{"pageNumber":"529","pageRowStart":"13200","pageSize":"25","recordCount":40778,"records":[{"id":70156871,"text":"70156871 - 2015 - Effects of urbanization and stormwater control measures on streamflows in the vicinity of Clarksburg, Maryland, USA","interactions":[],"lastModifiedDate":"2015-09-02T09:00:25","indexId":"70156871","displayToPublicDate":"2015-09-02T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of urbanization and stormwater control measures on streamflows in the vicinity of Clarksburg, Maryland, USA","docAbstract":"Understanding the efficacy of revised watershed management methods is important to mitigating the impacts of urbanization on streamflow. We evaluated the influence of land use change, primarily as urbanization, and stormwater control measures on the relationship between precipitation and stream discharge over an 8-year period for five catchments near Clarksburg, Montgomery County, Maryland, USA. A unit-hydrograph model based on a temporal transfer function was employed to account for and standardize temporal variation in rainfall pattern, and properly apportion rainfall to streamflow at different time lags. From these lagged relationships, we quantified a correction to the precipitation time series to achieve a hydrograph that showed good agreement between precipitation and discharge records. Positive corrections appeared to include precipitation events that were of limited areal extent and therefore not captured by our rain gages. Negative corrections were analysed for potential causal relationships. We used mixed-model statistical techniques to isolate different sources of variance as drivers that mediate the rainfall–runoff dynamic before and after management. Seasonal periodicity mediated rainfall–runoff relationships, and land uses (i.e. agriculture, natural lands, wetlands and stormwater control measures) were statistically significant predictors of precipitation apportionment to stream discharge. Our approach is one way to evaluate actual effectiveness of management efforts in the face of complicating circumstances and could be paired with cost data to understand economic efficiency or life cycle aspects of watershed management. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.10505","usgsCitation":"Rhea, L., Jarnagin, T., Hogan, D.M., Loperfido, J., and Shuster, W., 2015, Effects of urbanization and stormwater control measures on streamflows in the vicinity of Clarksburg, Maryland, USA: Hydrological Processes, v. 29, no. 20, p. 4413-4426, https://doi.org/10.1002/hyp.10505.","productDescription":"14 p.","startPage":"4413","endPage":"4426","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1998-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-053400","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":307802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Montgomery County","otherGeospatial":"Clarksburg","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.32315063476562,\n 39.17771552084858\n ],\n [\n -77.32315063476562,\n 39.32101883236063\n ],\n [\n -77.16865539550781,\n 39.32101883236063\n ],\n [\n -77.16865539550781,\n 39.17771552084858\n ],\n [\n -77.32315063476562,\n 39.17771552084858\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"20","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-11","publicationStatus":"PW","scienceBaseUri":"55e80f98e4b0dacf699e663a","chorus":{"doi":"10.1002/hyp.10505","url":"http://dx.doi.org/10.1002/hyp.10505","publisher":"Wiley-Blackwell","authors":"Rhea Lee, Jarnagin Taylor, Hogan Dianna, Loperfido J. V., Shuster William","journalName":"Hydrological Processes","publicationDate":"5/11/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"Rhea, Lee","contributorId":147260,"corporation":false,"usgs":false,"family":"Rhea","given":"Lee","affiliations":[{"id":16813,"text":"Sustainable Environments Branch, National Risk Management Research Laboratory, Office of Research and Development, EPA","active":true,"usgs":false}],"preferred":false,"id":570900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnagin, Taylor","contributorId":131140,"corporation":false,"usgs":false,"family":"Jarnagin","given":"Taylor","email":"","affiliations":[{"id":7258,"text":"Landscape Ecology Branch, U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":570901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":570899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loperfido, J. V. jloperfido@usgs.gov","contributorId":131139,"corporation":false,"usgs":true,"family":"Loperfido","given":"J. V.","email":"jloperfido@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":570902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shuster, William","contributorId":147261,"corporation":false,"usgs":false,"family":"Shuster","given":"William","affiliations":[{"id":16813,"text":"Sustainable Environments Branch, National Risk Management Research Laboratory, Office of Research and Development, EPA","active":true,"usgs":false}],"preferred":false,"id":570903,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70159967,"text":"70159967 - 2015 - Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories","interactions":[],"lastModifiedDate":"2015-12-04T14:47:17","indexId":"70159967","displayToPublicDate":"2015-09-02T00:00:00","publicationYear":"2015","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":"Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories","docAbstract":"Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of flexible model libraries and data from observationnetworks, within a learning framework, as a means to improve the accuracy and scope of model predictions.The framework is comprised of a data assimilation component that utilizes diverse data streams from sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identified as a means to develop and inte-grate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry,and, in doing so, also advance the role of prediction in aquatic ecosystem management.
","language":"English","publisher":"Wiley","doi":"10.1002/2015WR017175","usgsCitation":"Hipsey, M., Hamilton, D., Hanson, P.C., Carey, C.C., Coletti, J.Z., Read, J.S., Ibelings, B.W., Valensini, F.J., and Brookes, J.D., 2015, Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories: Water Resources Research, v. 51, no. 9, p. 7023-7043, https://doi.org/10.1002/2015WR017175.","productDescription":"21 p.","startPage":"7023","endPage":"7043","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063945","costCenters":[],"links":[{"id":471816,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr017175","text":"Publisher Index Page"},{"id":311939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"9","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-02","publicationStatus":"PW","scienceBaseUri":"5662c758e4b06a3ea36c67c7","contributors":{"authors":[{"text":"Hipsey, Matthew R.","contributorId":80968,"corporation":false,"usgs":true,"family":"Hipsey","given":"Matthew R.","affiliations":[],"preferred":false,"id":581334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hamilton, David P.","contributorId":18633,"corporation":false,"usgs":true,"family":"Hamilton","given":"David P.","affiliations":[],"preferred":false,"id":581335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":581336,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":581337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coletti, Janaine Z","contributorId":150282,"corporation":false,"usgs":false,"family":"Coletti","given":"Janaine","email":"","middleInitial":"Z","affiliations":[{"id":17958,"text":"Aquatic Ecodynamics, School of Earth and Environment, The University of Western Australia, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":581338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":581339,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ibelings, Bas W","contributorId":130973,"corporation":false,"usgs":false,"family":"Ibelings","given":"Bas","email":"","middleInitial":"W","affiliations":[{"id":7189,"text":"Institut F.A. Forel, Versoix, Switzerland","active":true,"usgs":false}],"preferred":false,"id":581340,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Valensini, Fiona J","contributorId":150283,"corporation":false,"usgs":false,"family":"Valensini","given":"Fiona","email":"","middleInitial":"J","affiliations":[{"id":17959,"text":"Centre for Fish and Fisheries Research, Murdoch University, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":581341,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brookes, Justin D","contributorId":130984,"corporation":false,"usgs":false,"family":"Brookes","given":"Justin","email":"","middleInitial":"D","affiliations":[{"id":7196,"text":"Water Research Centre, The Environment Institute, School of Earth and Environmental Science, University of Adelaide, South Australia, Australia","active":true,"usgs":false}],"preferred":false,"id":581342,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70157377,"text":"70157377 - 2015 - Landscape-scale distribution and density of raptor populations wintering in anthropogenic-dominated desert landscapes","interactions":[],"lastModifiedDate":"2017-11-24T18:08:55","indexId":"70157377","displayToPublicDate":"2015-09-01T12:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale distribution and density of raptor populations wintering in anthropogenic-dominated desert landscapes","docAbstract":"Anthropogenic development has great potential to affect fragile desert environments. Large-scale development of renewable energy infrastructure is planned for many desert ecosystems. Development plans should account for anthropogenic effects to distributions and abundance of rare or sensitive wildlife; however, baseline data on abundance and distribution of such wildlife are often lacking. We surveyed for predatory birds in the Sonoran and Mojave Deserts of southern California, USA, in an area designated for protection under the “Desert Renewable Energy Conservation Plan”, to determine how these birds are distributed across the landscape and how this distribution is affected by existing development. We developed species-specific models of resight probability to adjust estimates of abundance and density of each individual common species. Second, we developed combined-species models of resight probability for common and rare species so that we could make use of sparse data on the latter. We determined that many common species, such as red-tailed hawks, loggerhead shrikes, and especially common ravens, are associated with human development and likely subsidized by human activity. Species-specific and combined-species models of resight probability performed similarly, although the former model type provided higher quality information. Comparing abundance estimates with past surveys in the Mojave Desert suggests numbers of predatory birds associated with human development have increased while other sensitive species not associated with development have decreased. This approach gave us information beyond what we would have collected by focusing either on common or rare species, thus it provides a low-cost framework for others conducting surveys in similar desert environments outside of California.
","language":"English","publisher":"Springer","doi":"10.1007/s10531-015-0916-6","usgsCitation":"Duerr, A.E., Miller, T., Cornell Duerr, K.L., Lanzone, M.J., Fesnock-Parker, A., and Katzner, T., 2015, Landscape-scale distribution and density of raptor populations wintering in anthropogenic-dominated desert landscapes: Biodiversity and Conservation, v. 24, no. 10, p. 2365-2381, https://doi.org/10.1007/s10531-015-0916-6.","productDescription":"17 p.","startPage":"2365","endPage":"2381","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061915","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":308435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert, Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.63037109375,\n 37.97884504049713\n ],\n [\n -114.6533203125,\n 35.04798673426734\n ],\n [\n -114.60937499999999,\n 34.867904962568744\n ],\n [\n -114.45556640625,\n 34.687427949314845\n ],\n [\n -114.345703125,\n 34.452218472826566\n ],\n [\n -114.19189453125,\n 34.361576287484176\n ],\n [\n -114.3017578125,\n 34.125447565116126\n ],\n [\n -114.43359375,\n 33.99802726234877\n ],\n [\n -114.43359375,\n 33.8521697014074\n ],\n [\n -114.58740234375,\n 33.63291573870476\n ],\n [\n -114.71923828124999,\n 33.43144133557529\n ],\n [\n -114.71923828124999,\n 33.22949814144951\n ],\n [\n -114.6533203125,\n 33.119150226768866\n ],\n [\n -114.5654296875,\n 33.04550781490999\n ],\n [\n -114.54345703125,\n 32.879587173066305\n ],\n [\n -114.697265625,\n 32.713355353177555\n ],\n [\n -116.49902343749999,\n 32.62087018318113\n ],\n [\n -116.60888671874999,\n 32.97180377635759\n ],\n [\n -116.806640625,\n 33.61461929233378\n ],\n [\n -117.1142578125,\n 33.925129700072\n ],\n [\n -117.6416015625,\n 34.052659421375964\n ],\n [\n -118.23486328125,\n 34.23451236236987\n ],\n [\n -118.564453125,\n 34.56085936708387\n ],\n [\n -118.740234375,\n 34.75966612466248\n ],\n [\n -118.71826171875,\n 35.28150065789119\n ],\n [\n -118.91601562499999,\n 36.50963615733049\n ],\n [\n -118.7841796875,\n 37.78808138412046\n ],\n [\n -118.63037109375,\n 37.97884504049713\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-01","publicationStatus":"PW","scienceBaseUri":"5603cd46e4b03bc34f544b17","contributors":{"authors":[{"text":"Duerr, Adam E.","contributorId":102324,"corporation":false,"usgs":true,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":572915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Tricia A.","contributorId":64790,"corporation":false,"usgs":true,"family":"Miller","given":"Tricia A.","affiliations":[],"preferred":false,"id":572916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornell Duerr, Kerri L","contributorId":147850,"corporation":false,"usgs":false,"family":"Cornell Duerr","given":"Kerri","email":"","middleInitial":"L","affiliations":[{"id":16946,"text":"Westminster College","active":true,"usgs":false}],"preferred":false,"id":572917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lanzone, Michael J.","contributorId":147851,"corporation":false,"usgs":false,"family":"Lanzone","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13392,"text":"Cellular Tracking Technologies","active":true,"usgs":false}],"preferred":false,"id":572918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fesnock-Parker, Amy","contributorId":140129,"corporation":false,"usgs":false,"family":"Fesnock-Parker","given":"Amy","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":572919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":572914,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70157110,"text":"70157110 - 2015 - Trends in pesticide concentrations and use for major rivers of the United States","interactions":[],"lastModifiedDate":"2017-10-12T20:02:17","indexId":"70157110","displayToPublicDate":"2015-09-01T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Trends in pesticide concentrations and use for major rivers of the United States","docAbstract":"Trends in pesticide concentrations in 38 major rivers of the United States were evaluated in relation to use trends for 11 commonly occurring pesticide compounds. Pesticides monitored in water were analyzed for trends in concentration in three overlapping periods, 1992–2001, 1997–2006, and 2001–2010 to facilitate comparisons among sites with variable sample distributions over time and among pesticides with changes in use during different periods and durations. Concentration trends were analyzed using the SEAWAVE-Q model, which incorporates intra-annual variability in concentration and measures of long-term, mid-term, and short-term streamflow variability. Trends in agricultural use within each of the river basins were determined using interval-censored regression with high and low estimates of use.
\nPesticides strongly dominated by agricultural use (cyanazine, alachlor, atrazine and its degradate deethylatrazine, metolachlor, and carbofuran) had widespread agreement between concentration trends and use trends. Pesticides with substantial use in both agricultural and nonagricultural applications (simazine, chlorpyrifos, malathion, diazinon, and carbaryl) had concentration trends that were mostly explained by a combination of agricultural-use trends, regulatory changes, and urban use changes inferred from concentration trends in urban streams. When there were differences, concentration trends usually were greater than use trends (increased more or decreased less). These differences may occur because of such factors as unaccounted pesticide uses, delayed transport to the river through groundwater, greater uncertainty in the use data, or unquantified land use and management practice changes.
","language":"English","publisher":"Elsevier Pub. Co.","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2015.06.095","usgsCitation":"Ryberg, K.R., and Gilliom, R.J., 2015, Trends in pesticide concentrations and use for major rivers of the United States: Science of the Total Environment, v. 538, p. 431-444, https://doi.org/10.1016/j.scitotenv.2015.06.095.","productDescription":"14 p.","startPage":"431","endPage":"444","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059356","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":307996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"538","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55f15834e4b0dacf699eb987","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":571688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilliom, Robert J. rgilliom@usgs.gov","contributorId":488,"corporation":false,"usgs":true,"family":"Gilliom","given":"Robert","email":"rgilliom@usgs.gov","middleInitial":"J.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":571689,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70156879,"text":"70156879 - 2015 - Stock-specific advection of larval walleye (Sander vitreus) in western Lake Erie: Implications for larval growth, mixing, and stock discrimination","interactions":[],"lastModifiedDate":"2017-08-15T12:43:17","indexId":"70156879","displayToPublicDate":"2015-09-01T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Stock-specific advection of larval walleye (Sander vitreus) in western Lake Erie: Implications for larval growth, mixing, and stock discrimination","docAbstract":"Physical processes can generate spatiotemporal heterogeneity in habitat quality for fish and also influence the overlap of pre-recruit individuals (e.g., larvae) with high-quality habitat through hydrodynamic advection. In turn, individuals from different stocks that are produced in different spawning locations or at different times may experience dissimilar habitat conditions, which can underlie within- and among-stock variability in larval growth and survival. While such physically-mediated variation has been shown to be important in driving intra- and inter-annual patterns in recruitment in marine ecosystems, its role in governing larval advection, growth, survival, and recruitment has received less attention in large lake ecosystems such as the Laurentian Great Lakes. Herein, we used a hydrodynamic model linked to a larval walleye (Sander vitreus) individual-based model to explore how the timing and location of larval walleye emergence from several spawning sites in western Lake Erie (Maumee, Sandusky, and Detroit rivers; Ohio reef complex) can influence advection pathways and mixing among these local spawning populations (stocks), and how spatiotemporal variation in thermal habitat can influence stock-specific larval growth. While basin-wide advection patterns were fairly similar during 2011 and 2012, smaller scale advection patterns and the degree of stock mixing varied both within and between years. Additionally, differences in larval growth were evident among stocks and among cohorts within stocks which were attributed to spatiotemporal differences in water temperature. Using these findings, we discuss the value of linked physical–biological models for understanding the recruitment process and addressing fisheries management problems in the world's Great Lakes.
","language":"English","publisher":"International Association for Great Lakes Research","publisherLocation":"Toronto","doi":"10.1016/j.jglr.2015.04.008","usgsCitation":"Fraker, M.E., Anderson, E., May, C.J., Chen, K., Davis, J.J., DeVanna, K.M., DuFour, M., Marschall, E.A., Mayer, C.M., Miner, J.G., Pangle, K.L., Pritt, J., Roseman, E., Tyson, J.T., Zhao, Y., and Ludsin, S.A., 2015, Stock-specific advection of larval walleye (Sander vitreus) in western Lake Erie: Implications for larval growth, mixing, and stock discrimination: Journal of Great Lakes Research, v. 41, no. 3, p. 830-845, https://doi.org/10.1016/j.jglr.2015.04.008.","productDescription":"16 p.","startPage":"830","endPage":"845","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066933","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":307818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560ba84ae4b058f706e53abc","contributors":{"authors":[{"text":"Fraker, Michael E. 0000-0002-1813-706X","orcid":"https://orcid.org/0000-0002-1813-706X","contributorId":150962,"corporation":false,"usgs":false,"family":"Fraker","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":570938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Eric J.","contributorId":89434,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric J.","affiliations":[],"preferred":false,"id":570939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Cassandra J.","contributorId":150961,"corporation":false,"usgs":false,"family":"May","given":"Cassandra","email":"","middleInitial":"J.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":570940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Kuan-Yu","contributorId":140818,"corporation":false,"usgs":false,"family":"Chen","given":"Kuan-Yu","email":"","affiliations":[{"id":6714,"text":"Ohio State University, School of Earth Sciences, Columbus, Ohio, USA","active":true,"usgs":false}],"preferred":false,"id":570941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Jeremiah J.","contributorId":150963,"corporation":false,"usgs":false,"family":"Davis","given":"Jeremiah","email":"","middleInitial":"J.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":570942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeVanna, Kristen M.","contributorId":64991,"corporation":false,"usgs":true,"family":"DeVanna","given":"Kristen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":570943,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DuFour, Mark R.","contributorId":36451,"corporation":false,"usgs":true,"family":"DuFour","given":"Mark R.","affiliations":[],"preferred":false,"id":570944,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Marschall, Elizabeth A.","contributorId":41388,"corporation":false,"usgs":true,"family":"Marschall","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":570945,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mayer, Christine M.","contributorId":50814,"corporation":false,"usgs":true,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":570946,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miner, Jeffery G.","contributorId":150965,"corporation":false,"usgs":false,"family":"Miner","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":570947,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pangle, Kevin L.","contributorId":40947,"corporation":false,"usgs":true,"family":"Pangle","given":"Kevin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":570948,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pritt, Jeremy J. jpritt@usgs.gov","contributorId":139770,"corporation":false,"usgs":true,"family":"Pritt","given":"Jeremy J.","email":"jpritt@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":570949,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Roseman, Edward F. eroseman@usgs.gov","contributorId":147266,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","email":"eroseman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":570937,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tyson, Jeffrey T.","contributorId":104433,"corporation":false,"usgs":true,"family":"Tyson","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":570950,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zhao, Yingming","contributorId":49752,"corporation":false,"usgs":true,"family":"Zhao","given":"Yingming","affiliations":[],"preferred":false,"id":570951,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ludsin, Stuart A","contributorId":120607,"corporation":false,"usgs":true,"family":"Ludsin","given":"Stuart","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":570952,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70158591,"text":"70158591 - 2015 - Estimating the short-term recovery potential of little brown bats in the eastern United States in the face of White-nose syndrome","interactions":[],"lastModifiedDate":"2018-01-04T15:39:04","indexId":"70158591","displayToPublicDate":"2015-09-01T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the short-term recovery potential of little brown bats in the eastern United States in the face of White-nose syndrome","docAbstract":"White-nose syndrome (WNS) was first detected in North American bats in New York in 2006. Since that time WNS has spread throughout the northeastern United States, southeastern Canada, and southwest across Pennsylvania and as far west as Missouri. Suspect WNS cases have been identified in Minnesota and Iowa, and the causative agent of WNS (Pseudogymnoascus destructans) has recently been detected in Mississippi. The impact of WNS is devastating for little brown bats (Myotis lucifugus), causing up to 100% mortality in some overwintering populations, and previous research has forecast the extirpation of the species due to the disease. Recent evidence indicates that remnant populations may persist in areas where WNS is endemic. We developed a spatially explicit model of little brown bat population dynamics to investigate the potential for populations to recover under alternative scenarios. We used these models to investigate how starting population sizes, potential changes in the number of bats overwintering successfully in hibernacula, and potential changes in demographic rates of the population post WNS may influence the ability of the bats to recover to former levels of abundance. We found that populations of the little brown bat and other species that are highly susceptible to WNS are unlikely to return to pre-WNS levels in the near future under any of the scenarios we examined.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.ecolmodel.2015.07.016","usgsCitation":"Russell, R., Thogmartin, W.E., Erickson, R.A., Szymanski, J.A., and Tinsley, K., 2015, Estimating the short-term recovery potential of little brown bats in the eastern United States in the face of White-nose syndrome: Ecological Modelling, v. 314, p. 111-117, https://doi.org/10.1016/j.ecolmodel.2015.07.016.","productDescription":"7 p.","startPage":"111","endPage":"117","numberOfPages":"7","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":309365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"314","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560d07b5e4b058f706e54306","contributors":{"authors":[{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":10269,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[],"preferred":false,"id":576214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":576215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Szymanski, Jennifer A.","contributorId":51593,"corporation":false,"usgs":true,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":576217,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tinsley, Karl","contributorId":23457,"corporation":false,"usgs":false,"family":"Tinsley","given":"Karl","email":"","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":576218,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70223724,"text":"70223724 - 2015 - Wintering Bald Eagle count trends in the conterminous United States, 1986–2010","interactions":[],"lastModifiedDate":"2021-09-03T13:06:43.613522","indexId":"70223724","displayToPublicDate":"2015-09-01T07:59:20","publicationYear":"2015","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":"Wintering Bald Eagle count trends in the conterminous United States, 1986–2010","docAbstract":"We analyzed counts from the annual Midwinter Bald Eagle Survey to examine state, regional, and national trends in counts of wintering Bald Eagles (Haliaeetus leucocephalus) within the conterminous 48 United States from 1986 to 2010. Using hierarchical mixed model methods, we report trends in counts from 11 729 surveys along 844 routes in 44 states. Nationwide Bald Eagle counts increased 0.6% per yr over the 25-yr period, compared to an estimate of 1.9% per yr from 1986 to 2000. Trend estimates for Bald Eagles were significant (P ≤ 0.05) and positive in the northeastern and northwestern U.S. (3.9% and 1.1%, respectively), while trend estimates for Bald Eagles were negative (P ≤ 0.05) in the southwestern U.S. (−2.2%). After accounting for potential biases resulting from temporal and regional differences in surveys, we believe trends reflect post-DDT recovery and subsequent early effects of density-dependent population regulation.
Food web modeling is recognized as fundamental to understanding the complexities of aquatic systems. Ecopath is the most common mass-balance model used to represent food webs and quantify trophic interactions among groups. We constructed annual Ecopath models for four consecutive years during the first half-decade of a zebra mussel invasion in shallow, eutrophic Clear Lake, Iowa, USA, to evaluate changes in relative biomass and total system consumption among food web groups, evaluate food web impacts of non-native common carp and zebra mussels on food web groups, and to interpret food web impacts in light of on-going lake restoration. Total living biomass increased each year of the study; the majority of the increase due to a doubling in planktonic blue green algae, but several other taxa also increased including a more than two-order of magnitude increase in zebra mussels. Common carp accounted for the largest percentage of total fish biomass throughout the study even with on-going harvest. Chironomids, common carp, and zebra mussels were the top-three ranking consumer groups. Non-native common carp and zebra mussels accounted for an average of 42% of the total system consumption. Despite the relatively high biomass densities of common carp and zebra mussel, food web impacts was minimal due to excessive benthic and primary production in this eutrophic system. Consumption occurring via benthic pathways dominated system consumption in Clear Lake throughout our study, supporting the argument that benthic food webs are significant in shallow, eutrophic lake ecosystems and must be considered if ecosystem-level understanding is to be obtained.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2015.05.016","usgsCitation":"Colvin, M., Pierce, C., and Stewart, T.W., 2015, A food web modeling analysis of a Midwestern, USA eutrophic lake dominated by non-native Common Carp and Zebra Mussels: Ecological Modelling, v. 312, p. 26-40, https://doi.org/10.1016/j.ecolmodel.2015.05.016.","productDescription":"15 p.","startPage":"26","endPage":"40","ipdsId":"IP-040716","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":471825,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/139","text":"External Repository"},{"id":348687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.51390838623047,\n 43.106748182603205\n ],\n [\n -93.37657928466797,\n 43.106748182603205\n ],\n [\n -93.37657928466797,\n 43.14433468958596\n ],\n [\n -93.51390838623047,\n 43.14433468958596\n ],\n [\n -93.51390838623047,\n 43.106748182603205\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"312","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fe67e4b06e28e9c252f5","contributors":{"authors":[{"text":"Colvin, Michael E.","contributorId":140975,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":721780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Timothy W.","contributorId":171433,"corporation":false,"usgs":false,"family":"Stewart","given":"Timothy","email":"","middleInitial":"W.","affiliations":[{"id":26913,"text":"Iowa State University, Ames, Iowa","active":true,"usgs":false}],"preferred":false,"id":721781,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70173609,"text":"70173609 - 2015 - Linking resource selection and mortality modeling for population estimation of mountain lions in Montana","interactions":[],"lastModifiedDate":"2016-06-22T14:34:22","indexId":"70173609","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Linking resource selection and mortality modeling for population estimation of mountain lions in Montana","docAbstract":"To be most effective, the scale of wildlife management practices should match the range of a particular species’ movements. For this reason, combined with our inability to rigorously or regularly census mountain lion populations, several authors have suggested that mountain lions be managed in a source-sink or metapopulation framework. We used a combination of resource selection functions, mortality estimation, and dispersal modeling to estimate cougar population levels in Montana statewide and potential population level effects of planned harvest levels. Between 1980 and 2012, 236 independent mountain lions were collared and monitored for research in Montana. From these data we used 18,695 GPS locations collected during winter from 85 animals to develop a resource selection function (RSF), and 11,726 VHF and GPS locations from 142 animals along with the locations of 6343 mountain lions harvested from 1988–2011 to validate the RSF model. Our RSF model validated well in all portions of the State, although it appeared to perform better in Montana Fish, Wildlife and Parks (MFWP) Regions 1, 2, 4 and 6, than in Regions 3, 5, and 7. Our mean RSF based population estimate for the total population (kittens, juveniles, and adults) of mountain lions in Montana in 2005 was 3926, with almost 25% of the entire population in MFWP Region 1. Estimates based on a high and low reference population estimates produce a possible range of 2784 to 5156 mountain lions statewide. Based on a range of possible survival rates we estimated the mountain lion population in Montana to be stable to slightly increasing between 2005 and 2010 with lambda ranging from 0.999 (SD = 0.05) to 1.02 (SD = 0.03). We believe these population growth rates to be a conservative estimate of true population growth. Our model suggests that proposed changes to female harvest quotas for 2013–2015 will result in an annual statewide population decline of 3% and shows that, due to reduced dispersal, changes to harvest in one management unit may affect population growth in neighboring units where smaller or even no changes were made. Uncertainty regarding dispersal levels and initial population density may have a significant effect on predictions at a management unit scale (i.e. 2000 km2), while at a regional scale (i.e. 50,000 km2) large differences in initial population density result in relatively small changes in population growth rate, and uncertainty about dispersal may not be as influential. Doubling the presumed initial density from a low estimation of 2.19 total animals per 100 km2 resulted in a difference in annual population growth rate of only 2.6% statewide when compared to high density of 4.04 total animals per 100 km2 (low initial population estimate λ = 0.99, while high initial population estimate λ = 1.03). We suggest modeling tools such as this may be useful in harvest planning at a regional and statewide level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2015.05.013","usgsCitation":"Robinson, H.S., Ruth, T.K., Gude, J., Choate, D., DeSimone, R., Hebblewhite, M., Matchett, M.R., Mitchell, M.S., Murphy, K., and Williams, J., 2015, Linking resource selection and mortality modeling for population estimation of mountain lions in Montana: Ecological Modelling, v. 312, p. 11-25, https://doi.org/10.1016/j.ecolmodel.2015.05.013.","productDescription":"15 p.","startPage":"11","endPage":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057281","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":324240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"312","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576bb6b7e4b07657d1a228f1","contributors":{"authors":[{"text":"Robinson, Hugh S.","contributorId":139243,"corporation":false,"usgs":false,"family":"Robinson","given":"Hugh","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":640394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruth, Toni K.","contributorId":43657,"corporation":false,"usgs":true,"family":"Ruth","given":"Toni","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":640395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":640396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, David","contributorId":172339,"corporation":false,"usgs":false,"family":"Choate","given":"David","affiliations":[],"preferred":false,"id":640397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeSimone, Rich","contributorId":99451,"corporation":false,"usgs":true,"family":"DeSimone","given":"Rich","email":"","affiliations":[],"preferred":false,"id":640398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hebblewhite, Mark","contributorId":69455,"corporation":false,"usgs":true,"family":"Hebblewhite","given":"Mark","affiliations":[],"preferred":false,"id":640399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matchett, Marc R.","contributorId":35581,"corporation":false,"usgs":true,"family":"Matchett","given":"Marc","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":640400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":637398,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murphy, Kerry","contributorId":172340,"corporation":false,"usgs":false,"family":"Murphy","given":"Kerry","affiliations":[],"preferred":false,"id":640401,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams, Jim","contributorId":172341,"corporation":false,"usgs":false,"family":"Williams","given":"Jim","email":"","affiliations":[],"preferred":false,"id":640402,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70192652,"text":"70192652 - 2015 - Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations","interactions":[],"lastModifiedDate":"2017-11-08T15:46:00","indexId":"70192652","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations","docAbstract":"- Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.
- Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.
- Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.
- Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.
- Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.12370","usgsCitation":"Gerber, B.D., Kendall, W., Hooten, M., Dubovsky, J.A., and Drewien, R.C., 2015, Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations: Journal of Animal Ecology, v. 84, no. 5, p. 1299-1310, https://doi.org/10.1111/1365-2656.12370.","productDescription":"12 p.","startPage":"1299","endPage":"1310","ipdsId":"IP-061026","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471832,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12370","text":"Publisher Index Page"},{"id":348504,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-18","publicationStatus":"PW","scienceBaseUri":"5a0425c2e4b0dc0b45b453ff","contributors":{"authors":[{"text":"Gerber, Brian D.","contributorId":187620,"corporation":false,"usgs":false,"family":"Gerber","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":721374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":721375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dubovsky, James A.","contributorId":100763,"corporation":false,"usgs":true,"family":"Dubovsky","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":721376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drewien, Roderick C.","contributorId":195989,"corporation":false,"usgs":false,"family":"Drewien","given":"Roderick","email":"","middleInitial":"C.","affiliations":[{"id":342,"text":"Idaho Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":721377,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70169237,"text":"70169237 - 2015 - Rising methane emissions from northern wetlands associated with sea ice decline","interactions":[],"lastModifiedDate":"2016-03-24T11:47:51","indexId":"70169237","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Rising methane emissions from northern wetlands associated with sea ice decline","docAbstract":"The Arctic is rapidly transitioning toward a seasonal sea ice-free state, perhaps one of the most apparent examples of climate change in the world. This dramatic change has numerous consequences, including a large increase in air temperatures, which in turn may affect terrestrial methane emissions. Nonetheless, terrestrial and marine environments are seldom jointly analyzed. By comparing satellite observations of Arctic sea ice concentrations to methane emissions simulated by three process-based biogeochemical models, this study shows that rising wetland methane emissions are associated with sea ice retreat. Our analyses indicate that simulated high-latitude emissions for 2005–2010 were, on average, 1.7 Tg CH4 yr−1 higher compared to 1981–1990 due to a sea ice-induced, autumn-focused, warming. Since these results suggest a continued rise in methane emissions with future sea ice decline, observation programs need to include measurements during the autumn to further investigate the impact of this spatial connection on terrestrial methane emissions.
","language":"English","publisher":"Wiley","doi":"10.1002/2015GL065013","usgsCitation":"Parmentier, F.W., Zhang, W., Zhu, X., van Huissteden, J., Hayes, D.J., Zhuang, Q., Christensen, T.R., and McGuire, A.D., 2015, Rising methane emissions from northern wetlands associated with sea ice decline: Geophysical Research Letters, v. 42, no. 17, p. 7214-7222, https://doi.org/10.1002/2015GL065013.","productDescription":"9 p.","startPage":"7214","endPage":"7222","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063606","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471824,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gl065013","text":"Publisher Index Page"},{"id":319362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"17","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-10","publicationStatus":"PW","scienceBaseUri":"56f50fd1e4b0f59b85e1eba4","contributors":{"authors":[{"text":"Parmentier, Frans-Jan W.","contributorId":60537,"corporation":false,"usgs":true,"family":"Parmentier","given":"Frans-Jan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":623638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Wenxin","contributorId":167815,"corporation":false,"usgs":false,"family":"Zhang","given":"Wenxin","email":"","affiliations":[],"preferred":false,"id":623639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Xudong","contributorId":19684,"corporation":false,"usgs":true,"family":"Zhu","given":"Xudong","email":"","affiliations":[],"preferred":false,"id":623640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Huissteden, Jacobus","contributorId":167816,"corporation":false,"usgs":false,"family":"van Huissteden","given":"Jacobus","email":"","affiliations":[],"preferred":false,"id":623641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Daniel J.","contributorId":100237,"corporation":false,"usgs":true,"family":"Hayes","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":623642,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhuang, Qianlai","contributorId":101975,"corporation":false,"usgs":true,"family":"Zhuang","given":"Qianlai","affiliations":[],"preferred":false,"id":623643,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Christensen, Torben R.","contributorId":11946,"corporation":false,"usgs":true,"family":"Christensen","given":"Torben","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":623644,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":623375,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70186946,"text":"70186946 - 2015 - Heat flux from magmatic hydrothermal systems related to availability of fluid recharge","interactions":[],"lastModifiedDate":"2017-04-14T15:51:33","indexId":"70186946","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Heat flux from magmatic hydrothermal systems related to availability of fluid recharge","docAbstract":"Magmatic hydrothermal systems are of increasing interest as a renewable energy source. Surface heat flux indicates system resource potential, and can be inferred from soil CO2 flux measurements and fumarole gas chemistry. Here we compile and reanalyze results from previous CO2 flux surveys worldwide to compare heat flux from a variety of magma-hydrothermal areas. We infer that availability of water to recharge magmatic hydrothermal systems is correlated with heat flux. Recharge availability is in turn governed by permeability, structure, lithology, rainfall, topography, and perhaps unsurprisingly, proximity to a large supply of water such as the ocean. The relationship between recharge and heat flux interpreted by this study is consistent with recent numerical modeling that relates hydrothermal system heat output to rainfall catchment area. This result highlights the importance of recharge as a consideration when evaluating hydrothermal systems for electricity generation, and the utility of CO2 flux as a resource evaluation tool.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2015.07.003","usgsCitation":"Harvey, M., Rowland, J., Chiodini, G., Rissmann, C., Bloomberg, S., Hernandez, P., Mazot, A., Viveiros, F., and Werner, C.A., 2015, Heat flux from magmatic hydrothermal systems related to availability of fluid recharge: Journal of Volcanology and Geothermal Research, v. 302, p. 225-236, https://doi.org/10.1016/j.jvolgeores.2015.07.003.","productDescription":"12 p.","startPage":"225","endPage":"236","ipdsId":"IP-066187","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":339761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"302","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f1e0cae4b08144348b7e06","contributors":{"authors":[{"text":"Harvey, M. C.","contributorId":190955,"corporation":false,"usgs":false,"family":"Harvey","given":"M. C.","affiliations":[],"preferred":false,"id":691108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowland, J.V.","contributorId":190942,"corporation":false,"usgs":false,"family":"Rowland","given":"J.V.","email":"","affiliations":[],"preferred":false,"id":691109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chiodini, G.","contributorId":190943,"corporation":false,"usgs":false,"family":"Chiodini","given":"G.","affiliations":[],"preferred":false,"id":691110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rissmann, C.F.","contributorId":190944,"corporation":false,"usgs":false,"family":"Rissmann","given":"C.F.","email":"","affiliations":[],"preferred":false,"id":691111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bloomberg, S.","contributorId":190945,"corporation":false,"usgs":false,"family":"Bloomberg","given":"S.","email":"","affiliations":[],"preferred":false,"id":691112,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hernandez, P.A.","contributorId":190946,"corporation":false,"usgs":false,"family":"Hernandez","given":"P.A.","email":"","affiliations":[],"preferred":false,"id":691113,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mazot, A.","contributorId":190947,"corporation":false,"usgs":false,"family":"Mazot","given":"A.","email":"","affiliations":[],"preferred":false,"id":691114,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Viveiros, F.","contributorId":190948,"corporation":false,"usgs":false,"family":"Viveiros","given":"F.","email":"","affiliations":[],"preferred":false,"id":691115,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Werner, Cynthia A. cwerner@usgs.gov","contributorId":2540,"corporation":false,"usgs":true,"family":"Werner","given":"Cynthia","email":"cwerner@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":691107,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70159433,"text":"70159433 - 2015 - Spring plant phenology and false springs in the conterminous US during the 21st century","interactions":[],"lastModifiedDate":"2016-06-29T13:18:39","indexId":"70159433","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spring plant phenology and false springs in the conterminous US during the 21st century","docAbstract":"The onset of spring plant growth has shifted earlier in the year over the past several decades due to rising global temperatures. Earlier spring onset may cause phenological mismatches between the availability of plant resources and dependent animals, and potentially lead to more false springs, when subsequent freezing temperatures damage new plant growth. We used the extended spring indices to project changes in spring onset, defined by leaf out and by first bloom, and predicted false springs until 2100 in the conterminous United States (US) using statistically-downscaled climate projections from the Coupled Model Intercomparison Project 5 ensemble. Averaged over our study region, the median shift in spring onset was 23 days earlier in the Representative Concentration Pathway 8.5 scenario with particularly large shifts in the Western US and the Great Plains. Spatial variation in phenology was due to the influence of short-term temperature changes around the time of spring onset versus season long accumulation of warm temperatures. False spring risk increased in the Great Plains and portions of the Midwest, but remained constant or decreased elsewhere. We conclude that global climate change may have complex and spatially variable effects on spring onset and false springs, making local predictions of change difficult.
","language":"English","publisher":"American Geophysical Union","doi":"10.1088/1748-9326/10/10/104008","usgsCitation":"Allstadt, A.J., Vavrus, S.J., Heglund, P., Pidgeon, A.M., Thogmartin, W.E., and Radeloff, V.C., 2015, Spring plant phenology and false springs in the conterminous US during the 21st century: Geophysical Research Letters, v. 10, no. 10, e104008: 24 p., https://doi.org/10.1088/1748-9326/10/10/104008.","productDescription":"e104008: 24 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061582","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":471837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/10/10/104008","text":"Publisher Index Page"},{"id":310754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": 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Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":578606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heglund, Patricia J.","contributorId":141128,"corporation":false,"usgs":false,"family":"Heglund","given":"Patricia J.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":578607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pidgeon, Anna M.","contributorId":84243,"corporation":false,"usgs":true,"family":"Pidgeon","given":"Anna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":578608,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":578604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Radeloff, Volker C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":578609,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70188825,"text":"70188825 - 2015 - Mid-Cretaceous oblique rifting of West Antarctica: Emplacement and rapid cooling of the Fosdick Mountains migmatite-cored gneiss dome","interactions":[],"lastModifiedDate":"2018-03-23T12:22:12","indexId":"70188825","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2588,"text":"LITHOS","active":true,"publicationSubtype":{"id":10}},"title":"Mid-Cretaceous oblique rifting of West Antarctica: Emplacement and rapid cooling of the Fosdick Mountains migmatite-cored gneiss dome","docAbstract":"In Marie Byrd Land, West Antarctica, the Fosdick Mountains migmatite-cored gneiss dome was exhumed from mid- to lower middle crustal depths during the incipient stage of the West Antarctic Rift system in the mid-Cretaceous. Prior to and during exhumation, major crustal melting and deformation included transfer and emplacement of voluminous granitic material and numerous intrusions of mantle-derived diorite in dikes. A succession of melt- and magma-related structures formed at temperatures in excess of 665 ± 50 °C based on Ti-in-zircon thermometry. These record a transition from wrench to oblique extensional deformation that culminated in the development of the oblique South Fosdick Detachment zone. Solid-state fabrics within the detachment zone and overprinting brittle structures record translation of the detachment zone and dome to shallow levels.
To determine the duration of exhumation and cooling, we sampled granite and gneisses at high spatial resolution for U–Pb zircon geochronology and 40Ar/39Ar hornblende and biotite thermochronology. U–Pb zircon crystallization ages for the youngest granites are 102 Ma. Three hornblende ages are 103 to 100 Ma and 12 biotite ages are 101 to 99 Ma. All overlap within uncertainty. The coincidence of zircon crystallization ages with 40Ar/39Ar cooling ages indicates cooling rates > 100 °C/m.y. that, when considered together with overprinting structures, indicates rapid exhumation of granite and migmatite from deep to shallow crustal levels within a transcurrent setting. Orientations of structures and age-constrained crosscutting relationships indicate counterclockwise rotation of stretching axes from oblique extension into nearly orthogonal extension with respect to the Marie Byrd Land margin. The rotation may be a result of localized extension arising from unroofing and arching of the Fosdick dome, extensional opening within a pull-apart zone, or changes in plate boundary configuration.
The rapid tectonic and temperature evolution of the Fosdick Mountains dome lends support to recently developed numerical models of crustal flow and cooling in orogenic crust undergoing extension/transtension, and accords with numerous studies of migmatite-cored gneiss domes in transcurrent settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.lithos.2015.07.005","usgsCitation":"McFadden, R., Teyssier, C., Siddoway, C., Cosca, M.A., and Fanning, C.M., 2015, Mid-Cretaceous oblique rifting of West Antarctica: Emplacement and rapid cooling of the Fosdick Mountains migmatite-cored gneiss dome: LITHOS, v. 232, p. 306-318, https://doi.org/10.1016/j.lithos.2015.07.005.","productDescription":"13 p.","startPage":"306","endPage":"318","ipdsId":"IP-065165","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":342863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.3876953125,\n -73.3656394826721\n ],\n [\n -64.5556640625,\n -73.3656394826721\n ],\n [\n -64.5556640625,\n -69.09993967425088\n ],\n [\n -77.3876953125,\n -69.09993967425088\n ],\n [\n -77.3876953125,\n -73.3656394826721\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"232","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d21e4b062508e3c3681","contributors":{"authors":[{"text":"McFadden, Rory","contributorId":193449,"corporation":false,"usgs":false,"family":"McFadden","given":"Rory","email":"","affiliations":[],"preferred":false,"id":700508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teyssier, Christian","contributorId":193450,"corporation":false,"usgs":false,"family":"Teyssier","given":"Christian","email":"","affiliations":[],"preferred":false,"id":700509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siddoway, Christine","contributorId":193451,"corporation":false,"usgs":false,"family":"Siddoway","given":"Christine","email":"","affiliations":[],"preferred":false,"id":700510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cosca, Michael A. 0000-0002-0600-7663 mcosca@usgs.gov","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":1000,"corporation":false,"usgs":true,"family":"Cosca","given":"Michael","email":"mcosca@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fanning, C. Mark","contributorId":193428,"corporation":false,"usgs":false,"family":"Fanning","given":"C.","email":"","middleInitial":"Mark","affiliations":[],"preferred":false,"id":700511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70157198,"text":"70157198 - 2015 - The forcing of monthly precipitation variability over Southwest Asia during the Boreal cold season","interactions":[],"lastModifiedDate":"2018-03-27T13:00:59","indexId":"70157198","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2216,"text":"Journal of Climate","active":true,"publicationSubtype":{"id":10}},"title":"The forcing of monthly precipitation variability over Southwest Asia during the Boreal cold season","docAbstract":"Southwest Asia, deemed as the region containing the countries of Afghanistan, Iran, Iraq and Pakistan, is water scarce and receives nearly 75% of its annual rainfall during8 the boreal cold season of November-April. The forcing of Southwest Asia precipitation has been previously examined for the entire boreal cold season from the perspective of climate variability originating over the Atlantic and tropical Indo-Pacific Oceans. Here, we examine the inter-monthly differences in precipitation variability over Southwest Asia and the atmospheric conditions directly responsible in forcing monthly November-April precipitation. Seasonally averaged November-April precipitation over Southwest Asia is significantly correlated with sea surface temperature (SST) patterns consistent with Pacific Decadal Variability (PDV), the El Nino-Southern Oscillation (ENSO) and the warming trend of SST (Trend). On the contrary, the precipitation variability during individual months of November-April are unrelated and are correlated with SST signatures that include PDV, ENSO and Trend in different combinations. Despite strong inter-monthly differences in precipitation variability during November- April over Southwest Asia, similar atmospheric circulations, highlighted by a stationary equivalent barotropic Rossby wave centered over Iraq, force the monthly spatial distributions of precipitation. Tropospheric waves on the eastern side of the equivalent barotropic Rossby wave modifies the flux of moisture and advects the mean temperature gradient, resulting in temperature advection that is balanced by vertical motions over Southwest Asia. The forcing of monthly Southwest Asia precipitation by equivalent barotropic Rossby waves is different than the forcing by baroclinic Rossby waves associated with tropically-forced-only modes of climate variability.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JCLI-D-14-00757.1","usgsCitation":"Hoell, A., Shukla, S., Barlow, M., Cannon, F., Kelley, C., and Funk, C.C., 2015, The forcing of monthly precipitation variability over Southwest Asia during the Boreal cold season: Journal of Climate, v. 28, no. 18, p. 7038-7056, https://doi.org/10.1175/JCLI-D-14-00757.1.","productDescription":"19 p.","startPage":"7038","endPage":"7056","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066985","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jcli-d-14-00757.1","text":"Publisher Index Page"},{"id":308331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"18","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-11","publicationStatus":"PW","scienceBaseUri":"56012aaee4b03bc34f544439","contributors":{"authors":[{"text":"Hoell, Andrew","contributorId":145803,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":572236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":572237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barlow, Mathew","contributorId":145834,"corporation":false,"usgs":false,"family":"Barlow","given":"Mathew","affiliations":[{"id":16250,"text":"University of Massechusetts, Lowell","active":true,"usgs":false}],"preferred":false,"id":572238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cannon, Forest","contributorId":147642,"corporation":false,"usgs":false,"family":"Cannon","given":"Forest","email":"","affiliations":[{"id":16874,"text":"UC Santa Barbara, Geography","active":true,"usgs":false}],"preferred":false,"id":572239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelley, Colin","contributorId":147643,"corporation":false,"usgs":false,"family":"Kelley","given":"Colin","email":"","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":572240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Funk, Christopher C. 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":721,"corporation":false,"usgs":true,"family":"Funk","given":"Christopher","email":"cfunk@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":572235,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70159653,"text":"70159653 - 2015 - Factors affecting the thermal environment of Agassiz’s Desert Tortoise (Gopherus agassizii) cover sites in the Central Mojave Desert during periods of temperature extremes","interactions":[],"lastModifiedDate":"2017-01-12T11:37:21","indexId":"70159653","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Factors affecting the thermal environment of Agassiz’s Desert Tortoise (Gopherus agassizii) cover sites in the Central Mojave Desert during periods of temperature extremes","docAbstract":"Agassiz's Desert Tortoises (Gopherus agassizii) spend >95% of their lives underground in cover sites that serve as thermal buffers from temperatures, which can fluctuate >40°C on a daily and seasonal basis. We monitored temperatures at 30 active tortoise cover sites within the Soda Mountains, San Bernardino County, California, from February 2004 to September 2006. Cover sites varied in type and structural characteristics, including opening height and width, soil cover depth over the opening, aspect, tunnel length, and surficial geology. We focused our analyses on periods of extreme temperature: in summer, between July 1 and September 1, and winter, between November 1 and February 15. With the use of multivariate regression tree analyses, we found cover-site temperatures were influenced largely by tunnel length and subsequently opening width and soil cover. Linear regression models further showed that increasing tunnel length increased temperature stability and dampened seasonal temperature extremes. Climate change models predict increased warming for southwestern North America. Cover sites that buffer temperature extremes and fluctuations will become increasingly important for survival of tortoises. In planning future translocation projects and conservation efforts, decision makers should consider habitats with terrain and underlying substrate that sustain cover sites with long tunnels and expanded openings for tortoises living under temperature extremes similar to those described here or as projected in the future.
","language":"English","publisher":"The Society for the Study of Amphibians and Reptiles","doi":"10.1670/13-080","usgsCitation":"Mack, J.S., Berry, K.H., Miller, D., and Carlson, A.S., 2015, Factors affecting the thermal environment of Agassiz’s Desert Tortoise (Gopherus agassizii) cover sites in the Central Mojave Desert during periods of temperature extremes: Journal of Herpetology, v. 49, no. 3, p. 405-414, https://doi.org/10.1670/13-080.","productDescription":"10 p.","startPage":"405","endPage":"414","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-008005","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":311399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Mojave desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.14996337890625,\n 35.49757411565533\n ],\n [\n -116.663818359375,\n 35.50651802802079\n ],\n [\n -116.63497924804688,\n 35.40696093270201\n ],\n [\n -116.47430419921875,\n 35.40136418330354\n ],\n [\n -116.47979736328125,\n 35.3285710912542\n ],\n [\n -116.28341674804689,\n 35.34425514918409\n ],\n [\n -116.27517700195312,\n 35.306160014550784\n ],\n [\n -116.50039672851561,\n 35.112045209072974\n ],\n [\n -117.12112426757811,\n 35.055856273399804\n ],\n [\n -117.23098754882811,\n 35.016500995886005\n ],\n [\n -117.24472045898436,\n 35.07159307658134\n ],\n [\n -117.11975097656249,\n 35.088450570365396\n ],\n [\n -117.14996337890625,\n 35.49757411565533\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564b0c47e4b0ebfbef0d314a","contributors":{"authors":[{"text":"Mack, Jeremy S. jmack@usgs.gov","contributorId":3851,"corporation":false,"usgs":true,"family":"Mack","given":"Jeremy","email":"jmack@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":579893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":579892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":140769,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":579894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson, Andrea S.","contributorId":149879,"corporation":false,"usgs":false,"family":"Carlson","given":"Andrea","email":"","middleInitial":"S.","affiliations":[{"id":17847,"text":"USGS-WERC","active":true,"usgs":false}],"preferred":false,"id":579895,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70159958,"text":"70159958 - 2015 - Shifts in the eruptive styles at Stromboli in 2010–2014 revealed by ground-based InSAR data","interactions":[],"lastModifiedDate":"2015-12-04T16:11:07","indexId":"70159958","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Shifts in the eruptive styles at Stromboli in 2010–2014 revealed by ground-based InSAR data","docAbstract":"Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) is an efficient technique for capturing short, subtle episodes of conduit pressurization in open vent volcanoes like Stromboli (Italy), because it can detect very shallow magma storage, which is difficult to identify using other methods. This technique allows the user to choose the optimal radar location for measuring the most significant deformation signal, provides an exceptional geometrical resolution, and allows for continuous monitoring of the deformation. Here, we present and model ground displacements collected at Stromboli by GBInSAR from January 2010 to August 2014. During this period, the volcano experienced several episodes of intense volcanic activity, culminated in the effusive flank eruption of August 2014. Modelling of the deformation allowed us to estimate a source depth of 482 ± 46 m a.s.l. The cumulative volume change was 4.7 ± 2.6 × 105 m3. The strain energy of the source was evaluated 3–5 times higher than the surface energy needed to open the 6–7 August eruptive fissure. The analysis proposed here can help forecast shifts in the eruptive style and especially the onset of flank eruptions at Stromboli and at similar volcanic systems (e.g. Etna, Piton de La Fournaise, Kilauea).
","language":"English","publisher":"Nature Publishing Group (NPG)","doi":"10.1038/srep13569","usgsCitation":"Di Traglia, F., Battaglia, M., Nolesini, T., Lagomarsino, D., and Casaglia, N., 2015, Shifts in the eruptive styles at Stromboli in 2010–2014 revealed by ground-based InSAR data: Scientific Reports, no. 5, 11 p., https://doi.org/10.1038/srep13569.","productDescription":"11 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064541","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471838,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep13569","text":"Publisher Index Page"},{"id":311951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311915,"type":{"id":15,"text":"Index Page"},"url":"https://www.nature.com/articles/srep13569"}],"country":"Italy","otherGeospatial":"Stromboli","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 15.213661193847658,\n 38.81189098781871\n ],\n [\n 15.190315246582033,\n 38.79771102715645\n ],\n [\n 15.184478759765627,\n 38.790753788294424\n ],\n [\n 15.191688537597654,\n 38.7800490179011\n ],\n [\n 15.201988220214846,\n 38.77656962147866\n ],\n [\n 15.215721130371096,\n 38.77041335043523\n ],\n [\n 15.226364135742188,\n 38.77442837007637\n ],\n [\n 15.232543945312498,\n 38.78459874169886\n ],\n [\n 15.240097045898438,\n 38.79450007821985\n ],\n [\n 15.24421691894531,\n 38.80573776659133\n ],\n [\n 15.228080749511719,\n 38.812426025416734\n ],\n [\n 15.216751098632812,\n 38.81296105899589\n ],\n [\n 15.213661193847658,\n 38.81189098781871\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-01","publicationStatus":"PW","scienceBaseUri":"5662c759e4b06a3ea36c67cb","contributors":{"authors":[{"text":"Di Traglia, Federico","contributorId":150264,"corporation":false,"usgs":false,"family":"Di Traglia","given":"Federico","email":"","affiliations":[{"id":17947,"text":"Università di Firenze","active":true,"usgs":false}],"preferred":false,"id":581188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battaglia, Maurizio mbattaglia@usgs.gov","contributorId":139631,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":581187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolesini, Teresa","contributorId":150265,"corporation":false,"usgs":false,"family":"Nolesini","given":"Teresa","email":"","affiliations":[{"id":17947,"text":"Università di Firenze","active":true,"usgs":false}],"preferred":false,"id":581189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lagomarsino, Daniela","contributorId":150266,"corporation":false,"usgs":false,"family":"Lagomarsino","given":"Daniela","email":"","affiliations":[{"id":17947,"text":"Università di Firenze","active":true,"usgs":false}],"preferred":false,"id":581190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casaglia, Nicola","contributorId":150267,"corporation":false,"usgs":false,"family":"Casaglia","given":"Nicola","email":"","affiliations":[{"id":17947,"text":"Università di Firenze","active":true,"usgs":false}],"preferred":false,"id":581191,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70186566,"text":"70186566 - 2015 - Development of twelve microsatellite loci in the red tree corals Primnoa resedaeformis and Primnoa pacifica","interactions":[],"lastModifiedDate":"2017-04-05T15:57:17","indexId":"70186566","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"Development of twelve microsatellite loci in the red tree corals Primnoa resedaeformis and Primnoa pacifica","docAbstract":"A suite of tetra-, penta-, and hexa-nucleotide microsatellite loci were developed from Roche 454 pyrosequencing data for the cold-water octocorals Primnoa resedaeformis and P. pacifica. Twelve of 98 primer sets tested consistently amplified in 30 P. resedaeformis samples from Baltimore Canyon (western North Atlantic Ocean) and in 24 P. pacifica samples (Shutter Ridge, eastern Gulf of Alaska). The loci displayed moderate levels of allelic diversity (average 7.5 alleles/locus) and heterozygosity (average 47 %). Levels of genetic diversity were sufficient to produce unique multi-locus genotypes and to distinguish species. These common species are long-lived (hundreds of years) and provide essential fish habitat (P. pacifica), yet populations are provided little protection from human activities. These loci will be used to determine regional patterns of population connectivity to inform effective marine spatial planning and ecosystem-based fisheries management.
","language":"English","publisher":"Springer","doi":"10.1007/s12686-015-0455-1","usgsCitation":"Morrison, C.L., Springmann, M.J., Shroades, K., and Stone, R.P., 2015, Development of twelve microsatellite loci in the red tree corals Primnoa resedaeformis and Primnoa pacifica: Conservation Genetics Resources, v. 7, no. 3, p. 763-765, https://doi.org/10.1007/s12686-015-0455-1.","productDescription":"3 p.","startPage":"763","endPage":"765","ipdsId":"IP-061828","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":339267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-21","publicationStatus":"PW","scienceBaseUri":"58e60273e4b09da6799ac685","contributors":{"authors":[{"text":"Morrison, Cheryl L. 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":146488,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":689603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springmann, Marcus J. mspringmann@usgs.gov","contributorId":4372,"corporation":false,"usgs":true,"family":"Springmann","given":"Marcus","email":"mspringmann@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":689604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shroades, Kelsey kshroades@usgs.gov","contributorId":190568,"corporation":false,"usgs":true,"family":"Shroades","given":"Kelsey","email":"kshroades@usgs.gov","affiliations":[],"preferred":true,"id":689605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stone, Robert P.","contributorId":190569,"corporation":false,"usgs":false,"family":"Stone","given":"Robert","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":689606,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70148613,"text":"70148613 - 2015 - Wetland occupancy of pond-breeding amphibians in Yosemite National Park, USA","interactions":[],"lastModifiedDate":"2015-09-16T09:49:22","indexId":"70148613","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3895,"text":"Journal of North American Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Wetland occupancy of pond-breeding amphibians in Yosemite National Park, USA","docAbstract":"We estimated wetland occupancy and population trends for three species of pond-breeding anurans in Yosemite National Park from 2007-2011. We used a double survey technique in which two observers independently surveyed each site on the same day. Double surveys allowed us to calculate detectability for the three most common anurans within the park: Rana sierrae, Anaxyrus canorus, and Pseudacris regilla. Annual estimates of detectability were generally high; mean detectability ranged from 73.7% + 0.6 (SE) for any life history stage of A. canorus to 86.7% + 0.7 for sites with P. regilla reproduction (eggs or larvae present). Detectability was most variable for Anaxyrus canorus, which ranged from 45.9% to 99.7%. The probability of occupancy for R. sierrae was highest in larger, low-elevation wetlands that lacked fish. Anaxyrus canorus were more common in shallow high-elevation ponds; their occurrence was minimally impacted by the presence of fish. Finally, occurrence of P. regilla was largely unrelated to wetland size and elevation, but like R. sierrae, they were less likely to occupy sites with fi sh. Occupancy showed no trend over the five years of our study for R. sierrae or A. canorus when considering either sites with any life stage or only sites with reproduction. However, P. regilla showed a modest downward trend for sites with any life stage and sites with reproduction. Our results for R. sierrae run counter to expectations given recent concern about the decline of this species, while our findings for P. regilla raise concerns for this widespread and generally common species.
","language":"English","publisher":"The Center for North American Herpetology","usgsCitation":"Fellers, G.M., Kleeman, P.M., and Miller, D., 2015, Wetland occupancy of pond-breeding amphibians in Yosemite National Park, USA: Journal of North American Herpetology, v. 2015, no. 1, p. 22-33.","productDescription":"12 p","startPage":"22","endPage":"33","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2007-01-01","temporalEnd":"2011-12-31","ipdsId":"IP-059719","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":308163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":305748,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://jnah.cnah.org/default.aspx","text":"Publisher Index Page"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.66583251953124,\n 37.41270958119496\n ],\n [\n -119.80590820312499,\n 37.66860332433055\n ],\n [\n -119.89929199218749,\n 37.808698976006795\n ],\n [\n -119.94529724121092,\n 38.129155479572624\n ],\n [\n -119.46258544921874,\n 38.13779704369439\n ],\n [\n -119.25796508789061,\n 37.938782346134396\n ],\n [\n -119.14810180664061,\n 37.74465712069939\n ],\n [\n -119.13162231445311,\n 37.54131068652799\n ],\n [\n -119.26895141601562,\n 37.42361656106772\n ],\n [\n -119.66583251953124,\n 37.41270958119496\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2015","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fa92d8e4b05d6c4e501aee","contributors":{"authors":[{"text":"Fellers, Gary M. 0000-0003-4092-0285 gary_fellers@usgs.gov","orcid":"https://orcid.org/0000-0003-4092-0285","contributorId":3150,"corporation":false,"usgs":true,"family":"Fellers","given":"Gary","email":"gary_fellers@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":548886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":548887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A.W.","contributorId":19423,"corporation":false,"usgs":true,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":548888,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70191146,"text":"70191146 - 2015 - Amphibole reaction rims as a record of pre-eruptive magmatic heating: An experimental approach","interactions":[],"lastModifiedDate":"2017-09-27T17:02:15","indexId":"70191146","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Amphibole reaction rims as a record of pre-eruptive magmatic heating: An experimental approach","docAbstract":"Magmatic minerals record the pre-eruptive timescales of magma ascent and mixing in crustal reservoirs and conduits. Investigations of the mineral records of magmatic processes are fundamental to our understanding of what controls eruption style, as ascent rates and magma mixing processes are well known to control and/or trigger potentially hazardous explosive eruptions. Thus, amphibole reaction rims are often used to infer pre-eruptive magma dynamics, and in particular to estimate magma ascent rates. However, while several experimental studies have investigated amphibole destabilization during decompression, only two investigated thermal destabilization relevant to magma mixing processes. This study examines amphibole decomposition experimentally through isobaric heating of magnesio-hornblende phenocrysts within a natural high-silica andesite glass. The experiments first equilibrated for 24 h at 870 °C and 140 MPa at H2O-saturated conditions and ƒO2 ∼ Re–ReO prior to rapid heating to 880, 900, or 920 °C and hold times of 3–48 h. At 920 °C, rim thicknesses increased from 17 μm after 3 h, to 55 μm after 12 h, and became pseudomorphs after longer durations. At 900 °C, rim thicknesses increased from 7 μm after 3 h, to 80 μm after 24 h, to pseudomorphs after longer durations. At 880 °C, rim thicknesses increased from 7 μm after 3 h, to 18 μm after 36 h, to pseudomorphs after 48 h. Reaction rim microlites vary from 5–16 μm in size, with no systematic relationship between crystal size and the duration or magnitude of heating. Time-averaged rim microlite growth rates decrease steadily with increasing experimental duration (from ![\"View](\"https://ars.els-cdn.com/content/image/1-s2.0-S0012821X15004148-si1.gif\" "\\"View")
to 3.1 to ![\"View](\"https://ars.els-cdn.com/content/image/1-s2.0-S0012821X15004148-si2.gif\" "\\"View")
). Time-averaged microlite nucleation rates also decrease with increasing experimental duration (from ![\"View](\"https://ars.els-cdn.com/content/image/1-s2.0-S0012821X15004148-si3.gif\" "\\"View")
to 5.3 mm−3 s−1). There is no systematic relationship between time-averaged growth or nucleation rates and the magnitude of the heating step. Ortho- and clinopyroxene together constitute 57–90 modal % mineralogy in each reaction rim. At constant temperature, clinopyroxene abundances decrease with increasing experimental duration, from 72 modal % (3 h at 900 °C) to 0% (48 h at 880 °C, and 36 h at 900 and 920 °C). Fe–Ti oxides increase from 6–12 modal % (after 3–6 h) to 26–34 modal % (after 36–48 h). Plagioclase occurs in relatively minor amounts (<1–11 modal %), with anorthite contents that increase from An56 to An88 from 3 to 36 h of heating. Distal glass compositions (>500 μm from reacted amphibole) are consistent with inter-microlite rim glasses (71.3–77.7 wt.% SiO2) within a given experiment and there is a weakly positive correlation between increasing run duration and inter-microlite melt SiO2 (68.9–78.5 wt.%). Our results indicate that experimental heating-induced amphibole reaction rims have thicknesses, textures, and mineralogies consistent with many of the natural reaction rims seen at arc-andesite volcanoes. They are also texturally consistent with experimental decompression reaction rims. On this basis it may be challenging to distinguish between decompression and heating mechanisms in nature.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2015.06.051","usgsCitation":"De Angelis, S.H., Larsen, J., Coombs, M.L., Dunn, A., and Hayden, L.A., 2015, Amphibole reaction rims as a record of pre-eruptive magmatic heating: An experimental approach: Earth and Planetary Science Letters, v. 426, p. 235-245, https://doi.org/10.1016/j.epsl.2015.06.051.","productDescription":"11 p.","startPage":"235","endPage":"245","ipdsId":"IP-051815","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471826,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2015.06.051","text":"Publisher Index Page"},{"id":346142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"426","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ccb8a6e4b017cf314383e2","contributors":{"authors":[{"text":"De Angelis, S. H.","contributorId":196732,"corporation":false,"usgs":false,"family":"De Angelis","given":"S.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":711354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, J.","contributorId":74544,"corporation":false,"usgs":true,"family":"Larsen","given":"J.","affiliations":[],"preferred":false,"id":711355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":711356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunn, A.","contributorId":196733,"corporation":false,"usgs":false,"family":"Dunn","given":"A.","affiliations":[],"preferred":false,"id":711357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayden, Leslie A. lhayden@usgs.gov","contributorId":5926,"corporation":false,"usgs":true,"family":"Hayden","given":"Leslie","email":"lhayden@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":711358,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70174875,"text":"70174875 - 2015 - Model averaging and muddled multimodel inferences","interactions":[],"lastModifiedDate":"2017-05-04T10:07:30","indexId":"70174875","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Model averaging and muddled multimodel inferences","docAbstract":"Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-1639.1","usgsCitation":"Cade, B.S., 2015, Model averaging and muddled multimodel inferences: Ecology, v. 96, no. 9, p. 2370-7382, https://doi.org/10.1890/14-1639.1.","startPage":"2370","endPage":"7382","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051478","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":325441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"578f4f2fe4b0ad6235cf002e","contributors":{"authors":[{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":642943,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70192438,"text":"70192438 - 2015 - The climate space of fire regimes in north-western North America","interactions":[],"lastModifiedDate":"2017-10-26T14:10:10","indexId":"70192438","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"The climate space of fire regimes in north-western North America","docAbstract":"Aim. Studies of fire activity along environmental gradients have been undertaken, but the results of such studies have yet to be integrated with fire-regime analysis. We characterize fire-regime components along climate gradients and a gradient of human influence.
Location. We focus on a climatically diverse region of north-western North America extending from northern British Columbia, Canada, to northern Utah and Colorado, USA.
Methods. We used a multivariate framework to collapse 12 climatic variables into two major climate gradients and binned them into 73 discrete climate domains. We examined variation in fire-regime components (frequency, size, severity, seasonality and cause) across climate domains. Fire-regime attributes were compiled from existing databases and Landsat imagery for 1897 large fires. Relationships among the fire-regime components, climate gradients and human influence were examined through bivariate regressions. The unique contribution of human influence was also assessed.
Results. A primary climate gradient of temperature and summer precipitation and a secondary gradient of continentality and winter precipitation in the study area were identified. Fire occupied a distinct central region of such climate space, within which fire-regime components varied considerably. We identified significant interrelations between fire-regime components of fire size, frequency, burn severity and cause. The influence of humans was apparent in patterns of burn severity and ignition cause.
Main conclusions. Wildfire activity is highest where thermal and moisture gradients converge to promote fuel production, flammability and ignitions. Having linked fire-regime components to large-scale climate gradients, we show that fire regimes – like the climate that controls them – are a part of a continuum, expanding on models of varying constraints on fire activity. The observed relationships between fire-regime components, together with the distinct role of climatic and human influences, generate variation in biotic communities. Thus, future changes to climate may lead to ecological changes through altered fire regimes.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.12533","usgsCitation":"Whitman, E., Batllori, E., Parisien, M., Miller, C., Coop, J.D., Krawchuk, M.A., Chong, G.W., and Haire, S.L., 2015, The climate space of fire regimes in north-western North America: Journal of Biogeography, v. 42, no. 9, p. 1736-1749, https://doi.org/10.1111/jbi.12533.","productDescription":"14 p.","startPage":"1736","endPage":"1749","ipdsId":"IP-060450","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.4130859375,\n 45.644768217751924\n ],\n [\n -112.32421875,\n 49.49667452747045\n ],\n [\n -115.57617187499999,\n 53.592504809039376\n ],\n [\n -120.76171875,\n 54.92714186454645\n ],\n [\n -125.41992187499999,\n 57.040729838360875\n ],\n [\n -131.8359375,\n 57.040729838360875\n ],\n [\n -121.201171875,\n 49.89463439573421\n ],\n [\n -122.4755859375,\n 48.80686346108517\n ],\n [\n -121.9921875,\n 43.35713822211053\n ],\n [\n -114.12597656249999,\n 43.61221676817573\n ],\n [\n -114.0380859375,\n 45.27488643704891\n ],\n [\n -112.8955078125,\n 44.15068115978094\n ],\n [\n -111.62109375,\n 44.08758502824516\n ],\n [\n -111.181640625,\n 40.84706035607122\n ],\n [\n -108.7646484375,\n 40.04443758460856\n ],\n [\n -106.4794921875,\n 41.0130657870063\n ],\n [\n -108.4130859375,\n 45.644768217751924\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-26","publicationStatus":"PW","scienceBaseUri":"5a07eb44e4b09af898c8ccd0","contributors":{"authors":[{"text":"Whitman, Ellen","contributorId":187429,"corporation":false,"usgs":false,"family":"Whitman","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":715828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Batllori, Enric","contributorId":198367,"corporation":false,"usgs":false,"family":"Batllori","given":"Enric","email":"","affiliations":[],"preferred":false,"id":715829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parisien, Marc-Andre","contributorId":198368,"corporation":false,"usgs":false,"family":"Parisien","given":"Marc-Andre","email":"","affiliations":[],"preferred":false,"id":715830,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Carol","contributorId":187430,"corporation":false,"usgs":false,"family":"Miller","given":"Carol","email":"","affiliations":[],"preferred":false,"id":715832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coop, Jonathan D.","contributorId":187427,"corporation":false,"usgs":false,"family":"Coop","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":715833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krawchuk, Meg A.","contributorId":187425,"corporation":false,"usgs":false,"family":"Krawchuk","given":"Meg","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":715827,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haire, Sandra L.","contributorId":187426,"corporation":false,"usgs":false,"family":"Haire","given":"Sandra","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":715831,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70187279,"text":"70187279 - 2015 - Catchment-wide survival of wild- and hatchery-reared Atlantic salmon smolts in a changing system","interactions":[],"lastModifiedDate":"2017-04-28T10:47:35","indexId":"70187279","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Catchment-wide survival of wild- and hatchery-reared Atlantic salmon smolts in a changing system","docAbstract":"We developed a hierarchical multistate model to estimate survival of Atlantic salmon (Salmo salar) smolts in the Penobscot River, USA, over a decade during which two mainstem dams were removed from the catchment. We investigated effects of (i) environmental factors, (ii) rearing history, and (iii) management actions, including dam removal, turbine shutdown, and installation of new powerhouses. Mean ± SD smolt survival per kilometre was higher through free-flowing reaches of the catchment (0.995 ± 0.004·km−1) than through reaches containing dams that remain in the system (0.970 ± 0.019·km−1). We observed maximum survival between 12 and 17 °C and at intermediate discharges (1200 m3·s−1). Smolt survival increased concurrent with dam removal and decreased following increases in hydropower generation. The greatest increase in smolt survival followed seasonal turbine shutdowns at a dam located on the largest tributary to the Penobscot River, while other shutdowns had little influence. Our model provides a useful tool for assessing changes to survival of migratory species and will be useful for informing stocking plans to maximize numbers of smolts leaving coastal systems.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2014-0573","usgsCitation":"Stich, D.S., Bailey, M.M., Holbrook, C., Kinnison, M.T., and Zydlewski, J.D., 2015, Catchment-wide survival of wild- and hatchery-reared Atlantic salmon smolts in a changing system: Canadian Journal of Fisheries and Aquatic Sciences, v. 72, no. 9, p. 1352-1365, https://doi.org/10.1139/cjfas-2014-0573.","productDescription":"14 p.","startPage":"1352","endPage":"1365","ipdsId":"IP-060933","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590454a7e4b022cee40dc24e","contributors":{"authors":[{"text":"Stich, Daniel S.","contributorId":139212,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":12606,"text":"University of Maine, Dept of Plant, Soil, & Envir Sciences","active":true,"usgs":false}],"preferred":false,"id":693447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Michael M.","contributorId":169684,"corporation":false,"usgs":false,"family":"Bailey","given":"Michael","email":"","middleInitial":"M.","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":693448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":4198,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher M.","email":"cholbrook@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":693449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinnison, Michael T.","contributorId":169617,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":693450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"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":693210,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70187291,"text":"70187291 - 2015 - Monitoring landscape-level distribution and migration Phenology of Raptors using a volunteer camera-trap network","interactions":[],"lastModifiedDate":"2017-11-27T09:18:57","indexId":"70187291","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring landscape-level distribution and migration Phenology of Raptors using a volunteer camera-trap network","docAbstract":"Conservation of animal migratory movements is among the most important issues in wildlife management. To address this need for landscape-scale monitoring of raptor populations, we developed a novel, baited photographic observation network termed the “Appalachian Eagle Monitoring Program” (AEMP). During winter months of 2008–2012, we partnered with professional and citizen scientists in 11 states in the United States to collect approximately 2.5 million images. To our knowledge, this represents the largest such camera-trap effort to date. Analyses of data collected in 2011 and 2012 revealed complex, often species-specific, spatial and temporal patterns in winter raptor movement behavior as well as spatial and temporal resource partitioning between raptor species. Although programmatic advances in data analysis and involvement are needed, the continued growth of the program has the potential to provide a long-term, cost-effective, range-wide monitoring tool for avian and terrestrial scavengers during the winter season. Perhaps most importantly, by relying heavily on citizen scientists, AEMP has the potential to improve long-term interest and support for raptor conservation and serve as a model for raptor conservation programs in other portions of the world.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.571","usgsCitation":"Jachowski, D.S., Katzner, T., Rodrigue, J.L., and Ford, W.M., 2015, Monitoring landscape-level distribution and migration Phenology of Raptors using a volunteer camera-trap network: Wildlife Society Bulletin, v. 39, no. 3, p. 553-563, https://doi.org/10.1002/wsb.571.","productDescription":"11 p.","startPage":"553","endPage":"563","ipdsId":"IP-057779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500021,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/cb007dbfa04643b9ad65dc256e614b86","text":"External Repository"},{"id":340544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-11","publicationStatus":"PW","scienceBaseUri":"59030327e4b0e862d230f737","contributors":{"authors":[{"text":"Jachowski, David S.","contributorId":82966,"corporation":false,"usgs":true,"family":"Jachowski","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":693223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodrigue, Jane L.","contributorId":150352,"corporation":false,"usgs":false,"family":"Rodrigue","given":"Jane","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693292,"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":693222,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70188145,"text":"70188145 - 2015 - Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach","interactions":[],"lastModifiedDate":"2017-06-01T12:52:37","indexId":"70188145","displayToPublicDate":"2015-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach","docAbstract":"Hurricane flood impacts to residential buildings in coastal zones are caused by a number of hazards, such as inundation, overflow currents, erosion, and wave attack. However, traditional hurricane damage models typically make use of stage-damage functions, where the stage is related to flooding depth only. Moreover, these models are deterministic and do not consider the large amount of uncertainty associated with both the processes themselves and with the predictions. This uncertainty becomes increasingly important when multiple hazards (flooding, wave attack, erosion, etc.) are considered simultaneously. This paper focusses on establishing relationships between observed damage and multiple hazard indicators in order to make better probabilistic predictions. The concept consists of (1) determining Local Hazard Indicators (LHIs) from a hindcasted storm with use of a nearshore morphodynamic model, XBeach, and (2) coupling these LHIs and building characteristics to the observed damages. We chose a Bayesian Network approach in order to make this coupling and used the LHIs ‘Inundation depth’, ‘Flow velocity’, ‘Wave attack’, and ‘Scour depth’ to represent flooding, current, wave impacts, and erosion related hazards.
The coupled hazard model was tested against four thousand damage observations from a case site at the Rockaway Peninsula, NY, that was impacted by Hurricane Sandy in late October, 2012. The model was able to accurately distinguish ‘Minor damage’ from all other outcomes 95% of the time and could distinguish areas that were affected by the storm, but not severely damaged, 68% of the time. For the most heavily damaged buildings (‘Major Damage’ and ‘Destroyed’), projections of the expected damage underestimated the observed damage. The model demonstrated that including multiple hazards doubled the prediction skill, with Log-Likelihood Ratio test (a measure of improved accuracy and reduction in uncertainty) scores between 0.02 and 0.17 when only one hazard is considered and a score of 0.37 when multiple hazards are considered simultaneously. The LHIs with the most predictive skill were ‘Inundation depth’ and ‘Wave attack’. The Bayesian Network approach has several advantages over the market-standard stage-damage functions: the predictive capacity of multiple indicators can be combined; probabilistic predictions can be obtained, which include uncertainty; and quantitative as well as descriptive information can be used simultaneously.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2015.05.006","usgsCitation":"van Verseveld, H., Van Dongeren, A., Plant, N.G., Jager, W., and den Heijer, C., 2015, Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach: Coastal Engineering, v. 103, p. 1-14, https://doi.org/10.1016/j.coastaleng.2015.05.006.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-083654","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593127b0e4b0e9bd0ea9ef12","contributors":{"authors":[{"text":"van Verseveld, H.C.W.","contributorId":192572,"corporation":false,"usgs":false,"family":"van Verseveld","given":"H.C.W.","email":"","affiliations":[],"preferred":false,"id":696882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Dongeren, A. R.","contributorId":55572,"corporation":false,"usgs":true,"family":"Van Dongeren","given":"A. R.","affiliations":[],"preferred":false,"id":696883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":696881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jager, W.S.","contributorId":192574,"corporation":false,"usgs":false,"family":"Jager","given":"W.S.","email":"","affiliations":[],"preferred":false,"id":696884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"den Heijer, C.","contributorId":192575,"corporation":false,"usgs":false,"family":"den Heijer","given":"C.","affiliations":[],"preferred":false,"id":696885,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
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