{"pageNumber":"460","pageRowStart":"11475","pageSize":"25","recordCount":40783,"records":[{"id":70179090,"text":"70179090 - 2016 - Petroleum system model of the Upper Devonian-Lower Mississippian Bakken Formation in the northern Williston Basin, Saskatchewan, southwestern Manitoba, and southeastern Alberta, Canada","interactions":[],"lastModifiedDate":"2023-09-05T15:00:26.792955","indexId":"70179090","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Petroleum system model of the Upper Devonian-Lower Mississippian Bakken Formation in the northern Williston Basin, Saskatchewan, southwestern Manitoba, and southeastern Alberta, Canada","docAbstract":"<p>No abstract available.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Rocky Mountain Association of Geologists","usgsCitation":"Higley, D.K., and Gianoutsos, N.J., 2016, Petroleum system model of the Upper Devonian-Lower Mississippian Bakken Formation in the northern Williston Basin, Saskatchewan, southwestern Manitoba, and southeastern Alberta, Canada, chap. <i>of</i> Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook, p. 172-189.","productDescription":"18 p.","startPage":"172","endPage":"189","ipdsId":"IP-063119","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333117,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/publications"},{"id":333118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a48ce4b04df303d95812","contributors":{"authors":[{"text":"Higley, Debra K. 0000-0001-8024-9954 higley@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-9954","contributorId":152663,"corporation":false,"usgs":true,"family":"Higley","given":"Debra","email":"higley@usgs.gov","middleInitial":"K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":656024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":656025,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178626,"text":"70178626 - 2016 - Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2017-04-27T10:16:08","indexId":"70178626","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico","docAbstract":"<p><span>Coastal ecosystem management typically relies on subjective interpretation of scientific understanding, with limited methods for explicitly incorporating process knowledge into decisions that must meet multiple, potentially competing stakeholder objectives. Conversely, the scientific community lacks methods for identifying which advancements in system understanding would have the highest value to decision-makers. A case in point is barrier island restoration, where decision-makers lack tools to objectively use system understanding to determine how to optimally use limited contingency funds when project construction in this dynamic environment does not proceed as expected. In this study, collaborative structured decision-making (SDM) was evaluated as an approach to incorporate process understanding into mid-construction decisions and to identify priority gaps in knowledge from a management perspective. The focus was a barrier island restoration project at Ship Island, Mississippi, where sand will be used to close an extensive breach that currently divides the island. SDM was used to estimate damage that may occur during construction, and guide repair decisions within the confines of limited availability of sand and funding to minimize adverse impacts to project objectives. Sand was identified as more limiting than funds, and unrepaired major breaching would negatively impact objectives. Repairing minor damage immediately was determined to be generally more cost effective (depending on the longshore extent) than risking more damage to a weakened project. Key gaps in process-understanding relative to project management were identified as the relationship of island width to breach formation; the amounts of sand lost during breaching, lowering, or narrowing of the berm; the potential for minor breaches to self-heal versus developing into a major breach; and the relationship between upstream nourishment and resiliency of the berm to storms. This application is a prototype for using structured decision-making in support of engineering projects in dynamic environments where mid-construction decisions may arise; highlights uncertainty about barrier island physical processes that limit the ability to make robust decisions; and demonstrates the potential for direct incorporation of process-based models in a formal adaptive management decision framework.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2016.08.078","usgsCitation":"Dalyander, P.S., Meyers, M.B., Mattsson, B., Steyer, G., Godsey, E., McDonald, J., Byrnes, M.R., and Ford, M., 2016, Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico: Journal of Environmental Management, v. 183, no. 3, p. 497-509, https://doi.org/10.1016/j.jenvman.2016.08.078.","productDescription":"13 p.","startPage":"497","endPage":"509","ipdsId":"IP-068842","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2016.08.078","text":"Publisher Index Page"},{"id":331388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"East Ship Island, West Ship Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.000244140625,\n              30.18994745521063\n            ],\n            [\n              -89.000244140625,\n              30.267370168467806\n            ],\n            [\n              -88.85639190673828,\n              30.267370168467806\n            ],\n            [\n              -88.85639190673828,\n              30.18994745521063\n            ],\n            [\n              -89.000244140625,\n              30.18994745521063\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"183","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144dce4b04fc80e507373","contributors":{"authors":[{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":654607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyers, Michelle B. 0000-0002-5937-1012 mmeyers@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-1012","contributorId":5608,"corporation":false,"usgs":true,"family":"Meyers","given":"Michelle","email":"mmeyers@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":654608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mattsson, Brady","contributorId":59692,"corporation":false,"usgs":true,"family":"Mattsson","given":"Brady","affiliations":[],"preferred":false,"id":654609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":27797,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[],"preferred":false,"id":654610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godsey, Elizabeth","contributorId":177095,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","affiliations":[],"preferred":false,"id":654611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Justin","contributorId":171407,"corporation":false,"usgs":false,"family":"McDonald","given":"Justin","email":"","affiliations":[{"id":26898,"text":"University of Auckland, New Zealand","active":true,"usgs":false}],"preferred":false,"id":654612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Byrnes, Mark R.","contributorId":102504,"corporation":false,"usgs":true,"family":"Byrnes","given":"Mark","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":654613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ford, Mark","contributorId":177097,"corporation":false,"usgs":false,"family":"Ford","given":"Mark","email":"","affiliations":[],"preferred":false,"id":654614,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70178358,"text":"70178358 - 2016 - The 2016 groundwater flow model for Dane County, Wisconsin","interactions":[],"lastModifiedDate":"2017-01-03T14:13:59","indexId":"70178358","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":242,"text":"Bulletin","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"110","title":"The 2016 groundwater flow model for Dane County, Wisconsin","docAbstract":"<p>A new groundwater flow model for Dane County, Wisconsin, replaces an earlier model developed in the 1990s by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS). This modeling study was conducted cooperatively by the WGNHS and the USGS with funding from the Capital Area Regional Planning Commission (CARPC). Although the overall conceptual model of the groundwater system remains largely unchanged, the incorporation of newly acquired high-quality datasets, recent research findings, and improved modeling and calibration techniques have led to the development of a more detailed and sophisticated model representation of the groundwater system. The new model is three-dimensional and transient, and conceptualizes the county’s hydrogeology as a 12-layer system including all major unlithified and bedrock hydrostratigraphic units and two high-conductivity horizontal fracture zones. </p><p>Beginning from the surface down, the model represents the unlithified deposits as two distinct model layers (1 and 2). A single layer (3) simulates the Ordovician sandstone and dolomite of the Sinnipee, Ancell, and Prairie du Chien Groups. Sandstone of the Jordan Formation (layer 4) and silty dolostone of the St. Lawrence Formation (layer 5) each comprise separate model layers. The underlying glauconitic sandstone of the Tunnel City Group makes up three distinct layers: an upper aquifer (layer 6), a fracture feature (layer 7), and a lower aquifer (layer 8). The fracture layer represents a network of horizontal bedding-plane fractures that serve as a preferential pathway for groundwater flow. The model simulates the sandstone of the Wonewoc Formation as an upper aquifer (layer 9) with a bedding-plane fracture feature (layer 10) at its base. The Eau Claire aquitard (layer 11) includes shale beds within the upper portion of the Eau Claire Formation. This layer, along with overlying bedrock units, is mostly absent in the preglacially eroded valleys along the Yahara River valley and in northeastern Dane County. Layer 12 represents the Mount Simon sandstone as the lowermost model layer. It directly overlies the Precambrian crystalline basement rock, whose top surface forms the lower boundary of the model. </p><p>The model uses the USGS MODFLOW-NWT finite-difference code, a standalone version of MODFLOW-2005 that incorporates the Newton (NWT) solver. MODFLOW-NWT improves the handling of unconfined conditions by smoothing the transition from wet to dry cells. The model explicitly simulates groundwater–surface-water interaction with streamflow routing and lake-level fluctuation. Model input included published and unpublished hydrogeologic data from recent estimates of aquifer hydraulic conductivities. A spatial groundwater recharge distribution was obtained from a recent GIS-based, soil-water-balance model for Dane County. Groundwater withdrawals from pumping were simulated for 572 wells across the entire model domain, which includes Dane County and portions of seven neighboring counties—Columbia, Dodge, Green, Iowa, Jefferson, Lafayette, and Rock. These wells withdrew an average of 60 million gallons per day (mgd) over the 5-year period from 2006 through 2010. Within Dane County, 385 wells were simulated with an average withdrawal rate of 52 mgd.</p><p>Model calibration used the parameter estimation code PEST, and calibration targets included heads, stream and spring flows, lake levels, and borehole flows. Steady-state calibration focused on the period 2006 through 2010; the transient calibration focused on the 7-week drought period from late May through July 2012. </p><p>This model represents a significant step forward from previous work because of its finer grid resolution, improved hydrostratigraphic discretization, transient capabilities, and more sophisticated representation of surface-water features and multi-aquifer wells.</p><p>Potential applications of the model include evaluation of potential sites for and impacts of new high-capacity wells, development of wellhead protection plans, evaluating the effects of changing land use and climate on groundwater, and quantifying the relationships between groundwater and surface water.</p>","language":"English","publisher":"Wisconsin Geological and Natural History Survey","isbn":"978-0-88169-992-0","usgsCitation":"Parsen, M.J., Bradbury, K.R., Hunt, R.J., and Feinstein, D.T., 2016, The 2016 groundwater flow model for Dane County, Wisconsin: Bulletin 110, 56 p.","productDescription":"56 p.","numberOfPages":"64","ipdsId":"IP-071783","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":332790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330992,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.uwex.edu/dane-county-groundwater-model/"}],"country":"United States","state":"Wisconsin","county":"Dane County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.0094,43.286],[-89.0084,43.2555],[-89.0094,43.2],[-89.01,43.1131],[-89.0109,43.0849],[-89.0107,43.0271],[-89.0132,42.9353],[-89.013,42.8762],[-89.0119,42.8471],[-89.132,42.8479],[-89.2488,42.8478],[-89.3689,42.8484],[-89.3688,42.8575],[-89.4832,42.858],[-89.6026,42.8575],[-89.7196,42.8587],[-89.8377,42.8598],[-89.8375,42.9471],[-89.8386,43.0317],[-89.8384,43.1181],[-89.8394,43.205],[-89.8325,43.2123],[-89.825,43.2187],[-89.8175,43.226],[-89.8125,43.2342],[-89.8088,43.2369],[-89.8012,43.2365],[-89.7874,43.2356],[-89.771,43.237],[-89.7579,43.2379],[-89.7529,43.2443],[-89.7485,43.2507],[-89.7391,43.2548],[-89.7259,43.2644],[-89.7171,43.2739],[-89.714,43.2821],[-89.7165,43.2867],[-89.7235,43.2935],[-89.7209,43.2935],[-89.6008,43.2932],[-89.4819,43.2942],[-89.3617,43.2954],[-89.3624,43.2832],[-89.246,43.2834],[-89.1271,43.2827],[-89.0094,43.286]]]},\"properties\":{\"name\":\"Dane\",\"state\":\"WI\"}}]}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc695e4b0f5ce109fa953","contributors":{"authors":[{"text":"Parsen, Michael J.","contributorId":176845,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradbury, Kenneth R.","contributorId":49419,"corporation":false,"usgs":true,"family":"Bradbury","given":"Kenneth","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":657412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657414,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70179636,"text":"70179636 - 2016 - mizuRoute version 1: A river network routing tool for a continental domain water resources applications","interactions":[],"lastModifiedDate":"2017-01-09T11:33:05","indexId":"70179636","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"mizuRoute version 1: A river network routing tool for a continental domain water resources applications","docAbstract":"<p><span>This paper describes the first version of a stand-alone runoff routing tool, mizuRoute. The mizuRoute tool post-processes runoff outputs from any distributed hydrologic model or land surface model to produce spatially distributed streamflow at various spatial scales from headwater basins to continental-wide river systems. The tool can utilize both traditional grid-based river network and vector-based river network data. Both types of river network include river segment lines and the associated drainage basin polygons, but the vector-based river network can represent finer-scale river lines than the grid-based network. Streamflow estimates at any desired location in the river network can be easily extracted from the output of mizuRoute. The routing process is simulated as two separate steps. First, hillslope routing is performed with a gamma-distribution-based unit-hydrograph to transport runoff from a hillslope to a catchment outlet. The second step is river channel routing, which is performed with one of two routing scheme options: (1)&nbsp;a kinematic wave tracking (KWT) routing procedure; and (2)&nbsp;an impulse response function – unit-hydrograph (IRF-UH) routing procedure. The mizuRoute tool also includes scripts (python, NetCDF operators) to pre-process spatial river network data. This paper demonstrates mizuRoute's capabilities to produce spatially distributed streamflow simulations based on river networks from the United States Geological Survey (USGS) Geospatial Fabric (GF) data set in which over 54 000 river segments and their contributing areas are mapped across the contiguous United States (CONUS). A brief analysis of model parameter sensitivity is also provided. The mizuRoute tool can assist model-based water resources assessments including studies of the impacts of climate change on streamflow.</span></p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-9-2223-2016","usgsCitation":"Mizukami, N., Clark, M.P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R.J., Markstrom, S.L., Hay, L.E., Woods, R., Arnold, J.R., and Brekke, L.D., 2016, mizuRoute version 1: A river network routing tool for a continental domain water resources applications: Geoscientific Model Development, v. 9, p. 2223-2238, https://doi.org/10.5194/gmd-9-2223-2016.","productDescription":"16 p.","startPage":"2223","endPage":"2238","ipdsId":"IP-075055","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-9-2223-2016","text":"Publisher Index Page"},{"id":332987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-23","publicationStatus":"PW","scienceBaseUri":"5874b0ade4b0a829a320bb67","contributors":{"authors":[{"text":"Mizukami, Naoki","contributorId":178120,"corporation":false,"usgs":false,"family":"Mizukami","given":"Naoki","email":"","affiliations":[],"preferred":false,"id":657982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Martyn P.","contributorId":178121,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":657983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sampson, Kevin","contributorId":178122,"corporation":false,"usgs":false,"family":"Sampson","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":657984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nijssen, Bart","contributorId":178123,"corporation":false,"usgs":false,"family":"Nijssen","given":"Bart","email":"","affiliations":[],"preferred":false,"id":657985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mao, Yixin","contributorId":139783,"corporation":false,"usgs":false,"family":"Mao","given":"Yixin","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":657986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McMillan, Hilary","contributorId":176321,"corporation":false,"usgs":false,"family":"McMillan","given":"Hilary","email":"","affiliations":[],"preferred":false,"id":657987,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":657988,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":657989,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":657981,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Woods, Ross","contributorId":178124,"corporation":false,"usgs":false,"family":"Woods","given":"Ross","affiliations":[],"preferred":false,"id":657990,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Arnold, Jeffrey R.","contributorId":178125,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":657991,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Brekke, Levi D.","contributorId":178126,"corporation":false,"usgs":false,"family":"Brekke","given":"Levi","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":657992,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70192877,"text":"70192877 - 2016 - Panel regressions to estimate low-flow response to rainfall variability in ungaged basins","interactions":[],"lastModifiedDate":"2018-04-03T11:40:41","indexId":"70192877","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Panel regressions to estimate low-flow response to rainfall variability in ungaged basins","docAbstract":"<p><span>Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.</span></p>","language":"English","publisher":"AGU","doi":"10.1002/2016WR018718","usgsCitation":"Bassiouni, M., Vogel, R.M., and Archfield, S.A., 2016, Panel regressions to estimate low-flow response to rainfall variability in ungaged basins: Water Resources Research, v. 52, no. 12, p. 9470-9494, https://doi.org/10.1002/2016WR018718.","productDescription":"25 p.","startPage":"9470","endPage":"9494","ipdsId":"IP-064919","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":470377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr018718","text":"Publisher Index 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 \"}}]}","volume":"52","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-19","publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f12","contributors":{"authors":[{"text":"Bassiouni, Maoya 0000-0001-5795-9894 mbassiou@usgs.gov","orcid":"https://orcid.org/0000-0001-5795-9894","contributorId":4639,"corporation":false,"usgs":true,"family":"Bassiouni","given":"Maoya","email":"mbassiou@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":false,"id":717274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vogel, Richard M.","contributorId":66811,"corporation":false,"usgs":true,"family":"Vogel","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":717275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":717276,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70193163,"text":"70193163 - 2016 - Bayesian analysis of Jolly-Seber type models","interactions":[],"lastModifiedDate":"2017-11-20T15:57:21","indexId":"70193163","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1573,"text":"Environmental and Ecological Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian analysis of Jolly-Seber type models","docAbstract":"<p><span>We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (</span><i class=\"EmphasisTypeItalic \">Calidris pussila</i><span>) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10651-016-0352-0","usgsCitation":"Matechou, E., Nicholls, G.K., Morgan, B.J., Collazo, J., and Lyons, J.E., 2016, Bayesian analysis of Jolly-Seber type models: Environmental and Ecological Statistics, v. 23, no. 4, p. 531-547, https://doi.org/10.1007/s10651-016-0352-0.","productDescription":"17 p.","startPage":"531","endPage":"547","ipdsId":"IP-057563","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10651-016-0352-0","text":"Publisher Index Page"},{"id":349161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-04","publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f0f","contributors":{"authors":[{"text":"Matechou, Eleni","contributorId":200631,"corporation":false,"usgs":false,"family":"Matechou","given":"Eleni","email":"","affiliations":[],"preferred":false,"id":722930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicholls, Geoff K.","contributorId":200632,"corporation":false,"usgs":false,"family":"Nicholls","given":"Geoff","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":722931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgan, Byron J. T.","contributorId":200633,"corporation":false,"usgs":false,"family":"Morgan","given":"Byron","email":"","middleInitial":"J. T.","affiliations":[],"preferred":false,"id":722932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@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":718111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyons, James E. 0000-0002-9810-8751 jelyons@usgs.gov","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":177546,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"jelyons@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":722933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70185004,"text":"70185004 - 2016 - Latent spatial models and sampling design for landscape genetics","interactions":[],"lastModifiedDate":"2017-11-27T11:01:49","indexId":"70185004","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Latent spatial models and sampling design for landscape genetics","docAbstract":"<p><span>We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (</span><i>Centrocercus urophasianus</i><span>) population genetics in the western United States.</span></p>","language":"English","publisher":"Project Euclid","doi":"10.1214/16-AOAS929","usgsCitation":"Hanks, E., Hooten, M., Knick, S.T., Oyler-McCance, S.J., Fike, J.A., Cross, T.B., and Schwartz, M.K., 2016, Latent spatial models and sampling design for landscape genetics: Annals of Applied Statistics, v. 10, no. 2, p. 1041-1062, https://doi.org/10.1214/16-AOAS929.","productDescription":"22 p.","startPage":"1041","endPage":"1062","ipdsId":"IP-057367","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":470379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1214/16-aoas929","text":"Publisher Index Page"},{"id":337532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c90124e4b0849ce97abcc7","contributors":{"authors":[{"text":"Hanks, Ephraim M.","contributorId":104630,"corporation":false,"usgs":true,"family":"Hanks","given":"Ephraim M.","affiliations":[],"preferred":false,"id":684297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":683912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knick, Steven T. 0000-0003-4025-1704 steve_knick@usgs.gov","orcid":"https://orcid.org/0000-0003-4025-1704","contributorId":159,"corporation":false,"usgs":true,"family":"Knick","given":"Steven","email":"steve_knick@usgs.gov","middleInitial":"T.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":684298,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":684299,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fike, Jennifer A. fikej@usgs.gov","contributorId":4564,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer","email":"fikej@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":684300,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":684301,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schwartz, Michael K.","contributorId":102326,"corporation":false,"usgs":true,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":684302,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70193635,"text":"70193635 - 2016 - Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering","interactions":[],"lastModifiedDate":"2017-11-13T14:58:13","indexId":"70193635","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"title":"Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering","docAbstract":"<p><span>Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (</span><i>Odobenus rosmarus divergens</i><span>) and fishers (</span><i>Pekania pennanti</i><span>). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.</span></p>","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.160457","usgsCitation":"Sethi, S., Linden, D., Wenburg, J., Lewis, C., Lemons, P.R., Fuller, A.K., and Hare, M.P., 2016, Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering: Royal Society Open Science, v. 3, p. 1-14, https://doi.org/10.1098/rsos.160457.","productDescription":"Article 160457; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-076769","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470349,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.160457","text":"Publisher Index Page"},{"id":348725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f09","contributors":{"authors":[{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linden, Daniel","contributorId":199671,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel","affiliations":[],"preferred":false,"id":719698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wenburg, John","contributorId":199672,"corporation":false,"usgs":false,"family":"Wenburg","given":"John","affiliations":[],"preferred":false,"id":719699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Cara","contributorId":199673,"corporation":false,"usgs":false,"family":"Lewis","given":"Cara","affiliations":[],"preferred":false,"id":719700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lemons, Patrick R.","contributorId":11014,"corporation":false,"usgs":true,"family":"Lemons","given":"Patrick","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":719701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hare, Matthew P.","contributorId":171454,"corporation":false,"usgs":false,"family":"Hare","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719702,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70191724,"text":"70191724 - 2016 - MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA","interactions":[],"lastModifiedDate":"2017-10-25T12:30:57","indexId":"70191724","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1536,"text":"Environmental Entomology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (<i>Cephus cinctus</i>, Hymenoptera: Cephidae) in Colorado, USA","title":"MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA","docAbstract":"<p><span>Wheat stem sawfly (</span><i>Cephus cinctus</i><span><span>&nbsp;</span>Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of<span>&nbsp;</span></span><i>C. cinctus</i><span><span>&nbsp;</span>and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for<span>&nbsp;</span></span><i>C. cinctus</i><span><span>&nbsp;</span>in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with<span>&nbsp;</span></span><i>C. cinctus</i><span><span>&nbsp;</span>distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of<span>&nbsp;</span></span><i>C. cinctus</i><span><span>&nbsp;</span>establishment in currently uninfested regions.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvw095","usgsCitation":"Lestina, J., Cook, M., Kumar, S., Morisette, J.T., Ode, P.J., and Peirs, F., 2016, MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA: Environmental Entomology, v. 45, no. 6, p. 1343-1351, https://doi.org/10.1093/ee/nvw095.","productDescription":"9 p.","startPage":"1343","endPage":"1351","ipdsId":"IP-077680","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":347349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","volume":"45","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-22","publicationStatus":"PW","scienceBaseUri":"59f1a2a7e4b0220bbd9d9f72","contributors":{"authors":[{"text":"Lestina, Jordan","contributorId":197312,"corporation":false,"usgs":false,"family":"Lestina","given":"Jordan","email":"","affiliations":[],"preferred":false,"id":713173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Maxwell","contributorId":197313,"corporation":false,"usgs":false,"family":"Cook","given":"Maxwell","email":"","affiliations":[],"preferred":false,"id":713174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":713175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ode, Paul J.","contributorId":197314,"corporation":false,"usgs":false,"family":"Ode","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peirs, Frank","contributorId":197315,"corporation":false,"usgs":false,"family":"Peirs","given":"Frank","email":"","affiliations":[],"preferred":false,"id":713178,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191260,"text":"70191260 - 2016 - Geochemistry, Nd-Pb Isotopes, and Pb-Pb Ages of the Mesoproterozoic Pea Ridge Iron Oxide-Apatite–Rare Earth Element Deposit, Southeast Missouri","interactions":[],"lastModifiedDate":"2017-10-02T16:32:15","indexId":"70191260","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry, Nd-Pb Isotopes, and Pb-Pb Ages of the Mesoproterozoic Pea Ridge Iron Oxide-Apatite–Rare Earth Element Deposit, Southeast Missouri","docAbstract":"<p id=\"p-1\">Iron oxide-apatite and iron oxide-copper-gold deposits occur within ~1.48 to 1.47 Ga volcanic rocks of the St. Francois Mountains terrane near a regional boundary separating crustal blocks having contrasting depleted-mantle Sm-Nd model ages (T<sub>DM</sub>). Major and trace element analyses and Nd and Pb isotope data were obtained to characterize the Pea Ridge deposit, improve identification of exploration targets, and better understand the regional distribution of mineralization with respect to crustal blocks. The Pea Ridge deposit is spatially associated with felsic volcanic rocks and plutons. Mafic to intermediate-composition rocks are volumetrically minor. Data for major element variations are commonly scattered and strongly suggest element mobility. Ratios of relatively immobile elements indicate that the felsic rocks are evolved subalkaline dacite and rhyolite; the mafic rocks are basalt to basaltic andesite. Granites and rhyolites display geochemical features typical of rocks produced by subduction. Rare earth element (REE) variations for the rhyolites are diagnostic of rocks affected by hydrothermal alteration and associated REE mineralization. The magnetite-rich rocks and REE-rich breccias show similar REE and mantle-normalized trace element patterns.</p><p id=\"p-2\">Nd isotope compositions (age corrected) show that: (1) host rhyolites have<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>from 3.44 to 4.25 and T<sub>DM</sub><span>&nbsp;</span>from 1.51 to 1.59 Ga; (2) magnetite ore and specular hematite rocks display<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>from 3.04 to 4.21 and T<sub>DM</sub><span>&nbsp;</span>from 1.6 to 1.51 Ga, and<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>from 2.23 to 2.81, respectively; (3) REE-rich breccias have<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>from 3.04 to 4.11 and T<sub>DM</sub><span>&nbsp;</span>from 1.6 to 1.51 Ga; and (4) mafic to intermediate-composition rocks range in<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>from 2.35 to 3.66 and in T<sub>DM</sub><span>&nbsp;</span>from 1.66 to 1.56. The<span>&nbsp;</span><i>ɛ</i><sub>Nd</sub><span>&nbsp;</span>values of the magnetite and specular hematite samples show that the REE mineralization is magmatic; no evidence exists for major overprinting by younger, crustal meteoric fluids, or by externally derived Nd. Host rocks, breccias, and magnetite ore shared a common origin from a similar source.</p><p id=\"p-3\">Lead isotope ratios are diverse: (1) host rhyolite has<span>&nbsp;</span><sup>206</sup>Pb/<sup>204</sup>Pb from 24.261 to 50.091; (2) Pea Ridge and regional galenas have<span>&nbsp;</span><sup>206</sup>Pb/<sup>204</sup>Pb from 16.030 to 33.548; (3) REE-rich breccia, magnetite ore, and specular hematite rock are more radiogenic than galena; (4) REE-rich breccias have high<span>&nbsp;</span><sup>206</sup>Pb/<sup>204</sup>Pb (38.122–1277.61) compared to host rhyolites; and (5) REE-rich breccias are more radiogenic than magnetite ore and specular-hematite rock, having<span>&nbsp;</span><sup>206</sup>Pb/<sup>204</sup>Pb up to 230.65. Radiogenic<span>&nbsp;</span><sup>207</sup>Pb/<sup>206</sup>Pb age estimates suggest the following: (1) rhyolitic host rocks have ages of ~1.50 Ga, (2) magnetite ore is ~1.44 Ga, and (3) REE-rich breccias are ~1.48 Ga. These estimates are broadly consistent and genetically link the host rhyolite, REE-rich breccia, and magnetite ore as being contemporaneous.</p><p id=\"p-4\">Alteration style and mineralogical or textural distinctions among the magnetite-rich rocks and REE-rich breccias do not correlate with different isotopic sources. In our model, magmatic fluids leached metals from the coeval felsic rocks (rhyolites), which provided the metal source reflected in the compositions of the REE-rich breccias and mineralized rocks. This model allows for the likelihood of contributions from other genetically related felsic and intermediate to more mafic rocks stored deeper in the crust. The deposit thus records an origin as a magmatic-hydrothermal system that was not affected by Nd and Pb remobilization processes, particularly if these processes also triggered mixing with externally sourced metal-bearing fluids. The Pea Ridge deposit was part of a single, widespread, homogeneous mixing system that produced a uniform isotopic composition, thus representing an excellent example of an igneous-dominated system that generated coeval magmatism and REE mineralization. Geochemical features suggest that components in the Pea Ridge deposit originated from sources in an orogenic margin. Basaltic magmatism produced by mantle decompression melting provided heat for extracting melts from the middle or lower crust. Continual addition of mafic magmas to the base of the subcontinental lithosphere, in a back-arc setting, remelted calc-alkaline rocks enriched in metals that were stored in the crust.</p><p id=\"p-5\">The St. Francois Mountains terrane is adjacent to the regional T<sub>DM</sub><span>&nbsp;</span>line (defined at a value of 1.55 Ga) that separates ~1600 Ma basement to the west, from younger basements to the east. Data for Pea Ridge straddle the T<sub>DM</sub><span>&nbsp;</span>values proposed for the line. The Sm-Nd isotope system has been closed since formation of the deposit and the original igneous signatures have not been affected by cycles of alteration or superimposed mineralizing events. No evidence exists for externally derived Nd or Sm. The source region for metals within the Pea Ridge deposit had a moderate compositional variation and the REE-rich breccias and mineralized rocks are generally isotopically homogeneous. The Pea Ridge deposit thus constitutes a distinctive isotopic target for use as a model in identifying other mineralized systems that may share the same metal source in the St. Francois Mountains terrane and elsewhere in the eastern Granite-Rhyolite province.</p>","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1935","usgsCitation":"Ayuso, R.A., Slack, J.F., Day, W.C., and McCafferty, A.E., 2016, Geochemistry, Nd-Pb Isotopes, and Pb-Pb Ages of the Mesoproterozoic Pea Ridge Iron Oxide-Apatite–Rare Earth Element Deposit, Southeast Missouri: Economic Geology, v. 111, no. 8, p. 1935-1962, https://doi.org/10.2113/econgeo.111.8.1935.","productDescription":"28 p.","startPage":"1935","endPage":"1962","ipdsId":"IP-070054","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":346336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.5,\n              37\n            ],\n            [\n              -89,\n              37\n            ],\n            [\n              -89,\n              38.5\n            ],\n            [\n              -91.5,\n              38.5\n            ],\n            [\n              -91.5,\n              37\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"111","issue":"8","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"59d35028e4b05fe04cc34d5f","contributors":{"authors":[{"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":711725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":711726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":711727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":711728,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70191261,"text":"70191261 - 2016 - Abiotic raw-materials in life cycle impact assessments: An emerging consensus across disciplines","interactions":[],"lastModifiedDate":"2018-03-27T17:26:57","indexId":"70191261","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5507,"text":"Resources","printIssn":"2079-9276","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic raw-materials in life cycle impact assessments: An emerging consensus across disciplines","docAbstract":"<p><span>This paper captures some of the emerging consensus points that came out of the workshop “Mineral Resources in Life Cycle Impact Assessment: Mapping the path forward”, held at the Natural History Museum London on 14 October 2015: that current practices rely in many instances on obsolete data, often confuse resource depletion with impacts on resource availability, which can therefore provide inconsistent decision support and lead to misguided claims about environmental performance. Participants agreed it would be helpful to clarify which models estimate depletion and which estimate availability, so that results can be correctly reported in the most appropriate framework. Most participants suggested that resource availability will be more meaningfully addressed within a comprehensive Life Cycle Sustainability Assessment framework rather than limited to an environmental Life Cycle Assessment or Footprint. Presentations from each of the authors are available for download.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/resources5010012","usgsCitation":"Drielsma, J., Allington, R., Brady, T., Guinee, J., Hammarstrom, J.M., Hummen, T., Russell-Vaccari, A., Schneider, L., Sonnemann, G., and Weihed, P., 2016, Abiotic raw-materials in life cycle impact assessments: An emerging consensus across disciplines: Resources, v. 5, no. 1, Article 12; 10 p., https://doi.org/10.3390/resources5010012.","productDescription":"Article 12; 10 p.","ipdsId":"IP-071618","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":462005,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/resources5010012","text":"Publisher Index Page"},{"id":346314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-26","publicationStatus":"PW","scienceBaseUri":"59d35027e4b05fe04cc34d5c","contributors":{"authors":[{"text":"Drielsma, Johannes","contributorId":196826,"corporation":false,"usgs":false,"family":"Drielsma","given":"Johannes","affiliations":[],"preferred":false,"id":711730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allington, Ruth","contributorId":196827,"corporation":false,"usgs":false,"family":"Allington","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":711731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brady, Thomas","contributorId":196828,"corporation":false,"usgs":false,"family":"Brady","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":711732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guinee, Jeroen","contributorId":196829,"corporation":false,"usgs":false,"family":"Guinee","given":"Jeroen","email":"","affiliations":[],"preferred":false,"id":711733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammarstrom, Jane M. 0000-0003-2742-3460 jhammars@usgs.gov","orcid":"https://orcid.org/0000-0003-2742-3460","contributorId":1226,"corporation":false,"usgs":true,"family":"Hammarstrom","given":"Jane","email":"jhammars@usgs.gov","middleInitial":"M.","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":711729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hummen, Torsten","contributorId":196830,"corporation":false,"usgs":false,"family":"Hummen","given":"Torsten","email":"","affiliations":[],"preferred":false,"id":711734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Russell-Vaccari, Andrea","contributorId":196831,"corporation":false,"usgs":false,"family":"Russell-Vaccari","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":711735,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schneider, Laura","contributorId":196832,"corporation":false,"usgs":false,"family":"Schneider","given":"Laura","email":"","affiliations":[],"preferred":false,"id":711736,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sonnemann, Guido","contributorId":196833,"corporation":false,"usgs":false,"family":"Sonnemann","given":"Guido","email":"","affiliations":[],"preferred":false,"id":711737,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weihed, Par","contributorId":196834,"corporation":false,"usgs":false,"family":"Weihed","given":"Par","email":"","affiliations":[],"preferred":false,"id":711738,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70187209,"text":"70187209 - 2016 - Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats","interactions":[],"lastModifiedDate":"2017-04-26T12:50:55","indexId":"70187209","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":770,"text":"Animal Behaviour","active":true,"publicationSubtype":{"id":10}},"title":"Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats","docAbstract":"<p><span>Fission–fusion dynamics appear common among temperate bats where females form roost groups that change in size and composition, as females switch roosts almost daily. One hypothesis for frequent roost switching is that females move to find suitable thermal conditions as ambient conditions change. Tests of this hypothesis have, however, been conducted mostly at roosts in artificial structures where microclimate is relatively stable. The goal of our study was to determine whether roost switching and roost use by northern long-eared bats, </span><i>Myotis septentrionalis</i><span>, that roost in trees are related to ambient conditions. We used generalized linear fixed effects models to explore the influence of roost characteristics and changes in ambient conditions on the likelihood of roost switching. We used canonical correlation analyses to examine the relationship between ambient conditions and roost characteristics. Roost switching was indeed linked to ambient conditions together with characteristics of roosts on the previous day; the best descriptors of roost switching differed between the two geographical regions we analysed. In Nova Scotia, females were less likely to switch roosts when it rained, particularly if they were in roosts below surrounding canopy whereas they were more likely to switch roosts when they were in roosts of high decay. Females roosted in shorter trees in earlier decay classes on warm days, as well as on windy and rainy days. In Kentucky, females were more likely to switch roosts at high temperatures, particularly when they were in roosts in high decay. Females roosted in shorter, decayed trees on warm days, and in less decayed trees with small diameter on windy and rainy days. Our results suggest bats switch roosts in response to changes in ambient conditions to select suitable roosting conditions, which may explain some of the proximate factors shaping fission–fusion dynamics of bats.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2016.09.022","usgsCitation":"Patriquin, K.J., Leonard, M.L., Broders, H.G., Ford, W.M., Britzke, E.R., and Silvis, A., 2016, Weather as a proximate explanation for fission–fusion dynamics in female northern long-eared bats: Animal Behaviour, v. 122, p. 47-57, https://doi.org/10.1016/j.anbehav.2016.09.022.","productDescription":"11 p.","startPage":"47","endPage":"57","ipdsId":"IP-071165","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5901b1bbe4b0c2e071a99b9a","contributors":{"authors":[{"text":"Patriquin, Krista J.","contributorId":191434,"corporation":false,"usgs":false,"family":"Patriquin","given":"Krista","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":693039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, Marty L.","contributorId":191435,"corporation":false,"usgs":false,"family":"Leonard","given":"Marty","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broders, Hugh G.","contributorId":191436,"corporation":false,"usgs":false,"family":"Broders","given":"Hugh","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":693041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark wford@usgs.gov","contributorId":3858,"corporation":false,"usgs":true,"family":"Ford","given":"W.","email":"wford@usgs.gov","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britzke, Eric R.","contributorId":8327,"corporation":false,"usgs":true,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":693042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":693043,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193722,"text":"70193722 - 2016 - A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm","interactions":[],"lastModifiedDate":"2017-11-04T13:27:11","indexId":"70193722","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm","docAbstract":"<p>In microseismicity analyses, reliable focal mechanisms can typically be obtained for only a small subset of located events. We address this limitation here, presenting a framework for determining robust focal mechanisms for entire populations of very small events. To achieve this, we resolve relative <i>P</i> and <i>S</i> wave polarities between pairs of waveforms by using their signed correlation coefficients—a by-product of previously performed precise earthquake relocation. We then use cluster analysis to group events with similar patterns of polarities across the network. Finally, we apply a standard mechanism inversion to the grouped data, using either catalog or correlation-derived <i>P</i> wave polarity data sets. This approach has great potential for enhancing analyses of spatially concentrated microseismicity such as earthquake swarms, mainshock-aftershock sequences, and industrial reservoir stimulation or injection-induced seismic sequences. To demonstrate its utility, we apply this technique to the 2014 Long Valley Caldera earthquake swarm. In our analysis, 85% of the events (7212 out of 8494 located by <i>Shelly et al</i>. [2016]) fall within five well-constrained mechanism clusters, more than 12 times the number with network-determined mechanisms. Of the earthquakes we characterize, 3023 (42%) have magnitudes smaller than 0.0. We find that mechanism variations are strongly associated with corresponding hypocentral structure, yet mechanism heterogeneity also occurs where it cannot be resolved by hypocentral patterns, often confined to small-magnitude events. Small (5–20°) rotations between mechanism orientations and earthquake location trends persist when we apply 3-D velocity models and might reflect a geometry of en echelon, interlinked shear, and dilational faulting.</p>","language":"English","publisher":"AGU","doi":"10.1002/2016JB013437","usgsCitation":"Shelly, D.R., Hardebeck, J.L., Ellsworth, W.L., and Hill, D.P., 2016, A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm: Journal of Geophysical Research B: Solid Earth, v. 121, no. 12, p. 8622-8641, https://doi.org/10.1002/2016JB013437.","productDescription":"20 p.","startPage":"8622","endPage":"8641","ipdsId":"IP-078358","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":348194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Long Valley Caldera","volume":"121","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-03","publicationStatus":"PW","scienceBaseUri":"59fedfb4e4b0531197b573c4","contributors":{"authors":[{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":720060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780 jhardebeck@usgs.gov","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":841,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"jhardebeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":720061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":720062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hill, David P. hill@usgs.gov","contributorId":2600,"corporation":false,"usgs":true,"family":"Hill","given":"David","email":"hill@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":720063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70179656,"text":"70179656 - 2016 - Development of an adaptive harvest management program for Taiga bean geese","interactions":[],"lastModifiedDate":"2017-01-20T10:17:45","indexId":"70179656","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Development of an adaptive harvest management program for Taiga bean geese","docAbstract":"This report describes recent progress in specifying the elements of an adaptive harvest program for taiga bean goose. It describes harvest levels appropriate for first rebuilding the population of the Central Management Unit and then maintaining it near the goal specified in the AEWA International Single Species Action Plan (ISSAP). This report also provides estimates of the length of time it would take under ideal conditions (no density dependence and no harvest) to rebuild depleted populations in the Western and Eastern Management Units. We emphasize that our estimates are a first approximation because detailed demographic information is lacking for taiga bean geese. Using allometric relationships, we estimated parameters of a thetalogistic matrix population model. The mean intrinsic rate of growth was estimated as r = 0.150 (90% credible interval: 0.120 – 0.182). We estimated the mean form of density dependence as   2.361 (90% credible interval: 0.473 – 11.778), suggesting the strongest density dependence occurs when the population is near its carrying capacity. Based on expert opinion, carrying capacity (i.e., population size expected in the absence of hunting) for the Central Management Unit was estimated as K  87,900 (90% credible interval: 82,000 – 94,100). The ISSAP specifies a population goal for the Central Management Unit of 60,000 – 80,000 individuals in winter; thus, we specified a preliminary objective function as one which would minimize the difference between this goal and population size. Using the concept of stochastic dominance to explicitly account for uncertainty in demography, we determined that optimal harvest rates for 5, 10, 15, and 20-year time horizons were h = 0.00, 0.02, 0.05, and 0.06, respectively. These optima represent a tradeoff between the harvest rate and the time required to achieve and maintain a population size within desired bounds. We recognize, however, that regulation of absolute harvest rather than harvest rate is more practical, but our matrix model does not permit one to calculate an exact harvest associated with a specific harvest rate. Approximate harvests for current population size in the Central Management Unit are 0, 1,200, 2,300, and 3,500 for the 5, 10, 15, and 20-year time horizons, respectively. Populations of taiga bean geese in the Western and Eastern Units would require at least 10 and 13 years, respectively, to reach their minimum goals under the most optimistic of scenarios. The presence of harvest, density dependence, or environmental variation could extend these time frames considerably. Finally, we stress that development and implementation of internationally coordinated monitoring programs will be essential to further development and implementation of an adaptive harvest management program.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"First meeting of the AEWA European Goose Management International Working Group","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"First meeting of the AEWA European Goose Management International Working Group","conferenceDate":"December 14-16, 2016","conferenceLocation":"Kristianstad, Sweden","language":"English","publisher":"Agreement on the Conservation of African-Eurasian Migratory Waterbirds","usgsCitation":"Johnson, F.A., Alhainen, M., Fox, A.D., and Madsen, J., 2016, Development of an adaptive harvest management program for Taiga bean geese, <i>in</i> First meeting of the AEWA European Goose Management International Working Group, Kristianstad, Sweden, December 14-16, 2016, p. 1-28.","productDescription":"28 p.","startPage":"1","endPage":"28","ipdsId":"IP-077422","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":333524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":333022,"type":{"id":15,"text":"Index Page"},"url":"https://www.unep-aewa.org/en/document/development-adaptive-harvest-management-program-taiga-bean-geese"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58833022e4b0d0023163778c","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":658095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alhainen, Mikko","contributorId":141140,"corporation":false,"usgs":false,"family":"Alhainen","given":"Mikko","email":"","affiliations":[{"id":13690,"text":"Finnish Wildlife Agency","active":true,"usgs":false}],"preferred":false,"id":658101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fox, Anthony D.","contributorId":130960,"corporation":false,"usgs":false,"family":"Fox","given":"Anthony","email":"","middleInitial":"D.","affiliations":[{"id":7177,"text":"Dept of Bioscience, Aahus Univ, Denmark","active":true,"usgs":false}],"preferred":false,"id":658102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":658103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70179443,"text":"70179443 - 2016 - Modeling and simulation of storm surge on Staten Island to understand inundation mitigation strategies","interactions":[],"lastModifiedDate":"2017-01-03T11:32:41","indexId":"70179443","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Modeling and simulation of storm surge on Staten Island to understand inundation mitigation strategies","docAbstract":"<p><span>Hurricane Sandy made landfall on October 29, 2012, near Brigantine, New Jersey, and had a transformative impact on Staten Island and the New York Metropolitan area. Of the 43 New York City fatalities, 23 occurred on Staten Island. The borough, with a population of approximately 500,000, experienced some of the most devastating impacts of the storm. Since Hurricane Sandy, protective dunes have been constructed on the southeast shore of Staten Island. ADCIRC+SWAN model simulations run on The City University of New York's Cray XE6M, housed at the College of Staten Island, using updated topographic data show that the coast of Staten Island is still susceptible to tidal surge similar to those generated by Hurricane Sandy. Sandy hindcast simulations of storm surges focusing on Staten Island are in good agreement with observed storm tide measurements. Model results calculated from fine-scaled and coarse-scaled computational grids demonstrate that finer grids better resolve small differences in the topography of critical hydraulic control structures, which affect storm surge inundation levels. The storm surge simulations, based on post-storm topography obtained from high-resolution lidar, provide much-needed information to understand Staten Island's changing vulnerability to storm surge inundation. The results of fine-scale storm surge simulations can be used to inform efforts to improve resiliency to future storms. For example, protective barriers contain planned gaps in the dunes to provide for beach access that may inadvertently increase the vulnerability of the area.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI76-013","usgsCitation":"Kress, M.E., Benimoff, A.I., Fritz, W.J., Thatcher, C.A., Blanton, B.O., and Dzedzits, E., 2016, Modeling and simulation of storm surge on Staten Island to understand inundation mitigation strategies: Journal of Coastal Research, v. Special Issue 76, p. 149-161, https://doi.org/10.2112/SI76-013.","productDescription":"13 p.","startPage":"149","endPage":"161","ipdsId":"IP-062638","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":462021,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.17615/7b0p-2a36","text":"External Repository"},{"id":332735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Staten Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.13625717163086,\n              40.52593506631396\n            ],\n            [\n              -74.04544830322264,\n              40.605090749765786\n            ],\n            [\n              -74.06364440917969,\n              40.61890405098613\n            ],\n            [\n              -74.1525650024414,\n              40.53806878053114\n            ],\n            [\n              -74.13625717163086,\n              40.52593506631396\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"Special Issue 76","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc695e4b0f5ce109fa94f","contributors":{"authors":[{"text":"Kress, Michael E.","contributorId":177814,"corporation":false,"usgs":false,"family":"Kress","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":657220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benimoff, Alan I.","contributorId":177815,"corporation":false,"usgs":false,"family":"Benimoff","given":"Alan","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":657221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritz, William J.","contributorId":177816,"corporation":false,"usgs":false,"family":"Fritz","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thatcher, Cindy A. 0000-0003-0331-071X thatcherc@usgs.gov","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":2868,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"thatcherc@usgs.gov","middleInitial":"A.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":657223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blanton, Brian O.","contributorId":177817,"corporation":false,"usgs":false,"family":"Blanton","given":"Brian","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":657224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dzedzits, Eugene","contributorId":177818,"corporation":false,"usgs":false,"family":"Dzedzits","given":"Eugene","email":"","affiliations":[],"preferred":false,"id":657225,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70179442,"text":"70179442 - 2016 - Creating a Coastal National Elevation Database (CoNED) for science and conservation applications","interactions":[],"lastModifiedDate":"2022-04-22T14:43:57.030153","indexId":"70179442","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Creating a Coastal National Elevation Database (CoNED) for science and conservation applications","docAbstract":"<p><span>The U.S. Geological Survey is creating the Coastal National Elevation Database, an expanding set of topobathymetric elevation models that extend seamlessly across coastal regions of high societal or ecological significance in the United States that are undergoing rapid change or are threatened by inundation hazards. Topobathymetric elevation models are raster datasets useful for inundation prediction and other earth science applications, such as the development of sediment-transport and storm surge models. These topobathymetric elevation models are being constructed by the broad regional assimilation of numerous topographic and bathymetric datasets, and are intended to fulfill the pressing needs of decision makers establishing policies for hazard mitigation and emergency preparedness, coastal managers tasked with coastal planning compatible with predictions of inundation due to sea-level rise, and scientists investigating processes of coastal geomorphic change. A key priority of this coastal elevation mapping effort is to foster collaborative lidar acquisitions that meet the standards of the USGS National Geospatial Program's 3D Elevation Program, a nationwide initiative to systematically collect high-quality elevation data. The focus regions are located in highly dynamic environments, for example in areas subject to shoreline change, rapid wetland loss, hurricane impacts such as overwash and wave scouring, and/or human-induced changes to coastal topography.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI76-007","usgsCitation":"Thatcher, C.A., Brock, J., Danielson, J.J., Poppenga, S.K., Gesch, D.B., Palaseanu-Lovejoy, M., Barras, J., Evans, G.A., and Gibbs, A., 2016, Creating a Coastal National Elevation Database (CoNED) for science and conservation applications: Journal of Coastal Research, v. Special Issue 76, p. 64-74, https://doi.org/10.2112/SI76-007.","productDescription":"11 p.","startPage":"64","endPage":"74","ipdsId":"IP-065916","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470361,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.2112/SI76-007","text":"External Repository"},{"id":332736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"Special Issue 76","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc695e4b0f5ce109fa951","contributors":{"authors":[{"text":"Thatcher, Cindy A. 0000-0003-0331-071X thatcherc@usgs.gov","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":2868,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"thatcherc@usgs.gov","middleInitial":"A.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":657210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brock, John 0000-0002-5289-9332 jbrock@usgs.gov","orcid":"https://orcid.org/0000-0002-5289-9332","contributorId":2261,"corporation":false,"usgs":true,"family":"Brock","given":"John","email":"jbrock@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":657211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","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":657212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poppenga, Sandra K. 0000-0002-2846-6836 spoppenga@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-6836","contributorId":3327,"corporation":false,"usgs":true,"family":"Poppenga","given":"Sandra","email":"spoppenga@usgs.gov","middleInitial":"K.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"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":657213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"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":657214,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":657215,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barras, John 0000-0002-4207-2972 jbarras@usgs.gov","orcid":"https://orcid.org/0000-0002-4207-2972","contributorId":177812,"corporation":false,"usgs":true,"family":"Barras","given":"John","email":"jbarras@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":657216,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evans, Gayla A. 0000-0001-5072-4232 gevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-4232","contributorId":3125,"corporation":false,"usgs":true,"family":"Evans","given":"Gayla","email":"gevans@usgs.gov","middleInitial":"A.","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":657217,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gibbs, Ann agibbs@usgs.gov","contributorId":177813,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","affiliations":[],"preferred":true,"id":657218,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70184183,"text":"70184183 - 2016 - Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America","interactions":[],"lastModifiedDate":"2018-03-27T09:54:46","indexId":"70184183","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America","docAbstract":"Fire refugia, sometimes referred to as fire islands, shadows, skips, residuals, or fire remnants, are an important element of the burn mosaic, but we lack a quantitative framework that links observations of fire refugia from different environmental contexts. Here, we develop and test a conceptual model for how predictability of fire refugia varies according to topographic complexity and fire weather conditions. Refugia were quantified as areas unburned or burned at comparatively low severity based on remotely sensed burn severity data. We assessed the relationship between refugia and a suite of terrain-related explanatory metrics by fitting a collection of boosted regression tree models. The models were developed\nfor seven study fires that burned in conifer-dominated forested landscapes of the Western Cordillera of Canada between 2001 and 2014. We fit nine models, each for distinct levels of fire weather and terrain ruggedness. Our framework revealed that the predictability and abundance of fire refugia varied among these environmental settings. We observed highest predictability under moderate fire weather conditions and moderate terrain ruggedness (ROC-AUC = 0.77), and lowest predictability in flatter landscapes and under high fire weather conditions (ROC-AUC = 0.63–0.68). Catchment slope, local aspect, relative position, topographic wetness, topographic convergence, and local slope all contributed to discriminating where refugia occur but the relative importance of these topographic controls differed among environments. Our framework allows us to characterize the predictability of contemporary fire refugia across multiple environmental settings and provides important insights for ecosystem resilience, wildfire management, conservation planning, and climate change adaptation.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1632","usgsCitation":"Krawchuk, M.A., Haire, S.L., Coop, J.D., Parisien, M., Whitman, E., Chong, G.W., and Miller, C., 2016, Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America: Ecosphere, v. 7, no. 12, p. 1-18, https://doi.org/10.1002/ecs2.1632.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-074382","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":462015,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1632","text":"Publisher Index Page"},{"id":336773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-29","publicationStatus":"PW","scienceBaseUri":"58b7eba4e4b01ccd5500baeb","contributors":{"authors":[{"text":"Krawchuk, Meg A.","contributorId":187425,"corporation":false,"usgs":false,"family":"Krawchuk","given":"Meg","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":680389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haire, Sandra L.","contributorId":187426,"corporation":false,"usgs":false,"family":"Haire","given":"Sandra","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coop, Jonathan D.","contributorId":187427,"corporation":false,"usgs":false,"family":"Coop","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parisien, Marc-André","contributorId":187428,"corporation":false,"usgs":false,"family":"Parisien","given":"Marc-André","affiliations":[],"preferred":false,"id":680392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Ellen","contributorId":187429,"corporation":false,"usgs":false,"family":"Whitman","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":680393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chong, Geneva W. 0000-0003-3883-5153 geneva_chong@usgs.gov","orcid":"https://orcid.org/0000-0003-3883-5153","contributorId":419,"corporation":false,"usgs":true,"family":"Chong","given":"Geneva","email":"geneva_chong@usgs.gov","middleInitial":"W.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":680388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Carol","contributorId":187430,"corporation":false,"usgs":false,"family":"Miller","given":"Carol","email":"","affiliations":[],"preferred":false,"id":680394,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70178940,"text":"70178940 - 2016 - Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and Greater sage-grouse in their eastern range: A strategic multi-scale approach","interactions":[],"lastModifiedDate":"2017-11-22T12:13:12","indexId":"70178940","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-GTR-356","title":"Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and Greater sage-grouse in their eastern range: A strategic multi-scale approach","docAbstract":"This report provides a strategic approach developed by a Western Association of Fish and Wildlife Agencies interagency working group for conservation of sagebrush ecosystems, Greater sage-grouse, and Gunnison sage-grouse. It uses information on (1) factors that influence sagebrush ecosystem resilience to disturbance and resistance to nonnative invasive annual grasses and (2) distribution and relative abundance of sage-grouse populations to address persistent ecosystem threats, such as invasive annual grasses and wildfire, and land use and development threats, such as oil and gas development and cropland conversion, to develop effective management strategies. A sage-grouse habitat matrix links relative resilience and resistance of sagebrush ecosystems with modeled sage-grouse breeding habitat probabilities to help decisionmakers assess risks and determine appropriate management strategies at both landscape and site scales. Areas for targeted management are assessed by overlaying matrix components with Greater sage-grouse Priority Areas for Conservation and Gunnison sage-grouse critical habitat and linkages, breeding bird concentration areas, and specific habitat threats. Decision tools are discussed for determining the suitability of target areas for management and the most appropriate management actions. A similar approach was developed for the Great Basin that was incorporated into the Federal land use plan amendments and served as the basis of a Bureau of Land Management Fire and Invasives Assessment Tool, which was used to prioritize sage-grouse habitat for targeted management activities.","language":"English","publisher":"U.S. Forest Service","usgsCitation":"Chambers, J., Beck, J.L., Campbell, S., Carlson, J., Christiansen, T.J., Clause, K.J., Dinkins, J.B., Doherty, K., Griffin, K.A., Havlina, D.W., Mayer, K.F., Hennig, J.D., Kurth, L.L., Maestas, J.D., Manning, M., Mealor, B.A., McCarthy, C., Perea, M.A., and Pyke, D.A., 2016, Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and Greater sage-grouse in their eastern range: A strategic multi-scale approach: General Technical Report RMRS-GTR-356, iv., 143 p.","productDescription":"iv., 143 p.","ipdsId":"IP-076666","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":332336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331932,"type":{"id":15,"text":"Index Page"},"url":"https://www.treesearch.fs.fed.us/pubs/53201"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585a51bde4b01224f329b5e3","contributors":{"authors":[{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":656231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Jeffrey L.","contributorId":14753,"corporation":false,"usgs":true,"family":"Beck","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":656232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Steve","contributorId":116058,"corporation":false,"usgs":true,"family":"Campbell","given":"Steve","affiliations":[],"preferred":false,"id":656233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson, John","contributorId":177563,"corporation":false,"usgs":false,"family":"Carlson","given":"John","affiliations":[],"preferred":false,"id":656234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christiansen, Thomas J.","contributorId":24237,"corporation":false,"usgs":true,"family":"Christiansen","given":"Thomas","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":656235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clause, Karen J.","contributorId":177564,"corporation":false,"usgs":false,"family":"Clause","given":"Karen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":656236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dinkins, Jonathan B.","contributorId":177565,"corporation":false,"usgs":false,"family":"Dinkins","given":"Jonathan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":656237,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Doherty, Kevin E.","contributorId":99490,"corporation":false,"usgs":true,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":656238,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Griffin, Kathleen A.","contributorId":177566,"corporation":false,"usgs":false,"family":"Griffin","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":656239,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Havlina, Douglas W.","contributorId":177567,"corporation":false,"usgs":false,"family":"Havlina","given":"Douglas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":656240,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mayer, Kenneth F.","contributorId":117083,"corporation":false,"usgs":true,"family":"Mayer","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":656241,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hennig, Jacob D.","contributorId":177569,"corporation":false,"usgs":false,"family":"Hennig","given":"Jacob","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":656242,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kurth, Laurie L.","contributorId":47050,"corporation":false,"usgs":true,"family":"Kurth","given":"Laurie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":656243,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Maestas, Jeremy D.","contributorId":117298,"corporation":false,"usgs":true,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":656244,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Manning, Mary E.","contributorId":177570,"corporation":false,"usgs":false,"family":"Manning","given":"Mary E.","affiliations":[],"preferred":false,"id":656245,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Mealor, Brian A.","contributorId":152584,"corporation":false,"usgs":false,"family":"Mealor","given":"Brian","email":"","middleInitial":"A.","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":656246,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"McCarthy, Clinton","contributorId":177571,"corporation":false,"usgs":false,"family":"McCarthy","given":"Clinton","email":"","affiliations":[],"preferred":false,"id":656247,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Perea, Marco A.","contributorId":177572,"corporation":false,"usgs":false,"family":"Perea","given":"Marco","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":656248,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":656249,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70178258,"text":"70178258 - 2016 - Using large-scale flow experiments to rehabilitate Colorado River ecosystem function in Grand Canyon: Basis for an adaptive climate-resilient strategy","interactions":[],"lastModifiedDate":"2020-08-27T15:36:52.359768","indexId":"70178258","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Using large-scale flow experiments to rehabilitate Colorado River ecosystem function in Grand Canyon: Basis for an adaptive climate-resilient strategy","docAbstract":"<p>Adaptive management of Glen Canyon Dam is improving downstream resources of the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. The Glen Canyon Dam Adaptive Management Program (AMP), a federal advisory committee of 25 members with diverse special interests tasked to advise the U.S. Department of the Interior), was established in 1997 in response to the 1992 Grand Canyon Protection Act. Adaptive management assumes that ecosystem responses to management policies are inherently complex and unpredictable, but that understanding and management can be improved through monitoring. Best known for its high-flow experiments intended to benefit physical and biological resources by simulating one aspect of pre-dam conditions—floods, the AMP promotes collaboration among tribal, recreation, hydropower, environmental, water and other natural resource management interests. Monitoring has shown that high flow experiments move limited new tributary sand inputs below the dam from the bottom of the Colorado River to shorelines; rebuilding eroded sandbars that support camping areas and other natural and cultural resources. Spring-timed high flows have also been shown to stimulate aquatic productivity by disturbing the river bed below the dam in Glen Canyon. Understanding about how nonnative tailwater rainbow trout (<i>Oncorhynchus mykiss</i>), and downstream endangered humpback chub (<i>Gila cypha</i>) respond to dam operations has also increased, but this learning has mostly posed “surprise” adaptation opportunities to managers. Since reoperation of the dam to Modified Low Fluctuating Flows in 1996, rainbow trout now benefit from more stable daily flows and high spring releases, but possibly at a risk to humpback chub and other native fishes downstream. In contrast, humpback chub have so far proven robust to all flows, and native fish have increased under the combination of warmer river temperatures associated with reduced storage in Lake Powell, and a system-wide reduction in trout from 2000-06, possibly due to several years of natural reproduction under limited food supply. Uncertainties about dam operations and ecosystem responses remain, including how native and nonnative fish will interact and respond to possible increased river temperatures under drier basin conditions. Ongoing assessment of operating policies by the AMP’s diverse stakeholders represents a major commitment to the river’s valued resources, while surprise learning opportunities can also help identify a resilient climate-change strategy for co-managing nonnative and endangered native fish, sandbar habitats and other river resources in a region with already complex and ever-increasing water demands.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Water policy and planning in a variable and changing climate","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press - Taylor and Francis Group","usgsCitation":"Melis, T., Pine, W.E., Korman, J., Yard, M., Jain, S., and Pulwarty, R.S., 2016, Using large-scale flow experiments to rehabilitate Colorado River ecosystem function in Grand Canyon: Basis for an adaptive climate-resilient strategy, chap. 17 <i>of</i> Water policy and planning in a variable and changing climate, p. 315-345.","productDescription":"31 p.","startPage":"315","endPage":"345","ipdsId":"IP-081318","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":335454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335453,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/Water-Policy-and-Planning-in-a-Variable-and-Changing-Climate/Miller-Hamlet-Kenney-Redmond/p/book/9781482227970"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.7197265625,\n              35.68407153314097\n            ],\n            [\n              -111.588134765625,\n              35.68407153314097\n            ],\n            [\n              -111.588134765625,\n              36.54053616262899\n            ],\n            [\n              -112.7197265625,\n              36.54053616262899\n            ],\n            [\n              -112.7197265625,\n              35.68407153314097\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a576c2e4b057081a24ed53","contributors":{"editors":[{"text":"Miller, Kathleen","contributorId":176765,"corporation":false,"usgs":false,"family":"Miller","given":"Kathleen","email":"","affiliations":[],"preferred":false,"id":669250,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Hamlet, Alan F.","contributorId":15529,"corporation":false,"usgs":true,"family":"Hamlet","given":"Alan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":669251,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Kenney, Douglas S.","contributorId":181736,"corporation":false,"usgs":false,"family":"Kenney","given":"Douglas","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":669252,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Redmond, Kelly T.","contributorId":45677,"corporation":false,"usgs":true,"family":"Redmond","given":"Kelly","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":669253,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Melis, Theodore S. 0000-0003-0473-3968 tmelis@usgs.gov","orcid":"https://orcid.org/0000-0003-0473-3968","contributorId":1829,"corporation":false,"usgs":true,"family":"Melis","given":"Theodore S.","email":"tmelis@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":669244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":669245,"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":669246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":2889,"corporation":false,"usgs":true,"family":"Yard","given":"Michael D.","email":"myard@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":669247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jain, Shaleen","contributorId":181735,"corporation":false,"usgs":false,"family":"Jain","given":"Shaleen","email":"","affiliations":[],"preferred":false,"id":669248,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pulwarty, Roger S.","contributorId":30715,"corporation":false,"usgs":true,"family":"Pulwarty","given":"Roger","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":669249,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193693,"text":"70193693 - 2016 - Use of noninvasive genetics to assess nest and space use by white-tailed eagles","interactions":[],"lastModifiedDate":"2017-11-22T17:19:58","indexId":"70193693","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Use of noninvasive genetics to assess nest and space use by white-tailed eagles","docAbstract":"<p>Movement and space use are important components of animal interactions with the environment. However, for hard-to-monitor raptor species, there are substantial gaps in our understanding of these key determinants. We used noninvasive genetic tools to evaluate the details of space use over a 3-yr period by White-tailed Eagles (<i><i>Haliaeetus albicilla</i></i>) at the Naurzum Zapovednik in northern Kazakhstan. We genotyped, at 10 microsatellite markers and one mitochondrial marker, 859 eagle feathers and assigned naturally shed feathers to individuals. We identified 124 White-tailed Eagles, including both members of 5–10 pairs per year, and were able to monitor birds across years. Distances between eagle nests and hunting perches were always greater than nearest neighbor distances, eagles never used the closest available hunting perch, and hunting perches were always shared with other eagles. When eagles switched nests between years, the nests they chose were almost always well outside the space that theory predicted they defended the prior year. Our data are inconsistent with classical territorial and colonial models of resource use; they more closely resemble semi-colonial behavior. It is unlikely that standard methods of animal tracking (e.g., marking and telemetry), would have provided a similarly cost-effective mechanism to gain these insights into spatial and temporal aspects of eagle behavior. When combined with existing information on space use of other local species, these data suggest that partitioning of spatial resources among White-tailed Eagles and other eagles at the Zapovednik may be facilitated by the alternative strategies of space use they employ.</p>","language":"English","publisher":"The Raptor Research Foundation","doi":"10.3356/JRR-15-84.1","usgsCitation":"Bulut, Z., Bragin, E.A., DeWoody, J.A., Braham, M.A., Katzner, T., and Doyle, J.M., 2016, Use of noninvasive genetics to assess nest and space use by white-tailed eagles: Journal of Raptor Research, v. 50, no. 4, p. 351-362, https://doi.org/10.3356/JRR-15-84.1.","productDescription":"12 p.","startPage":"351","endPage":"362","ipdsId":"IP-069074","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":470350,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.3356/JRR-15-84.1","text":"External Repository"},{"id":348205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","otherGeospatial":"Naurzum Zapovednik","volume":"50","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a003151e4b0531197b5a74e","contributors":{"authors":[{"text":"Bulut, Zafer","contributorId":182413,"corporation":false,"usgs":false,"family":"Bulut","given":"Zafer","email":"","affiliations":[{"id":30222,"text":"Selcuk University","active":true,"usgs":false}],"preferred":false,"id":720398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":720399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeWoody, J. Andrew","contributorId":175103,"corporation":false,"usgs":false,"family":"DeWoody","given":"J.","email":"","middleInitial":"Andrew","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":720400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Braham, Melissa A.","contributorId":199740,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":34303,"text":"West Virginia University, Department of Geology & Geography","active":true,"usgs":false}],"preferred":false,"id":720401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":5979,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":720402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":720403,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70179121,"text":"70179121 - 2016 - Using structure from motion photogrammetry to examine glide snow avalanches","interactions":[],"lastModifiedDate":"2017-01-12T13:54:37","indexId":"70179121","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using structure from motion photogrammetry to examine glide snow avalanches","docAbstract":"Structure from Motion (SfM), a photogrammetric technique, has been used extensively\nand successfully in many fields including geosciences over the past few years to create 3D models and\nhigh resolution digital elevation models (DEMs) from aerial or oblique photographs. SfM has recently\nbeen used in a limited capacity in snow avalanche research and shows promise as a tool for broader applications.\nIn this study, we used SfM to examine glide avalanches along the Going-to-the-Sun Road\n(GTSR) corridor in Glacier National Park (GNP), Montana. Glide avalanches pose substantial hazard to\nrailroads, highways, and other infrastructure in many avalanche prone regions around the world, and yet\nbasic measurements of crown depth and ground/snow interface can be hard to observe. Along the GTSR,\nglide avalanches can impact worker and public safety, but accessing glide avalanche crowns or glide\ncracks is often prohibited by inaccessible terrain or residual avalanche hazard. We used SfM techniques\nto derive high resolution DEMs for four glide avalanches that occurred in the spring of 2016. This allowed\nus to estimate selected full depth glide avalanche dimensions without visiting the site. However, our analysis\nwas limited to qualitative assessments of the glide avalanche dimensions as the high resolution coordinates\nnecessary to analyze the vertical dimension of avalanche crowns were not available. Despite this,\nour results suggest SfM can be a robust tool for examining glide avalanche crowns and of sufficient resolution\nto accurately characterize glide avalanche dimensions. Under a warming and more variable future\nclimate, glide avalanches could become a more prevalent problem, and using SfM as a tool to help characterize\nglide avalanches over a larger spatial area will help us to better document and further understand\nthese phenomena.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the International Snow Science Workshop","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop","conferenceDate":"October 3-7, 2016","conferenceLocation":"Breckenridge, CO","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Peitzsch, E.H., Hendrikx, J., and Fagre, D.B., 2016, Using structure from motion photogrammetry to examine glide snow avalanches, <i>in</i> Proceedings of the International Snow Science Workshop, Breckenridge, CO, October 3-7, 2016, p. 12-16.","productDescription":"5 p.","startPage":"12","endPage":"16","ipdsId":"IP-079064","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":333114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a48ce4b04df303d9580e","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":656092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrikx, Jordy","contributorId":166967,"corporation":false,"usgs":false,"family":"Hendrikx","given":"Jordy","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":656093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":656094,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70179546,"text":"70179546 - 2016 - Perspectives on bay-delta science and policy","interactions":[],"lastModifiedDate":"2017-10-30T09:40:23","indexId":"70179546","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Perspectives on bay-delta science and policy","docAbstract":"<p>The State of Bay–Delta Science 2008 highlighted seven emerging perspectives on science and management of the Delta. These perspectives had important effects on policy and legislation concerning management of the Delta ecosystem and water exports. From the collection of papers that make up the State of Bay–Delta Science 2016, we derive another seven perspectives that augment those published in 2008. The new perspectives address nutrient and contaminant&nbsp;concentrations in Delta waters, the failure of the Delta food web to support native species, the role of multiple stressors in driving species toward extinction, and the emerging importance of extreme events in driving change in the ecosystem and the water supply. The scientific advances that underpin these new perspectives were made possible by new measurement and analytic tools. We briefly discuss some of these, including miniaturized acoustic fish tags, sensors for monitoring of water quality, analytic techniques for disaggregating complex contaminant mixtures, remote sensing to assess levee vulnerability, and multidimensional hydrodynamic modeling. Despite these new tools and scientific insights, species conservation objectives for the Delta are not being met. We believe that this lack of progress stems in part from the fact that science and policy do not incorporate sufficiently long-term perspectives. Looking forward half a century was central to the Delta Visioning process, but science and policy have not embraced this conceptual breadth. We are also concerned that protection and enhancement of the unique cultural, recreational, natural resource, and agricultural values of the Delta as an evolving place, as required by the Delta Reform Act, has received no critical study and analysis. Adopting wider and longer science and policy perspectives immediately encourages recognition of the need for evaluation, analysis, and public discourse on novel conservation approaches. These longer and wider perspectives also encourage more attention to the opportunities provided by heavily invaded ecosystems. It is past time to turn scientific and policy attention to these issues. </p>","language":"English","publisher":"University of California","doi":"10.15447/sfews.2016v14iss4art6","usgsCitation":"Healey, M., Dettinger, M.D., and Norgaard, R., 2016, Perspectives on bay-delta science and policy: San Francisco Estuary and Watershed Science, v. 14, no. 4, p. 1-25, https://doi.org/10.15447/sfews.2016v14iss4art6.","productDescription":"Article 6; 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-077666","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":470409,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2016v14iss4art6","text":"Publisher Index Page"},{"id":332907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","volume":"14","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-28","publicationStatus":"PW","scienceBaseUri":"586e1822e4b0f5ce109fcadd","contributors":{"authors":[{"text":"Healey, Michael","contributorId":146519,"corporation":false,"usgs":false,"family":"Healey","given":"Michael","email":"","affiliations":[],"preferred":false,"id":657632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":657631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norgaard, Richard","contributorId":177948,"corporation":false,"usgs":false,"family":"Norgaard","given":"Richard","email":"","affiliations":[],"preferred":false,"id":657633,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70178571,"text":"70178571 - 2016 - Clinal patterns in genetic variation for northern leopard frog (<i>Rana pipiens</i>): Conservation status and population histories","interactions":[],"lastModifiedDate":"2017-01-03T16:01:46","indexId":"70178571","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Clinal patterns in genetic variation for northern leopard frog (<i>Rana pipiens</i>): Conservation status and population histories","docAbstract":"<p><span>The security of the northern leopard frog (</span><i class=\"EmphasisTypeItalic \">Rana pipiens)</i><span> varies spatially with populations east and west of North Dakota considered as secure and at risk, respectively. We used genetic markers to characterize the conservation status of northern leopard frog populations across North Dakota. We used multiple regression analyses and model selection to evaluate correlations of expected heterozygosity (</span><i class=\"EmphasisTypeItalic \">H</i><sub><i class=\"EmphasisTypeItalic \">E</i></sub><span>) with the direct and additive effects of: </span><strong class=\"EmphasisTypeBold \">i</strong><span>) </span><i class=\"EmphasisTypeItalic \">geographic location,</i><strong class=\"EmphasisTypeBold \">ii</strong><span>) </span><i class=\"EmphasisTypeItalic \">wetland density</i><span> and </span><strong class=\"EmphasisTypeBold \">iii</strong><span>) </span><i class=\"EmphasisTypeItalic \">average annual precipitation</i><span>. There was lower genetic diversity in the western portion of the state due to lower levels of diversity for populations southwest of the Missouri River. This may reflect a refugial/colonization signature for the only non-glaciated area of North Dakota. Genetic diversity was also positively associated with </span><i class=\"EmphasisTypeItalic \">wetland densities</i><span> which is consistent with the reliance of this species on a mosaic of wetlands. Our findings suggest that populations in the southwestern part of North Dakota are of higher conservation concern, a finding consistent with the higher risk noted for northern leopard frog populations in most states west of North Dakota. Our findings also pose the hypothesis that climate change induced changes in wetland densities will reduce genetic diversity of northern leopard frog populations.</span></p>","language":"English","publisher":"Sprinker","doi":"10.1007/s13157-016-0847-3","usgsCitation":"Stockwell, C., Fisher, J.D., and McLean, K.I., 2016, Clinal patterns in genetic variation for northern leopard frog (<i>Rana pipiens</i>): Conservation status and population histories: Wetlands, v. 36, no. s2, p. 437-443, https://doi.org/10.1007/s13157-016-0847-3.","productDescription":"7 p.","startPage":"437","endPage":"443","ipdsId":"IP-077323","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":331368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North 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Center","active":true,"usgs":true}],"preferred":false,"id":654592,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70178700,"text":"70178700 - 2016 - Modeling the effects of land cover and use on landscape capability for urban ungulate populations","interactions":[],"lastModifiedDate":"2020-08-25T16:33:19.998207","indexId":"70178700","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"11","title":"Modeling the effects of land cover and use on landscape capability for urban ungulate populations","docAbstract":"Expanding ungulate populations are causing concerns for wildlife professionals and residents in many urban areas worldwide.  Nowhere is the phenomenon more apparent than in the eastern US, where urban white-tailed deer (Odocoileus virginianus) populations are increasing.  Most habitat suitability models for deer have been developed in rural areas and across large (>1000 km2) spatial extents.  Only recently have we begun to understand the factors that contribute to space use by deer over much smaller spatial extents.  In this study, we explore the concepts, terminology, methodology and state-of-the-science in wildlife abundance modeling as applied to overabundant deer populations across heterogeneous urban landscapes.  We used classified, high-resolution digital orthoimagery to extract landscape characteristics in several urban areas of upstate New York.  In addition, we assessed deer abundance and distribution in 1-km2 blocks across each study area from either aerial surveys or ground-based distance sampling.  We recorded the number of detections in each block and used binomial mixture models to explore important relationships between abundance and key landscape features.  Finally, we cross-validated statistical models of abundance and compared covariate relationships across study sites.  Study areas were characterized along a gradient of urbanization based on the proportions of impervious surfaces and natural vegetation which, based on the best-supported models, also distinguished blocks potentially occupied by deer.  Models performed better at identifying occurrence of deer and worse at predicting abundance in cross-validation comparisons.  We attribute poor predictive performance to differences in deer population trajectories over time.  The proportion of impervious surfaces often yielded better predictions of abundance and occurrence than did the proportion of natural vegetation, which we attribute to a lack of certain land cover classes during cold and snowy winters.  Merits and limitations of our approach to habitat suitability modeling are discussed in detail.","largerWorkType":{"id":4,"text":"Book"},"largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"International Association for Landscape Ecology","publisherLocation":"Urban landscape ecology: Science, policy and practice","usgsCitation":"Underwood, H.B., and Kilheffer, C.R., 2016, Modeling the effects of land cover and use on landscape capability for urban ungulate populations, p. 181-208.","productDescription":"28 p.","startPage":"181","endPage":"208","ipdsId":"IP-061055","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":331585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331463,"type":{"id":15,"text":"Index Page"},"url":"https://www.routledge.com/Urban-Landscape-Ecology-Science-policy-and-practice/Francis-Millington-Chadwick/p/book/9781138888517"}],"publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5847dc7de4b06d80b7af6aaf","contributors":{"editors":[{"text":"Francis, Robert A.","contributorId":112146,"corporation":false,"usgs":true,"family":"Francis","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":655011,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Millington, James D. A.","contributorId":169900,"corporation":false,"usgs":false,"family":"Millington","given":"James","email":"","middleInitial":"D. A.","affiliations":[{"id":25615,"text":"Department of Geography, King's College London","active":true,"usgs":false}],"preferred":false,"id":655012,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Chadwick, Michael A.","contributorId":177208,"corporation":false,"usgs":false,"family":"Chadwick","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":655013,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Underwood, H. Brian 0000-0002-2064-9128 hbunderw@usgs.gov","orcid":"https://orcid.org/0000-0002-2064-9128","contributorId":140185,"corporation":false,"usgs":true,"family":"Underwood","given":"H.","email":"hbunderw@usgs.gov","middleInitial":"Brian","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kilheffer, Chellby R.","contributorId":177173,"corporation":false,"usgs":false,"family":"Kilheffer","given":"Chellby","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":654864,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70179123,"text":"70179123 - 2016 - Case study: 2016 Natural glide and wet slab avalanche cycle, Going-to-the-Sun Road, Glacier National Park, Montana, USA","interactions":[],"lastModifiedDate":"2017-01-12T13:53:11","indexId":"70179123","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Case study: 2016 Natural glide and wet slab avalanche cycle, Going-to-the-Sun Road, Glacier National Park, Montana, USA","docAbstract":"The Going-to-the-Sun Road (GTSR) is the premier tourist attraction in Glacier\nNational Park, Montana. The GTSR also traverses through and under 40 avalanche paths which\npose a hazard to National Park Service (NPS) road crews during the annual spring snow plowing\noperation. Through a joint collaboration between the NPS and the U.S. Geological Survey\n(USGS), a forecasting program primarily dealing with wet snow avalanche problems serves to\naid worker safety. The objective of this case study is to examine the meteorological metrics and\nsnowpack characteristics leading up to a noteworthy wet slab and glide avalanche cycle that occurred\n16-22 April, 2016 during a period of unseasonably warm and sunny weather. Continuous\nabove freezing temperatures at upper elevations with daily maximum values reaching 10-15° C\npersisted for four days. The nearby Flattop Mountain SNOTEL station reported a steady loss of\nSWE of approximately 1.25 cm/day. River height and discharge on the Middle Fork of the Flathead\nRiver (app. 20-35 km away from starting zones) increased from 1.26 m and 139.60 m3/s\n(4930 cfs), respectively, on April 18 to 1.69 m and 267.59 m3/s (9450 cfs) on April 22. The ensuing\navalanche cycle began with three small glide avalanches on 17 April and culminated in three\nlarge wet slab avalanches that released on wet, basal facets. These wet slabs were triggered by\nglide avalanches releasing above and cascading over cliffs. Four of these avalanches crossed\nplowed sections of the road, resulting in a three day delay in plowing operations. Finally, this\nspecific case was compared to previous statistical models for wet snow avalanches in this transportation\ncorridor. Out of 12 avalanche days, the model correctly predicted six of those days as\navalanche days. This case study allowed for a greater understanding of a wet slab and glide avalanche\ncycle that occurred during a prolonged spring warming event and can serve as a reference\nfor future similar cycles.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the International Snow Science Workshop","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop","conferenceDate":"October 3-7, 2016","conferenceLocation":"Breckenridge, CO","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Hutchinson, J., Peitzsch, E.H., and Clark, A., 2016, Case study: 2016 Natural glide and wet slab avalanche cycle, Going-to-the-Sun Road, Glacier National Park, Montana, USA, <i>in</i> Proceedings of the International Snow Science Workshop, Breckenridge, CO, October 3-7, 2016, p. 66-73.","productDescription":"8 p.","startPage":"66","endPage":"73","ipdsId":"IP-079047","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":333112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a48ce4b04df303d9580c","contributors":{"authors":[{"text":"Hutchinson, Jacob","contributorId":177528,"corporation":false,"usgs":false,"family":"Hutchinson","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":656098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":656097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Adam 0000-0002-8863-1434 amclark@usgs.gov","orcid":"https://orcid.org/0000-0002-8863-1434","contributorId":177529,"corporation":false,"usgs":true,"family":"Clark","given":"Adam","email":"amclark@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":656099,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
]}