-0001-7705-1509 ghatcher@usgs.gov","orcid":"https://orcid.org/0000-0001-7705-1509","contributorId":208239,"corporation":false,"usgs":true,"family":"Hatcher","given":"Gerry","email":"ghatcher@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70198658,"text":"ofr20181130 - 2018 - U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Haynesville Formation, U.S. Gulf Coast, 2016","interactions":[],"lastModifiedDate":"2018-09-27T15:15:32","indexId":"ofr20181130","displayToPublicDate":"2018-09-27T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1130","title":"U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Haynesville Formation, U.S. Gulf Coast, 2016","docAbstract":"<h1>Introduction</h1><p>In 2016, the U.S. Geological Survey (USGS) completed an updated assessment of undiscovered, technically recoverable oil and gas resources in the Upper Jurassic Haynesville Formation of the onshore U.S. Gulf Coast Province (Paxton and others, 2017). The Haynesville Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for four assessment units (AUs): (1) Haynesville Western Shelf Carbonate Gas and Oil AU, (2) Haynesville Eastern Shelf Sandstone and Carbonate Oil and Gas AU, (3) Haynesville Shale Continuous Gas AU, and (4) Haynesville Shale Peripheral Continuous Gas AU. The revised assessment resulted in total estimated mean resources of 1.1 billion barrels of oil, 195.8 trillion cubic feet of gas, and 866 million barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2016 Haynesville Formation assessment.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181130","usgsCitation":"Paxton, S.T., Pitman, J.K., Kinney, S.A., Gianoutsos, N.J., Pearson, O.N., Whidden, K.J., Dubiel, R.F., Schenk, C.J., Burke, L.A., Klett, T.R., Leathers-Miller, H.M., Mercier, T.J., Haines, S.S., Varela, B.A., Le, P.A., Finn, T.M., Gaswirth, S.B., Hawkins, S.J., Marra, K.R., and Tennyson, M.E., 2018, U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Haynesville Formation, U.S. Gulf Coast, 2016: U.S. Geological Survey Open-File Report 2018–1130, 62 p., https://doi.org/10.3133/ofr20181130.","productDescription":"iii, 62 p.","onlineOnly":"Y","ipdsId":"IP-098784","costCenters":[{"id":164,"text":"Central Energy Resources Science 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The Bossier Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for three assessment units (AUs): (1) Bossier Eastern Shelf Sandstone Gas and Oil AU, (2) Bossier Western Shelf Sandstone Gas AU, and (3) Bossier Shale Continuous Gas AU. A fourth assessment unit, the Upper Jurassic Downdip Continuous Gas AU, was also defined but was not quantitatively assessed because of limited well data within the extent of the AU. The revised assessment resulted in total estimated mean resources of 2.9 billion barrels of oil, 108.6 trillion cubic feet of gas, and 1.1 billion barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2016 Bossier Formation assessment.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181134","usgsCitation":"Paxton, S.T., Pitman, J.K., Kinney, S.A., Gianoutsos, N.J., Pearson, O.N., Whidden, K.J., Dubiel, R.F., Schenk, C.J., Burke, L.A., Klett, T.R., Leathers-Miller, H.M., Mercier, T.J., Haines, S.S., Varela, B.A., Le, P.A., Finn, T.M., Gaswirth, S.B., Hawkins, S.J., Marra, K.R., and Tennyson, M.E., 2018, U.S. Geological Survey input-data forms for the assessment of the Upper Jurassic Bossier Formation, U.S. Gulf Coast, 2016: U.S. Geological Survey Open-File Report 2018–1134, 48 p., https://doi.org/10.3133/ofr20181134.","productDescription":"iii, 48 p.","onlineOnly":"Y","ipdsId":"IP-098783","costCenters":[{"id":164,"text":"Central Energy Resources Science 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,{"id":70198096,"text":"70198096 - 2018 - Variation in the vital rates of an Antarctic marine predator: the role of individual heterogeneity","interactions":[],"lastModifiedDate":"2018-10-04T13:09:02","indexId":"70198096","displayToPublicDate":"2018-09-26T12:31:33","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variation in the vital rates of an Antarctic marine predator: the role of individual heterogeneity","docAbstract":"<p><span>Variation in life‐history traits such as lifespan and lifetime reproductive output is thought to arise, in part, due to among‐individual differences in the underlying probabilities of survival and reproduction. However, the stochastic nature of demographic processes can also generate considerable variation in fitness‐related traits among otherwise‐identical individuals. An improved understanding of life‐history evolution and population dynamics therefore depends on evaluating the relative role of each of these processes. Here, we used a 33‐yr data set with reproductive histories for 1,274 female Weddell seals from Erebus Bay, Antarctica, to assess the strength of evidence for among‐individual heterogeneity in the probabilities of survival and reproduction, while accounting for multiple other sources of variation in vital rates. Our analysis used recent advances in Bayesian model selection techniques and diagnostics to directly compare model fit and predictive power between models that included individual effects on survival and reproduction to those that did not. We found strong evidence for costs of reproduction to both survival and future reproduction, with breeders having rates of survival and subsequent reproduction that were 3% and 6% lower than rates for non‐breeders. We detected age‐related changes in the rates of survival and reproduction, but the patterns differed for the two rates. Survival rates steadily declined from 0.92 at age 7 to 0.56 at the maximal age of 31&nbsp;yr. In contrast, reproductive rates increased from 0.68 at age 7 to 0.79 at age 16 and then steadily declined to 0.37 for the oldest females. Models that included individual effects explained more variation in observed life histories and had better estimated predictive power than those that did not, indicating their importance in understanding sources of variation among individuals in life‐history traits. We found that among‐individual heterogeneity in survival was small relative to that for reproduction. Our study, which found patterns of variation in vital rates that are consistent with a series of predictions&nbsp;from life‐history theory, is the first to provide a thorough assessment of variation in important vital rates for a long‐lived, high‐latitude marine mammal while taking full advantage of recent developments in model evaluation.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2481","usgsCitation":"Paterson, J.T., Rotella, J.J., Link, W.A., and Garrott, R.A., 2018, Variation in the vital rates of an Antarctic marine predator: the role of individual heterogeneity: Ecology, v. 99, no. 10, p. 2385-2396, https://doi.org/10.1002/ecy.2481.","productDescription":"12 p.","startPage":"2385","endPage":"2396","ipdsId":"IP-099409","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":460843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2481","text":"Publisher Index Page"},{"id":357774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"10","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-12","publicationStatus":"PW","scienceBaseUri":"5bc02f8be4b0fc368eb538a9","contributors":{"authors":[{"text":"Paterson, J. Terrill","contributorId":206296,"corporation":false,"usgs":false,"family":"Paterson","given":"J.","email":"","middleInitial":"Terrill","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":740000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rotella, Jay J.","contributorId":37271,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay","email":"","middleInitial":"J.","affiliations":[{"id":5098,"text":"Department of Ecology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":740001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":739999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrott, Robert A.","contributorId":171537,"corporation":false,"usgs":false,"family":"Garrott","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":740002,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70199711,"text":"70199711 - 2018 - A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language","interactions":[],"lastModifiedDate":"2018-09-26T11:05:12","indexId":"70199711","displayToPublicDate":"2018-09-26T11:05:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language","docAbstract":"<p><span>Recent studies hypothesize various causes of species‐level trait covariation, namely size (e.g., metabolic theory of ecology and leaf economics spectrum), pace‐of‐life (e.g., slow‐to‐fast continuum; lifestyle continuum), evolutionary history (e.g., phylogenetic conservatism), and ecological conditions (e.g., stabilizing selection). Various methods have been used in attempts to partition trait correlation among these influences (e.g., univariate analysis, principal components analysis, and factor analysis). However, it is not clear that the implied causal structure assumed by these methods matches the hypothesized causal structure driving trait correlations, a situation that can potentially lead to biased estimates and incorrect partitioning among mechanisms. Here, we propose the application of graphical causal models (GCM) for across‐kingdom synthesis and to aid researchers in their selection of correct analytical strategies. Graphical causal models use causal diagrams (i.e., box‐and‐arrow graphs) to represent expert knowledge of the data‐generating processes to analytically investigate the possibility of identifying hypothesized causal associations. We developed a causal diagram that synthesizes prominent hypotheses of trait covariation. Using the causal diagram, we (1) derived a quantitative expression to partition trait covariance among its hypothesized causal elements (i.e., size, pace‐of‐life, evolutionary history, and ecological conditions) and (2) developed analytic strategies to attribute trait covariance among the hypothesized causal elements under real‐world data availability, namely unobserved variables (i.e., pace‐of‐life) and confounding variables (i.e., evolutionary history and ecological conditions). Finally, we tested each analytic strategy by simulating trait datasets and, after incorporating the data limitations, tested their ability to correctly partition trait covariance. The analytical strategies were able to correctly partition trait covariance into the hypothesized causal elements of size, pace‐of‐life, and the historical effects of evolutionary history and ecological conditions. We demonstrate the efficacy of these strategies by applying them to a widely used trait dataset. Overall, the application of GCM revealed that researchers have used inappropriate measures to represent their theoretical constructs and have relied on analytical strategies that violated their causal assumptions, likely resulting in biased estimates. We discuss how this mismatch between theoretical language and statistical methods is prevalent in species‐level, trait‐based research and call for future studies to address these limitations.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2422","usgsCitation":"Cronin, J.P., and Schoolmaster, D., 2018, A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language: Ecosphere, v. 9, no. 9, p. 1-15, https://doi.org/10.1002/ecs2.2422.","productDescription":"e02422; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-066629","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468366,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2422","text":"Publisher Index Page"},{"id":357754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"9","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-17","publicationStatus":"PW","scienceBaseUri":"5bc02f8ce4b0fc368eb538b1","contributors":{"authors":[{"text":"Cronin, James P. 0000-0001-6791-5828 jcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-6791-5828","contributorId":5834,"corporation":false,"usgs":true,"family":"Cronin","given":"James","email":"jcronin@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":746297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoolmaster, Donald 0000-0003-0910-4458 schoolmasterd@usgs.gov","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":156350,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","email":"schoolmasterd@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":746298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
]}