{"pageNumber":"497","pageRowStart":"12400","pageSize":"25","recordCount":40783,"records":[{"id":70193158,"text":"70193158 - 2016 - Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales","interactions":[],"lastModifiedDate":"2017-11-16T16:07:49","indexId":"70193158","displayToPublicDate":"2016-03-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales","docAbstract":"<p><span>The loss of historic ecosystem conditions has led forest managers to implement woodland and savanna ecosystem restoration on a landscape scale (≥10,000 ha) in the Ozark Plateau of Arkansas. Managers are attempting to restore and conserve these ecosystems through the reintroduction of disturbance, mainly short-rotation early-growing-season prescribed fire. Short-rotation early-growing season prescribed fire in the Ozarks typically occurs immediately before bud-break, through bud-break, and before leaf-out, and fire events occur on a three-to five-year interval. We examined short-rotation early-growing season prescribed fire as a restoration tool on vegetation characteristics. We collected vegetation measurements at 70 locations annually from 2011 to 2012 in and around the White Rock Ecosystem Restoration Area (WRERA), Ozark-St. Francis National Forest, Arkansas, and used generalized linear models to investigate the impact and efficacy of prescribed fire on vegetation structure. We found the number of large shrubs (&gt;5 cm base diameter) decreased and small shrubs (&lt;5 cm ground diameter) increased with prescribed fire severity. We found that horizontal understory cover from ground level to 1 m in height increased with time-since-prescribed-fire and woody ground cover decreased with the number of prescribed fire treatments. Using LANDFIRE datasets at the landscape scale, we found that since the initiation of a short-rotation early-growing season prescribed fire management regime, forest canopy cover has not reverted to levels characteristic of woodlands and savannas or reached restoration objectives over large areas. Without greater reductions in forest canopy cover and increases in forest-canopy cover heterogeneity, advanced regeneration will be limited in success, and woodland and savanna conditions will not return soon or to the extent desired.</span></p>","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Pittman, H.T., and Krementz, D.G., 2016, Efficacy of landscape scale woodland and savanna restoration at multiple spatial and temporal scales: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 3, p. 233-242.","productDescription":"10 p.","startPage":"233","endPage":"242","ipdsId":"IP-059541","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.seafwa.org/publications/journal/?id=402058"},{"id":349022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Ozark Highlands","volume":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fd6be4b06e28e9c24d6e","contributors":{"authors":[{"text":"Pittman, H. Tyler","contributorId":200530,"corporation":false,"usgs":false,"family":"Pittman","given":"H.","email":"","middleInitial":"Tyler","affiliations":[],"preferred":false,"id":722562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718106,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70168683,"text":"ofr20161026 - 2016 - Geologic assessment of undiscovered oil and gas resources in the Albian Clastic and Updip Albian Clastic Assessment Units, U.S. Gulf Coast Region","interactions":[],"lastModifiedDate":"2016-05-23T09:06:15","indexId":"ofr20161026","displayToPublicDate":"2016-03-11T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1026","title":"Geologic assessment of undiscovered oil and gas resources in the Albian Clastic and Updip Albian Clastic Assessment Units, U.S. Gulf Coast Region","docAbstract":"<p>U.S. Geological Survey National Oil and Gas Assessments (NOGA) of Albian aged clastic reservoirs in the U.S. Gulf Coast region indicate a relatively low prospectivity for undiscovered hydrocarbon resources due to high levels of past production and exploration. Evaluation of two assessment units (AUs), (1) the Albian Clastic AU 50490125, and (2) the Updip Albian Clastic AU 50490126, were based on a geologic model incorporating consideration of source rock, thermal maturity, migration, events timing, depositional environments, reservoir rock characteristics, and production analyses built on well and field-level production histories. The Albian Clastic AU is a mature conventional hydrocarbon prospect with undiscovered accumulations probably restricted to small faulted and salt-associated structural traps that could be revealed using high resolution subsurface imaging and from targeting structures at increased drilling depths that were unproductive at shallower intervals. Mean undiscovered accumulation volumes from the probabilistic assessment are 37 million barrels of oil (MMBO), 152 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL). Limited exploration of the Updip Albian Clastic AU reflects a paucity of hydrocarbon discoveries updip of the periphery fault zones in the northern Gulf Coastal region. Restricted migration across fault zones is a major factor behind the small discovered fields and estimation of undiscovered resources in the AU. Mean undiscovered accumulation volumes from the probabilistic assessment are 1 MMBO and 5 BCFG for the Updip Albian Clastic AU.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161026","usgsCitation":"Merrill, M.D., 2016, Geologic assessment of undiscovered oil and gas resources in the Albian clastic and updip Albian clastic assessment units, U.S. Gulf Coast region: U.S. Geological Survey Open-File Report 2016–1026, 31 p., https://dx.doi.org/10.3133/ofr20161026.","productDescription":"Report: v, 27; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038743","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":318712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1026//ofr20161026.pdf","text":"Report","size":"2.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1026"},{"id":318714,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1026/ofr20161026_appendix2.pdf","text":"Appendix 2 - Basic Input Data for the Updip Albian Clastic Assessment Unit","size":"32.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1026"},{"id":318713,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1026/ofr20161026_appendix1.pdf","text":"Appendix 1 - Basic Input Data for the Albian Clastic Assessment Unit","size":"32.4 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1026"},{"id":318711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1026/coverthb.jpg"}],"country":"United States","otherGeospatial":"Gulf Coast Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n 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Basic Input Data for the Albian Clastic Assessment Unit</li>\n<li>Appendix 2. Basic Input Data for the Updip Albian Clastic Assessment Unit</li>\n</ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2016-03-11","noUsgsAuthors":false,"publicationDate":"2016-03-11","publicationStatus":"PW","scienceBaseUri":"56e3ec2be4b0f59b85d42dee","contributors":{"authors":[{"text":"Merrill, Matthew D. 0000-0003-3766-847X mmerrill@usgs.gov","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":167161,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew D.","email":"mmerrill@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":621256,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70169028,"text":"70169028 - 2016 - Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China","interactions":[],"lastModifiedDate":"2016-03-11T09:22:50","indexId":"70169028","displayToPublicDate":"2016-03-11T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2184,"text":"Journal of Asian Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China","docAbstract":"<p><span>The NWW-striking North Qilian Orogenic Belt records the Paleozoic accretion&ndash;collision processes in NW China, and hosts Paleozoic Cu&ndash;Pb&ndash;Zn mineralization that was temporally and spatially related to the closure of the Paleo Qilian-Qinling Ocean. The Wangdian Cu deposit is located in the eastern part of the North Qilian Orogenic Belt, NW China. Copper mineralization is spatially associated with an altered early Paleozoic porphyritic granodiorite, which intruded tonalites and volcaniclastic rocks. Alteration zones surrounding the mineralization progress outward from a potassic to a feldspar-destructive phyllic assemblage. Mineralization consists mainly of quartz-sulfide stockworks and disseminated sulfides, with ore minerals chalcopyrite, pyrite, molybdenite, and minor galena and sphalerite. Gangue minerals include quartz, orthoclase, biotite, sericite, and K-feldspar. Zircon LA-ICPMS U&ndash;Pb dating of the ore-bearing porphyritic granodiorite yielded a mean&nbsp;</span><sup>206</sup><span>Pb/</span><sup>238</sup><span>U age of 444.6&nbsp;&plusmn;&nbsp;7.8&nbsp;Ma, with a group of inherited zircons yielding a mean U&ndash;Pb age of 485&nbsp;&plusmn;&nbsp;12&nbsp;Ma, consistent with the emplacement age (485.3&nbsp;&plusmn;&nbsp;6.2&nbsp;Ma) of the barren precursor tonalite. Rhenium and osmium analyses of molybdenite grains returned model ages of 442.9&nbsp;&plusmn;&nbsp;6.8&nbsp;Ma and 443.3&nbsp;&plusmn;&nbsp;6.2&nbsp;Ma, indicating mineralization was coeval with the emplacement of the host porphyritic granodiorite. Rhenium concentrations in molybdenite (208.9&ndash;213.2&nbsp;ppm) suggest a mantle Re source. The tonalities are medium-K calc-alkaline. They are characterized by enrichment of light rare-earth elements (LREEs) and large-ion lithophile elements (LILEs), depletion of heavy rare-earth elements (HREEs) and high-field-strength elements (HFSEs), and minor negative Eu anomalies. They have&nbsp;</span><i>&epsilon;</i><sub>Hf</sub><span>(</span><i>t</i><span>) values in the range of +3.6 to +11.1, with two-stage Hf model ages of 0.67&ndash;1.13&nbsp;Ga, suggesting that the ca. 485&nbsp;Ma barren tonalites were products of arc magmatism incorporating melts from the mantle wedge and the lithosphere. In contrast, the 40-m.y.-younger ore-bearing porphyritic granodiorite is sub-alkaline and peraluminous. They are enriched in LREEs and LILEs, depleted in HFSEs, and show weak negative Eu anomalies. They display</span><i>&epsilon;</i><sub>Hf</sub><span>(</span><i>t</i><span>) values of captured or inherited zircons in the range of +8.5 to +10.0, and younger two-stage Hf model ages of 0.78&nbsp;Ga and 0.86&nbsp;Ga, similar to those of ca. 485&nbsp;Ma tonalite. The ca. 445&nbsp;Ma zircons have&nbsp;</span><i>&epsilon;</i><sub>Hf</sub><span>(</span><i>t</i><span>) values of &minus;2.1 to +9.9, with two-stage Hf model ages of 0.75&ndash;1.27&nbsp;Ga. Moreover, they have relatively high oxygen fugacity than that of the precursor barren tonalite. The ca. 445&nbsp;Ma magmas at Wangdian thus formed in a subduction setting, and incorporated melts from the subduction-modified lithosphere that had previously been enriched by additions of chalcophile and siderophile element-rich materials by the earlier magmatism and metasomatism during the Paleo Qilian-Qinling Ocean subduction event.</span></p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Asian Earth Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jseaes.2016.02.007","collaboration":"Kunfeng Qiu; Jun Deng; Liqiang Yang; Ryan D Taylor; Kairui Song, Yaohui Song; Quanzhong Li; Richard J Goldfarb","usgsCitation":"Qiu, K., Deng, J., Taylor, R.D., Song, K., Song, Y., Li, Q., and Goldfarb, R.J., 2016, Paleozoic magmatism and porphyry Cu-mineralization in an evolving tectonic setting in the North Qilian Orogenic Belt, NW China: Journal of Asian Earth Sciences, v. 122, p. 20-40, https://doi.org/10.1016/j.jseaes.2016.02.007.","productDescription":"21 p.","startPage":"20","endPage":"40","numberOfPages":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063294","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":318811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"North Qilian Orogenic Belt","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              89.56054687499999,\n              31.203404950917395\n            ],\n            [\n              89.56054687499999,\n              41.37680856570233\n            ],\n            [\n              112.412109375,\n              41.37680856570233\n            ],\n            [\n              112.412109375,\n              31.203404950917395\n            ],\n            [\n              89.56054687499999,\n              31.203404950917395\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"122","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e3ec2be4b0f59b85d42df1","contributors":{"authors":[{"text":"Qiu, Kun-Feng","contributorId":167527,"corporation":false,"usgs":false,"family":"Qiu","given":"Kun-Feng","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deng, Jun","contributorId":167528,"corporation":false,"usgs":false,"family":"Deng","given":"Jun","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Song, Kai-Rui","contributorId":167530,"corporation":false,"usgs":false,"family":"Song","given":"Kai-Rui","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622596,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Song, Yao-Hui","contributorId":167531,"corporation":false,"usgs":false,"family":"Song","given":"Yao-Hui","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":622597,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Quan-Zhong","contributorId":167532,"corporation":false,"usgs":false,"family":"Li","given":"Quan-Zhong","email":"","affiliations":[{"id":24738,"text":"Hefei University of Technology","active":true,"usgs":false}],"preferred":false,"id":622598,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldfarb, Richard J. goldfarb@usgs.gov","contributorId":1205,"corporation":false,"usgs":true,"family":"Goldfarb","given":"Richard","email":"goldfarb@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":622599,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70173767,"text":"70173767 - 2016 - Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region","interactions":[],"lastModifiedDate":"2016-06-22T15:40:36","indexId":"70173767","displayToPublicDate":"2016-03-11T07:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region","docAbstract":"<p><span class=\"pb_abstract\">&nbsp;A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models and compare them with observations from 268 Russian stations. There are large across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (&Delta;<i>T</i>), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96&deg;C/&deg;C), and in the relationship between &Delta;<i>T</i>&nbsp;and snow depth. The observed relationship between &Delta;<i>T</i>&nbsp;and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, and hence guide improvements to the model&rsquo;s conceptual structure and process parameterizations. Models with better performance apply multi-layer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (12&ndash;16 million km<sup><span>2</span></sup>). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.</span></p>\n<p>&nbsp;</p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-2016-36","usgsCitation":"Wang, W., Rinke, A., Moore, J., Ji, D., Cui, X., Peng, S., Lawrence, D.M., McGuire, A., Burke, E.J., Chen, X., Delire, C., Koven, C., MacDougall, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Decharme, B., Gouttevin, I., Hajima, T., Krinner, G., Lettenmaier, D.P., Miller, P.A., Smith, B., and Sueyoshi, T., 2016, Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region: The Cryosphere, no. Online First, https://doi.org/10.5194/tc-2016-36.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059649","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-2016-36","text":"Publisher Index Page"},{"id":324264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"Online First","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576bb6b4e4b07657d1a228a5","contributors":{"authors":[{"text":"Wang, Wenli","contributorId":172351,"corporation":false,"usgs":false,"family":"Wang","given":"Wenli","email":"","affiliations":[],"preferred":false,"id":640442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rinke, Annette","contributorId":172352,"corporation":false,"usgs":false,"family":"Rinke","given":"Annette","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":640443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, John C.","contributorId":152072,"corporation":false,"usgs":false,"family":"Moore","given":"John C.","affiliations":[],"preferred":false,"id":640444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Duoying","contributorId":172353,"corporation":false,"usgs":false,"family":"Ji","given":"Duoying","email":"","affiliations":[],"preferred":false,"id":640445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cui, Xuefeng","contributorId":172354,"corporation":false,"usgs":false,"family":"Cui","given":"Xuefeng","email":"","affiliations":[],"preferred":false,"id":640446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peng, Shushi","contributorId":172355,"corporation":false,"usgs":false,"family":"Peng","given":"Shushi","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":640447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, David M.","contributorId":105206,"corporation":false,"usgs":false,"family":"Lawrence","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":640448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, A. David","contributorId":18494,"corporation":false,"usgs":true,"family":"McGuire","given":"A. David","affiliations":[],"preferred":false,"id":640449,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burke, Eleanor J.","contributorId":172358,"corporation":false,"usgs":false,"family":"Burke","given":"Eleanor","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":640450,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chen, Xiaodong","contributorId":172359,"corporation":false,"usgs":false,"family":"Chen","given":"Xiaodong","email":"","affiliations":[{"id":16995,"text":"School of Earth and Space Exploration, Arizona State University","active":true,"usgs":false}],"preferred":false,"id":640451,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Delire, Christine","contributorId":172360,"corporation":false,"usgs":false,"family":"Delire","given":"Christine","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":638144,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Koven, Charles","contributorId":51143,"corporation":false,"usgs":true,"family":"Koven","given":"Charles","affiliations":[],"preferred":false,"id":640452,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"MacDougall, Andrew","contributorId":102378,"corporation":false,"usgs":true,"family":"MacDougall","given":"Andrew","affiliations":[],"preferred":false,"id":640453,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Saito, Kazuyuki","contributorId":172361,"corporation":false,"usgs":false,"family":"Saito","given":"Kazuyuki","email":"","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":640454,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zhang, Wenxin","contributorId":167815,"corporation":false,"usgs":false,"family":"Zhang","given":"Wenxin","email":"","affiliations":[],"preferred":false,"id":640455,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Alkama, Ramdane","contributorId":172362,"corporation":false,"usgs":false,"family":"Alkama","given":"Ramdane","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":640456,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Bohn, Theodore J.","contributorId":172363,"corporation":false,"usgs":false,"family":"Bohn","given":"Theodore","email":"","middleInitial":"J.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":640457,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Ciais, Philippe","contributorId":40719,"corporation":false,"usgs":true,"family":"Ciais","given":"Philippe","affiliations":[],"preferred":false,"id":640458,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Decharme, Bertrand","contributorId":172364,"corporation":false,"usgs":false,"family":"Decharme","given":"Bertrand","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":640459,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Gouttevin, Isabelle","contributorId":172365,"corporation":false,"usgs":false,"family":"Gouttevin","given":"Isabelle","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":640460,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Hajima, Tomohiro","contributorId":172366,"corporation":false,"usgs":false,"family":"Hajima","given":"Tomohiro","email":"","affiliations":[],"preferred":false,"id":640461,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Krinner, Gerhard","contributorId":172367,"corporation":false,"usgs":false,"family":"Krinner","given":"Gerhard","email":"","affiliations":[{"id":16636,"text":"CNRS","active":true,"usgs":false}],"preferred":false,"id":640462,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Lettenmaier, Dennis P.","contributorId":139779,"corporation":false,"usgs":false,"family":"Lettenmaier","given":"Dennis","email":"","middleInitial":"P.","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":640463,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Miller, Paul A.","contributorId":57372,"corporation":false,"usgs":true,"family":"Miller","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":640464,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Smith, Benjamin","contributorId":171835,"corporation":false,"usgs":false,"family":"Smith","given":"Benjamin","affiliations":[],"preferred":false,"id":640465,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sueyoshi, Tetsuo","contributorId":172368,"corporation":false,"usgs":false,"family":"Sueyoshi","given":"Tetsuo","email":"","affiliations":[],"preferred":false,"id":640466,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}}
,{"id":70168922,"text":"70168922 - 2016 - Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","interactions":[],"lastModifiedDate":"2018-03-05T16:49:57","indexId":"70168922","displayToPublicDate":"2016-03-11T03:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6","docAbstract":"<p>Floodplains can be viewed as complex adaptive systems (Levin, 1998) because they are comprised of many different biophysical components, such as morphological features, soil groups and vegetation communities as well as being sites of key biogeochemical processing (Stanford et al., 2005). Interactions and feedbacks among the biophysical components often result in additional phenomena occuring over a range of scales, often in the absence of any controlling factors (sensu Hallet, 1990). This emergence of new biophysical features and rates of processing can lead to alternative stable states which feed back into floodplain adaptive cycles (cf. Hughes, 1997; Stanford et al., 2005). Interactions between different biophysical components, feedbacks, self emergence and scale are all key properties of complex adaptive systems (Levin, 1998; Phillips, 2003; Murray et al., 2014) and therefore will influence the manner in which we study and view spatial patterns. Measuring the spatial patterns of floodplain biophysical components is a prerequisite to examining and understanding these ecosystems as complex adaptive systems. Elucidating relationships between pattern and process, which are intrinsically linked within floodplains (Ward et al., 2002), is dependent upon an understanding of spatial pattern. This knowledge can help river scientists determine the major drivers, controllers and responses of floodplain structure and function, as well as the consequences of altering those drivers and controllers (Hughes and Cass, 1997; Whited et al., 2007). Interactions and feedbacks between physical, chemical and biological components of floodplain ecosystems create and maintain a structurally diverse and dynamic template (Stanford et al., 2005). This template influences subsequent interactions between components that consequently affect system trajectories within floodplains (sensu Bak et al., 1988). Constructing and evaluating models used to predict floodplain ecosystem responses to natural and anthropogenic disturbances therefore require quantification of spatial pattern (Asselman and Middelkoop, 1995; Walling and He, 1998). Quantifying these patterns also provides insights into the spatial and temporal domains of structuring processes as well as enabling the detection of self-emergent phenomena, environmental constraints or anthropogenic interference (Turner et al., 1990; Holling, 1992; De Jager and Rohweder, 2012). Thus, quantifying spatial pattern is an important building block on which to examine floodplains as complex adaptive systems (Levin, 1998). Approaches to measuring spatial pattern in floodplains must be cognisant of scale, self-emergent phenomena, spatial organisation, and location. Fundamental problems may arise when patterns observed at a site or transect scale are scaled-up to infer processes and patterns over entire floodplain surfaces (Wiens, 2002; Thorp et al., 2008). Likewise, patterns observed over the entire spatial extent of a landscape can mask important variation and detail at finer scales (Riitters et al., 2002). Indeed, different patterns often emerge at different scales (Turner et al., 1990) because of hierarchical structuring processes (O'Neill et al., 1991). Categorising data into discrete, homogeneous and predefined spatial units at a particular scale (e.g. polygons) creates issues and errors associated with scale and subjective classification (McGarigal et al., 2009; Cushman et al., 2010). These include, loss of information within classified &lsquo;patches&rsquo;, as well as the ability to detect the emergence of new features that do not fit the original classification scheme. Many of these issues arise because floodplains are highly heterogeneous and have complex spatial organizations (Carbonneau et al., 2012; Legleiter, 2013). As a result, the scale and location at which measurements are made can influence the observed spatial patterns; and patterns may not be scale independent or applicable in different geomorp</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River science: Research and management for the 21st century","language":"English","publisher":"John Wiley & Sons, Ltd","doi":"10.1002/9781118643525.ch6","isbn":"978-1-119-99434-3","usgsCitation":"Scown, M.W., Thoms, M.C., and De Jager, N.R., 2016, Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6, chap. <i>of</i> River science: Research and management for the 21st century, p. 103-131, https://doi.org/10.1002/9781118643525.ch6.","productDescription":"29 p.","startPage":"103","endPage":"131","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056619","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":321675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5746ccbee4b07e28b662dcf0","contributors":{"editors":[{"text":"Gilvear, David J.","contributorId":169613,"corporation":false,"usgs":false,"family":"Gilvear","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630282,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Greenwood, Malcolm T.","contributorId":169614,"corporation":false,"usgs":false,"family":"Greenwood","given":"Malcolm","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":630283,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":630284,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Wood, Paul J.","contributorId":169615,"corporation":false,"usgs":false,"family":"Wood","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":630285,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Scown, Murray W.","contributorId":145709,"corporation":false,"usgs":false,"family":"Scown","given":"Murray","email":"","middleInitial":"W.","affiliations":[{"id":24492,"text":"Riverine Landscapes Research Laboratory, University of New England, Armidale, Australia","active":true,"usgs":false}],"preferred":false,"id":622119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":622120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":622118,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70170764,"text":"70170764 - 2016 - Application of effective discharge analysis to environmental flow decision-making","interactions":[],"lastModifiedDate":"2016-05-02T15:14:07","indexId":"70170764","displayToPublicDate":"2016-03-10T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Application of effective discharge analysis to environmental flow decision-making","docAbstract":"<p><span>Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60&nbsp;years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.</span></p>","language":"English","publisher":"Springer-Verlag","publisherLocation":"New York","doi":"10.1007/s00267-016-0684-4","usgsCitation":"McKay, S.K., Freeman, M., and Covich, A., 2016, Application of effective discharge analysis to environmental flow decision-making: Environmental Management, v. 575, no. 6, p. 1153-1165, https://doi.org/10.1007/s00267-016-0684-4.","productDescription":"13 p.","startPage":"1153","endPage":"1165","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073347","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":320849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"575","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a2be4b0b13d391865af","contributors":{"authors":[{"text":"McKay, S. Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":628390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Covich, A.P.","contributorId":14965,"corporation":false,"usgs":true,"family":"Covich","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":628392,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70170765,"text":"70170765 - 2016 - Study of biological communities subject to imperfect detection: Bias and precision of community <i>N</i>-mixture abundance models in small-sample situations","interactions":[],"lastModifiedDate":"2016-05-02T15:06:29","indexId":"70170765","displayToPublicDate":"2016-03-10T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1461,"text":"Ecological Research","active":true,"publicationSubtype":{"id":10}},"title":"Study of biological communities subject to imperfect detection: Bias and precision of community <i>N</i>-mixture abundance models in small-sample situations","docAbstract":"<p><span>Community&nbsp;</span><i class=\"EmphasisTypeItalic \">N</i><span>-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance&nbsp;</span><span id=\"IEq1\" class=\"InlineEquation\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mo stretchy=&quot;false&quot;&gt;(&lt;/mo&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;&amp;#x03BB;&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mo\">(</span><span id=\"MathJax-Span-4\" class=\"texatom\"><span id=\"MathJax-Span-5\" class=\"mrow\"><span id=\"MathJax-Span-6\" class=\"munderover\"><span><span><span id=\"MathJax-Span-7\" class=\"mi\">&lambda;</span></span><span><span id=\"MathJax-Span-8\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>: 0.1, 0.5, 1, 5), detection probability&nbsp;</span><span id=\"IEq2\" class=\"InlineEquation\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mo stretchy=&quot;false&quot;&gt;(&lt;/mo&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-9\" class=\"math\"><span><span><span id=\"MathJax-Span-10\" class=\"mrow\"><span id=\"MathJax-Span-11\" class=\"mo\">(</span><span id=\"MathJax-Span-12\" class=\"texatom\"><span id=\"MathJax-Span-13\" class=\"mrow\"><span id=\"MathJax-Span-14\" class=\"munderover\"><span><span><span id=\"MathJax-Span-15\" class=\"mi\">p</span></span><span><span id=\"MathJax-Span-16\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>: 0.1, 0.2, 0.5), and number of sampling sites (</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><sub><i class=\"EmphasisTypeItalic \">site</i></sub>&nbsp;</span><span>: 10, 20, 40) and visits (</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><sub><i class=\"EmphasisTypeItalic \">visit</i></sub>&nbsp;</span><span>: 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables&nbsp;</span><span id=\"IEq3\" class=\"InlineEquation\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mo stretchy=&quot;false&quot;&gt;(&lt;/mo&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;&amp;#x03BB;&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-17\" class=\"math\"><span><span><span id=\"MathJax-Span-18\" class=\"mrow\"><span id=\"MathJax-Span-19\" class=\"mo\">(</span><span id=\"MathJax-Span-20\" class=\"texatom\"><span id=\"MathJax-Span-21\" class=\"mrow\"><span id=\"MathJax-Span-22\" class=\"munderover\"><span><span><span id=\"MathJax-Span-23\" class=\"mi\">&lambda;</span></span><span><span id=\"MathJax-Span-24\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>,&nbsp;</span><span id=\"IEq4\" class=\"InlineEquation\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-25\" class=\"math\"><span><span><span id=\"MathJax-Span-26\" class=\"mrow\"><span id=\"MathJax-Span-27\" class=\"texatom\"><span id=\"MathJax-Span-28\" class=\"mrow\"><span id=\"MathJax-Span-29\" class=\"munderover\"><span><span><span id=\"MathJax-Span-30\" class=\"mi\">p</span></span><span><span id=\"MathJax-Span-31\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>,&nbsp;</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><sub><i class=\"EmphasisTypeItalic \">site</i></sub>&nbsp;</span><span>,&nbsp;</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><sub><i class=\"EmphasisTypeItalic \">visit</i></sub>&nbsp;</span><span>) increased. Detection probability&nbsp;</span><span id=\"IEq5\" class=\"InlineEquation\"><span id=\"MathJax-Element-5-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-32\" class=\"math\"><span><span><span id=\"MathJax-Span-33\" class=\"mrow\"><span id=\"MathJax-Span-34\" class=\"texatom\"><span id=\"MathJax-Span-35\" class=\"mrow\"><span id=\"MathJax-Span-36\" class=\"munderover\"><span><span><span id=\"MathJax-Span-37\" class=\"mi\">p</span></span><span><span id=\"MathJax-Span-38\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>&nbsp;was most important for the estimates of mean abundance, while&nbsp;</span><span id=\"IEq6\" class=\"InlineEquation\"><span id=\"MathJax-Element-6-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;&amp;#x03BB;&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-39\" class=\"math\"><span><span><span id=\"MathJax-Span-40\" class=\"mrow\"><span id=\"MathJax-Span-41\" class=\"texatom\"><span id=\"MathJax-Span-42\" class=\"mrow\"><span id=\"MathJax-Span-43\" class=\"munderover\"><span><span><span id=\"MathJax-Span-44\" class=\"mi\">&lambda;</span></span><span><span id=\"MathJax-Span-45\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>&nbsp;was most influential for covariate effect and species richness estimates. For all parameters, increasing&nbsp;</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><i class=\"EmphasisTypeItalic \">site</i>&nbsp;</span><span>was more beneficial than increasing&nbsp;</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><sub><i class=\"EmphasisTypeItalic \">visit</i></sub>&nbsp;</span><span>. Minimal conditions for obtaining adequate performance of community abundance models were&nbsp;</span><i class=\"EmphasisTypeItalic \">n</i><span>&nbsp;</span><span><i class=\"EmphasisTypeItalic \">site</i>&nbsp;</span><span>&nbsp;&ge;&nbsp;20,&nbsp;</span><span id=\"IEq7\" class=\"InlineEquation\"><span id=\"MathJax-Element-7-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-46\" class=\"math\"><span><span><span id=\"MathJax-Span-47\" class=\"mrow\"><span id=\"MathJax-Span-48\" class=\"texatom\"><span id=\"MathJax-Span-49\" class=\"mrow\"><span id=\"MathJax-Span-50\" class=\"munderover\"><span><span><span id=\"MathJax-Span-51\" class=\"mi\">p&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>&nbsp;&ge;&nbsp;0.2, and&nbsp;</span><span id=\"IEq8\" class=\"InlineEquation\"><span id=\"MathJax-Element-8-Frame\" class=\"MathJax\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mover&gt;&lt;mi&gt;&amp;#x03BB;&lt;/mi&gt;&lt;mo stretchy=&quot;false&quot;&gt;&amp;#x00AF;&lt;/mo&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;\"><span id=\"MathJax-Span-53\" class=\"math\"><span><span><span id=\"MathJax-Span-54\" class=\"mrow\"><span id=\"MathJax-Span-55\" class=\"texatom\"><span id=\"MathJax-Span-56\" class=\"mrow\"><span id=\"MathJax-Span-57\" class=\"munderover\"><span><span><span id=\"MathJax-Span-58\" class=\"mi\">&lambda;</span></span><span><span id=\"MathJax-Span-59\" class=\"mo\">&macr;</span></span></span></span></span></span></span></span></span></span></span></span><span>&nbsp;&ge;&nbsp;0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate &beta; diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community&nbsp;</span><i class=\"EmphasisTypeItalic \">N</i><span>-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.</span></p>","language":"English","publisher":"Blackwell Science","doi":"10.1007/s11284-016-1340-4","collaboration":"Yuichi Yamaura;\nMarc Kery","usgsCitation":"Yamaura, Y., Kery, M., and Royle, A., 2016, Study of biological communities subject to imperfect detection: Bias and precision of community <i>N</i>-mixture abundance models in small-sample situations: Ecological Research, v. 31, no. 3, p. 289-305, https://doi.org/10.1007/s11284-016-1340-4.","productDescription":"17 p.","startPage":"289","endPage":"305","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072120","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11284-016-1340-4","text":"Publisher Index Page"},{"id":320845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"57287a33e4b0b13d391865dd","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":169067,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","affiliations":[{"id":25402,"text":"Hokkaido Univ.","active":true,"usgs":false}],"preferred":false,"id":628332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":628333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628331,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70169085,"text":"70169085 - 2016 - Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (<i>Tamias palmeri</i>): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA","interactions":[],"lastModifiedDate":"2016-12-16T11:08:53","indexId":"70169085","displayToPublicDate":"2016-03-10T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (<i>Tamias palmeri</i>): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA","docAbstract":"<p><span>Although montane sky islands surrounded by desert scrub and shrub steppe comprise a large part of the biological diversity of the Basin and Range Province of southwestern North America, comprehensive ecological and population demographic studies for high-elevation small mammals within these areas are rare. Here, we examine the ecology and population parameters of the Palmer&rsquo;s chipmunk (</span><i>Tamias palmeri</i><span>) in the Spring Mountains of southern Nevada, and present a predictive GIS-based distribution and probability of occurrence model at both home range and geographic spatial scales. Logistic regression analyses and Akaike Information Criterion model selection found variables of forest type, slope, and distance to water sources as predictive of chipmunk occurrence at the geographic scale. At the home range scale, increasing population density, decreasing overstory canopy cover, and decreasing understory canopy cover contributed to increased survival rates.</span></p>","language":"English","publisher":"American Society of Mammalogists","publisherLocation":"Lawrence, KS","doi":"10.1093/jmammal/gyw026","usgsCitation":"Lowrey, C.E., Longshore, K.M., Riddle, B., and Mantooth, S., 2016, Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (<i>Tamias palmeri</i>): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA: Journal of Mammalogy, v. 97, no. 4, p. 1033-1043, https://doi.org/10.1093/jmammal/gyw026.","productDescription":"11 p.","startPage":"1033","endPage":"1043","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028807","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471159,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyw026","text":"Publisher Index Page"},{"id":318914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Spring Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.08428955078125,\n              36.53832942872816\n            ],\n            [\n              -116.070556640625,\n              36.45884507478879\n            ],\n            [\n              -115.99090576171875,\n              36.34610265300638\n            ],\n            [\n              -115.95520019531249,\n              36.27085020723905\n            ],\n            [\n              -115.9002685546875,\n              36.24870331653198\n      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Mammalogy","publicationDate":"3/10/2016"},"contributors":{"authors":[{"text":"Lowrey, Chris E. 0000-0001-5084-7275 clowrey@usgs.gov","orcid":"https://orcid.org/0000-0001-5084-7275","contributorId":3225,"corporation":false,"usgs":true,"family":"Lowrey","given":"Chris","email":"clowrey@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Longshore, Kathleen M. 0000-0001-6621-1271 longshore@usgs.gov","orcid":"https://orcid.org/0000-0001-6621-1271","contributorId":2677,"corporation":false,"usgs":true,"family":"Longshore","given":"Kathleen","email":"longshore@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":622835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riddle, Brett R.","contributorId":93016,"corporation":false,"usgs":true,"family":"Riddle","given":"Brett R.","affiliations":[],"preferred":false,"id":622837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mantooth, Stacy","contributorId":167608,"corporation":false,"usgs":false,"family":"Mantooth","given":"Stacy","email":"","affiliations":[{"id":24777,"text":"Nevada State College","active":true,"usgs":false}],"preferred":false,"id":622838,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70169123,"text":"70169123 - 2016 - Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management","interactions":[],"lastModifiedDate":"2016-08-25T10:26:16","indexId":"70169123","displayToPublicDate":"2016-03-10T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management","docAbstract":"<p>Mangrove forest rehabilitation should begin much sooner than at the point of catastrophic loss. We describe the need for “mangrove forest heart attack prevention”, and how that might be accomplished in a general sense by embedding plot and remote sensing monitoring within coastal management plans. The major cause of mangrove stress at many sites globally is often linked to reduced tidal flows and exchanges. Blocked water flows can reduce flushing not only from the seaward side, but also result in higher salinity and reduced sediments when flows are blocked landward. Long-term degradation of function leads to acute mortality prompted by acute events, but created by a systematic propensity for long-term neglect of mangroves. Often, mangroves are lost within a few years; however, vulnerability is re-set decades earlier when seemingly innocuous hydrological modifications are made (e.g., road construction, blocked tidal channels), but which remain undetected without reasonable large-scale monitoring.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Pollution Bulletin","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.marpolbul.2016.03.006","usgsCitation":"Lewis, R.R., Milbrandt, E.C., Brown, B., Krauss, K.W., Rovai, A.S., Beever, J.W., and Flynn, L., 2016, Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management: Marine Pollution Bulletin, v. 109, no. 2, p. 764-771, https://doi.org/10.1016/j.marpolbul.2016.03.006.","productDescription":"8 p.","startPage":"764","endPage":"771","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070524","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":319080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Worldwide","volume":"109","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b70e4b0f59b85ddc517","contributors":{"authors":[{"text":"Lewis, Roy R","contributorId":167668,"corporation":false,"usgs":false,"family":"Lewis","given":"Roy","email":"","middleInitial":"R","affiliations":[{"id":24798,"text":"Coastal Resources Group, Salt Springs, FL","active":true,"usgs":false}],"preferred":false,"id":623077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milbrandt, Eric C","contributorId":167669,"corporation":false,"usgs":false,"family":"Milbrandt","given":"Eric","email":"","middleInitial":"C","affiliations":[{"id":24799,"text":"Sanibel-Captiva Conservation Foundation, Sanibel, FL","active":true,"usgs":false}],"preferred":false,"id":623078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Benjamin","contributorId":167670,"corporation":false,"usgs":false,"family":"Brown","given":"Benjamin","email":"","affiliations":[{"id":24800,"text":"Charles Darwin University, Research Institute for Environment and Livelihoolds, AUS","active":true,"usgs":false}],"preferred":false,"id":623079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":623076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":623080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beever, James W.","contributorId":167672,"corporation":false,"usgs":false,"family":"Beever","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":24802,"text":"Southwest Florida Regional Planning Council, Fort Myers, FL","active":true,"usgs":false}],"preferred":false,"id":623081,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flynn, Laura L","contributorId":167673,"corporation":false,"usgs":false,"family":"Flynn","given":"Laura L","affiliations":[{"id":24798,"text":"Coastal Resources Group, Salt Springs, FL","active":true,"usgs":false}],"preferred":false,"id":623082,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70169001,"text":"70169001 - 2016 - Application of lime (CaCO<sub>3</sub>) to promote forest recovery from severe acidification increases potential for earthworm invasion","interactions":[],"lastModifiedDate":"2016-08-17T11:06:43","indexId":"70169001","displayToPublicDate":"2016-03-10T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Application of lime (CaCO<sub>3</sub>) to promote forest recovery from severe acidification increases potential for earthworm invasion","docAbstract":"<p>The application of lime (calcium carbonate) may be a cost-effective strategy to promote forest ecosystem recovery from acid impairment, under contemporary low levels of acidic deposition. However, liming acidified soils may create more suitable habitat for invasive earthworms that cause significant damage to forest floor communities and may disrupt ecosystem processes. We investigated the potential effects of liming in acidified soils where earthworms are rare in conjunction with a whole-ecosystem liming experiment in the chronically acidified forests of the western Adirondacks (USA). Using a microcosm experiment that replicated the whole-ecosystem treatment, we evaluated effects of soil liming on Lumbricus terrestris survivorship and biomass growth. We found that a moderate lime application (raising pH from 3.1 to 3.7) dramatically increased survival and biomass of L. terrestris, likely via increases in soil pH and associated reductions in inorganic aluminum, a known toxin. Very few L. terrestris individuals survived in unlimed soils, whereas earthworms in limed soils survived, grew, and rapidly consumed leaf litter. We supplemented this experiment with field surveys of extant earthworm communities along a gradient of soil pH in Adirondack hardwood forests, ranging from severely acidified (pH &lt; 3) to well-buffered (pH &gt; 5). In the field, no earthworms were observed where soil pH &lt; 3.6. Abundance and species richness of earthworms was greatest in areas where soil pH &gt; 4.4 and human dispersal vectors, including proximity to roads and public fishing access, were most prevalent. Overall our results suggest that moderate lime additions can be sufficient to increase earthworm invasion risk where dispersal vectors are present.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2016.03.002","usgsCitation":"Homan, C., Beirer, C.M., McCay, T.S., and Lawrence, G.B., 2016, Application of lime (CaCO<sub>3</sub>) to promote forest recovery from severe acidification increases potential for earthworm invasion: Forest Ecology and Management, v. 368, p. 39-44, https://doi.org/10.1016/j.foreco.2016.03.002.","productDescription":"6 p.","startPage":"39","endPage":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071766","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471165,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.03.002","text":"Publisher Index Page"},{"id":318768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.87268447875977,\n              43.50423881694708\n            ],\n            [\n              -74.87268447875977,\n              43.53672718543221\n            ],\n            [\n              -74.79852676391602,\n              43.53672718543221\n            ],\n            [\n              -74.79852676391602,\n              43.50423881694708\n            ],\n            [\n              -74.87268447875977,\n              43.50423881694708\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"368","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56e29aaae4b0f59b85d3274d","chorus":{"doi":"10.1016/j.foreco.2016.03.002","url":"http://dx.doi.org/10.1016/j.foreco.2016.03.002","publisher":"Elsevier BV","authors":"Homan Caitlin, Beier Colin, McCay Timothy, Lawrence Gregory","journalName":"Forest Ecology and Management","publicationDate":"5/2016"},"contributors":{"authors":[{"text":"Homan, Caitlin","contributorId":167484,"corporation":false,"usgs":false,"family":"Homan","given":"Caitlin","email":"","affiliations":[{"id":24722,"text":"Graduate Student, SUNY College of Environmental Science & Forestry","active":true,"usgs":false}],"preferred":false,"id":622462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beirer, Colin M","contributorId":167485,"corporation":false,"usgs":false,"family":"Beirer","given":"Colin","email":"","middleInitial":"M","affiliations":[{"id":24723,"text":"Associate Professor, Forest & Natural Resources, SUNY College of ESF","active":true,"usgs":false}],"preferred":false,"id":622463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCay, Timothy S","contributorId":167486,"corporation":false,"usgs":false,"family":"McCay","given":"Timothy","email":"","middleInitial":"S","affiliations":[{"id":24724,"text":"Professor of Biology & Environmental Studies, Colgate University","active":true,"usgs":false}],"preferred":false,"id":622464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622461,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70169297,"text":"70169297 - 2016 - The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence","interactions":[],"lastModifiedDate":"2016-12-16T11:22:24","indexId":"70169297","displayToPublicDate":"2016-03-10T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence","docAbstract":"<p><span>Competition &ndash; colonization tradeoffs occur in many systems, and theory predicts that they can strongly promote species coexistence. However, there is little empirical evidence that observed competition &ndash; colonization tradeoffs are strong enough to maintain diversity in natural systems. This is due in part to a mismatch between theoretical assumptions and biological reality in some systems. We tested whether a competition &ndash; colonization tradeoff explains how a diverse trematode guild coexists in California horn snail populations, a system that meets the requisite criteria for the tradeoff to promote coexistence. A field experiment showed that subordinate trematode species tended to have higher colonization rates than dominant species. This tradeoff promoted coexistence in parameterized models but did not fully explain trematode diversity and abundance, suggesting a role of additional diversity maintenance mechanisms. Spatial heterogeneity is an alternative way to promote coexistence if it isolates competing species. We used scale transition theory to expand the competition &ndash; colonization tradeoff model to include spatial variation. The parameterized model showed that spatial variation in trematode prevalence did not isolate most species sufficiently to explain the overall high diversity, but could benefit some rare species. Together, the results suggest that several mechanisms combine to maintain diversity, even when a competition &ndash; colonization tradeoff occurs.</span></p>","language":"English","publisher":"The Ecological Society of America","doi":"10.1890/15-0753.1","usgsCitation":"Mordecai, E., Jaramillo, A.G., Ashford, J.E., Hechinger, R., and Lafferty, K.D., 2016, The role of competition – colonization tradeoffs and spatial heterogeneity in promoting trematode coexistence: Ecology, v. 97, no. 6, p. 1484-1496, https://doi.org/10.1890/15-0753.1.","productDescription":"13 p.","startPage":"1484","endPage":"1496","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067090","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":319334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f50fd3e4b0f59b85e1ebd8","contributors":{"authors":[{"text":"Mordecai, Erin A.","contributorId":9113,"corporation":false,"usgs":true,"family":"Mordecai","given":"Erin A.","affiliations":[],"preferred":false,"id":623479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaramillo, Alejandra G.","contributorId":149800,"corporation":false,"usgs":false,"family":"Jaramillo","given":"Alejandra","email":"","middleInitial":"G.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":623480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ashford, Jacob E.","contributorId":149801,"corporation":false,"usgs":false,"family":"Ashford","given":"Jacob","email":"","middleInitial":"E.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":623481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hechinger, Ryan F.","contributorId":73730,"corporation":false,"usgs":true,"family":"Hechinger","given":"Ryan F.","affiliations":[],"preferred":false,"id":623482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623478,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70168948,"text":"70168948 - 2016 - Uncertainty in V<sub>s30</sub>-based site response","interactions":[],"lastModifiedDate":"2016-04-07T11:37:08","indexId":"70168948","displayToPublicDate":"2016-03-09T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in V<sub>s30</sub>-based site response","docAbstract":"<p><span>Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic‐hazard analysis usually relies on ground‐motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time‐averaged shear‐wave velocity to 30&nbsp;m (</span><i>V</i><sub><i>S</i>30</sub><span>) and basin depth parameters. Because&nbsp;</span><i>V</i><sub><i>S</i>30</sub><span>&nbsp;is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which&nbsp;</span><i>V</i><sub><i>S</i>30</sub><span>&nbsp;has been measured to address effects of&nbsp;</span><i>V</i><sub><i>S</i>30</sub><span>&nbsp;proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground‐motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured&nbsp;</span><i>V</i><sub><i>S</i>30</sub><span>&nbsp;values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150214","usgsCitation":"Thompson, E.M., and Wald, D.J., 2016, Uncertainty in V<sub>s30</sub>-based site response: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 453-463, https://doi.org/10.1785/0120150214.","productDescription":"11 p.","startPage":"453","endPage":"463","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072079","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":318755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-01","publicationStatus":"PW","scienceBaseUri":"56e1492ce4b00e6e7616095c","contributors":{"authors":[{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":622183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":622184,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70168932,"text":"70168932 - 2016 - Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery","interactions":[],"lastModifiedDate":"2016-03-08T15:52:05","indexId":"70168932","displayToPublicDate":"2016-03-08T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery","docAbstract":"<p><span>Nearly all of the ecosystem services supported by rangelands, including production of livestock forage, carbon sequestration, and provisioning of clean water, are negatively impacted by soil erosion. Accordingly, monitoring the severity, spatial extent, and rate of soil erosion is essential for long-term sustainable management. Traditional field-based methods of monitoring erosion (sediment traps, erosion pins, and bridges) can be labor intensive and therefore are generally limited in spatial intensity and/or extent. There is a growing effort to monitor natural resources at broad scales, which is driving the need for new soil erosion monitoring tools. One remote-sensing technique that can be used to monitor soil movement is a time series of digital elevation models (DEMs) created using aerial photogrammetry methods. By geographically coregistering the DEMs and subtracting one surface from the other, an estimate of soil elevation change can be created. Such analysis enables spatially explicit quantification and visualization of net soil movement including erosion, deposition, and redistribution. We constructed DEMs (12-cm ground sampling distance) on the basis of aerial photography immediately before and 1 year after a vegetation removal treatment on a 31-ha Pi&ntilde;on-Juniper woodland in southeastern Utah to evaluate the use of aerial photography in detecting soil surface change. On average, we were able to detect surface elevation change of &plusmn;&nbsp;8&minus;9cm and greater, which was sufficient for the large amount of soil movement exhibited on the study area. Detecting more subtle soil erosion could be achieved using the same technique with higher-resolution imagery from lower-flying aircraft such as unmanned aerial vehicles. DEM differencing and process-focused field methods provided complementary information and a more complete assessment of soil loss and movement than any single technique alone. Photogrammetric DEM differencing could be used as a technique to quantitatively monitor surface change over time relative to management activities.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2015.10.012","usgsCitation":"Gillan, J.K., Karl, J., Barger, N., Elaksher, A., and Duniway, M.C., 2016, Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery: Rangeland Ecology and Management, v. 69, no. 2, p. 95-107, https://doi.org/10.1016/j.rama.2015.10.012.","productDescription":"13 p.","startPage":"95","endPage":"107","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059477","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":318694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Shay Mesa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.6,\n              37.9\n            ],\n            [\n              -109.6,\n              38\n            ],\n            [\n              -109.5,\n              38\n            ],\n            [\n              -109.5,\n              37.9\n            ],\n            [\n              -109.6,\n              37.9\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"69","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56dff7b3e4b015c306fcda06","contributors":{"authors":[{"text":"Gillan, Jeffrey K.","contributorId":51656,"corporation":false,"usgs":true,"family":"Gillan","given":"Jeffrey","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":622150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Jason W.","contributorId":22616,"corporation":false,"usgs":true,"family":"Karl","given":"Jason W.","affiliations":[],"preferred":false,"id":622151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barger, Nichole N.","contributorId":102392,"corporation":false,"usgs":true,"family":"Barger","given":"Nichole N.","affiliations":[],"preferred":false,"id":622152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elaksher, Ahmed","contributorId":72305,"corporation":false,"usgs":true,"family":"Elaksher","given":"Ahmed","affiliations":[],"preferred":false,"id":622153,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":622149,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70168905,"text":"70168905 - 2016 - Slow-moving and far-travelled dense pyroclastic flows during the Peach Spring super-eruption","interactions":[],"lastModifiedDate":"2016-03-08T09:04:46","indexId":"70168905","displayToPublicDate":"2016-03-08T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Slow-moving and far-travelled dense pyroclastic flows during the Peach Spring super-eruption","docAbstract":"<p><span>Explosive volcanic super-eruptions of several hundred cubic kilometres or more generate long run-out pyroclastic density currents the dynamics of which are poorly understood and controversial. Deposits of one such event in the southwestern USA, the 18.8 Ma Peach Spring Tuff, were formed by pyroclastic flows that travelled &gt;170</span><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>km from the eruptive centre and entrained blocks up to ~70&ndash;90</span><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>cm diameter from the substrates along the flow paths. Here we combine these data with new experimental results to show that the flow&rsquo;s base had high-particle concentration and relatively modest speeds of ~5&ndash;20</span><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>m</span><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>s</span><sup>&minus;1</sup><span>, fed by an eruption discharging magma at rates up to ~10</span><sup>7</sup><span>&ndash;10</span><sup>8</sup><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>m</span><sup>3</sup><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>s</span><sup>&minus;1</sup><span>&nbsp;for a minimum of 2.5&ndash;10</span><span class=\"mb\"><span class=\"mb\">&thinsp;</span></span><span>h. We conclude that sustained high-eruption discharge and long-lived high-pore pressure in dense granular dispersion can be more important than large initial velocity and turbulent transport with dilute suspension in promoting long pyroclastic flow distance.</span></p>","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/ncomms10890","usgsCitation":"Roche, O., Buesch, D.C., and Valentine, G.A., 2016, Slow-moving and far-travelled dense pyroclastic flows during the Peach Spring super-eruption: Nature Communications, v. 7, p. 1-8, https://doi.org/10.1038/ncomms10890.","productDescription":"Article 10890; 8 p.","startPage":"1","endPage":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064658","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":471171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms10890","text":"Publisher Index Page"},{"id":318678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.301025390625,\n              34.15272698011818\n            ],\n            [\n              -117.301025390625,\n              35.6126508187567\n            ],\n            [\n              -113.0712890625,\n              35.6126508187567\n            ],\n            [\n              -113.0712890625,\n              34.15272698011818\n            ],\n            [\n              -117.301025390625,\n              34.15272698011818\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-07","publicationStatus":"PW","scienceBaseUri":"56dff7afe4b015c306fcd9fd","contributors":{"authors":[{"text":"Roche, Olivier","contributorId":167382,"corporation":false,"usgs":false,"family":"Roche","given":"Olivier","email":"","affiliations":[{"id":24702,"text":"Laboratoire Magmas et Volcans, Université Blaise Pascal-CNRS-IRD, OPGC, F-63038 6 Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":622108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":622106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valentine, Greg A.","contributorId":167383,"corporation":false,"usgs":false,"family":"Valentine","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":24703,"text":"Department of Geology and Center for Geohazards Studies, University at Buffalo, Buffalo, 9 NY 14260, USA","active":true,"usgs":false}],"preferred":false,"id":622109,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70168921,"text":"70168921 - 2016 - The pace of past climate change vs. potential bird distributions and land use in the United States","interactions":[],"lastModifiedDate":"2016-03-08T08:58:06","indexId":"70168921","displayToPublicDate":"2016-03-08T09:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"The pace of past climate change vs. potential bird distributions and land use in the United States","docAbstract":"<p><span>Climate change may drastically alter patterns of species distributions and richness, but predicting future species patterns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species&rsquo; suitable climate space over the past 60&nbsp;years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27&nbsp;km&nbsp;yr</span><sup><span>&minus;1</span></sup><span>, about double the pace of prior distribution shift estimates across terrestrial systems globally (0.61&nbsp;km&nbsp;yr</span><sup><span>&minus;1</span></sup><span>). The direction of shifts was not uniform. The majority of species&rsquo; distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). However, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have supported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing.</span></p>","language":"English","publisher":"Wiley Online Library","doi":"10.1111/gcb.13154","usgsCitation":"Bateman, B.L., Pidgeon, A.M., Radeloff, V., VanDerWal, J., Thogmartin, W.E., Vavrus, S.J., and Heglund, P., 2016, The pace of past climate change vs. potential bird distributions and land use in the United States: Global Change Biology, v. 22, no. 3, p. 1130-1144, https://doi.org/10.1111/gcb.13154.","productDescription":"15 p.","startPage":"1130","endPage":"1144","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056225","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":318677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-22","publicationStatus":"PW","scienceBaseUri":"56dff7bce4b015c306fcda17","contributors":{"authors":[{"text":"Bateman, Brooke L.","contributorId":141122,"corporation":false,"usgs":false,"family":"Bateman","given":"Brooke","email":"","middleInitial":"L.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pidgeon, Anna M.","contributorId":141123,"corporation":false,"usgs":false,"family":"Pidgeon","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Radeloff, Volker C.","contributorId":76169,"corporation":false,"usgs":true,"family":"Radeloff","given":"Volker C.","affiliations":[],"preferred":false,"id":622114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"VanDerWal, Jeremy","contributorId":167387,"corporation":false,"usgs":false,"family":"VanDerWal","given":"Jeremy","email":"","affiliations":[{"id":24704,"text":"Centre for Tropical Biodiversity and Climate Change Research, School of Marine and Tropical Biology, James Cook University, Townsville, Queensland","active":true,"usgs":false}],"preferred":false,"id":622115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":622111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vavrus, Stephen J.","contributorId":149491,"corporation":false,"usgs":false,"family":"Vavrus","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":17750,"text":"Nelson Institute Center for Climatic Research, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":622116,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heglund, Patricia J.","contributorId":141128,"corporation":false,"usgs":false,"family":"Heglund","given":"Patricia J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":622117,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70162634,"text":"sir20165009 - 2016 - Network global navigation satellite system surveys to harmonize American and Canadian datum for the Lake Champlain Basin","interactions":[],"lastModifiedDate":"2016-04-06T11:51:17","indexId":"sir20165009","displayToPublicDate":"2016-03-08T05:00:00","publicationYear":"2016","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":"2016-5009","title":"Network global navigation satellite system surveys to harmonize American and Canadian datum for the Lake Champlain Basin","docAbstract":"<p>Historically high flood levels were observed during flooding in Lake Champlain and the Richelieu River from late April through May 2011. Flooding was caused by record spring precipitation and snowmelt from the third highest cumulative snowfall year on record, which included a warm, saturated late spring snowpack. Flood stage was exceeded for a total of 67 days from April 13 to June 19, 2011. During this flooding, shoreline erosion and lake flood inundation were exacerbated by wind-driven waves associated with local fetch and lake-wide seiche effects. In May 2011, a new water-surface-elevation record was set for Lake Champlain. Peak lake-level water-surface elevations varied at the three U.S. Geological Survey lake-level gages on Lake Champlain in 2011. The May 2011 peak water-surface elevations for Lake Champlain ranged from 103.20 feet above the National Geodetic Vertical Datum of 1929 at the northern end of Lake Champlain (at its outlet into the Richelieu River at Rouses Point, New York) to 103.57 feet above the National Geodetic Vertical Datum of 1929 at the southern end of the Lake in Whitehall, New York. The water-surface elevations for the Richelieu River in Canada are referenced to a different vertical datum than are those in Lake Champlain in the United States, which causes difficulty in assessing real-time flood water-surface elevations and comparing of flood peaks in the Lake Champlain Basin in the United States and Canada.</p>\n<p>On March 19, 2012, as a result of the flood event of April and May 2011, the Governments of Canada and the United States asked the International Joint Commission to draft a plan of study to examine the causes and the effects of the spring 2011 flooding on Lake Champlain and the Richelieu River and develop potential mitigation measures. Specific challenges noted by the International Lake Champlain-Richelieu River Technical Working Group (established by the International Joint Commission) included harmonization of vertical datums within the drainage basin. Harmonization of the vertical datum discrepancy is needed for flood assessment and future efforts to model the flow of water through the Lake Champlain Basin in the United States and Canada.</p>\n<p>In April 2015, the U.S. Geological Survey and Environment Canada began a joint field effort with the goal of obtaining precise elevations representing a common vertical datum for select reference marks used to determine water-surface elevations throughout Lake Champlain and the Richelieu River. To harmonize the datum difference between the United States and Canada, Global Navigation Satellite System surveys were completed at nine locations in the Lake Champlain Basin to collect simultaneous satellite data. These satellite data were processed to produce elevations for two reference marks associated with dams and seven reference marks associated with active water-level gages (lake gages in Lake Champlain and streamgages in the Richelieu River) to harmonize vertical datums throughout the Lake Champlain Basin. The Global Navigation Satellite System surveys were completed from April 14 to 16, 2015, at locations ranging from southern Lake Champlain near Whitehall, New York, to the northern end of the Richelieu River in Sorel, Quebec, at its confluence with the St. Lawrence River in Canada.</p>\n<p>Lake-gage water-surface elevations determined during the 3 days of surveys were converted to water-surface elevations referenced to the North American Vertical Datum of 1988 by using calculated offsets and historical water-surface elevations. In this report, an &ldquo;offset&rdquo; refers to the adjustment that needs to be applied to published data from a particular gage to produce elevation data referenced to the North American Vertical Datum of 1988. Offsets presented in this report can be used in the evaluation of water-surface elevations in a common datum for Lake Champlain and the Richelieu River. In addition, the water-level data referenced to the common datum (as determined from the offsets) may be used to calibrate flow models and support future modeling studies developed for Lake Champlain and the Richelieu River.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165009","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., Rydlund, P.H., Jr., and Martin, D.J., 2016, Network global navigation satellite system surveys to harmonize American and Canadian datums for the Lake Champlain Basin (ver. 1.1, April 2016): U.S. Geological Survey Scientific Investigations Report 2016–5009, 17 p., https://dx.doi.org/10.3133/sir20165009.","productDescription":"Report: vii, 17 p.; Appendixes: 1-4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069015","costCenters":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"links":[{"id":319779,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5009/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5009"},{"id":318519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5009/sir20165009.pdf","text":"Report","size":"3.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5009"},{"id":318520,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix1.zip","text":"Appendix 1","size":"13.1 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5009","linkHelpText":"- Global navigation satellite system data collection information"},{"id":318518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5009/coverthb2.jpg"},{"id":318521,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix2.txt","text":"Appendix 2","size":"24 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016-5009","linkHelpText":"- Final coordinates for harmonization of datums"},{"id":318522,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix3.zip","text":"Appendix 3","size":"445 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5009","linkHelpText":"- Surveyor leveling information for sites with benchmarks that could not be surveyed directly with global navigation satellite systems"},{"id":318523,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5009/downloads/sir20165009_appendix4.xlsx","text":"Appendix 4","size":"19 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5009","linkHelpText":"- Elevation offset information for benchmarks surveyed with global navigation satellite systems"}],"country":"Canada, United States","state":"New York, Quebec, Vermont","otherGeospatial":"Lake Champlain Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.278564453125,\n              43.37710501700073\n            ],\n            [\n              -74.278564453125,\n              45.96642454131025\n            ],\n            [\n              -72.432861328125,\n              45.96642454131025\n            ],\n            [\n              -72.432861328125,\n              43.37710501700073\n            ],\n            [\n              -74.278564453125,\n              43.37710501700073\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: Originally posted March 8, 2016; Version 1.1: April 1, 2016","contact":"<p><a href=\"dc_nweng@usgs.gov\">Director</a>, New England Water Science Center<br /> U.S. Geological Survey<br /> 331 Commerce Way, Suite 2<br /> Pembroke, NH 03275</p>\n<p>Or visit our Web site at:<br /> <a href=\"http://newengland.water.usgs.gov/\">http://newengland.water.usgs.gov/</a></p>","tableOfContents":"<ul>\n<li>Acknowledgments</li>\n<li>Abstract</li>\n<li>Introduction</li>\n<li>Methods</li>\n<li>GNSS Survey Harmonization Results</li>\n<li>Summary</li>\n<li>References Cited</li>\n<li>Appendix 1. Global Navigation Satellite System Data Collection Information for All Benchmarks Surveyed in the Harmonization of American and Canadian Datums</li>\n<li>Appendix 2. Final Coordinates as Determined in and From the Online Positioning User Service Projects Least-Squares Adjustment for Harmonization of the American and Canadian Datum</li>\n<li>Appendix 3. Surveyor Leveling Information for Sites With Benchmarks That Could Not Be Surveyed Directly by Using Global Navigation Satellite Systems in Harmonization of the American and Canadian Datums</li>\n<li>Appendix 4. Elevation Offset Information for Benchmarks Surveyed Directly by Using Global Navigation Satellite Systems in Harmonization of the American and Canadian Datums</li>\n</ul>\n<p>&nbsp;</p>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2016-03-08","revisedDate":"2016-04-06","noUsgsAuthors":false,"publicationDate":"2016-03-08","publicationStatus":"PW","scienceBaseUri":"56dff7ade4b015c306fcd9f7","contributors":{"authors":[{"text":"Flynn, Robert H. rflynn@usgs.gov","contributorId":2137,"corporation":false,"usgs":true,"family":"Flynn","given":"Robert","email":"rflynn@usgs.gov","middleInitial":"H.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":589992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":589993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Daniel J. dmartin@usgs.gov","contributorId":152244,"corporation":false,"usgs":true,"family":"Martin","given":"Daniel","email":"dmartin@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":589994,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70168894,"text":"70168894 - 2016 - Landscape characteristics and livestock presence influence common ravens: Relevance to greater sage-grouse conservation","interactions":[],"lastModifiedDate":"2016-03-07T17:26:30","indexId":"70168894","displayToPublicDate":"2016-03-07T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Landscape characteristics and livestock presence influence common ravens: Relevance to greater sage-grouse conservation","docAbstract":"<p><span>Common raven (</span><i>Corvus corax</i><span>; hereafter, raven) population abundance in the sagebrush steppe of the American West has increased threefold during the previous four decades, largely as a result of unintended resource subsidies from human land-use practices. This is concerning because ravens frequently depredate nests of species of conservation concern, such as greater sage-grouse (</span><i>Centrocercus urophasianus</i><span>; hereafter, sage-grouse). Grazing by livestock in sagebrush ecosystems is common practice on most public lands, but associations between livestock and ravens are poorly understood. The primary objective of this study was to identify the effects of livestock on raven occurrence while accounting for landscape characteristics within human-altered sagebrush steppe habitat, particularly in areas occupied by breeding sage-grouse. Using data from southeastern Idaho collected during spring and summer across 3&nbsp;yr, we modeled raven occurrence as a function of the presence of livestock while accounting for multiple landscape covariates, including land cover features, topographical features, and proximity to sage-grouse lek sites (breeding grounds), as well as site-level anthropogenic features. While accounting for landscape characteristics, we found that the odds of raven occurrence increased 45.8% in areas where livestock were present. In addition, ravens selected areas near sage-grouse leks, with the odds of occurrence decreasing 8.9% for every 1-km distance, increase away from the lek. We did not find an association between livestock use and distance to lek. We also found that ravens selected sites with relatively lower elevation containing increased amounts of cropland, wet meadow, and urbanization. Limiting raven access to key anthropogenic subsidies and spatially segregating livestock from sage-grouse breeding areas would likely reduce exposure of predatory ravens to sage-grouse nests and chicks.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1203","usgsCitation":"Coates, P.S., Brussee, B.E., Howe, K., Gustafson, K.B., Casazza, M.L., and Delehanty, D., 2016, Landscape characteristics and livestock presence influence common ravens: Relevance to greater sage-grouse conservation: Ecosphere, v. 7, no. 2, https://doi.org/10.1002/ecs2.1203.","productDescription":"e01203; 20 p.","startPage":"e01203","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052300","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1203","text":"Publisher Index Page"},{"id":318663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Oneida County, Power County","city":"Holbrook","otherGeospatial":"Curlew National Grassland","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.71560668945311,\n              42.101788731521644\n            ],\n            [\n              -112.71560668945311,\n              42.25495072629938\n            ],\n            [\n              -112.58720397949219,\n              42.25495072629938\n            ],\n            [\n              -112.58720397949219,\n              42.101788731521644\n            ],\n            [\n              -112.71560668945311,\n              42.101788731521644\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"56dea628e4b015c306fb51d9","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howe, Kristy khowe@usgs.gov","contributorId":167379,"corporation":false,"usgs":true,"family":"Howe","given":"Kristy","email":"khowe@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delehanty, David J.","contributorId":86683,"corporation":false,"usgs":true,"family":"Delehanty","given":"David J.","affiliations":[],"preferred":false,"id":622099,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70171005,"text":"70171005 - 2016 - Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment","interactions":[],"lastModifiedDate":"2016-05-17T10:18:39","indexId":"70171005","displayToPublicDate":"2016-03-07T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment","docAbstract":"<p><span>As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%&ndash;85% of permafrost carbon release can still be avoided if human emissions are actively reduced.</span></p>","language":"English","publisher":"Institute of Physics and IOP Pub.","publisherLocation":"Bristol, U.K.","doi":"10.1088/1748-9326/11/3/034014","usgsCitation":"Abbott, B.W., Jeremy B. Jones, Schuur, E.A., Chapin, F., Bowden, W.B., Bret-Harte, M.S., Epstein, H.E., Flannigan, M.D., Harms, T.K., Hollingsworth, T.N., Mack, M.C., McGuire, A.D., Natali, S.M., Adrian V. Rocha, Tank, S.E., Turetsky, M.R., Vonk, J.E., Wickland, K.P., and Aiken, G.R., 2016, Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment: Environmental Research Letters, v. 11, no. 3, p. 1-13, https://doi.org/10.1088/1748-9326/11/3/034014.","productDescription":"13 p.","startPage":"1","endPage":"13","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065090","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/11/3/034014","text":"Publisher Index Page"},{"id":321285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-07","publicationStatus":"PW","scienceBaseUri":"574d644fe4b07e28b66835bb","contributors":{"authors":[{"text":"Abbott, Benjamin W.","contributorId":150799,"corporation":false,"usgs":false,"family":"Abbott","given":"Benjamin","email":"","middleInitial":"W.","affiliations":[{"id":18106,"text":"Universite de Rennes, Rennes, France","active":true,"usgs":false}],"preferred":false,"id":629477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jeremy B. Jones","contributorId":169385,"corporation":false,"usgs":false,"family":"Jeremy B. Jones","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":629478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schuur, Edward A.G.","contributorId":169386,"corporation":false,"usgs":false,"family":"Schuur","given":"Edward","email":"","middleInitial":"A.G.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":629479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapin, F.S.","contributorId":169387,"corporation":false,"usgs":false,"family":"Chapin","given":"F.S.","email":"","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":629480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowden, William B.","contributorId":169388,"corporation":false,"usgs":false,"family":"Bowden","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":6735,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":629481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bret-Harte, M. 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David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":629488,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Natali, Susan M.","contributorId":169395,"corporation":false,"usgs":false,"family":"Natali","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":629489,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Adrian V. Rocha","contributorId":169396,"corporation":false,"usgs":false,"family":"Adrian V. Rocha","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":629490,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tank, Suzanne E.","contributorId":150795,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":18102,"text":"University of Alberta, Edmonton, Canada","active":true,"usgs":false}],"preferred":false,"id":629491,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Turetsky, Merrit R.","contributorId":169397,"corporation":false,"usgs":false,"family":"Turetsky","given":"Merrit","email":"","middleInitial":"R.","affiliations":[{"id":25494,"text":"University of Geulph","active":true,"usgs":false}],"preferred":false,"id":629492,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Vonk, Jorien E.","contributorId":150794,"corporation":false,"usgs":false,"family":"Vonk","given":"Jorien","email":"","middleInitial":"E.","affiliations":[{"id":18101,"text":"Utrecht University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":629493,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":629476,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":629494,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70169053,"text":"70169053 - 2016 - Increased body mass of ducks wintering in California's Central Valley","interactions":[],"lastModifiedDate":"2016-12-16T11:05:08","indexId":"70169053","displayToPublicDate":"2016-03-06T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Increased body mass of ducks wintering in California's Central Valley","docAbstract":"<p><span>Waterfowl managers lack the information needed to fully evaluate the biological effects of their habitat conservation programs. We studied body condition of dabbling ducks shot by hunters at public hunting areas throughout the Central Valley of California during 2006&ndash;2008 compared with condition of ducks from 1979 to 1993. These time periods coincide with habitat increases due to Central Valley Joint Venture conservation programs and changing agricultural practices; we modeled to ascertain whether body condition differed among waterfowl during these periods. Three dataset comparisons indicate that dabbling duck body mass was greater in 2006&ndash;2008 than earlier years and the increase was greater in the Sacramento Valley and Suisun Marsh than in the San Joaquin Valley, differed among species (mallard [</span><i>Anas platyrhynchos</i><span>], northern pintail [</span><i>Anas acuta</i><span>], America wigeon [</span><i>Anas americana</i><span>], green-winged teal [</span><i>Anas crecca</i><span>], and northern shoveler [</span><i>Anas clypeata</i><span>]), and was greater in ducks harvested late in the season. Change in body mass also varied by age&ndash;sex cohort and month for all 5 species and by September&ndash;January rainfall for all except green-winged teal. The random effect of year nested in period, and sometimes interacting with other factors, improved models in many cases. Results indicate that improved habitat conditions in the Central Valley have resulted in increased winter body mass of dabbling ducks, especially those that feed primarily on seeds, and this increase was greater in regions where area of post-harvest flooding of rice and other crops, and wetland area, has increased. Conservation programs that continue to promote post-harvest flooding and other agricultural practices that benefit wintering waterfowl and continue to restore and conserve wetlands would likely help maintain body condition of wintering dabbling ducks in the Central Valley of California.</span></p>","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.1053","usgsCitation":"Fleskes, J., Yee, J.L., Yarris, G., and Loughman, D.L., 2016, Increased body mass of ducks wintering in California's Central Valley: Journal of Wildlife Management, v. 80, no. 4, p. 679-690, https://doi.org/10.1002/jwmg.1053.","productDescription":"12 p.","startPage":"679","endPage":"690","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072757","costCenters":[{"id":651,"text":"Western Ecological Research 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julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":622708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yarris, Gregory S.","contributorId":115361,"corporation":false,"usgs":true,"family":"Yarris","given":"Gregory S.","affiliations":[],"preferred":false,"id":622709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loughman, Daniel L.","contributorId":167556,"corporation":false,"usgs":false,"family":"Loughman","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":24747,"text":"California Waterfowl Association","active":true,"usgs":false}],"preferred":false,"id":622710,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70177886,"text":"70177886 - 2016 - Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","interactions":[],"lastModifiedDate":"2017-01-17T19:17:22","indexId":"70177886","displayToPublicDate":"2016-03-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites","docAbstract":"<p><span>Evapotranspiration (ET) is an important component of the water cycle &ndash; ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001&ndash;2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13&nbsp;mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (</span><i>ET<sub>o</sub></i><span>); and parameters, differential temperature (</span><i>dT</i><span>), and maximum ET scalar (</span><i>K<sub>max</sub></i><span>), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (</span><i>ET<sub>o</sub></i><span><span class=\"Apple-converted-space\">&nbsp;</span>and LST) and two key parameters (</span><i>K<sub>max</sub></i><span><span class=\"Apple-converted-space\">&nbsp;</span>and<span class=\"Apple-converted-space\">&nbsp;</span></span><i>dT</i><span>).</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.02.026","usgsCitation":"Chen, M., Senay, G.B., Singh, R.K., and Verdin, J.P., 2016, Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites: Journal of Hydrology, v. 536, p. 384-399, https://doi.org/10.1016/j.jhydrol.2016.02.026.","productDescription":"16 p.","startPage":"384","endPage":"399","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071555","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.02.026","text":"Publisher Index Page"},{"id":330417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"536","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f3e4b0f497e79a5a7b","chorus":{"doi":"10.1016/j.jhydrol.2016.02.026","url":"http://dx.doi.org/10.1016/j.jhydrol.2016.02.026","publisher":"Elsevier BV","authors":"Chen Mingshi, Senay Gabriel B., Singh Ramesh K., Verdin James P.","journalName":"Journal of Hydrology","publicationDate":"5/2016","auditedOn":"4/1/2016","publiclyAccessibleDate":"2/23/2016"},"contributors":{"authors":[{"text":"Chen, Mingshi mchen@usgs.gov","contributorId":4204,"corporation":false,"usgs":true,"family":"Chen","given":"Mingshi","email":"mchen@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":652026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":652237,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70164631,"text":"sir20165020 - 2016 - Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013","interactions":[],"lastModifiedDate":"2016-03-09T17:48:45","indexId":"sir20165020","displayToPublicDate":"2016-03-03T18:00:00","publicationYear":"2016","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":"2016-5020","title":"Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013","docAbstract":"<p>The Upper Black Squirrel Creek Basin is located about 25 kilometers east of Colorado Springs, Colorado. The primary aquifer is a productive section of unconsolidated deposits that overlies bedrock units of the Denver Basin and is a critical resource for local water needs, including irrigation, domestic, and commercial use. The primary aquifer also serves an important regional role by the export of water to nearby communities in the Colorado Springs area. Changes in land use and development over the last decade, which includes substantial growth of subdivisions in the Upper Black Squirrel Creek Basin, have led to uncertainty regarding the potential effects to water quality throughout the basin. In response, the U.S. Geological Survey, in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, and Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County conducted an assessment of groundwater quality and groundwater age with an emphasis on characterizing nitrate in the groundwater.</p>\n<p>Groundwater-quality samples were collected from 50 randomly selected wells between May and June 2013. The samples were analyzed for major ions, nutrients, dissolved gases, tritium (<sup>3</sup>H), chlorofluorocarbons (CFC-11, CFC-12, and CFC-113), and fuel products (such as benzene, toluene, ethylbenzene, and xylenes). None of the groundwater samples exceeded the U.S. Environmental Protection Agency (EPA) National Primary Drinking Water Regulations for primary maximum contaminant levels (MCL) for major ions. Secondary maximum contaminant levels, which are not health concerns and affect mainly taste, color, or odor of the water, were observed in rare instances for pH (2 samples), chloride (1 sample), iron (3 samples), and manganese (8 samples). The secondary maximum contaminant level for total dissolved solids was also exceeded for two samples.</p>\n<p>Nitrate (nitrite plus nitrate as nitrogen in groundwater) was elevated above the estimated background concentration of natural recharge waters of 1 milligram per liter (mg/L) in 44 of the 50 wells sampled and showed a median concentration of 5.4 mg/L. Nitrate concentrations were above the MCL of 10 mg/L in 5 of the 50 wells sampled and above half of the EPA MCL (5 mg/L) in 27 of the 50 wells sampled, which included samples above the MCL. Dissolved-oxygen concentrations exceeded 0.5 mg/L in 95 percent of reported values (40 of 42 samples) and exceeded 2.0 mg/L in 90 percent of reported values (38 of 42 samples). The oxidized conditions observed in most areas indicate that nitrate from fertilizers and animal or human waste was geochemically stable and could persist in the groundwater for decades or perhaps longer. A historical analysis of median nitrate concentrations over nearly three decades showed an increase in nitrate of approximately 1 mg/L from 4.3 to 5.4 mg/L, although the increase was not determined to be significantly different using nonparametric statistical methods.</p>\n<p>Major-ion data indicate that groundwater representative of the primary aquifer was classified as calcium-sodium bicarbonate type water. Other water samples from wells located mainly along the periphery of the primary aquifer had cation-anion compositions consistent with distinct water sources, including groundwater contributions from the underlying bedrock aquifers. The areas with differentiable water sources were located mainly where alluvial deposits were thin and geologic contacts to the underlying bedrock aquifers were relatively shallow.</p>\n<p>Nitrate concentrations in the groundwater were evaluated for relations to land use. An agricultural region was defined using a sequence of land satellite imagery. Groundwater flow directions interpreted from median water-table elevations measured from 2000 to 2013 were used in conjunction with cropland locations to define the agricultural region boundaries by encompassing potential pathways of nitrate transport in the groundwater from nitrogen-based fertilizers. A statistically significant higher median nitrate concentration was observed for areas inside the agricultural region (6.7 mg/L) compared to areas outside the agricultural region (2.3 mg/L), although median concentrations in both areas were below the MCL (10&nbsp;mg/L). Median nitrate concentration was also significantly greater in land parcels with septic use (4.9 mg/L) compared to nonseptic parcels (1.7 mg/L). In general, agriculture or septic use was identified as the primary source of nitrate, depending on location, while commercial, county, grazing, and residential land uses were generally secondary sources of nitrate.</p>\n<p>Apparent groundwater ages were estimated from chlorofluorocarbons (CFC-11, CFC-12, and CFC-113) and tritium (<sup>3</sup>H) data using models that assumed piston flow and binary mixing (dilution of a young component with old, tracer-free water). The mean and median groundwater ages were about 30&nbsp;years and the standard deviation was 6 years, indicating that most groundwater in the primary aquifer was &ldquo;young&rdquo; water that had recharged to the aquifer over the last few decades (post-1950s). The median fraction of young water was about 71 percent, and the standard deviation was 29 percent. The remaining water predated the 1950s, which may have originated from deeper geologic formations or may represent slow moving groundwater within the primary aquifer. Some of the oldest groundwater ages (older than 30 years) were observed in the upper reaches of the aquifer to the northwest where the primary aquifer is thin and intersects bedrock, supporting the hypothesis of geochemically distinct groundwater entering the primary aquifer from below. Groundwater that had reached the central part of the aquifer from upgradient areas of the basin was variable in age because of differences in flow paths and travel velocities. The groundwater age analysis showed that current (2013) land-use practices could affect water quality over decades to come, and that responses to remedial actions could be slow, especially for constituents, such as nitrate, that are stable under oxidized conditions.</p>\n<p>Fuel products (including acetone, benzene, diisopropyl ether, ethylbenzene, methyl acetate, methyl tertiary butyl ether (MTBE), methyl tert-pentyl ether, m- + p-xylene, o-xylene, tert-amyl alcohol, tert-butyl alcohol, tert-butyl ethyl ether, and toluene) were analyzed in groundwater from 49 of the 50&nbsp;wells. Water from seven sites had detections for fuel compounds; all concentrations were below MCL. The results provided assurance of water quality and a valuable baseline to evaluate future trends of fuel constituents as the region is further developed.</p>\n<p>Probability maps were developed from logistic regression models to examine the likelihood that nitrate concentrations in groundwater exceeded specified levels. Susceptibility analysis examined relations between mid-level (5.0 mg/L) nitrate concentrations and climatic, hydrologic, and geologic variables; the significant variables were identified as depth to groundwater, soil organic matter, and soil water storage to 25-centimeter (cm) depth. The vulnerability assessments included natural factors driving susceptibility but also human factors related to land use and septic use. Vulnerability to low-level (2.5 mg/L) nitrate was related to depth to groundwater, septic zoning, and soil organic matter. The results highlighted that septic zoning affected low-level nitrate concentrations. Vulnerability to mid-level (5.0 mg/L) nitrate was examined using all 50 samples and also with two data outliers removed, which showed relatively high nitrate concentrations but also anomalous water chemistry or were located beyond the primary study area. Vulnerability to mid-level (5.0 mg/L) nitrate using all 50 samples was related to depth to groundwater, land use, septic use within a 500-meter (m) radius, soil water storage to a 25-cm depth, soil organic matter, and whether a location was within the agricultural region. The mid-level (5.0 mg/L) vulnerability model using 48 samples (two outliers removed) produced the best overall fit and was related to the same variables as when using all samples except septic use. The results for mid-level vulnerability provided additional support that septic use was associated with low levels of nitrate in the groundwater. Soil properties and land use were identified as the main drivers of moderate nitrate concentrations. Probabilities of exceeding low-level nitrate concentrations were high in most areas with the lowest probabilities usually to the northwest along thin geologic deposits in the upper part of the basin.</p>\n<p>The results of this investigation offer the foundational information needed for developing best management practices to mitigate nitrate contamination, basic concepts on water quality to aid public education, and information to guide regulatory measures if policy makers determine this is warranted. Science-based decision making will require continued monitoring and analysis of water quality in the future.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165020","collaboration":"Prepared in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County","usgsCitation":"Wellman, T.P., and Rupert, M.G., 2016, Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013: U.S. Geological Survey Scientific Investigations Report, 2016–5020, 78 p., https://dx.doi.org/10.3133/sir20165020.","productDescription":"viii, 77 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068864","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":318534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5020/coverthb.jpg"},{"id":318535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5020/sir20165020.pdf","text":"Report","size":"63.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5020"}],"country":"United States","state":"Colorado","county":"El Paso","otherGeospatial":"Black Squirrel Management District","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.67361450195312,\n              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Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2016-03-03","noUsgsAuthors":false,"publicationDate":"2016-03-03","publicationStatus":"PW","scienceBaseUri":"56d96034e4b015c306f726d7","contributors":{"authors":[{"text":"Wellman, Tristan P.","contributorId":56500,"corporation":false,"usgs":true,"family":"Wellman","given":"Tristan P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":598071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rupert, Michael G. mgrupert@usgs.gov","contributorId":1194,"corporation":false,"usgs":true,"family":"Rupert","given":"Michael","email":"mgrupert@usgs.gov","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":598072,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70168792,"text":"70168792 - 2016 - Quantitative framework for preferential flow initiation and partitioning","interactions":[],"lastModifiedDate":"2016-03-03T10:38:45","indexId":"70168792","displayToPublicDate":"2016-03-03T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative framework for preferential flow initiation and partitioning","docAbstract":"<p><span>A model for preferential flow in macropores is based on the short-range spatial distribution of soil matrix infiltrability. It uses elementary areas at two different scales. One is the traditional representative elementary area (REA), which includes a sufficient heterogeneity to typify larger areas, as for measuring field-scale infiltrability. The other, called an elementary matrix area (EMA), is smaller, but large enough to represent the local infiltrability of soil matrix material, between macropores. When water is applied to the land surface, each EMA absorbs water up to the rate of its matrix infiltrability. Excess water flows into a macropore, becoming preferential flow. The land surface then can be represented by a mesoscale (EMA-scale) distribution of matrix infiltrabilities. Total preferential flow at a given depth is the sum of contributions from all EMAs. Applying the model, one case study with multi-year field measurements of both preferential and diffuse fluxes at a specific depth was used to obtain parameter values by inverse calculation. The results quantify the preferential&ndash;diffuse partition of flow from individual storms that differed in rainfall amount, intensity, antecedent soil water, and other factors. Another case study provided measured values of matrix infiltrability to estimate parameter values for comparison and illustrative predictions. These examples give a self-consistent picture from the combination of parameter values, directions of sensitivities, and magnitudes of differences caused by different variables. One major practical use of this model is to calculate the dependence of preferential flow on climate-related factors, such as varying soil wetness and rainfall intensity.</span></p>","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2015.05.0079","usgsCitation":"Nimmo, J.R., 2016, Quantitative framework for preferential flow initiation and partitioning: Vadose Zone Journal, v. 15, no. 2, https://doi.org/10.2136/vzj2015.05.0079.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069590","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":318537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-08","publicationStatus":"PW","scienceBaseUri":"56d96034e4b015c306f726de","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":621772,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70168794,"text":"70168794 - 2016 - Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions","interactions":[],"lastModifiedDate":"2016-05-19T10:27:29","indexId":"70168794","displayToPublicDate":"2016-03-03T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions","docAbstract":"<p><span>The invasion by winter-annual grasses (AGs) such as&nbsp;</span><i class=\"EmphasisTypeItalic \">Bromus tectorum</i><span>&nbsp;into sagebrush steppe throughout the western USA is a classic example of a biological invasion with multiple, interacting climate, soil and biotic factors driving the invasion, although few studies have examined all components together. Across a 6000-km</span><span>2</span><span>&nbsp;area of the northern Great Basin, we conducted a field assessment of 100 climate, soil, and biotic (functional group abundances, diversity) factors at each of 90 sites that spanned an invasion gradient ranging from 0 to 100&nbsp;% AG cover. We first determined which biotic and abiotic factors had the strongest correlative relationships with AGs and each resident functional group. We then used regression and structural equation modeling to explore how multiple ecological factors interact to influence AG abundance. Among biotic interactions, we observed negative relationships between AGs and biodiversity, perennial grass cover, resident species richness, biological soil crust cover and shrub density, whereas perennial and annual forb cover, tree cover and soil microbial biomass had no direct linkage to AG. Among abiotic factors, AG cover was strongly related to climate (increasing cover with increasing temperature&nbsp;and aridity), but had weak relationships with soil factors. Our structural equation model showed negative effects of perennial grasses and biodiversity on AG cover while integrating the negative effects of warmer climate and positive influence of belowground processes on resident functional groups. Our findings illustrate the relative importance of biotic interactions and climate on invasive abundance, while soil properties appear to have stronger relationships with resident biota than with invasives.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00442-016-3583-8","usgsCitation":"Bansal, S., and Sheley, R.L., 2016, Annual grass invasion in sagebrush-steppe: The relative importance of climate, soil properties and biotic interactions: Oecologia, v. 181, no. 2, p. 543-557, https://doi.org/10.1007/s00442-016-3583-8.","productDescription":"15 p.","startPage":"543","endPage":"557","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070148","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","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              -119.53125,\n              43.14909399920127\n            ],\n            [\n              -119.53125,\n              43.77109381775651\n            ],\n            [\n              -118.5205078125,\n              43.77109381775651\n            ],\n            [\n              -118.5205078125,\n              43.14909399920127\n            ],\n            [\n              -119.53125,\n              43.14909399920127\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"181","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"56d9602ce4b015c306f726b4","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":621776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheley, Roger L.","contributorId":167296,"corporation":false,"usgs":false,"family":"Sheley","given":"Roger","email":"","middleInitial":"L.","affiliations":[{"id":24676,"text":"USDA-ARS, Burns Oregon","active":true,"usgs":false}],"preferred":false,"id":621777,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70168798,"text":"70168798 - 2016 - A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags","interactions":[],"lastModifiedDate":"2017-01-12T11:06:16","indexId":"70168798","displayToPublicDate":"2016-03-03T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags","docAbstract":"<p><span>Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade</span><sup>−1</sup><span>) and a widening of the synchronized period (29 d decade</span><sup>−1</sup><span>). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.</span></p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.1727","usgsCitation":"Letcher, B., Hocking, D., O'Neil, K., Whiteley, A.R., Nislow, K., and O’Donnell, M., 2016, A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags: PeerJ, v. 4, e1727: 26 p., https://doi.org/10.7717/peerj.1727.","productDescription":"e1727: 26 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072906","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":471182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.1727","text":"Publisher Index Page"},{"id":318531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-29","publicationStatus":"PW","scienceBaseUri":"56d96027e4b015c306f726ad","contributors":{"authors":[{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":621791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hocking, Daniel 0000-0003-1889-9184 dhocking@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-9184","contributorId":149618,"corporation":false,"usgs":true,"family":"Hocking","given":"Daniel","email":"dhocking@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neil, Kyle","contributorId":82491,"corporation":false,"usgs":true,"family":"O'Neil","given":"Kyle","affiliations":[],"preferred":false,"id":621793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":621794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nislow, Keith H.","contributorId":60106,"corporation":false,"usgs":true,"family":"Nislow","given":"Keith H.","affiliations":[],"preferred":false,"id":621795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":621796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70176640,"text":"70176640 - 2016 - Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin","interactions":[],"lastModifiedDate":"2021-02-17T22:43:18.006221","indexId":"70176640","displayToPublicDate":"2016-03-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin","docAbstract":"<p><span>Over the last few years, canyons along the northern U.S. Atlantic continental margin have been the focus of intensive research examining canyon evolution, submarine geohazards, benthic ecology and deep-sea coral habitat. New high-resolution multibeam bathymetry and Remotely Operated Vehicle (ROV) dives in the major shelf-breaching and minor slope canyons, provided the opportunity to investigate the size of, and processes responsible for, canyon wall failures. The canyons cut through thick Late Cretaceous to Recent mixed siliciclastic and carbonate-rich lithologies which impart a primary control on the style of failures observed. Broad-scale canyon morphology across much of the margin can be correlated to the exposed lithology. Near vertical walls, sedimented benches, talus slopes, and canyon floor debris aprons were present in most canyons. The extent of these features depends on canyon wall cohesion and level of internal fracturing, and resistance to biological and chemical erosion. Evidence of brittle failure over different spatial and temporal scales, physical abrasion by downslope moving flows, and bioerosion, in the form of burrows and surficial scrape marks provide insight into the modification processes active in these canyons. The presence of sessile fauna, including long-lived, slow growing corals and sponges, on canyon walls, especially those affected by failure provide a critical, but as yet, poorly understood chronological record of geologic processes within these systems.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Submarine mass movements and their consequences: 7th international symposium part II","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer International Publishing","doi":"10.1007/978-3-319-20979-1_10","usgsCitation":"Chaytor, J., Demopoulos, A., ten Brink, U., Baxter, C.D., Quattrini, A.M., and Brothers, D., 2016, Assessment of canyon wall failure process from multibeam bathymetry and Remotely Operated Vehicle (ROV) observations, U.S. Atlantic continental margin, chap. 10 <i>of</i> Submarine mass movements and their consequences: 7th international symposium part II, p. 103-113, 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