{"pageNumber":"348","pageRowStart":"8675","pageSize":"25","recordCount":46619,"records":[{"id":70194112,"text":"70194112 - 2018 - Anticoagulant rodenticide toxicity to non-target wildlife under controlled exposure conditions","interactions":[],"lastModifiedDate":"2017-11-30T12:36:42","indexId":"70194112","displayToPublicDate":"2017-11-29T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Anticoagulant rodenticide toxicity to non-target wildlife under controlled exposure conditions","docAbstract":"Much of our understanding of anticoagulant rodenticide toxicity to non-target wildlife has been derived from molecular through whole animal research and registration studies in domesticated birds and mammals, and to a lesser degree from trials with captive wildlife. Using these data, an adverse outcome pathway identifying molecular initiating and anchoring events (inhibition of vitamin K epoxide reductase, failure to activate clotting factors), and established and plausible linkages (coagulopathy, hemorrhage, anemia, reduced fitness) associated with toxicity, is presented. Controlled exposure studies have demonstrated that second-generation anticoagulant rodenticides (e.g., brodifacoum) are more toxic than first- and intermediate-generation compounds (e.g., warfarin, diphacinone), however the difference in potency is diminished when first- and intermediate-generation compounds are administered on multiple days. Differences in species sensitivity are inconsistent among compounds. Numerous studies have compared mortality rate of predators fed prey or tissue containing anticoagulant rodenticides. In secondary exposure studies in birds, brodifacoum appears to pose the greatest risk, with bromadiolone, difenacoum, flocoumafen and difethialone being less hazardous than brodifacoum, and warfarin, coumatetralyl, coumafuryl, chlorophacinone and diphacinone being even less hazardous. In contrast, substantial mortality was noted in secondary exposure studies in mammals ingesting prey or tissue diets containing either second- or intermediate-generation compounds. Sublethal responses (e.g., prolonged clotting time, reduced hematocrit and anemia) have been used to study the sequelae of anticoagulant intoxication, and to some degree in the establishment of toxicity thresholds or toxicity reference values. Surprisingly few studies have undertaken histopathological evaluations to identify cellular lesions and hemorrhage associated with anticoagulant rodenticide exposure in non-target wildlife. Ecological risk assessments of anticoagulant rodenticides would be improved with additional data on (i) interspecific differences in sensitivity, particularly for understudied taxa, (ii) sublethal effects unrelated to coagulopathy, (iii) responses to mixtures and sequential exposures, and (iv) the role of vitamin K status on toxicity, and significance of inclusion of supplemental vitamin K or menadione (provitamin) in the diet of test organisms. A more complete understanding of the toxicity of anticoagulant rodenticides in non-target wildlife would enable regulators and natural resource managers to better predict and even mitigate risk.","largerWorkTitle":"Anticoagulant rodenticides and wildlife","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-64377-9_3","usgsCitation":"Rattner, B.A., and Mastrota, F.N., 2018, Anticoagulant rodenticide toxicity to non-target wildlife under controlled exposure conditions, chap. <i>of</i> Anticoagulant rodenticides and wildlife, v. 5, p. 45-86, https://doi.org/10.1007/978-3-319-64377-9_3.","productDescription":"42 p.","startPage":"45","endPage":"86","ipdsId":"IP-073175","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":349531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-11","publicationStatus":"PW","scienceBaseUri":"5a60fad6e4b06e28e9c22782","contributors":{"editors":[{"text":"van den Brink, Nico","contributorId":127370,"corporation":false,"usgs":false,"family":"van den Brink","given":"Nico","affiliations":[{"id":6920,"text":"Wageningen University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":724035,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Elliott, J.","contributorId":200997,"corporation":false,"usgs":false,"family":"Elliott","given":"J.","affiliations":[],"preferred":false,"id":724036,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Shore, R.","contributorId":200998,"corporation":false,"usgs":false,"family":"Shore","given":"R.","email":"","affiliations":[],"preferred":false,"id":724037,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Rattner, B.","contributorId":51416,"corporation":false,"usgs":true,"family":"Rattner","given":"B.","affiliations":[],"preferred":false,"id":724038,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":722111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastrota, F. Nicholas","contributorId":200995,"corporation":false,"usgs":false,"family":"Mastrota","given":"F.","email":"","middleInitial":"Nicholas","affiliations":[],"preferred":false,"id":724034,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70194450,"text":"70194450 - 2018 - Characterizing storm response and recovery using the beach change envelope: Fire Island, New York","interactions":[],"lastModifiedDate":"2017-11-29T13:02:00","indexId":"70194450","displayToPublicDate":"2017-11-28T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing storm response and recovery using the beach change envelope: Fire Island, New York","docAbstract":"<p><span>Hurricane Sandy at Fire Island, New York presented unique challenges in the quantification of storm impacts using traditional metrics of coastal change, wherein measured changes (shoreline, dune crest, and volume change) did not fully reflect the substantial changes in sediment redistribution following the storm. We used a time series of beach profile data at Fire Island, New York to define a new contour-based morphologic change metric, the Beach Change Envelope (BCE). The BCE quantifies changes to the upper portion of the beach likely to sustain measurable impacts from storm waves and capture a variety of storm and post-storm beach states. We evaluated the ability of the BCE to characterize cycles of beach change by relating it to a conceptual beach recovery regime, and demonstrated that BCE width and BCE height from the profile time series correlate well with established stages of recovery. We also investigated additional applications of this metric to capture impacts from storms and human modification by applying it to several post-storm historical datasets in which impacts varied considerably; Nor'Ida (2009), Hurricane Irene (2011), Hurricane Sandy (2012), and a 2009 community replenishment. In each case, the BCE captured distinctive upper beach morphologic change characteristic of these different beach building and erosional events. Analysis of the beach state at multiple profile locations showed spatial trends in recovery consistent with recent morphologic island evolution, which other studies have linked with sediment availability and the geologic framework. Ultimately we demonstrate a new way of more effectively characterizing beach response and recovery cycles to evaluate change along sandy coasts.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.08.004","usgsCitation":"Brenner, O.T., Lentz, E.E., Hapke, C.J., Henderson, R.E., Wilson, K., and Nelson, T., 2018, Characterizing storm response and recovery using the beach change envelope: Fire Island, New York: Geomorphology, v. 300, p. 189-202, https://doi.org/10.1016/j.geomorph.2017.08.004.","productDescription":"14 p.","startPage":"189","endPage":"202","ipdsId":"IP-081355","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":461105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2017.08.004","text":"Publisher Index Page"},{"id":349532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -73.2242202758789,\n              40.62177060472069\n            ],\n            [\n              -73.14216613769531,\n              40.62177060472069\n            ],\n            [\n              -73.14216613769531,\n              40.65485736139743\n            ],\n            [\n              -73.2242202758789,\n              40.65485736139743\n            ],\n            [\n              -73.2242202758789,\n              40.62177060472069\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"300","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fad6e4b06e28e9c22791","contributors":{"authors":[{"text":"Brenner, Owen T. 0000-0002-1588-721X obrenner@usgs.gov","orcid":"https://orcid.org/0000-0002-1588-721X","contributorId":4933,"corporation":false,"usgs":true,"family":"Brenner","given":"Owen","email":"obrenner@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":723886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":723889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hapke, Cheryl J. 0000-0002-2753-4075 chapke@usgs.gov","orcid":"https://orcid.org/0000-0002-2753-4075","contributorId":2981,"corporation":false,"usgs":true,"family":"Hapke","given":"Cheryl","email":"chapke@usgs.gov","middleInitial":"J.","affiliations":[{"id":6676,"text":"USGS (retired)","active":true,"usgs":false}],"preferred":true,"id":723887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henderson, Rachel E. 0000-0001-5810-7941 rehenderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5810-7941","contributorId":194022,"corporation":false,"usgs":true,"family":"Henderson","given":"Rachel","email":"rehenderson@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":723890,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Kathleen 0000-0002-2810-7585 kwilson@usgs.gov","orcid":"https://orcid.org/0000-0002-2810-7585","contributorId":195620,"corporation":false,"usgs":true,"family":"Wilson","given":"Kathleen","email":"kwilson@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":723888,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Timothy 0000-0002-5005-7617 trnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-5005-7617","contributorId":191933,"corporation":false,"usgs":true,"family":"Nelson","given":"Timothy","email":"trnelson@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":723891,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70236704,"text":"70236704 - 2018 - The Station Information System (SIS): A centralized seismic station repository for populating, managing, and distributing metadata","interactions":[],"lastModifiedDate":"2024-01-08T23:03:03.984775","indexId":"70236704","displayToPublicDate":"2017-11-22T09:50:33","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The Station Information System (SIS): A centralized seismic station repository for populating, managing, and distributing metadata","docAbstract":"<p><span>Creating, maintaining, and archiving accurate station metadata is critical for successful seismic network operations, data discovery, and research. The Station Information System (SIS) is a centralized repository of seismic station equipment inventory, instrument response, and site information of stations operated by regional seismic networks (RSNs) of the Advanced National Seismic System (ANSS;&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf5\">Filson and Arabasz, 2017</a><span>). It has a web‐based user interface that enables the creation and manipulation of the corresponding metadata. The system can track the installation, maintenance, and removal of equipment from a site, which often results in the creation of new metadata epochs. SIS also computes the overall response, including gain, of a data channel by combining the responses of the underlying hardware components. SIS distributes this information in standard formats such as Federation of Digital Seismic Networks StationXML and dataless Standard for the Exchange of Earthquake Data. SIS can also be used to manage inventory of field equipment such as power, telemetry, or Global Positioning System antenna, as well as links to other site‐related repositories external to SIS to give the network operator the most complete view of a site and the overall network. This article summarizes the main features in SIS. We present its basic infrastructure, holdings, workflow, and how RSNs retrieve data from it. We also explain the reasoning to pursue one centralized repository and why it supports the goals of SIS and the ANSS. We demonstrate that by providing the ANSS network operator with a comprehensive site view, SIS enables the production of high‐quality metadata, a necessary prerequisite for producing high‐quality seismic data.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170130","usgsCitation":"Yu, E., Acharya, P., Jaramillo, J., Kientz, S., Thomas, V., and Hauksson, E., 2018, The Station Information System (SIS): A centralized seismic station repository for populating, managing, and distributing metadata: Seismological Research Letters, v. 89, no. 1, p. 47-55, https://doi.org/10.1785/0220170130.","productDescription":"9 p.","startPage":"47","endPage":"55","ipdsId":"IP-088648","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469155,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20171127-131244965","text":"External Repository"},{"id":406843,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"1","noUsgsAuthors":false,"publicationDate":"2017-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Yu, Ellen","contributorId":222020,"corporation":false,"usgs":false,"family":"Yu","given":"Ellen","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":851950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acharya, Prabha","contributorId":296601,"corporation":false,"usgs":false,"family":"Acharya","given":"Prabha","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":851951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaramillo, Justin","contributorId":296602,"corporation":false,"usgs":false,"family":"Jaramillo","given":"Justin","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":851952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kientz, Sue","contributorId":296603,"corporation":false,"usgs":false,"family":"Kientz","given":"Sue","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":851953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomas, Valerie I. 0000-0001-6170-5563","orcid":"https://orcid.org/0000-0001-6170-5563","contributorId":208162,"corporation":false,"usgs":true,"family":"Thomas","given":"Valerie I.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hauksson, Egill","contributorId":48174,"corporation":false,"usgs":false,"family":"Hauksson","given":"Egill","affiliations":[{"id":27150,"text":"Seismological Laboratory, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":851955,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70222616,"text":"70222616 - 2018 - The 2015 Gorkha (Nepal) Earthquake sequence: I. Source modeling and deterministic 3D ground shaking","interactions":[],"lastModifiedDate":"2021-08-09T13:26:10.316998","indexId":"70222616","displayToPublicDate":"2017-11-21T08:21:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"The 2015 Gorkha (Nepal) Earthquake sequence: I. Source modeling and deterministic 3D ground shaking","docAbstract":"<p><span>To better quantify the relatively long period (&lt;</span><span>&nbsp;</span><span>0.3</span><span>&nbsp;</span><span>Hz) shaking experienced during the 2015 Gorkha (Nepal) earthquake sequence, we study the finite rupture processes and the associated 3D ground motion of the Mw7.8 mainshock and the Mw7.2&nbsp;<a class=\"topic-link\" title=\"Learn more about Aftershock from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/aftershock\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/aftershock\">aftershock</a>. The 3D synthetics are then used in the broadband ground shaking in Kathmandu with a hybrid approach, summarized in a companion paper (Chen and Wei, 2017, submitted together). We determined the coseismic rupture process of the mainshock by joint inversion of InSAR/SAR,&nbsp;<a class=\"topic-link\" title=\"Learn more about Global Positioning System from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/global-positioning-system\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/global-positioning-system\">GPS</a>&nbsp;(static and high-rate), strong motion and teleseismic waveforms. Our inversion for the mainshock indicates unilateral rupture towards the ESE, with an average rupture speed of 3.0</span><span>&nbsp;</span><span>km/s and a total duration of ~</span><span>&nbsp;</span><span>60</span><span>&nbsp;</span><span>s. Additionally, we find that the beginning part of the rupture (5–18</span><span>&nbsp;</span><span>s) has about 40% longer rise time than the rest of the rupture, as well as slower rupture velocity. Our model shows two strong asperities occurring ~</span><span>&nbsp;</span><span>24</span><span>&nbsp;</span><span>s and ~</span><span>&nbsp;</span><span>36</span><span>&nbsp;</span><span>s after the origin and located ~</span><span>&nbsp;</span><span>30</span><span>&nbsp;</span><span>km to the northwest and northeast of the Kathmandu valley, respectively. In contrast, the Mw7.2 aftershock is more compact both in time and space, as revealed by joint inversion of teleseismic body waves and InSAR data. The different rupture features between the mainshock and the aftershock could be related to difference in fault zone structure. The mainshock and aftershock ground motions in the Kathmandu valley, recorded by both strong motion and high-rate GPS stations, exhibited strong amplification around 0.2</span><span>&nbsp;</span><span>Hz. A simplified 3D basin model, calibrated by an Mw5.2 aftershock, can match the observed waveforms reasonably well at 0.3</span><span>&nbsp;</span><span>Hz and lower frequency. The 3D simulations indicate that the basin structure trapped the wavefield and produced an extensive ground vibration. Our study suggests that the combination of rupture characteristics and propagational complexity are required to understand the ground shaking produced by hazardous earthquakes such as the Gorkha event.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2017.11.024","usgsCitation":"Wei, S., Chen, M., Wang, X., Graves, R., Lindsey, E., Wang, T., Karakas, C., and Helmberger, D., 2018, The 2015 Gorkha (Nepal) Earthquake sequence: I. Source modeling and deterministic 3D ground shaking: Tectonophysics, v. 722, p. 447-461, https://doi.org/10.1016/j.tecto.2017.11.024.","productDescription":"15 p.","startPage":"447","endPage":"461","ipdsId":"IP-090039","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2017.11.024","text":"Publisher Index Page"},{"id":387776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              86.539306640625,\n              26.696545111585152\n            ],\n            [\n              86.802978515625,\n              27.848790459862073\n            ],\n            [\n              82.650146484375,\n              29.6594160549124\n            ],\n            [\n              82.034912109375,\n              28.159189634046708\n            ],\n            [\n              85.4296875,\n              27.127591028502078\n            ],\n            [\n              86.539306640625,\n              26.696545111585152\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"722","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wei, Shengji","contributorId":192953,"corporation":false,"usgs":false,"family":"Wei","given":"Shengji","email":"","affiliations":[],"preferred":false,"id":820768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Meng","contributorId":261912,"corporation":false,"usgs":false,"family":"Chen","given":"Meng","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":820769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Xin","contributorId":177411,"corporation":false,"usgs":false,"family":"Wang","given":"Xin","email":"","affiliations":[],"preferred":false,"id":820770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":820771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Eric","contributorId":261913,"corporation":false,"usgs":false,"family":"Lindsey","given":"Eric","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":820772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Teng","contributorId":156235,"corporation":false,"usgs":false,"family":"Wang","given":"Teng","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":820773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karakas, Cagil","contributorId":261914,"corporation":false,"usgs":false,"family":"Karakas","given":"Cagil","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":820774,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Helmberger, Don","contributorId":192954,"corporation":false,"usgs":false,"family":"Helmberger","given":"Don","email":"","affiliations":[],"preferred":false,"id":820775,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70193527,"text":"ofr20171140 - 2018 - Characteristics of peak streamflows and extent of inundation in areas of West Virginia and southwestern Virginia affected by flooding, June 2016","interactions":[],"lastModifiedDate":"2018-09-27T15:11:09","indexId":"ofr20171140","displayToPublicDate":"2017-11-17T14:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1140","title":"Characteristics of peak streamflows and extent of inundation in areas of West Virginia and southwestern Virginia affected by flooding, June 2016","docAbstract":"<p>Heavy rainfall occurred across central and southern West<br>Virginia in June 2016 as a result of repeated rounds of torrential<br>thunderstorms. The storms caused major flooding and flash<br>flooding in central and southern West Virginia with Kanawha,<br>Fayette, Nicholas, and Greenbrier Counties among the hardest<br>hit. Over the duration of the storms, from 8 to 9.37 inches of<br>rain was reported in areas in Greenbrier County. Peak streamflows<br>were the highest on record at 7 locations, and streamflows<br>at 18 locations ranked in the top five for the period of<br>record at U.S. Geological Survey streamflow-gaging stations<br>used in this study. Following the storms, U.S. Geological Survey<br>hydrographers identified and documented 422 high-water<br>marks in West Virginia, noting location and height of the water<br>above land surface. Many of these high-water marks were<br>used to create flood-inundation maps for selected communities<br>of West Virginia that experienced flooding in June 2016.<br>Digital datasets of the inundation areas, mapping boundaries,<br>and water depth rasters are available online. </p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171140","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Austin, S.H., Watson, K.M., Lotspeich, R.R., Cauller, S.J., White, J.S., and Wicklein, S.M., 2018, Characteristics of peak streamflows and extent of inundation in areas of West Virginia and southwestern Virginia affected by flooding, June 2016 (ver. 1.1, September 2018): U.S. Geological Survey Open-File Report 2017–1140, 35 p., https://doi. org/10.3133/ofr20171140. ","productDescription":"Report: vi, 35 p.; Appendixes 1-3; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082022","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":348766,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140_appendix02.pdf","text":"Appendix 2","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Graphs Showing Annual Exceedance Probabilities in Relation to Annual Peak Streamflow, Determined Using the Expected Moments Algorithm and Bulletin 17B Methods, for Selected Streamflow-Gaging Stations for the Period of Record  through 1990, 2015, and 2016 and Annual Peak Streamflow, by Water Year 1900–2016"},{"id":348763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1140/coverthb2.jpg"},{"id":348857,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0K4K","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood Inundation, Flood Depth, and High-Water Marks for Selected Areas in West Virginia from the June 2016 Flood"},{"id":348768,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140_appendix03-table03-2.xlsx","text":"Appendix 3 (Table 3-2)","size":"168 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary from six analyses estimating peak-flow exceedance probabilities at 18 streamflow-gaging stations associated with June 2016 flooding in West Virginia and southwestern Virginia."},{"id":348955,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140_appendix03-table03-3.xlsx","text":"Appendix 3 (Table 3-3)","size":"46.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Expanded summary of percent changes since 1990 in estimated peak-flow annual exceedance probabilities from six analyses using two methods for 18 streamflow-gaging stations associated with June 2016 flooding in West Virginia and southwestern Virginia using data for the period of record through 1990, 2015, and 2016"},{"id":357784,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1140/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":348767,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140_appendix03-table03-1.xlsx","text":"Appendix 3 (Table 3-1)","size":"36.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Site description exceedance probabilities, equivalent recurrence intervals and summary statistics for 18 streamflow-gaging stations associated with June 2016 Flooding in West Virginia and southwestern Virginia\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":348765,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140_appendix01.pdf","text":"Appendix 1 ","size":"179 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Graphs Showing Selected Annual Exceedance Probabilities in Relation to Streamflow Using the Expected Moments Algorithm Method for Selected Streamflow-Gaging Stations in West Virginia for the Period of Record through 1990, 2015, and 2016 "},{"id":348764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1140/ofr20171140.pdf","text":"Report","size":"117 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1140"}],"country":"United States","state":"Virginia, West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -82,\n              37\n            ],\n            [\n              -79.5,\n              37\n            ],\n            [\n              -79.5,\n              39\n            ],\n            [\n              -82,\n              39\n            ],\n            [\n              -82,\n              37\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.1: September 2018; Version 1.0: November 2017","contact":"<p><a href=\"mailto:dc@wva.gov\" data-mce-href=\"mailto:dc@wva.gov\">Director</a>, <a href=\"http://va.water.usgs.gov/\" data-mce-href=\"http://va.water.usgs.gov/\">Virginia and West Virginia Water Science Center</a><br> U.S. Geological Survey <br> 1730 East Parham Road <br> Richmond, VA 23228</p>","tableOfContents":"<ul><li>Abstract&nbsp;</li><li>Introduction</li><li>Weather Conditions Before and During the Flood</li><li>Methods</li><li>Estimated Magnitudes and Flood Probabilities for Peak Streamflows</li><li>Flood-Inundation Maps&nbsp;</li><li>Flood Damages</li><li>Summary</li><li>References Cited</li><li>Appendix 1.&nbsp;Graphs Showing Selected Annual Exceedance Probabilities in Relation to Streamflow Using the Expected Moments Algorithm Method for Selected Streamflow-Gaging Stations in West Virginia for the Period of Record through 1990, 2015, and 2016&nbsp;</li><li>Appendix 2.&nbsp;Graphs Showing Annual Exceedance Probabilities in Relation to Annual Peak&nbsp;Streamflow, Determined Using the Expected Moments Algorithm and Bulletin 17B Methods, for Selected Streamflow-Gaging Stations for the Period of Record&nbsp; through 1990, 2015, and 2016 and Annual Peak Streamflow, by Water Year 1900–2016</li><li>Appendix 3.&nbsp;Three Tables Listing Expanded Summaries of Site Descriptions, Exceedance&nbsp;Probabilities, Equivalent Recurrence Intervals, Statistics, and Percent Change Since&nbsp;1990 in Estimated Peak-Flow Annual Exceedance Probabilities for 18 Streamflow-Gaging Stations Associated with June 2016 Flooding in West Virginia and Southwestern&nbsp;Virginia Using Data for the Period of Record through 1990, 2015, and 2016&nbsp;</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2017-11-17","revisedDate":"2018-09-27","noUsgsAuthors":false,"publicationDate":"2017-11-17","publicationStatus":"PW","scienceBaseUri":"5a60fb0ee4b06e28e9c22b73","contributors":{"authors":[{"text":"Austin, Samuel H. 0000-0001-5626-023X saustin@usgs.gov","orcid":"https://orcid.org/0000-0001-5626-023X","contributorId":153,"corporation":false,"usgs":true,"family":"Austin","given":"Samuel","email":"saustin@usgs.gov","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":719269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":719274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lotspeich, R. Russell 0000-0002-5572-9064 rlotspei@usgs.gov","orcid":"https://orcid.org/0000-0002-5572-9064","contributorId":194107,"corporation":false,"usgs":true,"family":"Lotspeich","given":"R. Russell","email":"rlotspei@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":719270,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cauller, Stephen J. 0000-0002-1823-8813 sjcaulle@usgs.gov","orcid":"https://orcid.org/0000-0002-1823-8813","contributorId":199484,"corporation":false,"usgs":true,"family":"Cauller","given":"Stephen","email":"sjcaulle@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":false,"id":719272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Jeremy S. 0000-0002-1501-1074 jswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-1501-1074","contributorId":3905,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jswhite@usgs.gov","middleInitial":"S.","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":719273,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wicklein, Shaun 0000-0003-4551-1237 smwickle@usgs.gov","orcid":"https://orcid.org/0000-0003-4551-1237","contributorId":3389,"corporation":false,"usgs":true,"family":"Wicklein","given":"Shaun","email":"smwickle@usgs.gov","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":719271,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192777,"text":"70192777 - 2018 - A spatial approach to combatting wildlife crime","interactions":[],"lastModifiedDate":"2018-05-21T13:21:31","indexId":"70192777","displayToPublicDate":"2017-11-17T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"A spatial approach to combatting wildlife crime","docAbstract":"<p><span>Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. Here, we show how geographic profiling – a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology – can be adapted for use in investigations of wildlife crime, using data from an eight-year study in Savé Valley Conservancy, Zimbabwe that in total includes more than 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. Using a subset of these data for which the illegal hunters’ identities are known, we show that the model can successfully identify the illegal hunters’ home villages using the spatial locations of hunting incidences (for example, snares) as input, and show how this can be improved by manipulating the probability surface inside the Conservancy to reflect the fact that – although the illegal hunters mostly live outside the Conservancy, the majority of hunting occurs inside (in criminology, ‘commuter crime’). The results of this analysis – combined with rigorous simulations – show for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns – for example, where landscape heterogeneity means that some parts of the study area are unsuitable (e.g. aquatic areas for terrestrial animals, or vice versa), or where landscape permeability differs (for example, forest bats tending not to fly over open areas). More broadly, these results show how geographic profiling can be used to target anti-poaching interventions more effectively and more efficiently, with important implications for the development of management strategies and conservation plans in a range of conservation scenarios.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/cobi.13027","usgsCitation":"Faulkner, S.C., Stevens, M.C., Romanach, S.S., Lindsey, P.A., and LeComber, S.C., 2018, A spatial approach to combatting wildlife crime: Conservation Biology, v. 32, no. 3, p. 685-693, https://doi.org/10.1111/cobi.13027.","productDescription":"9 p.","startPage":"685","endPage":"693","ipdsId":"IP-085198","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469158,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://qmro.qmul.ac.uk/xmlui/handle/123456789/25863","text":"External Repository"},{"id":349057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-06","publicationStatus":"PW","scienceBaseUri":"5a60fb0fe4b06e28e9c22b82","contributors":{"authors":[{"text":"Faulkner, Sally C.","contributorId":198703,"corporation":false,"usgs":false,"family":"Faulkner","given":"Sally","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Michael C.A.","contributorId":198704,"corporation":false,"usgs":false,"family":"Stevens","given":"Michael","email":"","middleInitial":"C.A.","affiliations":[],"preferred":false,"id":716892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":140419,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","email":"sromanach@usgs.gov","middleInitial":"S.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":716890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsey, Peter A.","contributorId":198705,"corporation":false,"usgs":false,"family":"Lindsey","given":"Peter","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":716893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeComber, Steven C.","contributorId":198706,"corporation":false,"usgs":false,"family":"LeComber","given":"Steven","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716894,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70194133,"text":"70194133 - 2018 - Estimating disperser abundance using open population models that incorporate data from continuous detection PIT arrays","interactions":[],"lastModifiedDate":"2018-08-31T11:07:02","indexId":"70194133","displayToPublicDate":"2017-11-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating disperser abundance using open population models that incorporate data from continuous detection PIT arrays","docAbstract":"<p><span>Autonomous passive integrated transponder (PIT) tag antenna systems continuously detect individually marked organisms at one or more fixed points over long time periods. Estimating abundance using data from autonomous antennae can be challenging, because these systems do not detect unmarked individuals. Here we pair PIT antennae data from a tributary with mark-recapture sampling data in a mainstem river to estimate the number of fish moving from the mainstem to the tributary. We then use our model to estimate abundance of non-native rainbow trout Oncorhynchus mykiss that move from the Colorado River to the Little Colorado River (LCR), the latter of which is important spawning and rearing habitat for federally-endangered humpback chub Gila cypha. We estimate 226 rainbow trout (95% CI: 127-370) entered the LCR from October 2013-April 2014. We discuss the challenges of incorporating detections from autonomous PIT antenna systems into mark-recapture population models, particularly in regards to using information about spatial location to estimate movement and detection probabilities.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2017-0304","usgsCitation":"Dzul, M.C., Yackulic, C.B., and Korman, J., 2018, Estimating disperser abundance using open population models that incorporate data from continuous detection PIT arrays: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 9, p. 1393-1404, https://doi.org/10.1139/cjfas-2017-0304.","productDescription":"12 p.","startPage":"1393","endPage":"1404","ipdsId":"IP-081814","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438073,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NZ86JV","text":"USGS data release","linkHelpText":"Continuous Detection PIT Array Data &amp; Model"},{"id":349027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.9342041015625,\n              36.121236902880185\n            ],\n            [\n              -111.66778564453125,\n              36.121236902880185\n            ],\n            [\n              -111.66778564453125,\n              36.465471886798134\n            ],\n            [\n              -111.9342041015625,\n              36.465471886798134\n            ],\n            [\n              -111.9342041015625,\n              36.121236902880185\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"75","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb10e4b06e28e9c22b91","contributors":{"authors":[{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":722302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":722304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":722306,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70193560,"text":"70193560 - 2018 - Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains","interactions":[],"lastModifiedDate":"2018-04-02T13:56:58","indexId":"70193560","displayToPublicDate":"2017-11-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2298,"text":"Journal of Forestry Research","active":true,"publicationSubtype":{"id":10}},"title":"Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains","docAbstract":"<p><span>In eastern North America, white-tailed deer (</span><i class=\"EmphasisTypeItalic \">Odocoileus virginianus</i><span>) can have profound influences on forest biodiversity and forest successional processes. Moderate to high deer populations in the central Appalachians have resulted in lower forest biodiversity. Legacy effects in some areas persist even following deer population reductions or declines. This has prompted managers to consider deer population management goals in light of policies designed to support conservation of biodiversity and forest regeneration while continuing to support ample recreational hunting opportunities. However, despite known relationships between herbivory intensity and biodiversity impact, little information exists on the predictability of herbivory intensity across the varied and spatially diverse habitat conditions of the central Appalachians. We examined the predictability of browsing rates across central Appalachian landscapes at four environmental scales: vegetative community characteristics, physical environment, habitat configuration, and local human and deer population demographics. In an<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">information</i><span>-</span><i class=\"EmphasisTypeItalic \">theoretic</i><span><span>&nbsp;</span>approach, we found that a model fitting the number of stems browsed relative to local vegetation characteristics received most (62%) of the overall support of all tested models assessing herbivory impact. Our data suggest that deer herbivory responded most predictably to differences in vegetation quantity and type. No other spatial factors or demographic factors consistently affected browsing intensity. Because herbivory, vegetation communities, and productivity vary spatially, we suggest that effective broad-scale herbivory impact assessment should include spatially-balanced vegetation monitoring that accounts for regional differences in deer forage preference. Effective monitoring is necessary to avoid biodiversity impacts and deleterious changes in vegetation community composition that are difficult to reverse and/or may not be detected using traditional deer-density based management goals.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11676-017-0476-6","usgsCitation":"Kniowski, A.B., and Ford, W., 2018, Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains: Journal of Forestry Research, v. 29, no. 3, p. 841-850, https://doi.org/10.1007/s11676-017-0476-6.","productDescription":"10 p.","startPage":"841","endPage":"850","ipdsId":"IP-086612","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469161,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/99324","text":"External Repository"},{"id":348769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Appalachian Mountains","volume":"29","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-21","publicationStatus":"PW","scienceBaseUri":"5a60fb13e4b06e28e9c22bd8","contributors":{"authors":[{"text":"Kniowski, Andrew B.","contributorId":191558,"corporation":false,"usgs":false,"family":"Kniowski","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":719363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":719362,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70193580,"text":"70193580 - 2018 - Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance","interactions":[],"lastModifiedDate":"2018-02-05T15:33:08","indexId":"70193580","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance","docAbstract":"<div id=\"ASec1\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Context</strong></p><p id=\"Par1\" class=\"Para\">Species-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning.</p></div><div id=\"ASec2\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Objectives</strong></p><p id=\"Par2\" class=\"Para\">We tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats.</p></div><div id=\"ASec3\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Methods</strong></p><p id=\"Par3\" class=\"Para\">We compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling.</p></div><div id=\"ASec4\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Results</strong></p><p id=\"Par4\" class=\"Para\">Blackpoll warbler (<i class=\"EmphasisTypeItalic \">Setophaga striata</i>), wood thrush (<i class=\"EmphasisTypeItalic \">Hylocichla mustelina</i>), and Louisiana (<i class=\"EmphasisTypeItalic \">Parkesia motacilla</i>) and northern waterthrush (<i class=\"EmphasisTypeItalic \">P. noveboracensis</i>) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird (<i class=\"EmphasisTypeItalic \">Seiurus aurocapilla</i>), blackburnian (<i class=\"EmphasisTypeItalic \">Setophaga fusca</i>) and cerulean warbler (<i class=\"EmphasisTypeItalic \">Setophaga cerulea</i>)], as all were positively related to occupancy data.</p></div><div id=\"ASec5\" class=\"AbstractSection\"><p class=\"Heading\"><strong>Conclusions</strong></p><p id=\"Par5\" class=\"Para\">LC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.</p></div>","language":"English","publisher":"Springer","doi":"10.1007/s10980-017-0582-z","usgsCitation":"Loman, Z., DeLuca, W., Harrison, D.J., Loftin, C., Rolek, B.W., and Wood, P.B., 2018, Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance: Landscape Ecology, v. 33, no. 1, p. 77-91, https://doi.org/10.1007/s10980-017-0582-z.","productDescription":"15 p.","startPage":"77","endPage":"91","ipdsId":"IP-080262","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-71.860513,41.320248],[-72.983751,41.235364],[-73.643478,41.002171],[-73.785964,40.800862],[-72.245348,41.161217],[-72.273657,41.051533],[-72.116368,40.999796],[-71.869558,41.075046],[-72.39585,40.86666],[-73.23914,40.6251],[-74.206731,40.594569],[-74.209788,40.447407],[-73.995683,40.468707],[-73.971381,40.371709],[-74.090945,39.799978],[-74.850748,38.954538],[-74.933571,38.928519],[-74.905181,39.174945],[-75.165979,39.201842],[-75.542894,39.470447],[-75.511743,39.674313],[-75.587147,39.651012],[-75.401193,39.088762],[-75.06551,38.66103],[-75.057288,38.404738],[-75.87767,37.135604],[-76.023664,37.268971],[-75.712065,37.936082],[-75.846621,37.925785],[-75.938577,38.272329],[-76.188644,38.267434],[-76.320843,38.459862],[-76.190902,38.621092],[-76.308922,38.813346],[-76.205063,38.892726],[-76.333703,38.984607],[-76.168332,38.996546],[-76.27566,39.160304],[-75.986298,39.510398],[-76.497977,39.204697],[-76.438845,39.0529],[-76.559697,38.767443],[-76.329433,38.073986],[-77.040638,38.444618],[-77.256412,38.396755],[-77.175969,38.604113],[-77.26443,38.582845],[-77.286202,38.347025],[-77.024866,38.386791],[-76.910832,38.197073],[-76.265998,37.91138],[-76.339892,37.655966],[-76.722156,37.83668],[-76.252415,37.447274],[-76.475927,37.250543],[-76.300352,37.00885],[-76.780532,37.209336],[-76.482407,36.917364],[-76.058154,36.916947],[-75.867044,36.550754],[-83.645586,36.600002],[-82.895445,36.882145],[-82.722097,37.120168],[-81.968297,37.537798],[-82.39968,37.829935],[-82.638398,38.152157],[-82.595382,38.382712],[-82.181967,38.599384],[-82.068864,38.984878],[-81.759995,38.925828],[-81.814155,39.073478],[-81.692203,39.236091],[-80.865575,39.662751],[-80.602895,40.327869],[-80.652436,40.562544],[-80.52566,40.636068],[-80.519345,41.929168],[-78.868556,42.770258],[-79.061388,43.251349],[-78.370221,43.376505],[-76.952174,43.270692],[-76.235834,43.529256],[-76.133697,43.940356],[-76.360306,44.070907],[-76.312647,44.199044],[-74.946686,44.984665],[-71.502487,45.013367],[-71.443882,45.235462],[-70.898482,45.244088],[-70.684614,45.395071],[-70.688214,45.563981],[-70.259117,45.890755],[-70.290896,46.185838],[-70.057061,46.415036],[-69.997086,46.69523],[-69.22442,47.459686],[-69.066715,47.43024],[-69.0402,47.2451],[-68.893204,47.182974],[-68.292679,47.359476],[-67.991871,47.212042],[-67.790515,47.067921],[-67.803148,45.696127],[-67.476704,45.604157],[-67.489464,45.282653],[-67.390579,45.154114],[-67.145652,45.146667],[-66.986318,44.820657],[-68.049334,44.33073],[-68.22939,44.463496],[-68.191924,44.306675],[-68.339498,44.222893],[-68.3791,44.430049],[-68.529905,44.39907],[-68.528153,44.241263],[-68.982449,44.426195],[-69.031878,44.079036],[-69.259838,43.921427],[-69.851297,43.703581],[-70.026193,43.822587],[-70.176023,43.76079],[-70.810999,42.892375],[-70.772267,42.711064],[-70.595474,42.660336],[-70.996097,42.271222],[-70.754488,42.228673],[-70.471552,41.761563],[-70.008462,41.800786],[-70.169781,42.059736],[-70.082624,42.054657],[-69.935952,41.809422],[-69.976478,41.603664],[-70.329924,41.634578],[-70.902763,41.421061],[-70.658659,41.543385],[-70.708193,41.730959],[-71.19302,41.457931],[-71.21616,41.62549],[-71.304394,41.454502],[-71.19564,41.67509],[-71.342786,41.728506],[-71.455371,41.407962],[-71.860513,41.320248]],[[-77.038598,38.791513],[-77.002498,38.96541],[-77.0915,38.95651],[-77.038598,38.791513]]],[[[-70.59628,41.471905],[-70.450431,41.420703],[-70.496162,41.346452],[-70.802083,41.314207],[-70.59628,41.471905]]],[[[-70.092142,41.297741],[-69.960277,41.278731],[-70.256164,41.288123],[-70.092142,41.297741]]],[[[-74.144428,40.53516],[-74.219787,40.502603],[-74.120186,40.642201],[-74.144428,40.53516]]]]},\"properties\":{\"name\":\"Connecticut\",\"nation\":\"USA  \"}}]}","volume":"33","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-16","publicationStatus":"PW","scienceBaseUri":"5a60fb14e4b06e28e9c22bfa","contributors":{"authors":[{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":721890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeLuca, William","contributorId":192836,"corporation":false,"usgs":false,"family":"DeLuca","given":"William","affiliations":[],"preferred":false,"id":721891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Daniel J.","contributorId":200256,"corporation":false,"usgs":false,"family":"Harrison","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":719435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rolek, Brian W.","contributorId":200318,"corporation":false,"usgs":false,"family":"Rolek","given":"Brian","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":721893,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Petra B. 0000-0002-8575-1705 pbwood@usgs.gov","orcid":"https://orcid.org/0000-0002-8575-1705","contributorId":199090,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":721894,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193612,"text":"70193612 - 2018 - Growth potential and habitat requirements of endangered age-0 pallid sturgeon (Scaphirhynchus albus) in the Missouri River, USA, determined using a individual-based model framework","interactions":[],"lastModifiedDate":"2017-12-11T13:07:48","indexId":"70193612","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Growth potential and habitat requirements of endangered age-0 pallid sturgeon (<i>Scaphirhynchus albus</i>) in the Missouri River, USA, determined using a individual-based model framework","title":"Growth potential and habitat requirements of endangered age-0 pallid sturgeon (Scaphirhynchus albus) in the Missouri River, USA, determined using a individual-based model framework","docAbstract":"<p><span>An individual-based model framework was used to evaluate growth potential of the federally endangered pallid sturgeon (</span><i>Scaphirhynchus albus</i><span>) in the Missouri River. The model, developed for age-0 sturgeon, combines information on functional feeding response, bioenergetics and swimming ability to regulate consumption and growth within a virtual foraging arena. Empirical data on water temperature, water velocity and prey density were obtained from three sites in the Missouri River and used as inputs in the model to evaluate hypotheses concerning factors affecting pallid sturgeon growth. The model was also used to evaluate the impacts of environmental heterogeneity and water velocity on individual growth variability, foraging success and dispersal ability. Growth was simulated for a period of 100&nbsp;days using 100 individuals (first feeding; 19&nbsp;mm and 0.035&nbsp;g) per scenario. Higher growth was shown to occur at sites where high densities of Ephemeroptera and Chironomidae larvae occurred throughout the growing season. Highly heterogeneous habitats (i.e., wide range of environmental conditions) and moderate water velocities (0.3&nbsp;m/s) were also found to positively affect growth rates. The model developed here provides an important management and conservation tool for evaluating growth hypotheses and(or) identifying habitats in the Missouri River that are favourable to age-0 pallid sturgeon growth.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/eff.12337","usgsCitation":"Deslauriers, D., Heironimus, L.B., Rapp, T., Graeb, B.D., Klumb, R.A., and Chipps, S.R., 2018, Growth potential and habitat requirements of endangered age-0 pallid sturgeon (Scaphirhynchus albus) in the Missouri River, USA, determined using a individual-based model framework: Ecology of Freshwater Fish, v. 27, no. 1, p. 198-208, https://doi.org/10.1111/eff.12337.","productDescription":"11 p.","startPage":"198","endPage":"208","ipdsId":"IP-080765","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-24","publicationStatus":"PW","scienceBaseUri":"5a60fad8e4b06e28e9c227cb","contributors":{"authors":[{"text":"Deslauriers, David","contributorId":187586,"corporation":false,"usgs":false,"family":"Deslauriers","given":"David","email":"","affiliations":[],"preferred":false,"id":719622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heironimus, Laura B.","contributorId":187587,"corporation":false,"usgs":false,"family":"Heironimus","given":"Laura","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":719623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rapp, Tobias","contributorId":199643,"corporation":false,"usgs":false,"family":"Rapp","given":"Tobias","email":"","affiliations":[],"preferred":false,"id":719624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graeb, Brian D. S.","contributorId":171851,"corporation":false,"usgs":false,"family":"Graeb","given":"Brian","email":"","middleInitial":"D. S.","affiliations":[{"id":26956,"text":"Departement of Natural Resource Management, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":719625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klumb, Robert A.","contributorId":86606,"corporation":false,"usgs":true,"family":"Klumb","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false},{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false},{"id":561,"text":"South Dakota Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":719626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719621,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193638,"text":"70193638 - 2018 - Catchment-scale determinants of nonindigenous minnow richness in the eastern United States","interactions":[],"lastModifiedDate":"2017-12-11T13:08:33","indexId":"70193638","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Catchment-scale determinants of nonindigenous minnow richness in the eastern United States","docAbstract":"<p><span>Understanding the drivers of biological invasions is critical for preserving aquatic biodiversity. Stream fishes make excellent model taxa for examining mechanisms driving species introduction success because their distributions are naturally limited by catchment boundaries. In this study, we compared the relative importance of catchment-scale abiotic and biotic predictors of native and nonindigenous minnow (Cyprinidae) richness in 170 catchments throughout the eastern United States. We compared historic and contemporary cyprinid distributional data to determine catchment-wise native/nonindigenous status for 152 species. Catchment-scale model predictor variables described natural (elevation, precipitation, flow accumulation) and anthropogenic (developed land cover, number of dams) abiotic features, as well as native congener richness. Native congener richness may represent either biotic resistance via interspecific competition, or trait preadaptation according to Darwin's naturalisation hypothesis. We used generalised linear mixed models to examine evidence supporting the relative roles of abiotic and biotic predictors of cyprinid introduction success. Native congener richness was positively correlated with nonindigenous cyprinid richness and was the most important variable predicting nonindigenous cyprinid richness. Mean elevation had a weak positive effect, and effects of other abiotic factors were insignificant and less important. Our results suggest that at this spatial scale, trait preadaptation may be more important than intrageneric competition for determining richness of nonindigenous fishes.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/eff.12331","usgsCitation":"Peoples, B.K., Midway, S.R., DeWeber, J.T., and Wagner, T., 2018, Catchment-scale determinants of nonindigenous minnow richness in the eastern United States: Ecology of Freshwater Fish, v. 27, no. 1, p. 138-145, https://doi.org/10.1111/eff.12331.","productDescription":"8 p.","startPage":"138","endPage":"145","ipdsId":"IP-074166","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461121,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12331","text":"Publisher Index Page"},{"id":348724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.78125,\n              29.53522956294847\n            ],\n            [\n              -66.62109375,\n              29.53522956294847\n            ],\n            [\n              -66.62109375,\n              47.487513008956554\n            ],\n            [\n              -85.78125,\n              47.487513008956554\n            ],\n            [\n              -85.78125,\n              29.53522956294847\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-13","publicationStatus":"PW","scienceBaseUri":"5a60fad8e4b06e28e9c227c7","contributors":{"authors":[{"text":"Peoples, Brandon K.","contributorId":177551,"corporation":false,"usgs":false,"family":"Peoples","given":"Brandon","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":719709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Midway, Stephen R.","contributorId":172159,"corporation":false,"usgs":false,"family":"Midway","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":719710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeWeber, Jefferson T.","contributorId":199675,"corporation":false,"usgs":false,"family":"DeWeber","given":"Jefferson","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":719711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719708,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192969,"text":"70192969 - 2018 - Pharmaceuticals in water, fish and osprey nestlings in Delaware River and Bay","interactions":[],"lastModifiedDate":"2017-11-12T16:52:21","indexId":"70192969","displayToPublicDate":"2017-11-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Pharmaceuticals in water, fish and osprey nestlings in Delaware River and Bay","docAbstract":"<p>Exposure of wildlife to Active Pharmaceutical Ingredients (APIs) is likely to occur but studies of risk are limited. One exposure pathway that has received attention is trophic transfer of APIs in a water-fish-osprey food chain. Samples of water, fish plasma and osprey plasma were collected from Delaware River and Bay, and analyzed for 21 APIs. Only 2 of 21 analytes exceeded method detection limits in osprey plasma (acetaminophen and diclofenac) with plasma levels typically 2–3 orders of magnitude below human therapeutic concentrations (HTC). We built upon a screening level model used to predict osprey exposure to APIs in Chesapeake Bay and evaluated whether exposure levels could have been predicted in Delaware Bay had we just measured concentrations in water or fish. Use of surface water and BCFs did not predict API concentrations in fish well, likely due to fish movement patterns, and partitioning and bioaccumulation uncertainties associated with these ionizable chemicals. Input of highest measured API concentration in fish plasma combined with pharmacokinetic data accurately predicted that diclofenac and acetaminophen would be the APIs most likely detected in osprey plasma. For the majority of APIs modeled, levels were not predicted to exceed 1&nbsp;ng/mL or method detection limits in osprey plasma. Based on the target analytes examined, there is little evidence that APIs represent a significant risk to ospreys nesting in Delaware Bay. If an API is present in fish orders of magnitude below HTC, sampling of fish-eating birds is unlikely to be necessary. However, several human pharmaceuticals accumulated in fish plasma within a recommended safety factor for HTC. It is now important to expand the scope of diet-based API exposure modeling to include alternative exposure pathways (e.g., uptake from landfills, dumps and wastewater treatment plants) and geographic locations (developing countries) where API contamination of the environment may represent greater risk.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2017.09.083","usgsCitation":"Bean, T., Rattner, B.A., Lazarus, R.S., Day, D.D., Burket, S.R., Brooks, B.W., Haddad, S.P., and Bowerman, W.W., 2018, Pharmaceuticals in water, fish and osprey nestlings in Delaware River and Bay: Environmental Pollution, v. 232, p. 533-545, https://doi.org/10.1016/j.envpol.2017.09.083.","productDescription":"13 p.","startPage":"533","endPage":"545","ipdsId":"IP-086763","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":461125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2017.09.083","text":"Publisher Index Page"},{"id":348631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware Bay, Delaware River","volume":"232","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a096bade4b09af898c94133","contributors":{"authors":[{"text":"Bean, Thomas G. 0000-0002-3577-1994 tbean@usgs.gov","orcid":"https://orcid.org/0000-0002-3577-1994","contributorId":195993,"corporation":false,"usgs":true,"family":"Bean","given":"Thomas G.","email":"tbean@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":717477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":717476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lazarus, Rebecca S. 0000-0003-1731-6469 rlazarus@usgs.gov","orcid":"https://orcid.org/0000-0003-1731-6469","contributorId":5594,"corporation":false,"usgs":true,"family":"Lazarus","given":"Rebecca","email":"rlazarus@usgs.gov","middleInitial":"S.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":717478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Day, Daniel D. 0000-0001-9070-7170 dday@usgs.gov","orcid":"https://orcid.org/0000-0001-9070-7170","contributorId":3985,"corporation":false,"usgs":true,"family":"Day","given":"Daniel","email":"dday@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":717479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burket, S. Rebekah","contributorId":198867,"corporation":false,"usgs":false,"family":"Burket","given":"S.","email":"","middleInitial":"Rebekah","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":717480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brooks, Bryan W. 0000-0002-6277-9852","orcid":"https://orcid.org/0000-0002-6277-9852","contributorId":198868,"corporation":false,"usgs":false,"family":"Brooks","given":"Bryan","email":"","middleInitial":"W.","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":717481,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haddad, Samuel P.","contributorId":198869,"corporation":false,"usgs":false,"family":"Haddad","given":"Samuel","email":"","middleInitial":"P.","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":717482,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bowerman, William W.","contributorId":198870,"corporation":false,"usgs":false,"family":"Bowerman","given":"William","email":"","middleInitial":"W.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":717483,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70193023,"text":"70193023 - 2018 - Association between degradation of pharmaceuticals and endocrine-disrupting compounds and microbial communities along a treated wastewater effluent gradient in Lake Mead","interactions":[],"lastModifiedDate":"2018-02-14T14:16:01","indexId":"70193023","displayToPublicDate":"2017-11-12T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Association between degradation of pharmaceuticals and endocrine-disrupting compounds and microbial communities along a treated wastewater effluent gradient in Lake Mead","docAbstract":"The role of microbial communities in the degradation of trace organic contaminants in the environment is little understood. In this study, the biotransformation potential of 27 pharmaceuticals and endocrine-disrupting compounds was examined in parallel with a characterization of the native microbial community in water samples from four sites variously impacted by urban run-off and wastewater discharge in Lake Mead, Nevada and Arizona, USA. Samples included relatively pristine Colorado River water at the upper end of the lake, nearly pure tertiary-treated municipal wastewater entering via the Las Vegas Wash, and waters of mixed influence (Las Vegas Bay and Boulder Basin), which represented a gradient of treated wastewater effluent impact. Microbial diversity analysis based on 16S rRNA gene censuses revealed the community at this site to be distinct from the less urban-impacted locations, although all sites were similar in overall diversity and richness. Similarly, Biolog EcoPlate assays demonstrated that the microbial community at Las Vegas Wash was the most metabolically versatile and active. Organic contaminants added as a mixture to laboratory microcosms were more rapidly and completely degraded in the most wastewater-impacted sites (Las Vegas Wash and Las Vegas Bay), with the majority exhibiting shorter half-lives than at the other sites or in a bacteriostatic control.  Although the reasons for enhanced degradation capacity in the wastewater-impacted sites remain to be established, these data are consistent with the acclimatization of native microorganisms (either through changes in community structure or metabolic regulation) to effluent-derived trace contaminants. This study suggests that in urban, wastewater-impacted watersheds, prior exposure to organic contaminants fundamentally alters the structure and function of microbial communities, which in turn translates into greater potential for the natural attenuation of these compounds compared to more pristine sites.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.10.052","usgsCitation":"Blunt, S.M., Sackett, J.D., Rosen, M.R., Benotti, M.J., Trenholm, R.A., Vanderford, B.J., Hedlund, B.P., and Moser, D.P., 2018, Association between degradation of pharmaceuticals and endocrine-disrupting compounds and microbial communities along a treated wastewater effluent gradient in Lake Mead: Science of the Total Environment, v. 622-623, p. 1640-1648, https://doi.org/10.1016/j.scitotenv.2017.10.052.","productDescription":"9 p.","startPage":"1640","endPage":"1648","ipdsId":"IP-088846","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":461131,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2017.10.052","text":"Publisher Index Page"},{"id":348622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Nevada","otherGeospatial":"Lake Mead","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.0762939453125,\n              35.89795019335754\n            ],\n            [\n              -113.8238525390625,\n              35.89795019335754\n            ],\n            [\n              -113.8238525390625,\n              36.4433803110554\n            ],\n            [\n              -115.0762939453125,\n            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Institute","active":true,"usgs":false},{"id":33776,"text":"University of Nevada, Las Vegas","active":true,"usgs":false}],"preferred":false,"id":717680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benotti, Mark J.","contributorId":190783,"corporation":false,"usgs":false,"family":"Benotti","given":"Mark","email":"","middleInitial":"J.","affiliations":[{"id":35387,"text":"Southern Nevada Water Authority","active":true,"usgs":false}],"preferred":false,"id":717681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trenholm, Rebecca A.","contributorId":198950,"corporation":false,"usgs":false,"family":"Trenholm","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":35387,"text":"Southern Nevada Water Authority","active":true,"usgs":false}],"preferred":false,"id":717684,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderford, Brett J.","contributorId":198951,"corporation":false,"usgs":false,"family":"Vanderford","given":"Brett","email":"","middleInitial":"J.","affiliations":[{"id":35387,"text":"Southern Nevada Water Authority","active":true,"usgs":false}],"preferred":false,"id":717685,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hedlund, Brian P.","contributorId":198948,"corporation":false,"usgs":false,"family":"Hedlund","given":"Brian","email":"","middleInitial":"P.","affiliations":[{"id":33776,"text":"University of Nevada, Las Vegas","active":true,"usgs":false}],"preferred":false,"id":717682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moser, Duane P.","contributorId":198949,"corporation":false,"usgs":false,"family":"Moser","given":"Duane","email":"","middleInitial":"P.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":717683,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70192867,"text":"70192867 - 2018 - Bipartite networks improve understanding of effects of waterbody size and angling method on angler–fish interactions","interactions":[],"lastModifiedDate":"2018-01-05T14:12:53","indexId":"70192867","displayToPublicDate":"2017-11-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Bipartite networks improve understanding of effects of waterbody size and angling method on angler–fish interactions","docAbstract":"<p><span>Networks used to study interactions could provide insights to fisheries. We compiled data from 27 297 interviews of anglers across waterbodies that ranged in size from 1 to 12 113 ha. Catch rates of fish species among anglers grouped by species targeted generally differed between angling methods (bank or boat). We constructed angler–catch bipartite networks (angling method specific) between anglers and fish and measured several network metrics. There was considerable variation in networks among waterbodies, with multiple metrics influenced by waterbody size. Number of species-targeting angler groups and number of fish species caught increased with increasing waterbody size. Mean number of links for species-targeting angler groups and fish species caught also increased with waterbody size. Connectance (realized proportion of possible links) of angler–catch interaction networks decreased slower for boat anglers than for bank anglers with increasing waterbody size. Network specialization (deviation of number of interactions from expected) was not significantly related to waterbody size or angling methods. Application of bipartite networks in fishery science requires careful interpretation of outputs, especially considering the numerous confounding factors prevalent in recreational fisheries.</span></p>","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2016-0176","usgsCitation":"Chizinski, C.J., Martin, D., Shizuka, D., and Pope, K.L., 2018, Bipartite networks improve understanding of effects of waterbody size and angling method on angler–fish interactions: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 1, p. 72-81, https://doi.org/10.1139/cjfas-2016-0176.","productDescription":"10 p.","startPage":"72","endPage":"81","ipdsId":"IP-075838","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469164,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2016-0176","text":"External Repository"},{"id":348428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425b1e4b0dc0b45b45304","contributors":{"authors":[{"text":"Chizinski, Christopher J.","contributorId":7178,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Dustin R.","contributorId":43482,"corporation":false,"usgs":true,"family":"Martin","given":"Dustin R.","affiliations":[],"preferred":false,"id":721084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shizuka, Daizaburo","contributorId":62048,"corporation":false,"usgs":true,"family":"Shizuka","given":"Daizaburo","email":"","affiliations":[],"preferred":false,"id":721085,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717245,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192975,"text":"70192975 - 2018 - The effectiveness of surrogate taxa to conserve freshwater biodiversity","interactions":[],"lastModifiedDate":"2018-01-05T14:14:42","indexId":"70192975","displayToPublicDate":"2017-11-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The effectiveness of surrogate taxa to conserve freshwater biodiversity","docAbstract":"<p><span>Establishing protected areas has long been an effective conservation strategy, and is often based on more readily surveyed species. The potential of any freshwater taxa to be a surrogate of other aquatic groups has not been fully explored. We compiled occurrence data on 72 species of freshwater fish, amphibians, mussels, and aquatic reptiles for the Great Plains, Wyoming. We used hierarchical Bayesian multi-species mixture models and MaxEnt models to describe species distributions, and program Zonation to identify conservation priority areas for each aquatic group. The landscape-scale factors that best characterized aquatic species distributions differed among groups. There was low agreement and congruence among taxa-specific conservation priorities (&lt;20%), meaning that no surrogate priority areas would include or protect the best habitats of other aquatic taxa. We found that common, wide-ranging aquatic species were included in taxa-specific priority areas, but rare freshwater species were not included. Thus, the development of conservation priorities based on a single freshwater aquatic group would not protect all species in the other aquatic groups.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/cobi.12967","usgsCitation":"Stewart, D., Underwood, Z.E., Rahel, F.J., and Walters, A.W., 2018, The effectiveness of surrogate taxa to conserve freshwater biodiversity: Conservation Biology, v. 32, no. 1, p. 183-194, https://doi.org/10.1111/cobi.12967.","productDescription":"12 p.","startPage":"183","endPage":"194","ipdsId":"IP-077166","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-14","publicationStatus":"PW","scienceBaseUri":"5a07e841e4b09af898c8cb1c","contributors":{"authors":[{"text":"Stewart, David R.","contributorId":141323,"corporation":false,"usgs":false,"family":"Stewart","given":"David R.","affiliations":[],"preferred":false,"id":720908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Zachary E.","contributorId":166946,"corporation":false,"usgs":false,"family":"Underwood","given":"Zachary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":720909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rahel, Frank J.","contributorId":171824,"corporation":false,"usgs":false,"family":"Rahel","given":"Frank","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":717506,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70203980,"text":"70203980 - 2018 - Origins of lead in populations of raptors","interactions":[],"lastModifiedDate":"2019-06-26T09:23:21","indexId":"70203980","displayToPublicDate":"2017-11-06T09:15:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Origins of lead in populations of raptors","docAbstract":"<p><span>Although poisoning from anthropogenically derived lead threatens wildlife of many species, routes of lead exposure are unclear and rarely empirically tested. We used blood lead concentration and isotope ratio (</span><sup>207</sup><span>Pb/</span><sup>206</sup><span>Pb) data from populations of four species of raptors from across North America to test hypotheses associated with lead exposure via inhalation versus ingestion. Mean variation in blood lead concentration among cohort siblings was non‐zero at nests of ferruginous hawks&nbsp;</span><i>Buteo regalis</i><span>&nbsp;and osprey&nbsp;</span><i>Pandion haliaetus</i><span>&nbsp;(</span><i>P&nbsp;</i><span>&lt;</span><i>&nbsp;</i><span>0.001 and&nbsp;</span><i>P&nbsp;</i><span>&lt;</span><i>&nbsp;</i><span>0.001), indicating exposure via episodic ingestion. However, within‐nest variation in blood lead concentration was not significantly different from zero among cohort siblings at nests of bald eagles&nbsp;</span><i>Haliaeetus leucocephalus</i><span>&nbsp;and golden eagles&nbsp;</span><i>Aquila chrysaetos</i><span>&nbsp;(</span><i>P&nbsp;</i><span>=</span><i>&nbsp;</i><span>0.014 and&nbsp;</span><i>P&nbsp;</i><span>=</span><i>&nbsp;</i><span>0.023), consistent with exposure via continuous inhalation. Isotope ratio data corroborated the lead concentration data and within‐nest average and variance of blood lead concentrations were positively correlated (</span><i>r</i><span>&nbsp;=&nbsp;0.70 to 0.94), indicating episodic ingestion. This study provides some of the first empirical population‐level data to evaluate mechanisms of lead exposure and demonstrates the importance of lead ingestion to avian predators and scavengers.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/acv.12379","usgsCitation":"Katzner, T., Stuber, M.J., Slabe, V.A., Anderson, J.T., Cooper, J.L., Rhea, L.L., and Milsap, B., 2018, Origins of lead in populations of raptors: Animal Conservation, v. 21, no. 3, p. 232-240, https://doi.org/10.1111/acv.12379.","productDescription":"9 p.","startPage":"232","endPage":"240","ipdsId":"IP-090831","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":365054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Arizona, California, Idaho, Michigan, Montana, Nevada, Virginia","volume":"21","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stuber, M J","contributorId":216569,"corporation":false,"usgs":false,"family":"Stuber","given":"M","email":"","middleInitial":"J","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":765074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slabe, V A","contributorId":216570,"corporation":false,"usgs":false,"family":"Slabe","given":"V","email":"","middleInitial":"A","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":765075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, J T","contributorId":216571,"corporation":false,"usgs":false,"family":"Anderson","given":"J","email":"","middleInitial":"T","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":765076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, J L","contributorId":216572,"corporation":false,"usgs":false,"family":"Cooper","given":"J","email":"","middleInitial":"L","affiliations":[{"id":35592,"text":"Virginia Department of Game and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":765077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rhea, L L","contributorId":216573,"corporation":false,"usgs":false,"family":"Rhea","given":"L","email":"","middleInitial":"L","affiliations":[{"id":37814,"text":"Former USGS","active":true,"usgs":false}],"preferred":false,"id":765078,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milsap, B A","contributorId":216574,"corporation":false,"usgs":false,"family":"Milsap","given":"B A","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":765079,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70193588,"text":"70193588 - 2018 - Linking spring phenology with mechanistic models of host movement to predict disease transmission risk","interactions":[],"lastModifiedDate":"2018-02-14T14:22:11","indexId":"70193588","displayToPublicDate":"2017-11-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Linking spring phenology with mechanistic models of host movement to predict disease transmission risk","docAbstract":"<ol id=\"jpe13022-list-0001\" class=\"o-list--numbered o-list--paragraph\"><li><p>Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.</p></li><li><p>Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [<i>Cervus Canadensis</i>] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.</p></li><li><p>Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.</p></li><li><p>Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.</p></li><li><p><i>Synthesis and applications</i>. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate risks posed by mobile disease hosts. More broadly, we demonstrate how mechanistic movement models can provide predictions of ecological conditions that are consistent with climate change but may be more extreme than has been observed historically.</p></li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13022","usgsCitation":"Merkle, J., Cross, P.C., Scurlock, B.M., Cole, E., Courtemanch, A.B., Dewey, S., and Kauffman, M., 2018, Linking spring phenology with mechanistic models of host movement to predict disease transmission risk: Journal of Applied Ecology, v. 55, no. 2, p. 810-819, https://doi.org/10.1111/1365-2664.13022.","productDescription":"10 p.","startPage":"810","endPage":"819","ipdsId":"IP-079776","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469167,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13022","text":"Publisher Index Page"},{"id":438078,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7474803","text":"USGS data release","linkHelpText":"Elk movement and predicted number of brucellosis-induced abortion events in the southern Greater Yellowstone Ecosystem (1993-2015)"},{"id":348261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","scienceBaseUri":"5a07e848e4b09af898c8cb32","contributors":{"authors":[{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":719500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":719499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scurlock, Brandon M.","contributorId":93788,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","email":"","middleInitial":"M.","affiliations":[{"id":6917,"text":"Wyoming Game and Fish Department, Laramie, USA","active":true,"usgs":false}],"preferred":false,"id":719501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Eric K. 0000-0002-2229-5853","orcid":"https://orcid.org/0000-0002-2229-5853","contributorId":145755,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":16228,"text":"U.S. Fish and Wildlife Service, National Elk Refuge, PO Box 510, Jackson, WY 83001 USA","active":true,"usgs":false}],"preferred":false,"id":719503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Courtemanch, Alyson B.","contributorId":198651,"corporation":false,"usgs":false,"family":"Courtemanch","given":"Alyson","email":"","middleInitial":"B.","affiliations":[{"id":35682,"text":"Wyoming Game and Fish Department, Jackson, WY","active":true,"usgs":false}],"preferred":false,"id":719504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dewey, Sarah","contributorId":145757,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah","affiliations":[{"id":16229,"text":"National Park Service, Grand Teton National Park, PO Drawer 170, Moose, WY 83012 USA","active":true,"usgs":false}],"preferred":false,"id":719505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":719502,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70193860,"text":"70193860 - 2018 - Quantifying changes and influences on mottled duck density in Texas","interactions":[],"lastModifiedDate":"2018-01-24T15:46:56","indexId":"70193860","displayToPublicDate":"2017-11-06T00:00:00","publicationYear":"2018","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":"Quantifying changes and influences on mottled duck density in Texas","docAbstract":"<p><span>Understanding the relative influence of environmental and intrinsic effects on populations is important for managing and conserving harvested species, especially those species inhabiting changing environments. Additionally, climate change can increase the uncertainty associated with management of species in these changing environments, making understanding factors affecting their populations even more important. Coastal ecosystems are particularly threatened by climate change; the combined effects of increasing severe weather events, sea level rise, and drought will likely have non-linear effects on coastal marsh wildlife species and their associated habitats. A species of conservation concern that persists in these coastal areas is the mottled duck (</span><i>Anas fulvigula</i><span>). Mottled ducks in the western Gulf Coast are approximately 50% below target abundance numbers established by the Gulf Coast Joint Venture for Texas and Louisiana, USA. Although evidence for declines in mottled duck abundance is apparent, specific causes of the decrease remain unknown. Our goals were to determine where the largest declines in mottled duck population were occurring along the system of Texas Gulf Coast National Wildlife Refuges and quantify the relative contribution of environmental and intrinsic effects on changes to relative population density. We modeled aerial survey data of mottled duck density along the Texas Gulf Coast from 1986–2015 to quantify effects of extreme weather events on an index to mottled duck density using the United States Climate Extremes Index and Palmer Drought Severity Index. Our results indicate that decreases in abundance are best described by an increase in days with extreme 1-day precipitation from June to November (hurricane season) and an increase in drought severity. Better understanding those portions of the life cycle affected by environmental conditions, and how to manage mottled duck habitat in conjunction with these events will likely be key to persistence of the species under future environmental conditions.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21373","usgsCitation":"Ross, B., Haukos, D.A., and Walther, P., 2018, Quantifying changes and influences on mottled duck density in Texas: Journal of Wildlife Management, v. 82, p. 374-382, https://doi.org/10.1002/jwmg.21373.","productDescription":"9 p.","startPage":"374","endPage":"382","ipdsId":"IP-083236","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -99.00878906249999,\n              25.898761936567023\n            ],\n            [\n              -93.482666015625,\n              25.898761936567023\n            ],\n            [\n              -93.482666015625,\n              30.41078179084589\n            ],\n            [\n              -99.00878906249999,\n              30.41078179084589\n            ],\n            [\n              -99.00878906249999,\n              25.898761936567023\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"82","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-25","publicationStatus":"PW","scienceBaseUri":"5a07e845e4b09af898c8cb26","contributors":{"authors":[{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":720705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walther, Patrick","contributorId":42153,"corporation":false,"usgs":true,"family":"Walther","given":"Patrick","affiliations":[],"preferred":false,"id":720750,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70200847,"text":"70200847 - 2018 - Seafloor fluid seeps on Kimki Ridge, offshore southern California: Links to active strike-slip faulting","interactions":[],"lastModifiedDate":"2019-07-26T14:47:25","indexId":"70200847","displayToPublicDate":"2017-11-02T08:56:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1371,"text":"Deep-Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Seafloor fluid seeps on Kimki Ridge, offshore southern California: Links to active strike-slip faulting","docAbstract":"<p><span>The Kimki Ridge fluid seeps are located in western Catalina Basin about 60</span><span>&nbsp;</span><span>km southwest of the southern California mainland and at a water depth of approximately 1100</span><span>&nbsp;</span><span>m. Multichannel&nbsp;seismic reflection&nbsp;profiles collected by the U.S.&nbsp;Geological Survey&nbsp;(USGS) in 2014 show&nbsp;acoustic&nbsp;transparency within the Kimki Ridge, suggesting the possibility of fluid seeps and possible sub-seafloor fluid pathways. Subsequent multibeam bathymetric and backscatter intensity data collected during a cooperative University of Washington/USGS cruise in early 2016 show subtle&nbsp;seafloor&nbsp;buildups with high acoustic backscatter (reflectivity) in three places along Kimki Ridge, supporting the existence of fluid&nbsp;seepage. A&nbsp;Remotely Operated Vehicle&nbsp;(ROV) dive, conducted as part of the&nbsp;</span><span><i>Nautilus</i></span><span>&nbsp;Exploration Program, took place in August 2016 to confirm the presence of these previously unknown seeps and document their characteristics as well as those of any associated&nbsp;biological communities. Two of the three seeps were explored by&nbsp;ROV, and showed abundant evidence of fluid seepage, including characteristic&nbsp;algal mats, chemosynthetic clams, and authigenic carbonate formation. The seeps are comprised of carbonate buildups 1–3</span><span>&nbsp;</span><span>m thick and 300–500</span><span>&nbsp;</span><span>m across. Within these areas, we interpret broad crater-like depressions 30–50</span><span>&nbsp;</span><span>m across and 1–2</span><span>&nbsp;</span><span>m deep to be individual seep vents. The seep areas appear to be broad zones of diffuse seepage that support chemosynthetic biologic communities; however, active venting was not observed. Geochemical analyses of rock samples collected from the seeps indicate microbially driven anaerobic oxidation of&nbsp;methane&nbsp;at or near the&nbsp;sediment water interface. Seismic-reflection profiles show chimney-like fluid pathways along the limbs and in the axis of the fold forming Kimki Ridge, and evidence of methane in shallow sediments can be traced into the adjacent Catalina Basin. A system of closely spaced faults located at the axis of the Kimki Ridge&nbsp;anticline&nbsp;may serve as pathways to allow&nbsp;fluid flow&nbsp;to the seafloor. Our data are consistent with other studies that suggest that&nbsp;transpression&nbsp;is an important component in the formation and localization of fluid seeps in a strike-slip setting, implying that seep formation may be a common occurrence at fault stepovers or transpressional bends in strike-slip systems.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2017.11.001","usgsCitation":"Conrad, J.E., Prouty, N.G., Walton, M.A., Kluesner, J.W., Maier, K.L., McGann, M., Brothers, D.S., Roland, E.C., and Dartnell, P., 2018, Seafloor fluid seeps on Kimki Ridge, offshore southern California: Links to active strike-slip faulting: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 150, p. 82-91, https://doi.org/10.1016/j.dsr2.2017.11.001.","productDescription":"10 p.","startPage":"82","endPage":"91","ipdsId":"IP-092415","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2017.11.001","text":"Publisher Index Page"},{"id":359267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kimki Ridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.97644042968749,\n              32.71797709835758\n            ],\n            [\n              -117.75146484375,\n              32.71797709835758\n            ],\n            [\n              -117.75146484375,\n              33.62376800118811\n            ],\n            [\n              -118.97644042968749,\n              33.62376800118811\n            ],\n            [\n              -118.97644042968749,\n              32.71797709835758\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"150","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5be40823e4b0b3fc5cf7cc0c","contributors":{"authors":[{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":750875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":750876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton, Maureen A. L.","contributorId":147200,"corporation":false,"usgs":false,"family":"Walton","given":"Maureen","email":"","middleInitial":"A. L.","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":750877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":167088,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":750878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maier, Katherine L. 0000-0003-2908-3340 kcoble@usgs.gov","orcid":"https://orcid.org/0000-0003-2908-3340","contributorId":4926,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine","email":"kcoble@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":750879,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":750880,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":750881,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roland, Emily C. eroland@usgs.gov","contributorId":5075,"corporation":false,"usgs":true,"family":"Roland","given":"Emily","email":"eroland@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":false,"id":750882,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dartnell, Peter 0000-0002-9554-729X pdartnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":2688,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","email":"pdartnell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":750883,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70193311,"text":"70193311 - 2018 - Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill","interactions":[],"lastModifiedDate":"2018-02-28T09:39:33","indexId":"70193311","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5536,"text":"Deep Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill","docAbstract":"<p><span>Research and monitoring activities over the 28 years since the T/V&nbsp;</span><i data-reactid=\"172\">Exxon Valdez</i><span><span>&nbsp;</span>ran aground and spilled oil into Prince William Sound, Alaska have led to an improved understanding of how wildlife populations were damaged, as well as the mechanisms and timelines of recovery. A key finding was that for some species, such as harlequin ducks and sea otters, chronic oil spill effects persisted for at least two decades and were a larger influence on population dynamics over the long term than acute effects of the spill. These data also offer insights into population variation resulting from factors other than the oil spill. For example, while many seabirds experienced direct and indirect effects of the spill, population trajectories of some piscivorous birds, including pigeon guillemots and marbled murrelets, were linked to long-term environmental changes independent of spill effects. Another species, killer whales, suffered population declines due to acute spill effects that have not been resolved despite lack of chronic direct effects, representing a novel pathway of long-term injury. The observed variation in mechanisms and timelines of recovery is linked to species specific life history and natural history traits, and thus may be useful for predicting population recovery for other species following other spills.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2017.04.007","usgsCitation":"Esler, D., Ballachey, B.E., Matkin, C.O., Cushing, D., Kaler, R., Bodkin, J.L., Monson, D., Esslinger, G.G., and Kloecker, K.A., 2018, Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill: Deep Sea Research Part II: Topical Studies in Oceanography, v. 147, p. 36-42, https://doi.org/10.1016/j.dsr2.2017.04.007.","productDescription":"7 p.","startPage":"36","endPage":"42","ipdsId":"IP-080594","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":461135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2017.04.007","text":"Publisher Index Page"},{"id":348051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -151.875,\n              58.6769376725869\n            ],\n            [\n              -143.08593749999997,\n              58.6769376725869\n            ],\n            [\n              -143.08593749999997,\n              62.30879369102805\n            ],\n            [\n              -151.875,\n              62.30879369102805\n            ],\n            [\n              -151.875,\n              58.6769376725869\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"147","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fadd1de4b0531197b13c60","contributors":{"authors":[{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ballachey, Brenda E. 0000-0003-1855-9171 bballachey@usgs.gov","orcid":"https://orcid.org/0000-0003-1855-9171","contributorId":2966,"corporation":false,"usgs":true,"family":"Ballachey","given":"Brenda","email":"bballachey@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matkin, Craig O.","contributorId":192145,"corporation":false,"usgs":false,"family":"Matkin","given":"Craig","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":718636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cushing, Daniel","contributorId":199323,"corporation":false,"usgs":false,"family":"Cushing","given":"Daniel","affiliations":[],"preferred":false,"id":718637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaler, Robert","contributorId":199324,"corporation":false,"usgs":false,"family":"Kaler","given":"Robert","email":"","affiliations":[],"preferred":false,"id":718638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology 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,{"id":70194480,"text":"70194480 - 2018 - Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change","interactions":[],"lastModifiedDate":"2017-11-29T12:39:47","indexId":"70194480","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change","docAbstract":"<p><span>Climate-change driven increases in water temperature pose challenges for aquatic organisms. Predictions of impacts typically do not account for fine-grained spatiotemporal thermal patterns in rivers. Patches of cooler water could serve as refuges for anadromous species like salmon that migrate during summer. We used high-resolution remotely sensed water temperature data to characterize summer thermal heterogeneity patterns for 11,308&nbsp;km of second–seventh-order rivers throughout the Pacific Northwest and northern California (USA). We evaluated (1) water temperature patterns at different spatial resolutions, (2) the frequency, size, and spacing of cool thermal patches suitable for Pacific salmon (i.e., contiguous stretches ≥ 0.25&nbsp;km, ≤ 15&nbsp;°C&nbsp;and ≥ 2&nbsp;°C, aooler than adjacent water), and (3) potential influences of climate change on availability of cool patches. Thermal heterogeneity was nonlinearly related to the spatial resolution of water temperature data, and heterogeneity at fine resolution (&lt; 1&nbsp;km) would have been difficult to quantify without spatially continuous data. Cool patches were generally &gt; 2.7 and &lt; 13.0&nbsp;km long, and spacing among patches was generally &gt; 5.7 and &lt; 49.4&nbsp;km. Thermal heterogeneity varied among rivers, some of which had long uninterrupted stretches of warm water ≥ 20&nbsp;°C, and others had many smaller cool patches. Our models predicted little change in future thermal heterogeneity among rivers, but within-river patterns sometimes changed markedly compared to contemporary patterns. These results can inform long-term monitoring programs as well as near-term climate-adaptation strategies.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00027-017-0557-9","usgsCitation":"Fullerton, A., Torgersen, C.E., Lawer, J., Steel, E.A., Ebersole, J.L., and Lee, S., 2018, Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change: Aquatic Sciences, v. 80, https://doi.org/10.1007/s00027-017-0557-9.","productDescription":"Article 3; 15p.","startPage":"15","ipdsId":"IP-090182","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":469171,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5854952","text":"External Repository"},{"id":349527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.71679687499999,\n              39\n            ],\n            [\n              -112.763671875,\n              39\n            ],\n            [\n              -112.763671875,\n              49.081062364320736\n            ],\n            [\n              -124.71679687499999,\n              49.081062364320736\n            ],\n            [\n              -124.71679687499999,\n              39\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"80","edition":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-21","publicationStatus":"PW","scienceBaseUri":"5a60fad8e4b06e28e9c227d5","contributors":{"authors":[{"text":"Fullerton, A.H.","contributorId":200991,"corporation":false,"usgs":false,"family":"Fullerton","given":"A.H.","email":"","affiliations":[],"preferred":false,"id":724027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":724026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawer, J.J.","contributorId":200992,"corporation":false,"usgs":false,"family":"Lawer","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":724028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steel, E. A.","contributorId":200993,"corporation":false,"usgs":false,"family":"Steel","given":"E.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebersole, J. L.","contributorId":74221,"corporation":false,"usgs":false,"family":"Ebersole","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":724030,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, S.Y.","contributorId":200994,"corporation":false,"usgs":false,"family":"Lee","given":"S.Y.","email":"","affiliations":[],"preferred":false,"id":724031,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193229,"text":"70193229 - 2018 - Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act","interactions":[],"lastModifiedDate":"2018-01-05T14:21:05","indexId":"70193229","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act","docAbstract":"In 2014, the Fish and Wildlife Service (FWS) and National Marine Fisheries Service announced a new policy interpretation for the U.S. Endangered Species Act (ESA). According to the act, a species must be listed as threatened or endangered if it is determined to be threatened or endangered in a significant portion of its range (SPR). The 2014 policy seeks to provide consistency by establishing that a portion of the range should be considered significant if the associated individuals’ “removal would cause the entire species to become endangered or threatened.” We reviewed 20 quantitative techniques used to assess whether a portion of a species’ range is significant according to the new guidance. Our assessments are based on the 3R criteria—redundancy (i.e., buffering from catastrophe), resiliency (i.e., ability to withstand stochasticity), and representation (i.e., ability to evolve)—that the FWS uses to determine if a species merits listing. We identified data needs for each quantitative technique and considered which methods could be implemented given the data limitations typical of rare species. We also identified proxies for the 3Rs that may be used with limited data. To assess potential data availability, we evaluated 7 example species by accessing data in their species status assessments, which document all the information used during a listing decision. In all species, an SPR could be evaluated with at least one metric for each of the 3Rs robustly or with substantial assumptions. Resiliency assessments appeared most constrained by limited data, and many species lacked information on connectivity between subpopulations, genetic variation, and spatial variability in vital rates. These data gaps will likely make SPR assessments for species with complex life histories or that cross national boundaries difficult. Although we reviewed techniques for the ESA, other countries require identification of significant areas and could benefit from this research.","language":"English","publisher":"Wiley","doi":"10.1111/cobi.12963","usgsCitation":"Earl, J.E., Nicol, S., Wiederholt, R., Diffendorfer, J.E., Semmens, D.J., Flockhart, D.T., Mattsson, B., McCracken, G., Norris, D.R., Thogmartin, W.E., and Lopez-Hoffman, L., 2018, Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act: Conservation Biology, v. 32, no. 1, p. 35-49, https://doi.org/10.1111/cobi.12963.","productDescription":"15 p.","startPage":"35","endPage":"49","ipdsId":"IP-084220","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":347838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70193306,"text":"70193306 - 2018 - Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy)","interactions":[],"lastModifiedDate":"2017-12-11T13:20:42","indexId":"70193306","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy)","docAbstract":"<p id=\"sp0080\">A geochemical survey was carried out to investigate metal contamination in the Domizio Littoral and Agro Aversano area (Southern Italy) by means of soil, groundwater, human hair and corn samples. Pb isotope ratios were also determined to identify the sources of metals. Specifically, the investigation focused on topsoils (<i>n</i>&nbsp;=&nbsp;1064), groundwater (<i>n</i>&nbsp;=&nbsp;26), 25 human hair (<i>n</i>&nbsp;=&nbsp;24) and corn samples (<i>n</i>&nbsp;=&nbsp;13). Topsoils have been sampled and analysed in a previous study for 53 elements (including potentially harmful ones), and determined by ICP-MS after dissolving with aqua regia. Groundwater was analysed for 72 elements by ICP-MS and by ICP-ES. Samples of human hair were prepared and analysed for 16 elements by ICP-MS. Dried corn collected at several farms were also analysed for 53 elements by ICP-MS. The isotopic ratios of <sup>206</sup>Pb/<sup>207</sup>Pb and <sup>208</sup>Pb/<sup>207</sup>Pb in selected topsoil (<i>n</i>&nbsp;=&nbsp;24), groundwater (<i>n</i>&nbsp;=&nbsp;9), human hair (<i>n</i>&nbsp;=&nbsp;9) and corn (<i>n</i>&nbsp;=&nbsp;4) samples were analysed from both eluates and residues to investigate possible anthropogenic contamination and geogenic contributions. All data were processed and mapped by ArcGis software to produce interpolated maps and contamination factor maps of potentially harmful elements, in accordance with Italian Environmental Law (Legislative Decree 152/06). Results show that soil sampling sites are characterized by As, Cd, Co, Cr, Cu, Hg, Pb, Se, and Zn contents exceeding the action limits established for residential land use (RAL) and, in some cases, also the action limits for industrial land use (IAL) as established by Legislative Decree 152/06. A map of contamination factors and a map showing the degrees of contamination indicate that the areas in the municipalities of Acerra, Casoria and Giugliano have been affected by considerable anthropogenic-related pollution. To interpret the isotopic data and roughly estimate proportion of Pb from an anthropogenic source we broadly defined possible natural and anthropogenic Pb end-member fields based on literature data. For example, we summarized data for Vesuvius and Campi Flegrei volcanic rocks, gasoline, and aerosol deposits.</p><p id=\"sp0085\">Lead isotope data show mixing between geogenic and anthropogenic sources. Topsoil, groundwater, human hair and corn samples show a greater contribution from geogenic sources like the Yellow Tuff (from Campi Flegrei) and volcanic rocks from Mt. Vesuvius. Aerosols, fly ash and gasoline (anthropogenic sources) have also been contributors. In detail, 46% of the topsoil residues, 96% of topsoil leachates, 88% of groundwater, 90% of human hair, and 25% of corn samples indicate that &gt;&nbsp;50% percent of the lead in this area can be ascribed to anthropogenic activity.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2017.01.007","usgsCitation":"Rezza, C., Albanese, S., Ayuso, R.A., Lima, A., Sorvari, J., and De Vivo, B., 2018, Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy): Journal of Geochemical Exploration, v. 184, no. B, p. 318-332, https://doi.org/10.1016/j.gexplo.2017.01.007.","productDescription":"15 p.","startPage":"318","endPage":"332","ipdsId":"IP-078832","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":347919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Campania region","otherGeospatial":"Agro Aversano, Domizio Flegreo","volume":"184","issue":"B","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98ba7e4b0531197af9f9c","contributors":{"authors":[{"text":"Rezza, Carmela","contributorId":199318,"corporation":false,"usgs":false,"family":"Rezza","given":"Carmela","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albanese, Stefano","contributorId":199319,"corporation":false,"usgs":false,"family":"Albanese","given":"Stefano","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":718742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lima, Annamaria","contributorId":176910,"corporation":false,"usgs":false,"family":"Lima","given":"Annamaria","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sorvari, Jaana","contributorId":199320,"corporation":false,"usgs":false,"family":"Sorvari","given":"Jaana","email":"","affiliations":[{"id":6718,"text":"Aalto University, Finland","active":true,"usgs":false}],"preferred":false,"id":718744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"De Vivo, Benedetto","contributorId":199321,"corporation":false,"usgs":false,"family":"De Vivo","given":"Benedetto","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718745,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192852,"text":"70192852 - 2018 - Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management","interactions":[],"lastModifiedDate":"2018-08-10T13:45:32","indexId":"70192852","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management","docAbstract":"<p><span>Understanding the causes and consequences of component change in sagebrush steppe is crucial for evaluating ecosystem sustainability. The sagebrush (</span><i class=\"EmphasisTypeItalic \">Artemisia</i><span><span>&nbsp;</span>spp.) steppe ecosystem of the northwest USA has been impacted by the invasion of exotic grasses, increasing fire return intervals, changing land management practices, and fragmentation, often lowering the overall resilience to change. We utilized contemporary and historical Landsat imagery, field data, and regression tree models to produce fractional cover maps of rangeland components (shrub, sagebrush, herbaceous, bare ground, and litter) through the last 30&nbsp;years. Our main goals were to (1) investigate rangeland component trends over 30&nbsp;years, (2) evaluate the magnitude and direction of trends in components and climate drivers and their relationship, and (3) assess component trends influenced by climate. Results indicated that over the study period, shrub, sage, herbaceous, and litter cover decreased, while bare ground cover increased. Measured rates of change ranged from −&nbsp;0.14%&nbsp;decade</span><sup>−1</sup><span><span>&nbsp;</span>for shrub cover to 0.05%&nbsp;decade</span><sup>−1</sup><span><span>&nbsp;</span>for bare ground, whereas herbaceous and litter cover trends were negligible. Net landscape cover changes were consistent with expectations of climate change and disturbance producing a loss of biotic cover, and converting a portion of shrub and sagebrush to herbaceous cover. Overall, fire and related successional recovery was the greatest change agent for all components in terms of area and cover change, while increasing minimum temperature, at a rate of 0.66°C&nbsp;decade</span><sup>−1</sup><span>, was found to be the most significant climate driver.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-017-0191-3","usgsCitation":"Shi, H., Rigge, M.B., Homer, C.G., Xian, G.Z., Meyer, D., and Bunde, B., 2018, Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management: Ecosystems, v. 21, no. 5, p. 913-929, https://doi.org/10.1007/s10021-017-0191-3.","productDescription":"17 p.","startPage":"913","endPage":"929","ipdsId":"IP-079676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192361,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717213,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191451,"text":"fs20173069 - 2018 - Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers","interactions":[],"lastModifiedDate":"2021-10-26T16:14:19.190775","indexId":"fs20173069","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3069","title":"Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers","docAbstract":"<h1>Introduction</h1><p>The Everglades Depth Estimation Network (EDEN) provides scientists and resource managers with regional maps of daily water levels and depths in the freshwater part of the Greater Everglades landscape. The EDEN domain includes all or parts of five Water Conservation Areas, Big Cypress National Preserve, Pennsuco Wetlands, and Everglades National Park. Daily water-level maps are interpolated from water-level data at monitoring gages, and depth is estimated by using a digital elevation model of the land surface. Online datasets provide time series of daily water levels at gages and rainfall and evapotranspiration data (<a href=\"https://sofia.usgs.gov/eden/\" data-mce-href=\"https://sofia.usgs.gov/eden/\">https://sofia.usgs.gov/eden/</a>). These datasets are used by scientists and resource managers to guide large-scale field operations, describe hydrologic changes, and support biological and ecological assessments that measure ecosystem response to the implementation of the Comprehensive Everglades Restoration Plan. EDEN water-level data have been used in a variety of biological and ecological studies including (1) the health of American alligators as a function of water depth, (2) the variability of post-fire landscape dynamics in relation to water depth, (3) the habitat quality for wading birds with dynamic habitat selection, and (4) an evaluation of the habitat of the Cape Sable seaside sparrow.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173069","collaboration":"Prepared as part of the U.S. Geological Survey Greater Everglades Priority Ecosystems Science and in collaboration with the<br />U.S. Army Corps of Engineers as part of the Comprehensive Everglades Restoration Plan REstoration COordination and VERification (RECOVER) Program","usgsCitation":"Patino, Eduardo, Conrads, Paul, Swain, Eric, and Beerens, James, 2018, Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers (ver. 1.1, January 2018): U.S. Geological Survey Fact Sheet 2017–3069, 6 p., https://doi.org/10.3133/fs20173069.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-071266","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":347419,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3069/fs20173069.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3069"},{"id":347418,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3069/coverthb1.jpg"},{"id":350307,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2017/3069/versionHist.txt","size":"1 MB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.29083251953124,\n              26.684275490019488\n            ],\n            [\n              -80.45013427734375,\n              26.686729520004036\n            ],\n            [\n              -80.56549072265625,\n              26.350036674507894\n            ],\n            [\n              -81.68609619140624,\n              26.33280692289788\n            ],\n            [\n              -81.70257568359375,\n              26.143110637100634\n            ],\n            [\n              -81.91955566406249,\n              26.06418490332395\n            ],\n            [\n              -81.134033203125,\n              25.008461758688334\n            ],\n            [\n              -80.41168212890625,\n              25.17760219565174\n            ],\n            [\n              -80.49407958984375,\n              25.693513062561056\n            ],\n            [\n              -80.35400390625,\n              26.115985925333536\n            ],\n            [\n              -80.2935791015625,\n              26.185018250078308\n            ],\n            [\n              -80.233154296875,\n              26.362342068998764\n            ],\n            [\n              -80.2056884765625,\n              26.524650377182763\n            ],\n            [\n              -80.29083251953124,\n              26.684275490019488\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: October 30, 2017; Version 1.1","contact":"<p>Director, <a href=\"https://www2.usgs.gov/water/caribbeanflorida/\" data-mce-href=\"https://www2.usgs.gov/water/caribbeanflorida/\">Caribbean-Florida Water Science Center</a><br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559<br></p>","tableOfContents":"<ul><li>Introduction<br></li><li>EDEN Water-Level Model<br></li><li>EDEN Web Applications<br></li><li>Looking Forward to the Next Decade<br></li><li>References<br></li><li>Special Acknowledgment</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2017-10-30","revisedDate":"2018-01-05","noUsgsAuthors":false,"publicationDate":"2017-10-30","publicationStatus":"PW","scienceBaseUri":"59f83a30e4b063d5d30980ab","contributors":{"authors":[{"text":"Patino, Eduardo 0000-0003-1016-3658 epatino@usgs.gov","orcid":"https://orcid.org/0000-0003-1016-3658","contributorId":1743,"corporation":false,"usgs":true,"family":"Patino","given":"Eduardo","email":"epatino@usgs.gov","affiliations":[{"id":269,"text":"FLWSC-Ft. 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