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Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from &gt;2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content.</span></p>","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.1716137115","usgsCitation":"Sequeira, A., Rodriguez, J., Eguíluz, V., Harcourt, R., Hindell, M., Sims, D., Duarte, C., Costa, D., Fernandez-Gracia, J., Ferreira, L., Hays, G., Heupel, M., Meekan, M., Aven, A., Bailleul, F., Baylis, A.M., Berumen, M.L., Braun, C.D., Burns, J., Caley, M., Campbell, R., Carmichael, R., Clua, E., Einoder, L.D., Friedlaender, A., Goebel, M.E., Goldsworthy, S., Guinet, C., Gunn, J., Hamer, D., Hammerschlag, N., Hammill, M., Hückstädt, L., Humphries, N., Lea, M., Lowther, A., Mackay, A., McHuron, E., Mckenzie, J., McLeay, L., McMahon, C., Mengersen, K., Muelbert, M.M., Pagano, A.M., Page, B., Queiroz, N., Robinson, P.W., Shaffer, S.A., Shivji, M., Skomal, G., Thorrold, S., Villegas-Amtmann, S., Weise, M., Wells, R., Wetherbee, B., Wiebkin, A., Wienecke, B., and Thums, M., 2018, Convergence of marine megafauna movement patterns in coastal and open oceans: Proceedings of the National Academy of Sciences, v. 115, no. 12, p. 3072-3077, https://doi.org/10.1073/pnas.1716137115.","productDescription":"6 p.","startPage":"3072","endPage":"3077","ipdsId":"IP-089008","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":468941,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1716137115","text":"Publisher Index Page"},{"id":437992,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RV0MK4","text":"USGS data release","linkHelpText":"Locations Collected 1985-2015 from Female Polar Bears (Ursus maritimus) with Dependent Young Instrumented in the Southern Beaufort Sea with Satellite-linked Transmitters by the USGS"},{"id":357121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5b98a2eae4b0702d0e84300e","contributors":{"authors":[{"text":"Sequeira, A. 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,{"id":70201634,"text":"70201634 - 2018 - Modeling wildfire-induced permafrost deformation in an Alaskan boreal forest using InSAR observations","interactions":[],"lastModifiedDate":"2022-04-22T16:43:48.161005","indexId":"70201634","displayToPublicDate":"2018-03-01T13:51:03","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Modeling wildfire-induced permafrost deformation in an Alaskan boreal forest using InSAR observations","docAbstract":"<p><span>The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs10030405","usgsCitation":"Molan, Y.E., Kim, J., Lu, Z., Wylie, B.K., and Zhu, Z., 2018, Modeling wildfire-induced permafrost deformation in an Alaskan boreal forest using InSAR observations: Remote Sensing, v. 10, no. 3, 405, 17 p., https://doi.org/10.3390/rs10030405.","productDescription":"405, 17 p.","ipdsId":"IP-090332","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":468942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10030405","text":"Publisher Index Page"},{"id":360566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -147.25,\n              65.67\n            ],\n            [\n              -146,\n              65.67\n            ],\n            [\n              -146,\n              66.33\n            ],\n            [\n              -147.25,\n              66.33\n            ],\n            [\n              -147.25,\n              65.67\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-06","publicationStatus":"PW","scienceBaseUri":"5c1b66e7e4b0708288c71d3a","contributors":{"authors":[{"text":"Molan, Yusef Eshqi","contributorId":211707,"corporation":false,"usgs":false,"family":"Molan","given":"Yusef","email":"","middleInitial":"Eshqi","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":754668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Jin-Woo","contributorId":211708,"corporation":false,"usgs":false,"family":"Kim","given":"Jin-Woo","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":754669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":754670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":754667,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70198481,"text":"70198481 - 2018 - A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","interactions":[],"lastModifiedDate":"2018-08-06T12:41:18","indexId":"70198481","displayToPublicDate":"2018-03-01T12:41:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2648,"text":"Malacologia","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A phylogenetic overview of the genus <i>Vertigo</i> O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","title":"A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","docAbstract":"<p><span>We document global phylogenetic pattern in the pupillid land snail genus&nbsp;</span><i>Vertigo</i><span>&nbsp;by analyses of nDNA (ITS1 and ITS2) and mtDNA (CytB and 16S) sequence from 424 individuals representing 91 putative specific and subspecific&nbsp;</span><i>Vertigo</i><span>&nbsp;taxa. nDNA and mtDNA data were separately subjected to neighbor-joining, minimum evolution, maximum likelihood and Bayesian reconstruction methods, with conclusions being drawn from shared topological structures. Six highly supported, reciprocally monophyletic subgeneric level clades were identified:&nbsp;</span><i>Vertigo</i><span>,&nbsp;</span><i>Alaea</i><span>,&nbsp;</span><strong><i>Boreovertigo</i></strong><span>&nbsp;new subgenus,&nbsp;</span><i>Isthmia</i><span>,&nbsp;</span><i>Staurodon</i><span>&nbsp;and&nbsp;</span><i>Vertilla</i><span>. 88 species or subspecies were also confirmed, nine of which are new and formally described herein:&nbsp;</span><strong><i>V. beringiana</i></strong><span>,&nbsp;</span><strong><i>V. chiricahuensis</i></strong><span>,&nbsp;</span><strong><i>V. chytryi</i></strong><span>,&nbsp;</span><strong><i>V. genesioides</i></strong><span>,&nbsp;</span><strong><i>V. kodamai</i></strong><span>,&nbsp;</span><strong><i>V. kurilensis</i></strong><span>,&nbsp;</span><strong><i>V. lilljeborgi vinlandica</i></strong><span>,&nbsp;</span><strong><i>V. pimuensis</i></strong><span>&nbsp;and&nbsp;</span><strong><i>V. pisewensis</i></strong><span>. Thirteen taxa were synonymized:&nbsp;</span><i>V. arthuri basidens</i><span>,&nbsp;</span><i>V. arthuri hubrichti</i><span>,&nbsp;</span><i>V. arthuri paradoxa</i><span>&nbsp;(=&nbsp;</span><i>V. arthuri</i><span>);&nbsp;</span><i>V. allyniana</i><span>(=&nbsp;</span><i>V. modesta</i><span>);&nbsp;</span><i>V. andrusiana</i><span>&nbsp;(=&nbsp;</span><i>V. columbiana</i><span>);&nbsp;</span><i>V. conecuhensis</i><span>&nbsp;(=&nbsp;</span><i>V. alabamensis</i><span>);&nbsp;</span><i>V. dedecora tamagonari</i><span>&nbsp;(=&nbsp;</span><i>V. dedecora</i><span>);&nbsp;</span><i>V. elatior</i><span>,&nbsp;</span><i>V. idahoensis</i><span>&nbsp;(=&nbsp;</span><i>V. ventricosa</i><span>);&nbsp;</span><i>V. eogea</i><span>&nbsp;(=&nbsp;</span><i>V. ovata</i><span>);&nbsp;</span><i>V. modesta insculpta</i><span>&nbsp;(=&nbsp;</span><i>V. modesta concinnula</i><span>),&nbsp;</span><i>V. modesta microphasma</i><span>,&nbsp;</span><i>V. modesta sculptilis</i><span>&nbsp;(=&nbsp;</span><i>V. modesta castanea</i><span>). Qualitative observations of conchological features, ecological preferences and geographic coverage were conducted for each subgenus and genetically supported species or subspecies-level taxon. These demonstrated that: (1) a suite of diagnostic shell features usually exists to demarcate each species-level taxon; (2) shell features were incapable of defining genetically validated subgenera; (3) all subgenera had transcontinental ranges; (4) ⅓ of all species possess continental or trans-continental ranges, with very few having range extents &lt; 1,000 km; (5) all subgenera and fully ⅔ of global&nbsp;</span><i>Vertigo</i><span>&nbsp;species and subspecies are found in North America, more than 2.5 times the number found in central and eastern Asia, the second most diverse region. This is similar to several other molluscan groups, such as the polygyrid land snails and unionid bivalves for which North America is the global biodiversity hotspot.</span></p>","language":"English","publisher":"Institute of Malacology","doi":"10.4002/040.062.0104","usgsCitation":"Nekola, J.C., Chiba, S., Coles, B.F., Drost, C.A., von Proschwitz, T., and Horsak, M., 2018, A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae): Malacologia, v. 62, no. 1, p. 21-161, https://doi.org/10.4002/040.062.0104.","productDescription":"141 p.","startPage":"21","endPage":"161","ipdsId":"IP-081461","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea98","contributors":{"authors":[{"text":"Nekola, Jeffrey C.","contributorId":26214,"corporation":false,"usgs":false,"family":"Nekola","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":741710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiba, Satoshi","contributorId":206770,"corporation":false,"usgs":false,"family":"Chiba","given":"Satoshi","email":"","affiliations":[],"preferred":false,"id":741711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coles, Brian F.","contributorId":206771,"corporation":false,"usgs":false,"family":"Coles","given":"Brian","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":741712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":741713,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"von Proschwitz, Ted","contributorId":206772,"corporation":false,"usgs":false,"family":"von Proschwitz","given":"Ted","email":"","affiliations":[],"preferred":false,"id":741714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Horsak, Michal","contributorId":206773,"corporation":false,"usgs":false,"family":"Horsak","given":"Michal","email":"","affiliations":[],"preferred":false,"id":741715,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70194087,"text":"ofr20171149 - 2018 - ModelArchiver—A program for facilitating the creation of groundwater model archives","interactions":[],"lastModifiedDate":"2018-03-02T10:13:23","indexId":"ofr20171149","displayToPublicDate":"2018-03-01T12:15:00","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":"2017-1149","title":"ModelArchiver—A program for facilitating the creation of groundwater model archives","docAbstract":"<p>ModelArchiver is a program designed to facilitate the creation of groundwater model archives that meet the requirements of the U.S. Geological Survey (USGS) policy (Office of Groundwater Technical Memorandum 2016.02, <a href=\"https://water.usgs.gov/admin/memo/GW/gw2016.02.pdf\" data-mce-href=\"https://water.usgs.gov/admin/memo/GW/gw2016.02.pdf\">https://water.usgs.gov/admin/memo/GW/gw2016.02.pdf</a>, <a href=\"https://water.usgs.gov/ogw/policy/gw-model/\" data-mce-href=\"https://water.usgs.gov/ogw/policy/gw-model/\">https://water.usgs.gov/ogw/policy/gw-model/</a>). ModelArchiver version 1.0 leads the user step-by-step through the process of creating a USGS groundwater model archive. The user specifies the contents of each of the subdirectories within the archive and provides descriptions of the archive contents. Descriptions of some files can be specified automatically using file extensions. Descriptions also can be specified individually. Those descriptions are added to a readme.txt file provided by the user. ModelArchiver moves the content of the archive to the archive folder and compresses some folders into .zip files.</p><p>As part of the archive, the modeler must create a metadata file describing the archive. The program has a built-in metadata editor and provides links to websites that can aid in creation of the metadata. The built-in metadata editor is also available as a stand-alone program named FgdcMetaEditor version 1.0, which also is described in this report. ModelArchiver updates the metadata file provided by the user with descriptions of the files in the archive. An optional archive list file generated automatically by ModelMuse can streamline the creation of archives by identifying input files, output files, model programs, and ancillary files for inclusion in the archive.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171149","usgsCitation":"Winston, R.B., 2018, ModelArchiver—A program for facilitating the creation of groundwater model archives: U.S. Geological Survey Open-File Report 2017–1149, 15 p., https://doi.org/10.3133/ofr20171149.","productDescription":"Report: vi, 15 p.; Application Site","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088876","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":437993,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73X85M1","text":"USGS data release","linkHelpText":"Software release: ModelArchiver and FgdcMetaEditor"},{"id":350293,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1149/coverthb.jpg"},{"id":350294,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1149/ofr20171149.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1149"},{"id":350295,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F73X85M1","text":"ModelArchiver and FgdcMetaEditor","linkFileType":{"id":5,"text":"html"}}],"contact":"<p><a href=\"https://ansers.usgs.gov\" data-mce-href=\"https://ansers.usgs.gov\">Director</a>,<a href=\"https://www.usgs.gov\" data-mce-href=\"https://www.usgs.gov\"> U.S. Geological Survey</a><br> 12201 Sunrise Valley Drive<br> Reston, VA 20192</p>","tableOfContents":"<ul><li>Preface</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Archive Structure</li><li>Creating Model Archives</li><li>FgdcMetaEditor</li><li>Examples of Modifications in .Archive Files</li><li>Modifications to ModelMuse to Support ModelArchiver</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Structure of Archive List Files and ModelMuse Integration</li><li>References Cited in Appendix 1</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2018-03-01","noUsgsAuthors":false,"publicationDate":"2018-03-01","publicationStatus":"PW","scienceBaseUri":"5afee70ee4b0da30c1bfc08e","contributors":{"authors":[{"text":"Winston, Richard B. 0000-0002-6287-8834 rbwinst@usgs.gov","orcid":"https://orcid.org/0000-0002-6287-8834","contributorId":3567,"corporation":false,"usgs":true,"family":"Winston","given":"Richard","email":"rbwinst@usgs.gov","middleInitial":"B.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":722080,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70196029,"text":"70196029 - 2018 - Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River","interactions":[],"lastModifiedDate":"2018-03-15T10:08:22","indexId":"70196029","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/WRD/NRR—2018/1603","title":"Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River","docAbstract":"The Upper Colorado River Endangered Fish Recovery Program has requested experimental flow releases from Flaming Gorge Dam for (1) elevated summer base flows to promote larval endangered Colorado pikeminnow, and (2) midsummer spike flows to disadvantage spawning invasive smallmouth bass. This white paper explores the effects of these proposed flow modifications on riparian vegetation and sediment deposition downstream along the Green River. Although modest in magnitude, the elevated base flows and possible associated reductions in magnitude or duration of peak flows would exacerbate a long-term trend of flow stabilization on the Green River that is already leading to proliferation of vegetation including invasive tamarisk along the channel and associated sediment deposition, channel narrowing and channel simplification. Midsummer spike flows could promote establishment of late-flowering plants like tamarisk. Because channel narrowing and simplification threaten persistence and quality of backwater and side channel features needed by endangered fish, the proposed flow modifications could lead to degradation of fish habitat. Channel narrowing and vegetation encroachment could be countered by increases in peak flows or reductions in base flows in some years and by prescription of rapid flow declines following midsummer spike flows. These strategies for reducing vegetation encroachment would need to be balanced with flow\nneeds of other riverine resources. Use of high flows to remove unwanted vegetation is constrained by current operational guidance for Flaming Gorge Dam, which attempts to limit spills (i.e., flows greater than 8600 ft3 /s) that might contribute to cavitation and lead to dam safety concerns. Therefore, reversing vegetation encroachment is more likely to succeed if implemented while plants are still small. Annual monitoring of near-channel vegetation and topography would enable managers to prescribe a timely hydrologic response in case the proposed flow experiments lead to vegetation encroachment and habitat degradation.","language":"English","publisher":"National Park Service","usgsCitation":"Friedman, J.M., 2018, Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River: Natural Resource Report NPS/NRSS/WRD/NRR—2018/1603, vi, 19 p.","productDescription":"vi, 19 p.","ipdsId":"IP-090611","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":352501,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2252016"},{"id":352542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Green River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.05529785156249,\n              38.190704293996504\n            ],\n            [\n              -108.15490722656249,\n              38.190704293996504\n            ],\n            [\n              -108.15490722656249,\n              41.253032440653186\n            ],\n            [\n              -111.05529785156249,\n              41.253032440653186\n            ],\n            [\n              -111.05529785156249,\n              38.190704293996504\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0c6","contributors":{"authors":[{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":731064,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70193270,"text":"70193270 - 2018 - Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations","interactions":[],"lastModifiedDate":"2018-03-28T15:17:43","indexId":"70193270","displayToPublicDate":"2018-03-01T00:00:00","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":"Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations","docAbstract":"<p><span>Conservation of small populations is often based on limited data from spatially and temporally restricted studies, resulting in management actions based on an incomplete assessment of the population drivers. If fluctuations in abundance are related to changes in weather, proper management is especially important, because extreme weather events could disproportionately affect population abundance. Conservation assessments, especially for vulnerable populations, are aided by a knowledge of how extreme events influence population status and trends. Although important for conservation efforts, data may be limited for small or vulnerable populations. Integrated population models maximize information from various sources of data to yield population estimates that fully incorporate uncertainty from multiple data sources while allowing for the explicit incorporation of environmental covariates of interest. Our goal was to assess the relative influence of population drivers for the Lesser Prairie-Chicken (</span><i>Tympanuchus pallidicinctus</i><span>) in the core of its range, western and southern Kansas, USA. We used data from roadside lek count surveys, nest monitoring surveys, and survival data from telemetry monitoring combined with climate (Palmer drought severity index) data in an integrated population model. Our results indicate that variability in population growth rate was most influenced by variability in juvenile survival. The Palmer drought severity index had no measurable direct effects on adult survival or mean number of offspring per female; however, there were declines in population growth rate following severe drought. Because declines in population growth rate occurred at a broad spatial scale, declines in response to drought were likely due to decreases in chick and juvenile survival rather than emigration outside of the study area. Overall, our model highlights the importance of accounting for environmental and demographic sources of variability, and provides a thorough method for simultaneously evaluating population demography in response to long-term climate effects.</span></p>","language":"English","publisher":"American Ornithological Society","doi":"10.1642/AUK-17-113.1","usgsCitation":"Ross, B., Haukos, D.A., Hagen, C.A., and Pitman, J., 2018, Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations: The Auk, v. 135, no. 2, p. 228-239, https://doi.org/10.1642/AUK-17-113.1.","productDescription":"12 p.","startPage":"228","endPage":"239","ipdsId":"IP-077342","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":352873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"135","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee713e4b0da30c1bfc0da","contributors":{"authors":[{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":731942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":731943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pitman, James","contributorId":176512,"corporation":false,"usgs":false,"family":"Pitman","given":"James","affiliations":[],"preferred":false,"id":731944,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70196870,"text":"70196870 - 2018 - Quantifying seining detection probability for fishes of Great Plains sand‐bed rivers","interactions":[],"lastModifiedDate":"2018-05-08T10:23:28","indexId":"70196870","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying seining detection probability for fishes of Great Plains sand‐bed rivers","docAbstract":"<p><span>Species detection error (i.e., imperfect and variable detection probability) is an essential consideration when investigators map distributions and interpret habitat associations. When fish detection error that is due to highly variable instream environments needs to be addressed, sand‐bed streams of the Great Plains represent a unique challenge. We quantified seining detection probability for diminutive Great Plains fishes across a range of sampling conditions in two sand‐bed rivers in Oklahoma. Imperfect detection resulted in underestimates of species occurrence using naïve estimates, particularly for less common fishes. Seining detection probability also varied among fishes and across sampling conditions. We observed a quadratic relationship between water depth and detection probability, in which the exact nature of the relationship was species‐specific and dependent on water clarity. Similarly, the direction of the relationship between water clarity and detection probability was species‐specific and dependent on differences in water depth. The relationship between water temperature and detection probability was also species dependent, where both the magnitude and direction of the relationship varied among fishes. We showed how ignoring detection error confounded an underlying relationship between species occurrence and water depth. Despite imperfect and heterogeneous detection, our results support that determining species absence can be accomplished with two to six spatially replicated seine hauls per 200‐m reach under average sampling conditions; however, required effort would be higher under certain conditions. Detection probability was low for the Arkansas River Shiner&nbsp;</span><i>Notropis girardi</i><span>, which is federally listed as threatened, and more than 10 seine hauls per 200‐m reach would be required to assess presence across sampling conditions. Our model allows scientists to estimate sampling effort to confidently assess species occurrence, which maximizes the use of available resources. Increased implementation of approaches that consider detection error promote ecological advancements and conservation and management decisions that are better informed.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10030","usgsCitation":"Mollenhauer, R., Logue, D.R., and Brewer, S.K., 2018, Quantifying seining detection probability for fishes of Great Plains sand‐bed rivers: Transactions of the American Fisheries Society, v. 147, no. 2, p. 329-341, https://doi.org/10.1002/tafs.10030.","productDescription":"13 p.","startPage":"329","endPage":"341","ipdsId":"IP-090661","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Canadian River, Cimarron River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -100.008544921875,\n              33.701492795584365\n            ],\n            [\n              -96.361083984375,\n              33.701492795584365\n            ],\n            [\n              -96.361083984375,\n              36.99816565700228\n            ],\n            [\n              -100.008544921875,\n              36.99816565700228\n            ],\n            [\n              -100.008544921875,\n              33.701492795584365\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"147","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc098","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":734844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Logue, Daniel R.","contributorId":177014,"corporation":false,"usgs":false,"family":"Logue","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":734845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734819,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196832,"text":"70196832 - 2018 - Oil and gas development footprint in the Piceance Basin, western Colorado","interactions":[],"lastModifiedDate":"2018-05-04T11:12:01","indexId":"70196832","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Oil and gas development footprint in the Piceance Basin, western Colorado","docAbstract":"<p><span>Understanding long-term implications of energy development on ecosystem functionrequires establishing regional datasets to quantify past development and determine relationships to predict future development. The Piceance Basin in western Colorado has a history of energy production and development is expected to continue into the foreseeable future due to abundant natural gas resources. To facilitate analyses of regional energy development we digitized all well pads in the Colorado portion of the basin, determined the previous land cover of areas converted to well pads over three time periods (2002–2006, 2007–2011, and 2012–2016), and explored the relationship between number of wells per pad and pad area to model future development. We also calculated the area of pads constructed prior to 2002. Over 21</span><span>&nbsp;</span><span>million<span>&nbsp;</span></span><i>m</i><sup>2</sup><span><span>&nbsp;</span>has been converted to well pads with approximately 13</span><span>&nbsp;</span><span>million<span>&nbsp;</span></span><i>m</i><sup>2</sup><span><span>&nbsp;</span>converted since 2002. The largest land conversion since 2002 occurred in shrub/scrub (7.9</span><span>&nbsp;</span><span>million<span>&nbsp;</span></span><i>m</i><sup>2</sup><span>), evergreen (2.1</span><span>&nbsp;</span><span>million<span>&nbsp;</span></span><i>m</i><sup>2</sup><span>), and deciduous (1.3</span><span>&nbsp;</span><span>million<span>&nbsp;</span></span><i>m</i><sup>2</sup><span>) forest environments based on National Land Cover Database classifications. Operational practices have transitioned from single well pads to multi-well pads, increasing the average number of wells per pad from 2.5 prior to 2002, to 9.1 between 2012 and 2016. During the same time period the pad area per well has increased from 2030<span>&nbsp;</span></span><i>m</i><sup>2</sup><span><span>&nbsp;</span>to 3504<span>&nbsp;</span></span><i>m</i><sup>2</sup><span>. Kernel density estimation was used to model the relationship between the number of wells per pad and pad area, with these curves exhibiting a lognormal distribution. Therefore, either kernel density estimation or lognormal probability distributions may potentially be used to model land use requirements for future development. Digitized well pad locations in the Piceance Basin contribute to a growing body of spatial data on energy infrastructure and, coupled with study results, will facilitate future regional and national studies assessing the spatial and temporal effects of energy development on ecosystem function.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.10.280","usgsCitation":"Martinez, C.D., and Preston, T.M., 2018, Oil and gas development footprint in the Piceance Basin, western Colorado: Science of the Total Environment, v. 616-617, p. 355-362, https://doi.org/10.1016/j.scitotenv.2017.10.280.","productDescription":"18 p.","startPage":"355","endPage":"362","ipdsId":"IP-089823","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":437998,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XG3","text":"USGS data release","linkHelpText":"Digitized oil and gas pads in the Piceance Basin of Western Colorado in 2015"},{"id":353959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Piceance Basin","volume":"616-617","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc09a","contributors":{"authors":[{"text":"Martinez, Cericia D. 0000-0001-6477-3190 cmartinez@usgs.gov","orcid":"https://orcid.org/0000-0001-6477-3190","contributorId":192866,"corporation":false,"usgs":true,"family":"Martinez","given":"Cericia","email":"cmartinez@usgs.gov","middleInitial":"D.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":734653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Preston, Todd M. 0000-0002-8812-9233","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":204676,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":734654,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70196690,"text":"70196690 - 2018 - Geoelectric hazard assessment: the differences of geoelectric responses during magnetic storms within common physiographic zones","interactions":[],"lastModifiedDate":"2018-04-24T16:59:59","indexId":"70196690","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1430,"text":"Earth, Planets and Space","active":true,"publicationSubtype":{"id":10}},"title":"Geoelectric hazard assessment: the differences of geoelectric responses during magnetic storms within common physiographic zones","docAbstract":"<p><span>Geomagnetic field data obtained through the INTERMAGNET program are convolved with with magnetotelluric surface impedance from four EarthScope USArray sites to estimate the geoelectric variations throughout the duration of a magnetic storm. A duration of time from&nbsp;June 22, 2016, to June 25, 2016, is considered which&nbsp;encompasses a magnetic storm of moderate size recorded at the Brandon, Manitoba and Fredericksburg, Virginia magnetic observatories over 3&nbsp;days. Two impedance sites were chosen in each case which represent different responses while being within close geographic proximity and within the same physiographic zone. This study produces estimated time series of the geoelectric field throughout the duration of a magnetic storm, providing an understanding of how the geoelectric field differs across small geographic distances within the same physiographic zone. This study shows that the geoelectric response of two sites within 200 km of one another can differ by up to two orders of magnitude (4484 mV/km at one site and 41 mV/km at another site 125 km away). This study demonstrates that the application of uniform 1-dimensional conductivity models of the subsurface to wide geographic regions is insufficient to predict the geoelectric hazard at a given site. This necessitates that an evaluation of the 3-dimensional conductivity distribution at a given location is necessary to produce a reliable estimation of how the geoelectric field evolves over the course of a magnetic storm.</span></p>","language":"English","publisher":"Springer","doi":"10.1186/s40623-018-0807-7","usgsCitation":"Cuttler, S.W., Love, J.J., and Swidinsky, A., 2018, Geoelectric hazard assessment: the differences of geoelectric responses during magnetic storms within common physiographic zones: Earth, Planets and Space, v. 70, Article 35; 9 p., https://doi.org/10.1186/s40623-018-0807-7.","productDescription":"Article 35; 9 p.","ipdsId":"IP-092919","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468963,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40623-018-0807-7","text":"Publisher Index Page"},{"id":353688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-08","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0a6","contributors":{"authors":[{"text":"Cuttler, Stephen W.","contributorId":204450,"corporation":false,"usgs":false,"family":"Cuttler","given":"Stephen","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":733978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":733979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swidinsky, Andrei","contributorId":146924,"corporation":false,"usgs":false,"family":"Swidinsky","given":"Andrei","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":733980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70193856,"text":"70193856 - 2018 - Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock","interactions":[],"lastModifiedDate":"2018-03-29T15:13:48","indexId":"70193856","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock","docAbstract":"<ol class=\"\"><li><p>Switchgrass (<i>Panicum virgatum</i>) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine (<i>Pinus taeda</i>) plantations in Mississippi, USA.</p></li><li><p>We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum<span>&nbsp;</span><i>n&nbsp;</i>=<i>&nbsp;</i>5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species.</p></li><li><p>Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters.</p></li><li><p>Covariates of nest placement indicated that switchgrass at the nest site (&lt;4&nbsp;m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival.</p></li><li><p><i>Synthesis and applications</i>. Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1&nbsp;km<sup>2</sup>), although at a local scale (50&nbsp;m<sup>2</sup>) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.</p></li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13015","usgsCitation":"Loman, Z., Monroe, A., Riffell, S.K., Miller, D.A., Vilella, F., Wheat, B.R., Rush, S.A., and Martin, J.A., 2018, Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock: Journal of Applied Ecology, v. 55, no. 2, p. 937-946, https://doi.org/10.1111/1365-2664.13015.","productDescription":"10 p.","startPage":"937","endPage":"946","ipdsId":"IP-067146","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13015","text":"Publisher Index Page"},{"id":352961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","scienceBaseUri":"5afee713e4b0da30c1bfc0d8","contributors":{"authors":[{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":720689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":720690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riffell, Samuel K.","contributorId":102386,"corporation":false,"usgs":true,"family":"Riffell","given":"Samuel","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":720691,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Darren A.","contributorId":203650,"corporation":false,"usgs":false,"family":"Miller","given":"Darren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wheat, Bradley R.","contributorId":145933,"corporation":false,"usgs":false,"family":"Wheat","given":"Bradley","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":720693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rush, Scott A.","contributorId":92139,"corporation":false,"usgs":true,"family":"Rush","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720694,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, James A.","contributorId":145934,"corporation":false,"usgs":false,"family":"Martin","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720695,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70197450,"text":"70197450 - 2018 - Distinguishing values from science in decision making: Setting harvest quotas for mountain lions in Montana","interactions":[],"lastModifiedDate":"2018-06-05T10:33:23","indexId":"70197450","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Distinguishing values from science in decision making: Setting harvest quotas for mountain lions in Montana","docAbstract":"<p><span>The relative roles of science and human values can be difficult to distinguish when informal processes are used to make complex and contentious decisions in wildlife management. Structured Decision Making (SDM) offers a formal process for making such decisions, where scientific results and concepts can be disentangled from the values of differing stakeholders. We used SDM to formally integrate science and human values for a citizen working group of ungulate hunting advocates, lion hunting advocates, and outfitters convened to address the contentious allocation of harvest quotas for mountain lions (</span><i>Puma concolor</i><span>) in west‐central Montana, USA, during 2014. A science team consisting of mountain lion biologists and population ecologists convened to support the working group. The science team used integrated population models that incorporated 4 estimates of mountain lion density to estimate population trajectories for 5 alternative harvest quotas developed by the working group. Results of the modeling predicted that effects of each harvest quota were consistent across the 4 density estimates; harvest quotas affected predicted population trajectories for 5 years after implementation but differences were not strong. Based on these results, the focus of the working group changed to differences in values among stakeholders that were the true impediment to allocating harvest quotas. By distinguishing roles of science and human values in this process, the working group was able to collaboratively recommend a compromise solution. This solution differed little from the status quo that had been the focus of debate, but the SDM process produced understanding and buy‐in among stakeholders involved, reducing disagreements, misunderstanding, and unproductive arguments founded on informal application of scientific data and concepts. Whereas investments involved in conducting SDM may be unnecessary for many decisions in wildlife management, the investment may be beneficial for complex, contentious, and multiobjective decisions that integrate science and human values.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/wsb.861","usgsCitation":"Mitchell, M.S., Cooley, H., Gude, J., Kolbe, J., Nowak, J.J., Proffitt, K.M., Sells, S.N., and Thompson, M., 2018, Distinguishing values from science in decision making: Setting harvest quotas for mountain lions in Montana: Wildlife Society Bulletin, v. 42, no. 1, p. 13-21, https://doi.org/10.1002/wsb.861.","productDescription":"9 p.","startPage":"13","endPage":"21","ipdsId":"IP-089989","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":499990,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/821cf25d1bd94a54a95a168864e646fc","text":"External Repository"},{"id":354717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.740966796875,\n              45.433153642271385\n            ],\n            [\n              -112.093505859375,\n              45.433153642271385\n            ],\n            [\n              -112.093505859375,\n              47.45037978769006\n            ],\n            [\n              -115.740966796875,\n              47.45037978769006\n            ],\n            [\n              -115.740966796875,\n              45.433153642271385\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"42","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5b46e5ade4b060350a15d202","contributors":{"authors":[{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":737199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":737227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":737228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolbe, Jay","contributorId":205415,"corporation":false,"usgs":false,"family":"Kolbe","given":"Jay","email":"","affiliations":[],"preferred":false,"id":737229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, J. Joshua","contributorId":171707,"corporation":false,"usgs":false,"family":"Nowak","given":"J.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":737230,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Proffitt, Kelly M.","contributorId":106783,"corporation":false,"usgs":true,"family":"Proffitt","given":"Kelly","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":737231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sells, Sarah N.","contributorId":171706,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":737232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Mike","contributorId":205416,"corporation":false,"usgs":false,"family":"Thompson","given":"Mike","email":"","affiliations":[],"preferred":false,"id":737233,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70196435,"text":"70196435 - 2018 - Golden Eagle Monitoring Plan for the Desert Renewable Energy Conservation Plan","interactions":[],"lastModifiedDate":"2018-04-06T16:37:36","indexId":"70196435","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesNumber":"CEC-500-2018-008","title":"Golden Eagle Monitoring Plan for the Desert Renewable Energy Conservation Plan","docAbstract":"<p>This report describes options for monitoring the status and population trends of the golden eagle (Aquila chrysaetos) within the Desert Renewable Energy Conservation Plan (DRECP) area of Southern California in maintaining stable or increasing population in the planning area. The report profiles the ecology of golden eagles in the region and provides a range of potential sampling options to address monitoring needs and objectives. This approach also focused on links between changes in human land-use, golden eagle nesting and foraging habitat conditions, and population dynamics. The report outlines how monitoring data from demographic, prey, and habitat studies were used to develop a predictive demographic model for golden eagles in the DRECP area. Results from the model simulations suggest increases in renewable energy development could have negative consequences for population trajectories. Results also suggest site-specific conservation actions could reduce the magnitude of negative impacts to the local population of eagles. </p><p>A monitoring framework is proposed including: (1) annual assessments of site-occupancy and reproduction by territorial pairs of golden eagles (including rates at which sites become colonized or vacated over time); (2) estimates of survival, movements, and intensity of use of landscapes by breeding and non-breeding golden eagles; (3) periodic (conducted every two to four years) assessments of nesting and foraging habitats, prey populations, and associations with land-use and management activities; and (4) updating the predictive demographic model with new information obtained on eagles and associated population stressors. </p><p>The results of this research were published in the Journal of Rapture Research, Wiens, David,Inman, Rich D., Esque, Todd C., Longshore, Kathleen M. and Nussear, Kenneth (2017). Spatial Demographic Models to Inform Conservation Planning of Golden Eagles in Renewable Energy Landscapes. 51(3):234-257. </p>","language":"English","publisher":"California Energy Commission","usgsCitation":"Wiens, D., Kolar, P., and Katzner, T., 2018, Golden Eagle Monitoring Plan for the Desert Renewable Energy Conservation Plan, 99 p.","productDescription":"99 p.","ipdsId":"IP-086130","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":353243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353224,"type":{"id":11,"text":"Document"},"url":"https://www.energy.ca.gov/2018publications/CEC-500-2018-008/CEC-500-2018-008.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0aa","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732903,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196830,"text":"70196830 - 2018 - Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients","interactions":[],"lastModifiedDate":"2018-05-04T11:41:55","indexId":"70196830","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients","docAbstract":"<p><span>Nitrogen (N) and phosphorus (P) commonly stimulate phytoplankton production in lakes, but recent observations from lakes from an agricultural region suggest that nitrate may have a subsidy‐stress effect on chlorophyll&nbsp;</span><i>a</i><span><span>&nbsp;</span>(Chl<span>&nbsp;</span></span><i>a</i><span>). It is unclear, however, how generalizable this effect might be. Here, we analyzed a large water quality dataset of 2385 lakes spanning 60 regions across 17 states in the Northeastern and Midwestern U.S. to determine if N subsidy‐stress effects on phytoplankton are common and to identify regional landscape characteristics promoting N stress effects in lakes. We used a Bayesian hierarchical modeling framework to test our hypothesis that Chl<span>&nbsp;</span></span><i>a</i><span>–total N (TN) threshold relationships would be common across the central agricultural region of the U.S. (“the Corn Belt”), where lake N and P concentrations are high. Data aggregated across all regions indicated that high TN concentrations had a negative effect on Chl<span>&nbsp;</span></span><i>a</i><span><span>&nbsp;</span>in lakes with concurrent high total P. This large‐scale pattern was driven by relationships within only a subset of regions, however. Eight regions were identified as having Chl<span>&nbsp;</span></span><i>a</i><span>–TN threshold relationships, but only two of these regions located within the Corn Belt clearly demonstrated this subsidy‐stress relationship. N stress effects were not consistent across other intense agricultural regions, as we hypothesized. These findings suggest that interactions among regional land use and land cover, climate, and hydrogeology may be important in determining the synergistic conditions leading to N subsidy‐stress effects on lake phytoplankton.</span></p>","language":"English","publisher":"ASLO","doi":"10.1002/lno.10742","usgsCitation":"Filstrup, C.T., Wagner, T., Oliver, S., Stow, C.A., Webster, K.E., Stanley, E.H., and Downing, J., 2018, Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients: Limnology and Oceanography, v. 63, no. S1, p. 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K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":734714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stow, Craig A.","contributorId":204103,"corporation":false,"usgs":false,"family":"Stow","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":36843,"text":"NOAA, Great Lakes Environmental Research Lab","active":true,"usgs":false}],"preferred":false,"id":734715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":734717,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downing, John A.","contributorId":70348,"corporation":false,"usgs":true,"family":"Downing","given":"John A.","affiliations":[],"preferred":false,"id":734718,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70196763,"text":"70196763 - 2018 - Timber harvest as the predominant disturbance regime in northeastern U.S. forests: Effects of harvest intensification","interactions":[],"lastModifiedDate":"2018-04-30T13:06:26","indexId":"70196763","displayToPublicDate":"2018-03-01T00:00:00","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":"Timber harvest as the predominant disturbance regime in northeastern U.S. forests: Effects of harvest intensification","docAbstract":"<p><span>Harvesting is the leading cause of adult tree mortality in forests of the northeastern United States. While current rates of timber harvest are generally sustainable, there is considerable pressure to increase the contribution of forest biomass to meet renewable energy goals. We estimated current harvest regimes for different forest types and regions across the U.S. states of New York, Vermont, New Hampshire, and Maine using data from the U.S. Forest Inventory and Analysis Program. We implemented the harvest regimes in SORTIE‐ND, an individual‐based model of forest dynamics, and simulated the effects of current harvest regimes and five additional harvest scenarios that varied by harvest frequency and intensity over 150&nbsp;yr. The best statistical model for the harvest regime described the annual probability of harvest as a function of forest type/region, total plot basal area, and distance to the nearest improved road. Forests were predicted to increase in adult aboveground biomass in all harvest scenarios in all forest type and region combinations. The magnitude of the increase, however, varied dramatically—increasing from 3% to 120% above current landscape averages as harvest frequency and intensity decreased. The variation can be largely explained by the disproportionately high harvest rates estimated for Maine as compared with the rest of the region. Despite steady biomass accumulation across the landscape, stands that exhibited old‐growth characteristics (defined as ≥300 metric tons of biomass/hectare) were rare (8% or less of stands). Intensified harvest regimes had little effect on species composition due to widespread partial harvesting in all scenarios, resulting in dominance by late‐successional species over time. Our analyses indicate that forest biomass can represent a sustainable, if small, component of renewable energy portfolios in the region, although there are tradeoffs between carbon sequestration in forest biomass and sustainable feedstock supply. Integrating harvest regimes into a disturbance theory framework is critical to understanding the dynamics of forested landscapes, especially given the predominance of logging as a disturbance agent and the increasing pressure to meet renewable energy needs.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2062","usgsCitation":"Brown, M.L., Canham, C.D., Murphy, L., and Donovan, T.M., 2018, Timber harvest as the predominant disturbance regime in northeastern U.S. forests: Effects of harvest intensification: Ecosphere, v. 9, no. 3, p. 1-19, https://doi.org/10.1002/ecs2.2062.","productDescription":"e02062; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-086575","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468953,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2062","text":"Publisher Index Page"},{"id":353856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-30","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0a2","contributors":{"authors":[{"text":"Brown, Michelle L.","contributorId":168990,"corporation":false,"usgs":false,"family":"Brown","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":734289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Canham, Charles D.","contributorId":152138,"corporation":false,"usgs":false,"family":"Canham","given":"Charles","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":734290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Lora","contributorId":196420,"corporation":false,"usgs":false,"family":"Murphy","given":"Lora","email":"","affiliations":[],"preferred":false,"id":734291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734288,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70198130,"text":"70198130 - 2018 - Drivers of variability in public‐supply water use across the contiguous United States","interactions":[],"lastModifiedDate":"2018-07-17T09:50:48","indexId":"70198130","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of variability in public‐supply water use across the contiguous United States","docAbstract":"<p><span>This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian‐hierarchical regression models for over 2,500 counties, 18 covariates, and three higher‐level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher‐level groups results in the best out‐of‐sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city‐level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county‐level model, the effect of each variable depends heavily on where a city is located.</span></p>","language":"English","publisher":"AGU","doi":"10.1002/2017WR021268","usgsCitation":"Worland, S.C., Steinschneider, S., and Hornberger, G.M., 2018, Drivers of variability in public‐supply water use across the contiguous United States: Water Resources Research, v. 54, no. 3, p. 1868-1889, https://doi.org/10.1002/2017WR021268.","productDescription":"22 p.","startPage":"1868","endPage":"1889","ipdsId":"IP-087770","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488785,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr021268","text":"Publisher Index Page"},{"id":355717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"54","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-15","publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea9c","contributors":{"authors":[{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinschneider, Scott 0000-0002-8882-1908","orcid":"https://orcid.org/0000-0002-8882-1908","contributorId":206359,"corporation":false,"usgs":false,"family":"Steinschneider","given":"Scott","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":740160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hornberger, George M.","contributorId":206360,"corporation":false,"usgs":false,"family":"Hornberger","given":"George","email":"","middleInitial":"M.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":740161,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196211,"text":"70196211 - 2018 - Ground ruptures attributed to groundwater overexploitation damaging Jocotepec city in Jalisco, Mexico: 2016 field excursion of IGCP-641","interactions":[],"lastModifiedDate":"2020-12-15T22:41:37.225266","indexId":"70196211","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5657,"text":"Episodes, Journal of International Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Ground ruptures attributed to groundwater overexploitation damaging Jocotepec city in Jalisco, Mexico: 2016 field excursion of IGCP-641","docAbstract":"IGCP Project 641 (Mechanisms, Monitoring and Modeling Earth\nFissure generation and Fault activation due to subsurface Fluid\nexploitation – M3EF3) held its second international workshop from\nNovember 2 to 6, 2016, in Puerto Vallarta and included a two-day\nfield trip to Guadalajara and Jocotepec in the Mexican state of Jalisco\n(Fig. 1a). M3EF3 is aimed at i) understanding the mechanisms that\ncause the formation of “ground ruptures”, as a response to changes in\nthe stress state in the subsoil, ii) monitoring their occurrence, and iii)\ndeveloping appropriate approaches to model their formation and\ngrowth. Notice that many times it is difficult to discern whether a particular\ninstance of a “ruptured” land surface is due to an earth fissure\nwith a surface manifestation or a surface failure associated with the\nactivation of a pre-existing fault, especially where a fault has not been\nmapped and where there is no obvious scarp. Therefore, in M3EF3\nwe preferred to use the term “ground rupture”, to refer to both such\noccurrences.","language":"English","publisher":"IUGS, Geological Society of Korea","doi":"10.18814/epiiugs/2018/v41i1/007","usgsCitation":"Teatini, P., Carreon-Freyre, D., Ochoa-Gonzalez, G., Ye, S., Galloway, D.L., and Hernandez-Marin, M., 2018, Ground ruptures attributed to groundwater overexploitation damaging Jocotepec city in Jalisco, Mexico: 2016 field excursion of IGCP-641: Episodes, Journal of International Geoscience, v. 41, no. 1, p. 69-73, https://doi.org/10.18814/epiiugs/2018/v41i1/007.","productDescription":"5 p.","startPage":"69","endPage":"73","ipdsId":"IP-086773","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":460999,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.18814/epiiugs/2018/v41i1/007","text":"Publisher Index Page"},{"id":352784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Jalisco","city":"Jocotepec","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -103.47833633422852,\n              20.25543335716953\n            ],\n            [\n              -103.4113883972168,\n              20.25543335716953\n            ],\n            [\n              -103.4113883972168,\n              20.320321149740877\n            ],\n            [\n              -103.47833633422852,\n              20.320321149740877\n            ],\n            [\n              -103.47833633422852,\n              20.25543335716953\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"41","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee710e4b0da30c1bfc0b8","contributors":{"authors":[{"text":"Teatini, Pietro","contributorId":203529,"corporation":false,"usgs":false,"family":"Teatini","given":"Pietro","email":"","affiliations":[{"id":36643,"text":"Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":731704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carreon-Freyre, Dora","contributorId":203530,"corporation":false,"usgs":false,"family":"Carreon-Freyre","given":"Dora","email":"","affiliations":[{"id":36644,"text":"Centro de Geociencias, Universidad Nacional Autónoma de México, Campus Juriquilla, Queretaro, Mexico","active":true,"usgs":false}],"preferred":false,"id":731705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ochoa-Gonzalez, Gil","contributorId":203531,"corporation":false,"usgs":false,"family":"Ochoa-Gonzalez","given":"Gil","email":"","affiliations":[{"id":36645,"text":"Western Institute of Technology and Higher Education (Instituto Tecnológico y de Estudios Superiores de Occidente ITESO), Jalisco, Mexico","active":true,"usgs":false}],"preferred":false,"id":731706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ye, Shujun","contributorId":203532,"corporation":false,"usgs":false,"family":"Ye","given":"Shujun","email":"","affiliations":[{"id":36646,"text":"Dept. of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing P. R. China","active":true,"usgs":false}],"preferred":false,"id":731707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galloway, Devin L. 0000-0003-0904-5355 dlgallow@usgs.gov","orcid":"https://orcid.org/0000-0003-0904-5355","contributorId":679,"corporation":false,"usgs":true,"family":"Galloway","given":"Devin","email":"dlgallow@usgs.gov","middleInitial":"L.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":731703,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hernandez-Marin, Martin","contributorId":203533,"corporation":false,"usgs":false,"family":"Hernandez-Marin","given":"Martin","email":"","affiliations":[{"id":36647,"text":"Department of Geotechnics and Hydraulics, Autonomous University of Aguascalientes (Universidad Autónoma de Aguascalientes), Aguascalientes, Mexico","active":true,"usgs":false}],"preferred":false,"id":731708,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70196176,"text":"70196176 - 2018 - Fire and climate suitability for woody vegetation communities in the south central United States","interactions":[],"lastModifiedDate":"2019-10-28T09:26:31","indexId":"70196176","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fire and climate suitability for woody vegetation communities in the south central United States","docAbstract":"Climate and fire are primary drivers of plant species distributions. Long-term management of south central United States woody vegetation communities can benefit from information on potential changes in climate and fire frequencies, and how these changes might affect plant communities. We used historical (1900 to 1929) and future (2040 to 2069 and 2070 to 2099) projected climate data for the conterminous US to estimate reference and future fire probabilities\nusing a physical chemistry fire frequency model. We then used the fire probability data with additional climate parameters to construct maximum entropy environmental suitability models for three south central US vegetation communities. The modeled communities included an oak type (dominated by post oak, Quercus stellata Wangenh., and blackjack oak, Q. marilandica Münchh.), a mesquite type (dominated by honey mesquite, Prosopis glandulosa Torr., and velvet mesquite, P. velutina Wooton), and a pinyon−juniper type (dominated by pinyon pine, Pinus edulis Engelm., and Utah juniper, Juniperus osteosperma [Torr.] Little). We mapped baseline and future mean fire-climate suitability using data from three global climate models for 2040 to 2069 and 2070 to 2099; we also mapped future locations of threshold conditions for which all three models agreed on suitability for each community. Future projections included northward, southward, and eastward shifts in suitable conditions for the oaks along a broad path of fire-climate stability; an overall reduction in suitable area for historic mesquite communities coupled with potential expansion to new areas; and constriction and isolation of suitable conditions for pinyon−juniper communities. The inclusion of fire probability adds an important driver of vegetation distribution to climate envelope modeling. The simple models showed good fit, but future projections failed to account for future management activities or land use changes. Results provided information on potential future de-coupling and spatial re-arrangement of environmental conditions under which these communities have historically persisted and been managed. In particular, consensus threshold maps can inform long-term planning for maintenance or restoration of these communities, and they can be used as a potential tool for other communities in fire-prone environments within the study area and beyond its borders.","language":"English","publisher":"Springer","doi":"10.4996/fireecology.140110612","usgsCitation":"Stroh, E., Struckhoff, M., Stambaugh, M.C., and Guyette, R.P., 2018, Fire and climate suitability for woody vegetation communities in the south central United States: Fire Ecology, v. 14, no. 1, p. 106-124, https://doi.org/10.4996/fireecology.140110612.","productDescription":"19 p.","startPage":"106","endPage":"124","ipdsId":"IP-088298","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":468948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4996/fireecology.140110612","text":"Publisher Index Page"},{"id":352785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-01","publicationStatus":"PW","scienceBaseUri":"5afee711e4b0da30c1bfc0bc","contributors":{"authors":[{"text":"Stroh, Esther 0000-0003-4291-4647","orcid":"https://orcid.org/0000-0003-4291-4647","contributorId":202825,"corporation":false,"usgs":true,"family":"Stroh","given":"Esther","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":731537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struckhoff, Matthew 0000-0002-4911-9956","orcid":"https://orcid.org/0000-0002-4911-9956","contributorId":201512,"corporation":false,"usgs":true,"family":"Struckhoff","given":"Matthew","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":731538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stambaugh, Michael C.","contributorId":202826,"corporation":false,"usgs":false,"family":"Stambaugh","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":13706,"text":"University of Missouri-Columbia","active":true,"usgs":false}],"preferred":false,"id":731539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guyette, Richard P.","contributorId":176595,"corporation":false,"usgs":false,"family":"Guyette","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":731540,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70196947,"text":"70196947 - 2018 - Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling","interactions":[],"lastModifiedDate":"2018-05-17T15:21:08","indexId":"70196947","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Monitoring Least Bitterns (<i>Ixobrychis exilis</i>) in Vermont: Detection probability and occupancy modeling","title":"Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling","docAbstract":"<p><i>Ixobrychus exillis</i><span><span>&nbsp;</span>(Least Bittern) is listed as a species of high concern in the North American Waterbird Conservation Plan and is a US Fish and Wildlife Service migratory bird species of conservation concern in the Northeast. Little is known about the population of Least Bitterns in the Northeast because of their low population density, tendency to nest in dense wetland vegetation, and secretive behavior. Urban and agricultural development is expected to encroach on and degrade suitable wetland habitat; however, we cannot predict the effects on Least Bittern populations without more accurate information on their abundance and distribution. We conducted surveys of wetlands in Vermont to assess the efficacy of a monitoring protocol and to establish baseline Least Bittern abundance and distribution data at a sample of 29 wetland sites. Surveys yielded detections of 31 individuals at 15 of 29 sites across 3 biophysical regions and at 5 sites where occupancy had not been previously reported. Probability of occupancy was positively related to wetland size and number of patches, though the relationships were not strong enough to conclude if these were true determinants of occupancy. Call—response broadcast surveys yielded 30 detections, while passive surveys yielded 13. Call—response broadcasts (</span><i>P</i><span><span>&nbsp;</span>= 0.897) increased the rate of detection by 55% compared to passive surveys (</span><i>P</i><span><span>&nbsp;</span>= 0.577). Our results suggest that call—response broadcast surveys are an effective means of assessing Least Bittern occupancy and may reduce bias in long-term monitoring programs.</span></p>","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/045.025.0104","usgsCitation":"Cherukuri, A., Strong, A., and Donovan, T.M., 2018, Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling: Northeastern Naturalist, v. 25, no. 1, p. 56-71, https://doi.org/10.1656/045.025.0104.","productDescription":"16 p.","startPage":"56","endPage":"71","ipdsId":"IP-084582","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70195821,"text":"70195821 - 2018 - Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","interactions":[],"lastModifiedDate":"2018-11-14T10:03:20","indexId":"70195821","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fire and grazing influence site resistance to <i>Bromus tectorum</i> through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","title":"Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","docAbstract":"<p><span>Shrubs, bunchgrasses and biological soil crusts (biocrusts) are believed to contribute to site resistance to plant invasions in the presence of cattle grazing. Although fire is a concomitant disturbance with grazing, little is known regarding their combined impacts on invasion resistance. We are the first to date to test the idea that biotic communities mediate the effects of disturbance on site resistance. We assessed cover of&nbsp;</span><i class=\"EmphasisTypeItalic \">Bromus tectorum</i><span>, shrubs, native bunchgrasses, lichens and mosses in 99 burned and unburned plots located on similar soils where fires occurred between 12 and 23&nbsp;years before sampling. Structural equation modeling was used to test hypothesized relationships between environmental and disturbance characteristics, the biotic community and resistance to<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum</i><span><span>&nbsp;</span>cover. Characteristics of fire and grazing did not directly relate to cover of<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum.</i><span><span>&nbsp;</span>Relationships were mediated through shrub, bunchgrass and biocrust communities. Increased site resistance following fire was associated with higher bunchgrass cover and recovery of bunchgrasses and mosses with time since fire. Evidence of grazing was more pronounced on burned sites and was positively correlated with the cover of<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum</i><span>, indicating an interaction between fire and grazing that decreases site resistance. Lichen cover showed a weak, negative relationship with cover of<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum</i><span>. Fire reduced near-term site resistance to<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum</i><span><span>&nbsp;</span>on actively grazed rangelands. Independent of fire, grazing impacts resulted in reduced site resistance to<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">B. tectorum</i><span>, suggesting that grazing management that enhances plant and biocrust communities will also enhance site resistance.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0230-8","usgsCitation":"Condon, L.A., and Pyke, D.A., 2018, Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA): Ecosystems, v. 21, no. 7, p. 1416-1431, https://doi.org/10.1007/s10021-018-0230-8.","productDescription":"16 p.","startPage":"1416","endPage":"1431","ipdsId":"IP-083278","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":352209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.03662109374999,\n              39.027718840211605\n            ],\n            [\n              -111.796875,\n              39.027718840211605\n            ],\n            [\n              -111.796875,\n              44.213709909702054\n            ],\n            [\n              -120.03662109374999,\n              44.213709909702054\n            ],\n            [\n              -120.03662109374999,\n              39.027718840211605\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"21","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0ca","contributors":{"authors":[{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":730169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":730168,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70198127,"text":"70198127 - 2018 - Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","interactions":[],"lastModifiedDate":"2018-07-17T09:55:03","indexId":"70198127","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","docAbstract":"<p><span>We compare the ability of eight machine-learning</span><span><span><span><span>&nbsp;</span>models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of<span> support vector machines</span>, M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary<span> kriging</span></span>, a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean<span> streamflow</span></span><span>&nbsp;</span>with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2017.12.021","usgsCitation":"Worland, S.C., Farmer, W.H., and Kiang, J.E., 2018, Improving predictions of hydrological low-flow indices in ungaged basins using machine learning: Environmental Modelling and Software, v. 101, p. 169-182, https://doi.org/10.1016/j.envsoft.2017.12.021.","productDescription":"14 p.","startPage":"169","endPage":"182","ipdsId":"IP-080308","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":468958,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2017.12.021","text":"Publisher Index Page"},{"id":437995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5S4T","text":"USGS data release","linkHelpText":"7Q10 records and basin characteristics for 224 basins in South Carolina, Georgia, and Alabama (2015)"},{"id":355718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, South Carolina","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-87.984916,35.005881],[-85.466713,34.982972],[-83.619985,34.986592],[-83.620185,34.992091],[-83.108714,35.000768],[-82.787867,35.085024],[-82.776357,35.081349],[-82.781973,35.066817],[-82.757704,35.068019],[-82.728961,35.091978],[-82.694898,35.098456],[-82.68604,35.124545],[-82.662381,35.118123],[-82.642237,35.129215],[-82.629031,35.126155],[-82.547436,35.160306],[-82.52593,35.156749],[-82.476136,35.175486],[-82.460092,35.178143],[-82.448969,35.165037],[-82.439595,35.165863],[-82.419744,35.198613],[-82.390439,35.215395],[-82.371298,35.181449],[-82.361469,35.190831],[-82.326917,35.185056],[-82.27492,35.200071],[-81.043625,35.149877],[-81.051204,35.133237],[-81.038968,35.126299],[-81.032806,35.108049],[-81.052078,35.096276],[-81.058029,35.07319],[-81.041489,35.044703],[-80.93495,35.107409],[-80.782042,34.935782],[-80.796997,34.823874],[-79.675299,34.804744],[-78.541087,33.851112],[-78.584841,33.844282],[-78.714116,33.800138],[-78.812931,33.743472],[-78.938076,33.639826],[-79.028516,33.533365],[-79.084588,33.483669],[-79.135441,33.403867],[-79.180318,33.254141],[-79.172394,33.206577],[-79.18787,33.173712],[-79.24609,33.124865],[-79.329909,33.089986],[-79.359961,33.006672],[-79.403712,33.003903],[-79.423447,33.015085],[-79.483499,33.001265],[-79.506923,33.032813],[-79.522449,33.03535],[-79.55756,33.021269],[-79.580725,33.006447],[-79.617611,32.952726],[-79.606194,32.925953],[-79.574951,32.934526],[-79.576006,32.906235],[-79.631149,32.888606],[-79.695141,32.850398],[-79.719879,32.825796],[-79.716761,32.813627],[-79.726389,32.805996],[-79.811021,32.77696],[-79.84035,32.756816],[-79.866742,32.757422],[-79.870336,32.727777],[-79.888028,32.695177],[-79.884961,32.684402],[-79.975248,32.639537],[-79.999374,32.611851],[-80.121368,32.590523],[-80.148406,32.578479],[-80.171764,32.546118],[-80.20523,32.555547],[-80.332438,32.478104],[-80.363956,32.496098],[-80.413487,32.470672],[-80.423454,32.497989],[-80.439407,32.503472],[-80.472068,32.496964],[-80.484617,32.460976],[-80.480156,32.447048],[-80.467588,32.425259],[-80.446075,32.423721],[-80.429941,32.401782],[-80.455192,32.326458],[-80.545688,32.282076],[-80.596394,32.273549],[-80.658634,32.248638],[-80.669166,32.216783],[-80.721463,32.160427],[-80.812503,32.109746],[-80.844431,32.109709],[-80.858735,32.099581],[-80.905378,32.051943],[-80.885517,32.0346],[-80.84313,32.024226],[-80.841913,32.002643],[-80.862814,31.969346],[-80.911207,31.943769],[-80.929101,31.944964],[-80.930279,31.956705],[-80.948491,31.95723],[-80.972392,31.94127],[-80.975714,31.923602],[-80.968494,31.915822],[-80.934508,31.90918],[-80.99269,31.857641],[-81.065255,31.877095],[-81.05907,31.850106],[-81.075812,31.829031],[-81.039808,31.823],[-81.036873,31.812721],[-81.068116,31.768735],[-81.130634,31.722692],[-81.192784,31.733245],[-81.203572,31.719448],[-81.161084,31.691401],[-81.149369,31.699304],[-81.131137,31.695774],[-81.133493,31.623348],[-81.173079,31.555908],[-81.186114,31.568032],[-81.204315,31.568183],[-81.214536,31.557601],[-81.254218,31.55594],[-81.260076,31.54828],[-81.258809,31.52906],[-81.217948,31.527284],[-81.199518,31.537596],[-81.17831,31.52241],[-81.278798,31.367214],[-81.282923,31.326491],[-81.25482,31.315452],[-81.274688,31.289454],[-81.288403,31.211065],[-81.314183,31.207938],[-81.339028,31.186918],[-81.368241,31.136534],[-81.399677,31.134113],[-81.401267,31.072781],[-81.420474,31.016703],[-81.451444,31.015515],[-81.493651,30.977528],[-81.475789,30.965976],[-81.453568,30.965573],[-81.447388,30.956732],[-81.426929,30.956615],[-81.420108,30.974076],[-81.408484,30.977718],[-81.403409,30.957914],[-81.405153,30.908203],[-81.460061,30.769912],[-81.45947,30.741979],[-81.444124,30.709714],[-81.472597,30.713312],[-81.489537,30.7261],[-81.528278,30.723359],[-81.544679,30.713969],[-81.601206,30.728141],[-81.617663,30.722046],[-81.625098,30.733017],[-81.646137,30.727591],[-81.662173,30.746521],[-81.672824,30.738935],[-81.692815,30.7471],[-81.719927,30.744634],[-81.745035,30.765039],[-81.768192,30.773954],[-81.779171,30.768062],[-81.806652,30.789683],[-81.868608,30.792754],[-81.89572,30.821098],[-81.910926,30.815889],[-81.949787,30.827493],[-81.959759,30.821168],[-81.961989,30.800443],[-81.979061,30.776415],[-82.017051,30.791657],[-82.022866,30.787991],[-82.011597,30.763122],[-82.035964,30.750998],[-82.043795,30.729641],[-82.036426,30.706585],[-82.050432,30.676266],[-82.049507,30.655548],[-82.027338,30.606726],[-82.012109,30.593773],[-82.005477,30.563495],[-82.018361,30.531184],[-82.016982,30.478779],[-82.036203,30.43846],[-82.036825,30.377884],[-82.047917,30.363265],[-82.081106,30.358806],[-82.116385,30.367335],[-82.165192,30.358035],[-82.189847,30.375938],[-82.210291,30.42459],[-82.201416,30.485164],[-82.225026,30.50783],[-82.234952,30.533066],[-82.231916,30.55627],[-82.214818,30.568517],[-83.499876,30.645671],[-84.86346,30.711506],[-84.8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 \"}}]}","volume":"101","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea9e","contributors":{"authors":[{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":740155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":740156,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70195819,"text":"70195819 - 2018 - Species distribution modeling in regions of high need and limited data: waterfowl of China","interactions":[],"lastModifiedDate":"2018-03-05T10:59:14","indexId":"70195819","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5641,"text":"Avian Research","active":true,"publicationSubtype":{"id":10}},"title":"Species distribution modeling in regions of high need and limited data: waterfowl of China","docAbstract":"<div id=\"ASec1\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Background</strong></p><p id=\"Par1\" class=\"Para\">A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources. Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.</p></div><div id=\"ASec2\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Methods</strong></p><p id=\"Par2\" class=\"Para\">Faced with limited data, we built species distribution models using a habitat suitability approach for China’s breeding and non-breeding (hereafter, wintering) waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.</p></div><div id=\"ASec3\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Results</strong></p><p id=\"Par3\" class=\"Para\">We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1&nbsp;km resolution for the extent of China, along with cumulative and genus level species richness maps. Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China. Wintering waterfowl suitability was highest in the lowland regions of southeastern China. Validation measures indicated strong performance in predicting species presence. Comparing our model outputs to China’s protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.</p></div><div id=\"ASec4\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Conclusions</strong></p><p id=\"Par4\" class=\"Para\">These suitability models are the first available for many of China’s waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.</p></div>","language":"English","publisher":"BMC","doi":"10.1186/s40657-018-0099-4","usgsCitation":"Prosser, D.J., Ding, C., Erwin, R.M., Mundkur, T., Sullivan, J.D., and Ellis, E.C., 2018, Species distribution modeling in regions of high need and limited data: waterfowl of China: Avian Research, v. 9, p. 1-14, https://doi.org/10.1186/s40657-018-0099-4.","productDescription":"Article 7; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-080933","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468961,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40657-018-0099-4","text":"Publisher Index Page"},{"id":437996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P904APIK","text":"USGS data release","linkHelpText":"Species distribution models from a habitat suitability approach: waterfowl of China"},{"id":352210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","volume":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-05","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0cc","contributors":{"authors":[{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":730162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ding, Changqing","contributorId":202909,"corporation":false,"usgs":false,"family":"Ding","given":"Changqing","email":"","affiliations":[],"preferred":false,"id":730175,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erwin, R. Michael","contributorId":87854,"corporation":false,"usgs":true,"family":"Erwin","given":"R.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":730176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mundkur, Taej","contributorId":199127,"corporation":false,"usgs":false,"family":"Mundkur","given":"Taej","email":"","affiliations":[],"preferred":false,"id":730177,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sullivan, Jeffery D.","contributorId":202910,"corporation":false,"usgs":false,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":730178,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ellis, Erle C.","contributorId":67400,"corporation":false,"usgs":true,"family":"Ellis","given":"Erle","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":730179,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70196334,"text":"70196334 - 2018 - Integrate urban‐scale seismic hazard analyses with the U.S. National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2019-08-30T06:57:40","indexId":"70196334","displayToPublicDate":"2018-03-01T00:00:00","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":"Integrate urban‐scale seismic hazard analyses with the U.S. National Seismic Hazard Model","docAbstract":"<div><p>For more than 20&nbsp;yrs, damage patterns and instrumental recordings have highlighted the influence of the local 3D geologic structure on earthquake ground motions (e.g.,<span>&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mi xmlns=&quot;&quot; mathvariant=&quot;bold&quot;>M</mi></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mi\">M</span></span></span></span><span class=\"MJX_Assistive_MathML\">M</span></span></span>&nbsp;6.7 Northridge, California,<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf14\">Gao<span>&nbsp;</span><i>et&nbsp;al.</i>, 1996</a>;<span>&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mi xmlns=&quot;&quot; mathvariant=&quot;bold&quot;>M</mi></math>\"><span id=\"MathJax-Span-4\" class=\"math\"><span><span id=\"MathJax-Span-5\" class=\"mrow\"><span id=\"MathJax-Span-6\" class=\"mi\">M</span></span></span></span><span class=\"MJX_Assistive_MathML\">M</span></span></span>&nbsp;6.9 Kobe, Japan,<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf18\">Kawase, 1996</a>;<span>&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mi xmlns=&quot;&quot; mathvariant=&quot;bold&quot;>M</mi></math>\"><span id=\"MathJax-Span-7\" class=\"math\"><span><span id=\"MathJax-Span-8\" class=\"mrow\"><span id=\"MathJax-Span-9\" class=\"mi\">M</span></span></span></span><span class=\"MJX_Assistive_MathML\">M</span></span></span>&nbsp;6.8 Nisqually, Washington,<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf9\">Frankel, Carver, and Williams, 2002</a>). Although this and other local‐scale features are critical to improving seismic hazard forecasts, historically they have not been explicitly incorporated into the U.S. National Seismic Hazard Model (NSHM, national model and maps), primarily because the necessary basin maps and methodologies were not available at the national scale. Instead, the U.S. Geological Survey (USGS), its partners, and external groups developed urban seismic hazard maps (urban models and maps) that consider detailed site effects in local areas (e.g.,<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf27\">Wong<span>&nbsp;</span><i>et&nbsp;al.</i>, 2002</a>;<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf6\">Cramer<span>&nbsp;</span><i>et&nbsp;al.</i>, 2006</a>;<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf13\">Frankel<span>&nbsp;</span><i>et&nbsp;al.</i>, 2007</a>;<span>&nbsp;</span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"rf15\">Graves<span>&nbsp;</span><i>et&nbsp;al.</i>, 2011</a>). The disconnect between the urban and national hazard models, however, means that the national models, which underlie U.S. building codes and other applications, do not make use of all of the scientific results informing earthquake ground‐shaking hazards.</p></div><div><p>We recommend that future U.S. national seismic hazard assessment make use of all available regional information, including that in urban models. In this column, we describe the roles of and differences between the urban and national models, and discuss the obstacles to and benefits of integrating the urban models with the national model. Future progress on this issue will require further coordination and implementation efforts between the USGS and external partners.</p></div>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170261","usgsCitation":"Moschetti, M.P., Luco, N., Frankel, A.D., Petersen, M.D., Aagaard, B.T., Baltay Sundstrom, A.S., Blanpied, M.L., Boyd, O.S., Briggs, R.W., Gold, R.D., Graves, R., Hartzell, S.H., Rezaeian, S., Stephenson, W.J., Wald, D.J., Williams, R., and Withers, K., 2018, Integrate urban‐scale seismic hazard analyses with the U.S. National Seismic Hazard Model: Seismological Research Letters, v. 89, no. 3, p. 967-970, https://doi.org/10.1785/0220170261.","productDescription":"4 p.","startPage":"967","endPage":"970","ipdsId":"IP-094326","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":353086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0ae","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":732357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":732358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":732359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":732360,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blanpied, Michael L. 0000-0002-3294-4458 mblanpied@usgs.gov","orcid":"https://orcid.org/0000-0002-3294-4458","contributorId":203801,"corporation":false,"usgs":true,"family":"Blanpied","given":"Michael","email":"mblanpied@usgs.gov","middleInitial":"L.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":732361,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732362,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732363,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732364,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":732365,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732366,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732367,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732368,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732369,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Williams, Robert A. 0000-0002-2973-8493","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":203802,"corporation":false,"usgs":false,"family":"Williams","given":"Robert A.","affiliations":[{"id":36721,"text":"USGS-Emeritus","active":true,"usgs":false}],"preferred":false,"id":732370,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":732371,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}}
,{"id":70190387,"text":"70190387 - 2018 - Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016","interactions":[],"lastModifiedDate":"2018-04-23T09:00:37","indexId":"70190387","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016","docAbstract":"<p> In December–February (DJF) of 2015/16, a strong El Niño (Niño‑3.4 SST &gt;29°C) contributed to a severe drought over southern Africa (SA; Funk et al. 2016). A 9-million ton cereal deficit resulted in 26 mil‑ lion people in need of humanitarian assistance (SADC 2016). While SA rainfall has a well-documented nega‑ tive teleconnection with Niño‑3.4 SSTs (Hoell et al. 2015, 2017; Jury et al. 1994; Lindesay 1988; Misra 2003; Nicholson and Entekhabi 1987; Nicholson and Kim 1997; Reason et al. 2000; Rocha and Simmonds 1997), the link between climate change and El Niño remains unclear (Christensen et al. 2013) due to the large natural variability of ENSO SSTs (Wittenberg 2009), uncertainties surrounding measurements and trends (Solomon and Newman 2012), intermodel differences in ENSO representation and feedbacks (Guilyardi et al. 2012; Kim et al. 2014), and difficulties associated with quantifying ENSO strength (Cai et al. 2015).</p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-17-0112.1","usgsCitation":"Funk, C., Davenport, F., Harrison, L., Magadzire, T., Galu, G., Artan, G.A., Shukla, S., Korecha, D., Indeje, M., Pomposi, C., Macharia, D., Husak, G., and Dieudonne Nsadisa, F., 2018, Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016: Bulletin of the American Meteorological Society, v. 99, no. 1, p. S91-S94, https://doi.org/10.1175/BAMS-D-17-0112.1.","productDescription":"4 p.","startPage":"S91","endPage":"S94","ipdsId":"IP-086759","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":461003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-17-0112.1","text":"Publisher Index Page"},{"id":352865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-26","publicationStatus":"PW","scienceBaseUri":"5afee713e4b0da30c1bfc0dc","contributors":{"authors":[{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":708831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":708832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":708833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Magadzire, Tamuka","contributorId":145822,"corporation":false,"usgs":false,"family":"Magadzire","given":"Tamuka","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":731922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galu, Gideon","contributorId":97360,"corporation":false,"usgs":true,"family":"Galu","given":"Gideon","affiliations":[],"preferred":false,"id":731923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Artan, Guleid A. 0000-0001-8409-6182 gartan@usgs.gov","orcid":"https://orcid.org/0000-0001-8409-6182","contributorId":2938,"corporation":false,"usgs":true,"family":"Artan","given":"Guleid","email":"gartan@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":731924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":708834,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Korecha, Diriba","contributorId":195983,"corporation":false,"usgs":false,"family":"Korecha","given":"Diriba","email":"","affiliations":[],"preferred":false,"id":708835,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Indeje, Matayo","contributorId":203615,"corporation":false,"usgs":false,"family":"Indeje","given":"Matayo","email":"","affiliations":[],"preferred":false,"id":731925,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pomposi, Catherine","contributorId":195984,"corporation":false,"usgs":false,"family":"Pomposi","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":708836,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Macharia, Denis","contributorId":195985,"corporation":false,"usgs":false,"family":"Macharia","given":"Denis","email":"","affiliations":[],"preferred":false,"id":708837,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":708838,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dieudonne Nsadisa, Faka","contributorId":203616,"corporation":false,"usgs":false,"family":"Dieudonne Nsadisa","given":"Faka","email":"","affiliations":[],"preferred":false,"id":731926,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70159602,"text":"sir20155164 - 2018 - Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models","interactions":[],"lastModifiedDate":"2023-06-08T16:40:09.114831","indexId":"sir20155164","displayToPublicDate":"2018-03-01T00:00:00","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":"2015-5164","displayTitle":"Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models","title":"Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models","docAbstract":"<p>Hawai‘i’s aquifers have limited capacity to store fresh groundwater because each island is small and surrounded by saltwater. Saltwater also underlies much of the fresh groundwater. Fresh groundwater resources are, therefore, particularly vulnerable to human activity, short-term climate cycles, and long-term climate change. Availability of fresh groundwater for human use is constrained by the degree to which the impacts of withdrawal—such as lowering of the water table, saltwater intrusion, and reduction in the natural discharge to springs, streams, wetlands, and submarine seeps—are deemed acceptable. This report describes the hydrogeologic framework, groundwater budgets (inflows and outflows), conceptual models of groundwater occurrence and movement, and the factors limiting groundwater availability for the largest and most populated of the Hawaiian Islands—Kaua‘i, O‘ahu, Maui, and Hawai‘i Island.</p><p>The bulk of each of Hawai‘i’s islands is built of many thin lava flows erupted from shield volcanoes; the great piles of lava flows form highly permeable aquifers. In some areas, low-permeability dikes cutting across the lava flows, or low-permeability ash and soil horizons interlayered with the lava flows, can substantially alter groundwater flow. On some islands, sedimentary rocks form thick semiconfining coastal-plain deposits, locally known as caprock, that impede natural groundwater discharge to the ocean. In some regions, thick lava flows that ponded in preexisting depressions form aquifers that are much less permeable than aquifers formed by thin lava flows.</p><p>Fresh groundwater inflow to Hawai‘i’s aquifers comes from recharge. For predevelopment conditions (1870), estimates of groundwater recharge from this study are 871, 675, 1,279, and 5,291 million gallons per day (Mgal/d) for Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. Estimates of recharge for recent conditions (2010 land cover and 1978–2007 rainfall for Kaua‘i, O‘ahu, and Maui; 2008 land cover and 1916–1983 rainfall for Hawai‘i Island) are 875, 660, 1,308, and 6,595 Mgal/d for Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. Recent recharge values differ from predevelopment recharge values by only a few percent for all islands except Hawai‘i Island, where changes in forest cover affected recharge. Spatial distribution of recharge mimics the orographic rainfall pattern—recharge is high on&nbsp;windward slopes and mountain peaks below the top of the trade-wind inversion. Human activity such as irrigation also contributes to recharge in some areas.</p><p>Outflows from Hawai‘i’s aquifers include withdrawals from wells and natural groundwater discharge to springs, streams, wetlands, and submarine seeps. Under predevelopment conditions, groundwater withdrawal is assumed to be negligible and natural groundwater discharge probably was equal, or close, to recharge. Under recent conditions (2000–2010), groundwater withdrawal averaged 19, 209, 104, and 103 Mgal/d on Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. If recent withdrawal and recharge rates are maintained until steady state is achieved, natural groundwater discharge will be reduced by an amount equal to the withdrawal rate. Total recent withdrawal for the four islands is only about 5 percent of total recharge, but about half of the withdrawal comes from O‘ahu, whereas O‘ahu receives only 7 percent of the total recharge. Effects of high withdrawals on O‘ahu cannot be mitigated by the lower withdrawals on other islands because no freshwater flows between islands. Even within an island, high withdrawals from one area cannot be completely mitigated by recharge in another area. Water-level, saltwater/freshwater-transition-zone, spring, and stream base-flow data indicate an overall reduction in storage for most areas where groundwater has been developed.</p><p>Groundwater occurrence and movement in Hawai‘i’s volcanic aquifers can be described in terms of four conceptual models: (1) fresh groundwater lenses in high-permeability lava-flow aquifers, (2) aquifers with groundwater impounded by dikes, (3) thickly saturated low-permeability aquifers, and (4) perched aquifers. In Hawai‘i, most fresh groundwater withdrawn for human use comes from freshwater lenses in the dike-free high-permeability lava-flow aquifers where the principal limiting factor to groundwater availability is saltwater intrusion, but impacts of reduced natural groundwater discharge may also limit availability. Dike-impounded groundwater is common near the center of Hawaiian shield volcanoes, where water moves and is stored in permeable lava flows between the dikes; groundwater availability in these aquifers is primarily limited by storage depletion and reduction of flow to adjacent aquifers and natural groundwater discharge. Thickly saturated low-permeability aquifers have been identified on Kaua‘i and Maui; groundwater availability is primarily limited by streamflow depletion and water-table decline. Perched groundwater is postulated to exist in some areas of Hawai‘i, but store much less water than other modes ofgroundwater occurrence. Limits on groundwater availability in perched aquifers include the potential of reducing inflow to other groundwater settings and reducing natural discharge and stream seepage. Some groundwater bodies in Hawai‘i are enigmatic; consequences of groundwater development in these bodies and their relation to groundwater availability are not completely understood.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155164","usgsCitation":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","productDescription":"Report: ix, 158 p.; Data Releases","numberOfPages":"172","ipdsId":"IP-058142","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":351940,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72F7KH4","linkHelpText":"Mean annual 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 \"}}]}","edition":"Version 1.0: June 13, 2016; Version 2.0: March 1, 2018","contact":"<p><a href=\"mailto:dc_hi@usgs.gov\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,&nbsp;<br><a href=\"https://hi.water.usgs.gov/\" data-mce-href=\"https://hi.water.usgs.gov/\">Pacific Islands Water Science Center</a><br><a href=\"https://usgs.gov/\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Study Area<br></li><li>Hydrogeologic Framework of the Islands<br></li><li>Fresh Groundwater-Flow Budget<br></li><li>Conceptual Models of Groundwater Occurrence and Movement<br></li><li>Study Limitations<br></li><li>Summary<br></li><li>References Cited<br></li><li>Appendix 1. Calculation of Groundwater Recharge<br></li><li>Appendix 2. Annual Groundwater Recharge, 2001–2010<br></li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2016-06-13","revisedDate":"2018-03-01","noUsgsAuthors":false,"publicationDate":"2016-06-13","publicationStatus":"PW","scienceBaseUri":"575fcb20e4b04f417c2b2683","contributors":{"authors":[{"text":"Izuka, Scot K. 0000-0002-8758-9414 skizuka@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-9414","contributorId":2645,"corporation":false,"usgs":true,"family":"Izuka","given":"Scot","email":"skizuka@usgs.gov","middleInitial":"K.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engott, John A. 0000-0003-1889-4519 jaengott@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-4519","contributorId":1142,"corporation":false,"usgs":true,"family":"Engott","given":"John","email":"jaengott@usgs.gov","middleInitial":"A.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotzoll, Kolja 0000-0002-5910-888X","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":201087,"corporation":false,"usgs":false,"family":"Rotzoll","given":"Kolja","affiliations":[],"preferred":false,"id":729255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bassiouni, Maoya 0000-0001-5795-9894 mbassiou@usgs.gov","orcid":"https://orcid.org/0000-0001-5795-9894","contributorId":4639,"corporation":false,"usgs":true,"family":"Bassiouni","given":"Maoya","email":"mbassiou@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":false,"id":729256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Adam G. 0000-0003-2448-5746 ajohnson@usgs.gov","orcid":"https://orcid.org/0000-0003-2448-5746","contributorId":4752,"corporation":false,"usgs":true,"family":"Johnson","given":"Adam","email":"ajohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729257,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Lisa D. 0000-0002-3523-0768 ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729258,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mair, Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729259,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70194641,"text":"tm14A2 - 2018 - THRESH—Software for tracking rainfall thresholds for landslide and debris-flow occurrence, user manual","interactions":[],"lastModifiedDate":"2018-03-01T11:10:52","indexId":"tm14A2","displayToPublicDate":"2018-02-28T17:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"14-A2","title":"THRESH—Software for tracking rainfall thresholds for landslide and debris-flow occurrence, user manual","docAbstract":"<p>Precipitation thresholds are used in many areas to provide early warning of precipitation-induced landslides and debris flows, and the software distribution THRESH is designed for automated tracking of precipitation, including precipitation forecasts, relative to thresholds for landslide occurrence. This software is also useful for analyzing multiyear precipitation records to compare timing of threshold exceedance with dates and times of historical landslides. This distribution includes the main program THRESH for comparing precipitation to several kinds of thresholds, two utility programs, and a small collection of Python and shell scripts to aid the automated collection and formatting of input data and the graphing and further analysis of output results. The software programs can be deployed on computing platforms that support Fortran 95, Python 2, and certain Unix commands. The software handles rainfall intensity-duration thresholds, cumulative recent-antecedent precipitation thresholds, and peak intensity thresholds as well as various measures of antecedent precipitation. Users should have predefined rainfall thresholds before running THRESH.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Modeling methods in Book 14: <i>Landslide and debris-flow assessment</i>","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm14A2","usgsCitation":"Baum, R.L., Fischer, S.J., and Vigil, J.C., 2018, THRESH—Software for tracking rainfall thresholds for landslide and debris-flow occurrence, user manual: U.S. Geological Survey Techniques and Methods, book 14, chap. A2, 33 p., https://doi.org/10.3133/tm14A2.","productDescription":"Report: v, 33 p.; Software release","onlineOnly":"Y","ipdsId":"IP-087132","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437999,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23XR0","text":"USGS data release","linkHelpText":"Thresh - Software for Tracking Rainfall Thresholds for Landslide and Debris Flow Occurrence, Code Repository"},{"id":352130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/14/a2/coverthb.jpg"},{"id":352140,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F7Q23XR0","text":"Software Release","linkHelpText":"THRESH"},{"id":352131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/14/a2/tm14a2.pdf","text":"Report","size":"7.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 14-A2"}],"publicComments":"This report is Chapter 2 of Section A: Modeling methods in Book 14: <i>Landslide and debris-flow assessment</i>.","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/geohazards\" data-mce-href=\"https://www.usgs.gov/centers/geohazards\">Geologic Hazards Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS 966<br>Denver, CO 80225-0046</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Features</li><li>Theoretical Basis of the Program</li><li>Program Operation</li><li>Utility Programs</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Seattle, Washington, Example</li><li>Appendix 2. Example of Using Rainfall Forecasts with Thresholds</li><li>Appendix 3. Hydrological Monitoring Sites</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2018-02-28","noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee713e4b0da30c1bfc0e0","contributors":{"authors":[{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":724706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischer, Sarah J.","contributorId":201201,"corporation":false,"usgs":false,"family":"Fischer","given":"Sarah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":724707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vigil, Jacob C.","contributorId":201202,"corporation":false,"usgs":false,"family":"Vigil","given":"Jacob","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":724708,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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