Large areas of forest in the tropics have been cleared and converted to pastureland. Hawai‘i Island is no exception, with over 100,000 ha of historically forested land now dominated by non-native grasses. Passive forest restoration has been unsuccessful because these grasslands tend to persist even after grazers have been removed, yet active outplanting of native tree species can be cost-prohibitive at the landscape scale. It is therefore essential to seek co-benefits of forest restoration to defray costs, such as accredited carbon offsets from increased carbon sequestration. We developed a reforestation scenario for non-native grasslands on Hawai‘i Island by outplanting endemic koa (Acacia koa) trees paid for with carbon offsets via the California Cap and Trade Program. This scenario entails reforesting 53,531 ha of non-native grassland at 2500 ha y-1 over 22 years. We estimated planting costs at \\$6,178 ha-1, a total cost of approximately \\$331,000,000. We used the Land Use and Carbon Simulator (LUCAS) model to estimate island-wide ecosystem carbon sequestration with and without koa reforestation using 100 Monte Carlo simulations per year over a 60-year period. Income from carbon offsets was set at \\$13.57 per ton of CO2 equivalent, the current California Cap and Trade Program carbon market price.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Acacia koa in Hawaiʻi: Facing the future: 2016 Koa symposium proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Acacia koa in Hawaiʻi: Facing the Future","conferenceDate":"October 5, 2016","conferenceLocation":"Hilo, HI","language":"English","publisher":"Tropical Hardwood Tree Improvement and Regeneration Center","usgsCitation":"Selmants, P., Sleeter, B.M., Koch, N., and Friday, J.B., 2016, The potential carbon benefit of reforesting Hawai‘i Island non-native grasslands with endemic Acacia koa trees, in Acacia koa in Hawaiʻi: Facing the future: 2016 Koa symposium proceedings, Hilo, HI, October 5, 2016, p. 54-55.","productDescription":"2 p.","startPage":"54","endPage":"55","ipdsId":"IP-090079","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":345038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":345037,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.ctahr.hawaii.edu/forestry/trees/koa_2016.html"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Island of Hawai'i","publicComments":"Extended abstract.","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599e9449e4b04935557fe9d7","contributors":{"editors":[{"text":"Ohara, Rebekah Dickens","contributorId":34016,"corporation":false,"usgs":false,"family":"Ohara","given":"Rebekah","email":"","middleInitial":"Dickens","affiliations":[],"preferred":false,"id":708241,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Friday, James B.","contributorId":195791,"corporation":false,"usgs":false,"family":"Friday","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":33500,"text":"University of Hawai`i at Manoa","active":true,"usgs":false}],"preferred":false,"id":708242,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":708201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":708202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Nicholas","contributorId":195790,"corporation":false,"usgs":false,"family":"Koch","given":"Nicholas","email":"","affiliations":[{"id":34387,"text":"Forest Solutions, Inc.","active":true,"usgs":false}],"preferred":false,"id":708203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friday, James B.","contributorId":195791,"corporation":false,"usgs":false,"family":"Friday","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":33500,"text":"University of Hawai`i at Manoa","active":true,"usgs":false}],"preferred":false,"id":708204,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184223,"text":"70184223 - 2016 - Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble","interactions":[],"lastModifiedDate":"2017-03-06T11:13:04","indexId":"70184223","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2316,"text":"Journal of Geophysical Research D: Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble","docAbstract":"We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015JD024053","usgsCitation":"Lorenz, R., Argueso, D., Donat, M.G., Pitman, A.J., van den Hurk, B., Berg, A., Lawrence, D.M., Cheruy, F., Ducharne, A., Hagemann, S., Meier, A., Milly, P., and Seneviratne, S., 2016, Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble: Journal of Geophysical Research D: Atmospheres, v. 121, no. 2, p. 607-623, https://doi.org/10.1002/2015JD024053.","productDescription":"17 p.","startPage":"607","endPage":"623","ipdsId":"IP-071044","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":471362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11858/00-001M-0000-0029-CE77-E","text":"External Repository"},{"id":336865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-19","publicationStatus":"PW","scienceBaseUri":"58be833be4b014cc3a3a99eb","contributors":{"authors":[{"text":"Lorenz, Ruth","contributorId":187491,"corporation":false,"usgs":false,"family":"Lorenz","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":680611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Argueso, Daniel","contributorId":187492,"corporation":false,"usgs":false,"family":"Argueso","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":680612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donat, Markus G.","contributorId":187493,"corporation":false,"usgs":false,"family":"Donat","given":"Markus","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":680613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pitman, Andrew J.","contributorId":187494,"corporation":false,"usgs":false,"family":"Pitman","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van den Hurk, Bart","contributorId":187495,"corporation":false,"usgs":false,"family":"van den Hurk","given":"Bart","email":"","affiliations":[],"preferred":false,"id":680615,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Berg, Alexis","contributorId":187496,"corporation":false,"usgs":false,"family":"Berg","given":"Alexis","email":"","affiliations":[],"preferred":false,"id":680616,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, David M.","contributorId":105206,"corporation":false,"usgs":false,"family":"Lawrence","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":680617,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cheruy, Frederique","contributorId":187497,"corporation":false,"usgs":false,"family":"Cheruy","given":"Frederique","affiliations":[],"preferred":false,"id":680618,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ducharne, Agnes","contributorId":187498,"corporation":false,"usgs":false,"family":"Ducharne","given":"Agnes","affiliations":[],"preferred":false,"id":680619,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hagemann, Stefan","contributorId":187499,"corporation":false,"usgs":false,"family":"Hagemann","given":"Stefan","email":"","affiliations":[],"preferred":false,"id":680620,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Meier, Arndt","contributorId":187500,"corporation":false,"usgs":false,"family":"Meier","given":"Arndt","email":"","affiliations":[],"preferred":false,"id":680621,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":680610,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Seneviratne, Sonia I","contributorId":187501,"corporation":false,"usgs":false,"family":"Seneviratne","given":"Sonia I","affiliations":[],"preferred":false,"id":680622,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70187351,"text":"70187351 - 2016 - Geomorphic evolution of the San Luis Basin and Rio Grande in southern Colorado and northern New Mexico","interactions":[],"lastModifiedDate":"2017-05-01T15:05:10","indexId":"70187351","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1724,"text":"GSA Field Guides","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic evolution of the San Luis Basin and Rio Grande in southern Colorado and northern New Mexico","docAbstract":"The San Luis Basin encompasses the largest structural and hydrologic basin of the Rio Grande rift. On this field trip, we will examine the timing of transition of the San Luis Basin from hydrologically closed, aggrading subbasins to a continuous fluvial system that eroded the basin, formed the Rio Grande gorge, and ultimately, integrated the Rio Grande from Colorado to the Gulf of Mexico. Waning Pleistocene neotectonic activity and onset of major glacial episodes, in particular Marine Isotope Stages 11–2 (~420–14 ka), induced basin fill, spillover, and erosion of the southern San Luis Basin. The combined use of new geologic mapping, fluvial geomorphology, reinterpreted surficial geology of the Taos Plateau, pedogenic relative dating studies, 3He surface exposure dating of basalts, and U-series dating of pedogenic carbonate supports a sequence of events wherein pluvial Lake Alamosa in the northern San Luis Basin overflowed, and began to drain to the south across the closed Sunshine Valley–Costilla Plain region ≤400 ka. By ~200 ka, erosion had cut through topographic highs at Ute Mountain and the Red River fault zone, and began deep-canyon incision across the southern San Luis Basin. Previous studies indicate that prior to 200 ka, the present Rio Grande terminated into a large bolson complex in the vicinity of El Paso, Texas, and systematic, headward erosional processes had subtly integrated discontinuously connected basins along the eastern flank of the Rio Grande rift and southern Rocky Mountains. We propose that the integration of the entire San Luis Basin into the Rio Grande drainage system (~400–200 ka) was the critical event in the formation of the modern Rio Grande, integrating hinterland basins of the Rio Grande rift from El Paso, Texas, north to the San Luis Basin with the Gulf of Mexico. This event dramatically affected basins southeast of El Paso, Texas, across the Chisos Mountains and southeastern Basin and Range province, including the Rio Conchos watershed and much of the Chihuahuan Desert, inducing broad regional landscape incision and exhumation.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.0044(13)","usgsCitation":"Ruleman, C.A., Machette, M., Thompson, R.A., Miggins, D.M., Goehring, B.M., and Paces, J.B., 2016, Geomorphic evolution of the San Luis Basin and Rio Grande in southern Colorado and northern New Mexico: GSA Field Guides, v. 44, p. 291-333, https://doi.org/10.1130/2016.0044(13).","productDescription":"43 p.","startPage":"291","endPage":"333","ipdsId":"IP-076013","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":340697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":" Colorado, New Mexico","otherGeospatial":"Rio Grande, San Luis Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105,\n 36.2\n ],\n [\n -106.5,\n 36.2\n ],\n [\n -106.5,\n 38.5\n ],\n [\n -105,\n 38.5\n ],\n [\n -105,\n 36.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59084929e4b0fc4e448ffd56","contributors":{"authors":[{"text":"Ruleman, Chester A. 0000-0002-1503-4591 cruleman@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-4591","contributorId":1264,"corporation":false,"usgs":true,"family":"Ruleman","given":"Chester","email":"cruleman@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Machette, Michael","contributorId":191604,"corporation":false,"usgs":false,"family":"Machette","given":"Michael","affiliations":[],"preferred":false,"id":693584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Ren A. 0000-0002-3044-3043 rathomps@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":1265,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren","email":"rathomps@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miggins, Dan M","contributorId":191605,"corporation":false,"usgs":false,"family":"Miggins","given":"Dan","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":693585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goehring, Brent M","contributorId":191606,"corporation":false,"usgs":false,"family":"Goehring","given":"Brent","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":693586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693587,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187721,"text":"70187721 - 2016 - An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus","interactions":[],"lastModifiedDate":"2018-06-16T17:49:28","indexId":"70187721","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2671,"text":"Marine Mammal Science","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus","docAbstract":"State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.
","language":"English","publisher":"Wiley","doi":"10.1111/mms.12332","usgsCitation":"Beatty, W.S., Jay, C.V., and Fischbach, A.S., 2016, An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus: Marine Mammal Science, v. 32, no. 4, p. 1299-1318, https://doi.org/10.1111/mms.12332.","productDescription":"20 p.","startPage":"1299","endPage":"1318","ipdsId":"IP-069772","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":438647,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M060G","text":"USGS data release","linkHelpText":"Walrus Bayesian State-space Model Output from the Bering Sea and Chukchi Sea, 2008-2012"},{"id":341325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-11","publicationStatus":"PW","scienceBaseUri":"591abe36e4b0a7fdb43c8bf5","contributors":{"authors":[{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":695273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":695274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":695275,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187375,"text":"70187375 - 2016 - Pinedale glacial history of the upper Arkansas River valley: New moraine chronologies, modeling results, and geologic mapping","interactions":[],"lastModifiedDate":"2019-06-19T12:58:42","indexId":"70187375","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5478,"text":"Geological Society of America Field Guides","active":true,"publicationSubtype":{"id":24}},"title":"Pinedale glacial history of the upper Arkansas River valley: New moraine chronologies, modeling results, and geologic mapping","docAbstract":"This field-trip guide outlines the glacial history of the upper Arkansas River valley, Colorado, and builds on a previous GSA field trip to the area in 2010. The following will be presented: (1) new cosmogenic 10Be exposure ages of moraine boulders from the Pinedale and Bull Lake glaciations (Marine Isotope Stages 2 and 6, respectively) located adjacent to the Twin Lakes Reservoir, (2) numerical modeling of glaciers during the Pinedale glaciation in major tributaries draining into the upper Arkansas River, (3) discharge estimates for glacial-lake outburst floods in the upper Arkansas River valley, and (4) 10Be ages on flood boulders deposited downvalley from the moraine sequences. This research was stimulated by a new geologic map of the Granite 7.5′ quadrangle, in which the mapping of surficial deposits was revised based in part on the interpretation of newly acquired LiDAR data and field investigations. The new 10Be ages of the Pinedale terminal moraine at Twin Lakes average 21.8 ± 0.7 ka (n = 14), which adds to nearby Pinedale terminal moraine ages of 23.6 ± 1.4 ka (n = 5), 20.5 ± 0.2 ka (n = 3), and 16.6 ± 1.0 ka (n = 7), and downvalley outburst flood terraces that date to 20.9 ± 0.9 ka (n = 4) and 19.0 ± 0.6 ka (n = 4). This growing chronology leads to improved understanding of the controls and timing of glaciation in the western United States, the modeling of glacial-lake outburst flooding, and the reconstruction of paleotemperature through glacier modeling.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Unfolding the Geology of the West","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.0044(14)","usgsCitation":"Schweinsberg, A.D., Briner, J.P., Shroba, R.R., Licciardi, J.M., Leonard, E.M., Brugger, K.A., and Russell, C.M., 2016, Pinedale glacial history of the upper Arkansas River valley: New moraine chronologies, modeling results, and geologic mapping, chap. of Unfolding the Geology of the West: Geological Society of America Field Guides, v. 44, p. 335-353, https://doi.org/10.1130/2016.0044(14).","productDescription":"19 p.","startPage":"335","endPage":"353","ipdsId":"IP-076090","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488635,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.morris.umn.edu/geol_facpubs/13","text":"External Repository"},{"id":340698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364811,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.geoscienceworld.org/books/book/1995/chapter/16277561/Pinedale-glacial-history-of-the-upper-Arkansas","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Arkansas River valley","volume":"44","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59084928e4b0fc4e448ffd52","contributors":{"authors":[{"text":"Schweinsberg, Avriel D.","contributorId":191619,"corporation":false,"usgs":false,"family":"Schweinsberg","given":"Avriel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":693639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briner, Jason P.","contributorId":191620,"corporation":false,"usgs":false,"family":"Briner","given":"Jason","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":693640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shroba, Ralph R. 0000-0002-2664-1813 rshroba@usgs.gov","orcid":"https://orcid.org/0000-0002-2664-1813","contributorId":1266,"corporation":false,"usgs":true,"family":"Shroba","given":"Ralph","email":"rshroba@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":693638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Licciardi, Joseph M.","contributorId":9759,"corporation":false,"usgs":false,"family":"Licciardi","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":693641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leonard, Eric M.","contributorId":127415,"corporation":false,"usgs":false,"family":"Leonard","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":693642,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brugger, Keith A. 0000-0003-0869-920X","orcid":"https://orcid.org/0000-0003-0869-920X","contributorId":191621,"corporation":false,"usgs":false,"family":"Brugger","given":"Keith","email":"","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":693643,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Russell, Charles M.","contributorId":191622,"corporation":false,"usgs":false,"family":"Russell","given":"Charles","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":693644,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70187368,"text":"70187368 - 2016 - Golden-winged Warbler nest-site habitat selection: Chapter 7","interactions":[],"lastModifiedDate":"2017-09-07T16:50:51","indexId":"70187368","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":5103,"text":"Studies in Avian Biology","printIssn":"0197-9922","active":true,"publicationSubtype":{"id":24}},"chapter":"7","title":"Golden-winged Warbler nest-site habitat selection: Chapter 7","docAbstract":"Avian habitat selection occurs at multiple spatial scales to incorporate life history requirements. Breeding habitat of Golden-winged Warblers (Vermivora chrysoptera) is characterized by largely forested landscapes containing natural or anthropogenic disturbance elements that maintain forest patches in early stages of succession. Breeding habitat occurs in a variety of settings, including shrub and forest swamps, regenerating forests following timber harvest, grazed pastures, and reclaimed mined lands. We identified structural components of nest sites for Golden-winged Warblers by measuring habitat characteristics across five states (North Carolina, New York, Pennsylvania, Tennessee, and West Virginia) in the Appalachian breeding-distribution segment and two states (Minnesota and Wisconsin) in the Great Lakes breeding-distribution segment. We measured habitat characteristics at the nest-site scale with a series of nested plots characterizing herbaceous vegetation (grasses and forbs), woody shrubs and saplings, and overstory trees. We measured similar variables at paired random plots located 25–50 m from the nest within the same territory to evaluate selection. We used conditional logistical regression to identify which parameters were important in habitat selection and Simple Saddlepoint Approximation (SSA) to aid in management interpretation of identified parameters for each study site. Study site was an important determinant for which parameters were significant in nest-site selection, although selection for some parameters was consistent across sites. The amount of woody cover at the nest-site scale was consistently present in the top nest-site selection models across sites, although the direction of the relationship was not the same across all sites. We also identified grass, forb, woody cover, and vegetation density as important components of Golden-winged Warbler nest-site selection. Based on SSA, we identified vegetation thresholds to aid in designing habitat management prescriptions to promote creation or restoration of Golden-winged Warbler nesting habitat across the eastern portion of their breeding distribution.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","isbn":"978-1-4822-4068-9","usgsCitation":"Terhune, T.M., Aldinger, K.R., Buehler, D.A., Flaspohler, D.J., Larkin, J.L., Loegering, J.P., Percy, K.L., Roth, A.M., Smalling, C.G., and Wood, P., 2016, Golden-winged Warbler nest-site habitat selection: Chapter 7, chap. 7 of Golden-winged Warbler ecology, conservation, and habitat management (Studies in Avian Biology, volume 49): Studies in Avian Biology, v. 49, p. 109-125.","productDescription":"17 p.","startPage":"109","endPage":"125","ipdsId":"IP-052635","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340749,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/11299/189700"}],"volume":"49","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59099aaee4b0fc4e449157f0","contributors":{"authors":[{"text":"Terhune, Theron M. II","contributorId":191720,"corporation":false,"usgs":false,"family":"Terhune","given":"Theron","suffix":"II","email":"","middleInitial":"M.","affiliations":[{"id":33355,"text":"Tall Timbers Research Station and Land Conservancy","active":true,"usgs":false}],"preferred":false,"id":693990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldinger, Kyle R.","contributorId":171892,"corporation":false,"usgs":false,"family":"Aldinger","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":693991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":693992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flaspohler, David J.","contributorId":191721,"corporation":false,"usgs":false,"family":"Flaspohler","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":16650,"text":"School of Forest Resources & Environmental Science, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931","active":true,"usgs":false},{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":693993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larkin, Jeffrey L.","contributorId":169747,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":34542,"text":"Department of Biology. Indiana University of Pennsylvania","active":true,"usgs":false},{"id":17929,"text":"American Bird Conservancy","active":true,"usgs":false}],"preferred":false,"id":693994,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loegering, John P.","contributorId":166933,"corporation":false,"usgs":false,"family":"Loegering","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":33353,"text":"University of Minnesota, Crookston","active":true,"usgs":false}],"preferred":false,"id":693995,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Percy, Katie L.","contributorId":191722,"corporation":false,"usgs":false,"family":"Percy","given":"Katie","email":"","middleInitial":"L.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":693996,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roth, Amber M.","contributorId":191723,"corporation":false,"usgs":false,"family":"Roth","given":"Amber","email":"","middleInitial":"M.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false},{"id":27866,"text":"University of Maine, Department of Wildlife, Fisheries, and Conservation Biology, Orono, ME","active":true,"usgs":false},{"id":25614,"text":"School of Forest Resources, University of Maine","active":true,"usgs":false}],"preferred":false,"id":693997,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smalling, Curtis G.","contributorId":191724,"corporation":false,"usgs":false,"family":"Smalling","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":33352,"text":"Audubon North Carolina","active":true,"usgs":false}],"preferred":false,"id":693998,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wood, Petra pbwood@usgs.gov","contributorId":169812,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","affiliations":[{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":693999,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70186896,"text":"70186896 - 2016 - Feeding ecology of non-native Siberian prawns, Palaemon modestus (Heller, 1862) (Decapoda, Palaemonidae), in the lower Snake River, Washington, U.S.A.","interactions":[],"lastModifiedDate":"2017-04-13T14:56:16","indexId":"70186896","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1348,"text":"Crustaceana","active":true,"publicationSubtype":{"id":10}},"title":"Feeding ecology of non-native Siberian prawns, Palaemon modestus (Heller, 1862) (Decapoda, Palaemonidae), in the lower Snake River, Washington, U.S.A.","docAbstract":"We used both stomach content and stable isotope analyses to describe the feeding ecology of Siberian prawns Palaemon modestus (Heller, 1862), a non-native caridean shrimp that is a relatively recent invader of the lower Snake River. Based on identifiable prey in stomachs, the opossum shrimp Neomysis mercedis Holmes, 1896 comprised up to 34-55% (by weight) of diets of juvenile to adult P. modestus, which showed little seasonal variation. Other predominant items/taxa consumed included detritus, amphipods, dipteran larvae, and oligochaetes. Stable isotope analysis supported diet results and also suggested that much of the food consumed by P. modestus that was not identifiable came from benthic sources — predominantly invertebrates of lower trophic levels and detritus. Palaemon modestus consumption of N. mercedis may pose a competitive threat to juvenile salmon and resident fishes which also rely heavily on that prey.
","language":"English","publisher":"BRILL","doi":"10.1163/15685403-00003553","usgsCitation":"Tiffan, K.F., and Hurst, W., 2016, Feeding ecology of non-native Siberian prawns, Palaemon modestus (Heller, 1862) (Decapoda, Palaemonidae), in the lower Snake River, Washington, U.S.A.: Crustaceana, v. 89, no. 6-7, p. 721-736, https://doi.org/10.1163/15685403-00003553.","productDescription":"16 p.","startPage":"721","endPage":"736","ipdsId":"IP-073452","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":339703,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Snake River","volume":"89","issue":"6-7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f08e60e4b06911a29fa854","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":690914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurst, William 0000-0001-5758-8210 whurst@usgs.gov","orcid":"https://orcid.org/0000-0001-5758-8210","contributorId":139838,"corporation":false,"usgs":true,"family":"Hurst","given":"William","email":"whurst@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":690915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189264,"text":"70189264 - 2016 - Local environmental context conditions the impact of Russian olive in a heterogeneous riparian ecosystem","interactions":[],"lastModifiedDate":"2020-12-21T15:40:05.137922","indexId":"70189264","displayToPublicDate":"2016-01-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2100,"text":"Invasive Plant Science and Management","active":true,"publicationSubtype":{"id":10}},"title":"Local environmental context conditions the impact of Russian olive in a heterogeneous riparian ecosystem","docAbstract":"Local abiotic and biotic conditions can alter the strength of exotic species impacts. To better understand the effects of exotic species on invaded ecosystems and to prioritize management efforts, it is important that exotic species impacts are put in local environmental context. We studied how differences in plant community composition, photosynthetically active radiation (PAR), and available soil N associated with Russian olive presence are conditioned by local environmental variation within a western U.S. riparian ecosystem. In four sites along the South Fork of the Republican River in Colorado, we established 200 pairs of plots (underneath and apart from Russian olive) to measure the effects of invasion across the ecosystem. We used a series of a priori mixed models to identify environmental variables that altered the effects of Russian olive. For all response variables, models that included the interaction of environmental characteristics, such as presence/absence of an existing cottonwood canopy, with the presence/absence of Russian olive canopy were stronger candidate models than those that just included Russian olive canopy presence as a factor. Compared with reference plots outside of Russian olive canopy, plots underneath Russian olive had higher relative exotic cover (exotic/total cover), lower perennial C4 grass cover, and higher perennial forb cover. These effects were reduced, however, in the presence of a cottonwood canopy. As expected, Russian olive was associated with reduced PAR and increased N, but these effects were reduced under cottonwood canopy. Our results demonstrate that local abiotic and biotic environmental factors condition the effects of Russian olive within a heterogeneous riparian ecosystem and suggest that management efforts should be focused in open areas where Russian olive impacts are strongest.
","language":"English","publisher":"Weed Science Society of America","doi":"10.1614/IPSM-D-16-00029.1","usgsCitation":"Tuttle, G.M., Katz, G.L., Friedman, J.M., and Norton, A., 2016, Local environmental context conditions the impact of Russian olive in a heterogeneous riparian ecosystem: Invasive Plant Science and Management, v. 9, no. 4, p. 272-289, https://doi.org/10.1614/IPSM-D-16-00029.1.","productDescription":"18 p.","startPage":"272","endPage":"289","ipdsId":"IP-060573","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471391,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1614/ipsm-d-16-00029.1","text":"Publisher Index Page"},{"id":343467,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Kit Carson County, Yuma County","otherGeospatial":"Republican River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.23004150390625,\n 38.90385833966778\n ],\n [\n -102.06573486328124,\n 38.90385833966778\n ],\n [\n -102.06573486328124,\n 40.027614437486655\n ],\n [\n -104.23004150390625,\n 40.027614437486655\n ],\n [\n -104.23004150390625,\n 38.90385833966778\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-03","publicationStatus":"PW","scienceBaseUri":"59609db8e4b0d1f9f0594c3e","contributors":{"authors":[{"text":"Tuttle, Graham M.","contributorId":194351,"corporation":false,"usgs":false,"family":"Tuttle","given":"Graham","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":703804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Gabrielle L.","contributorId":194352,"corporation":false,"usgs":false,"family":"Katz","given":"Gabrielle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":703805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":703803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norton, Andrew P.","contributorId":46436,"corporation":false,"usgs":true,"family":"Norton","given":"Andrew P.","affiliations":[],"preferred":false,"id":703806,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160804,"text":"70160804 - 2016 - Addressing potential local adaptation in species distribution models: implications for conservation under climate change","interactions":[],"lastModifiedDate":"2016-06-15T16:12:31","indexId":"70160804","displayToPublicDate":"2015-12-31T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Addressing potential local adaptation in species distribution models: implications for conservation under climate change","docAbstract":"Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0926","usgsCitation":"Hallfors, M.H., Liao, J., Dzurisin, J., Grundel, R., Hyvarinen, M., Towle, K., Wu, G.C., and Hellmann, J.J., 2016, Addressing potential local adaptation in species distribution models: implications for conservation under climate change: Ecological Applications, v. 26, no. 4, p. 1154-1169, https://doi.org/10.1890/15-0926.","productDescription":"16 p.","startPage":"1154","endPage":"1169","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064359","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":313139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-08","publicationStatus":"PW","scienceBaseUri":"568651b3e4b0e7594ee74c9b","contributors":{"authors":[{"text":"Hallfors, Maria Helena","contributorId":151004,"corporation":false,"usgs":false,"family":"Hallfors","given":"Maria","email":"","middleInitial":"Helena","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":583962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liao, Jishan","contributorId":151005,"corporation":false,"usgs":false,"family":"Liao","given":"Jishan","email":"","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":583963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzurisin, Jason D. K.","contributorId":151006,"corporation":false,"usgs":false,"family":"Dzurisin","given":"Jason D. K.","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":583964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hyvarinen, Marko","contributorId":151007,"corporation":false,"usgs":false,"family":"Hyvarinen","given":"Marko","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":583965,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Towle, Kevin","contributorId":151008,"corporation":false,"usgs":false,"family":"Towle","given":"Kevin","email":"","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":583966,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wu, Grace C.","contributorId":151009,"corporation":false,"usgs":false,"family":"Wu","given":"Grace","email":"","middleInitial":"C.","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":583967,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hellmann, Jessica J.","contributorId":149219,"corporation":false,"usgs":false,"family":"Hellmann","given":"Jessica","email":"","middleInitial":"J.","affiliations":[{"id":17677,"text":"Department of Biological Sciences, University of Notre Dame, Notre Dame, IN","active":true,"usgs":false}],"preferred":false,"id":583968,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70175457,"text":"70175457 - 2016 - Female sea lamprey shift orientation toward a conspecific chemical cue to escape a sensory trap","interactions":[],"lastModifiedDate":"2016-08-12T10:20:47","indexId":"70175457","displayToPublicDate":"2015-12-31T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":981,"text":"Behavioral Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Female sea lamprey shift orientation toward a conspecific chemical cue to escape a sensory trap","docAbstract":"The sensory trap model of signal evolution hypothesizes that signalers adapt to exploit a cue used by the receiver in another context. Although exploitation of receiver biases can result in conflict between the sexes, deceptive signaling systems that are mutually beneficial drive the evolution of stable communication systems. However, female responses in the nonsexual and sexual contexts may become uncoupled if costs are associated with exhibiting a similar response to a trait in both contexts. Male sea lamprey (Petromyzon marinus) signal with a mating pheromone, 3-keto petromyzonol sulfate (3kPZS), which may be a match to a juvenile cue used by females during migration. Upstream movement of migratory lampreys is partially guided by 3kPZS, but females only move toward 3kPZS with proximal accuracy during spawning. Here, we use in-stream behavioral assays paired with gonad histology to document the transition of female preference for juvenile- and male-released 3kPZS that coincides with the functional shift of 3kPZS as a migratory cue to a mating pheromone. Females became increasingly biased toward the source of synthesized 3kPZS as their maturation progressed into the reproductive phase, at which point, a preference for juvenile odor (also containing 3kPZS naturally) ceased to exist. Uncoupling of female responses during migration and spawning makes the 3kPZS communication system a reliable means of synchronizing mate search. The present study offers a rare example of a transition in female responses to a chemical cue between nonsexual and sexual contexts, provides insights into the origins of stable communication signaling systems.
","language":"English","publisher":"International Society for Behavioral Ecology","publisherLocation":"Oxford, UK","doi":"10.1093/beheco/arv224","usgsCitation":"Brant, C.O., Johnson, N., Li, K., Buchinger, T.J., and Li, W., 2016, Female sea lamprey shift orientation toward a conspecific chemical cue to escape a sensory trap: Behavioral Ecology, v. 27, no. 3, p. 810-819, https://doi.org/10.1093/beheco/arv224.","startPage":"810","endPage":"819","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070842","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":326449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-20","publicationStatus":"PW","scienceBaseUri":"57aef33ce4b0fc09faae0372","contributors":{"authors":[{"text":"Brant, Cory O.","contributorId":126746,"corporation":false,"usgs":false,"family":"Brant","given":"Cory","email":"","middleInitial":"O.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":645321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":645320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Ke","contributorId":94959,"corporation":false,"usgs":true,"family":"Li","given":"Ke","affiliations":[],"preferred":false,"id":645322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buchinger, Tyler J.","contributorId":40508,"corporation":false,"usgs":true,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":645323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Weiming","contributorId":126748,"corporation":false,"usgs":false,"family":"Li","given":"Weiming","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":645324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191820,"text":"70191820 - 2016 - Water-quality effects on phytoplankton species and density and trophic state indices at Big Base and Little Base Lakes, Little Rock Air Force Base, Arkansas, June through August, 2015","interactions":[],"lastModifiedDate":"2017-10-25T14:30:19","indexId":"70191820","displayToPublicDate":"2015-12-31T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2531,"text":"Journal of the Arkansas Academy of Science","active":true,"publicationSubtype":{"id":10}},"title":"Water-quality effects on phytoplankton species and density and trophic state indices at Big Base and Little Base Lakes, Little Rock Air Force Base, Arkansas, June through August, 2015","docAbstract":"Big Base and Little Base Lakes are located on\r\nLittle Rock Air Force Base, Arkansas, and their close\r\nproximity to a dense residential population and an\r\nactive military/aircraft installation make the lakes\r\nvulnerable to water-quality degradation. The U.S.\r\nGeological Survey (USGS) conducted a study from\r\nJune through August 2015 to investigate the effects of\r\nwater quality on phytoplankton species and density and\r\ntrophic state in Big Base and Little Base Lakes, with\r\nparticular regard to nutrient concentrations. Nutrient\r\nconcentrations, trophic-state indices, and the large part\r\nof the phytoplankton biovolume composed of\r\ncyanobacteria, indicate eutrophic conditions were\r\nprevalent for Big Base and Little Base Lakes,\r\nparticularly in August 2015. Cyanobacteria densities\r\nand biovolumes measured in this study likely pose a\r\nlow to moderate risk of adverse algal toxicity, and the\r\nhigh proportion of filamentous cyanobacteria in the\r\nlakes, in relation to other algal groups, is important\r\nfrom a fisheries standpoint because these algae are a\r\npoor food source for many aquatic taxa. In both lakes,\r\ntotal nitrogen to total phosphorus (N:P) ratios declined\r\nover the sampling period as total phosphorus\r\nconcentrations increased relative to nitrogen\r\nconcentrations. The N:P ratios in the August samples\r\n(20:1 and 15:1 in Big Base and Little Base Lakes,\r\nrespectively) and other indications of eutrophic\r\nconditions are of concern and suggest that exposure of\r\nthe two lakes to additional nutrients could cause\r\nunfavorable dissolved-oxygen conditions and increase\r\nthe risk of cyanobacteria blooms and associated\r\ncyanotoxin issues.","language":"English","publisher":"Arkansas Academy of Science","usgsCitation":"Driver, L., and Justus, B., 2016, Water-quality effects on phytoplankton species and density and trophic state indices at Big Base and Little Base Lakes, Little Rock Air Force Base, Arkansas, June through August, 2015: Journal of the Arkansas Academy of Science, v. 70, no. 1, p. 88-95.","productDescription":"8 p.","startPage":"88","endPage":"95","ipdsId":"IP-074006","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":347379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346820,"type":{"id":15,"text":"Index Page"},"url":"https://scholarworks.uark.edu/jaas/vol70/iss1/16"}],"country":"United States","state":"Arkansas","otherGeospatial":"Big Base Lake, Little Base Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.17520713806152,\n 34.888395122782164\n ],\n [\n -92.1556806564331,\n 34.888395122782164\n ],\n [\n -92.1556806564331,\n 34.904375309375645\n ],\n [\n -92.17520713806152,\n 34.904375309375645\n ],\n [\n -92.17520713806152,\n 34.888395122782164\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"70","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a8e4b0220bbd9d9f8d","contributors":{"authors":[{"text":"Driver, Lucas ldriver@usgs.gov","contributorId":197344,"corporation":false,"usgs":true,"family":"Driver","given":"Lucas","email":"ldriver@usgs.gov","affiliations":[],"preferred":true,"id":713230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Justus, Billy bjustus@usgs.gov","contributorId":152446,"corporation":false,"usgs":true,"family":"Justus","given":"Billy","email":"bjustus@usgs.gov","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713229,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159495,"text":"70159495 - 2016 - Evolution of mid-Atlantic coastal and back-barrier estuary environments in response to a hurricane: Implications for barrier-estuary connectivity","interactions":[],"lastModifiedDate":"2016-12-14T12:29:43","indexId":"70159495","displayToPublicDate":"2015-12-29T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Evolution of mid-Atlantic coastal and back-barrier estuary environments in response to a hurricane: Implications for barrier-estuary connectivity","docAbstract":"Assessments of coupled barrier island-estuary storm response are rare. Hurricane Sandy made landfall during an investigation in Barnegat Bay-Little Egg Harbor estuary that included water quality monitoring, geomorphologic characterization, and numerical modeling; this provided an opportunity to characterize the storm response of the barrier island-estuary system. Barrier island morphologic response was characterized by significant changes in shoreline position, dune elevation, and beach volume; morphologic changes within the estuary were less dramatic with a net gain of only 200,000 m3 of sediment. When observed, estuarine deposition was adjacent to the back-barrier shoreline or collocated with maximum estuary depths. Estuarine sedimentologic changes correlated well with bed shear stresses derived from numerically simulated storm conditions, suggesting that change is linked to winnowing from elevated storm-related wave-current interactions rather than deposition. Rapid storm-related changes in estuarine water level, turbidity, and salinity were coincident with minima in island and estuarine widths, which may have influenced the location of two barrier island breaches. Barrier-estuary connectivity, or the transport of sediment from barrier island to estuary, was influenced by barrier island land use and width. Coupled assessments like this one provide critical information about storm-related coastal and estuarine sediment transport that may not be evident from investigations that consider only one component of the coastal system.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-015-0057-x","usgsCitation":"Miselis, J.L., Andrews, B., Nicholson, R.S., Defne, Z., Ganju, N., and Navoy, A.S., 2016, Evolution of mid-Atlantic coastal and back-barrier estuary environments in response to a hurricane: Implications for barrier-estuary connectivity: Estuaries and Coasts, v. 39, no. 4, p. 916-934, https://doi.org/10.1007/s12237-015-0057-x.","productDescription":"19 p.","startPage":"916","endPage":"934","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061843","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471394,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s12237-015-0057-x","text":"External Repository"},{"id":313934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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40.059679976774355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-29","publicationStatus":"PW","scienceBaseUri":"568e48fee4b0e7a44bc41946","chorus":{"doi":"10.1007/s12237-015-0057-x","url":"http://dx.doi.org/10.1007/s12237-015-0057-x","publisher":"Springer Nature","authors":"Miselis Jennifer L., Andrews Brian D., Nicholson Robert S., Defne Zafer, Ganju Neil K., Navoy Anthony","journalName":"Estuaries and Coasts","publicationDate":"12/29/2015","auditedOn":"7/29/2016","publiclyAccessibleDate":"12/29/2015"},"contributors":{"authors":[{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":579221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrews, Brian D. bandrews@usgs.gov","contributorId":149612,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian D.","email":"bandrews@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":579222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nicholson, Robert S. rnichol@usgs.gov","contributorId":2283,"corporation":false,"usgs":true,"family":"Nicholson","given":"Robert","email":"rnichol@usgs.gov","middleInitial":"S.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":579223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":579224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ganju, Neil K. 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":149613,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","email":"nganju@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":579225,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Navoy, Anthony S. anavoy@usgs.gov","contributorId":2464,"corporation":false,"usgs":true,"family":"Navoy","given":"Anthony","email":"anavoy@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":579226,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70160573,"text":"70160573 - 2016 - Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data","interactions":[],"lastModifiedDate":"2018-02-04T13:28:33","indexId":"70160573","displayToPublicDate":"2015-12-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying watershed-scale groundwater loading and in-stream fate of nitrate using high-frequency water quality data","docAbstract":"We describe a new approach that couples hydrograph separation with high-frequency nitrate data to quantify time-variable groundwater and runoff loading of nitrate to streams, and the net in-stream fate of nitrate at the watershed-scale. The approach was applied at three sites spanning gradients in watershed size and land use in the Chesapeake Bay watershed. Results indicate that 58-73% of the annual nitrate load to the streams was groundwater-discharged nitrate. Average annual first order nitrate loss rate constants (k) were similar to those reported in both modelling and in-stream process-based studies, and were greater at the small streams (0.06 and 0.22 d-1) than at the large river (0.05 d-1), but 11% of the annual loads were retained/lost in the small streams, compared with 23% in the large river. Larger streambed area to water volume ratios in small streams result in greater loss rates, but shorter residence times in small streams result in a smaller fraction of nitrate loads being removed than in larger streams. A seasonal evaluation of k values suggests that nitrate was retained/lost at varying rates during the growing season. Consistent with previous studies, streamflow and nitrate concentration were inversely related to k. This new approach for interpreting high-frequency nitrate data and the associated findings furthers our ability to understand, predict, and mitigate nitrate impacts on streams and receiving waters by providing insights into temporal nitrate dynamics that would be difficult to obtain using traditional field-based studies.
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We examine the seasonal monsoon cycle, and briefly explore recent circulation changes. The spatial footprint of our analysis corresponds with the “Greater Horn of Africa” (GHA) region, extending from Tanzania in the south to Yemen and Sudan in the north. During boreal winter, when northeast trade winds flow across the northwest Indian Ocean and the equatorial moisture transports over the Indian Ocean exhibit strong westerly mean flows over the equatorial Indian Ocean, East African precipitation is limited to a few highland areas. As the Indian monsoon circulation transitions during boreal spring, the trade winds over the northwest Indian Ocean reverse, and East African moisture convergence supports the “long” rains. In boreal summer, the southwesterly Somali Jet intensifies over eastern Africa. Subsidence forms along the westward flank of this jet, shutting down precipitation over eastern portions of East Africa. In boreal fall, the Jet subsides, but easterly moisture transports support rainfall in limited regions of the eastern Horn of Africa. We use regressions with the trend mode of global sea surface temperatures to explore potential changes in the seasonal monsoon circulations. Significant reductions in total precipitable water are indicated in Kenya, Tanzania, Rwanda, Burundi, Uganda, Ethiopia, South Sudan, Sudan, and Yemen, with moisture transports broadly responding in ways that reinforce the climatological moisture transports over the Indian Ocean. Over Kenya, southern Ethiopia and Somalia, regressions with velocity potential indicate increased convergence aloft. Near the surface, this convergence appears to manifest as a surface high pressure system that modifies moisture transports in these countries as well as Uganda, Tanzania, Rwanda, and Burundi. An analysis of rainfall changes indicates significant declines in parts of Tanzania, Rwanda, Burundi, Uganda, Kenya, Somalia, Ethiopia, and Yemen.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The Monsoons and Climate Change","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham","doi":"10.1007/978-3-319-21650-8_8","usgsCitation":"Funk, C.C., Hoell, A., Shukla, S., Husak, G.J., and Michaelsen, J., 2016, The East African monsoon system: Seasonal climatologies and recent variations: Chapter 10, chap. of The Monsoons and Climate Change, p. 163-185, https://doi.org/10.1007/978-3-319-21650-8_8.","productDescription":"13 p.","startPage":"163","endPage":"185","ipdsId":"IP-062072","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":339820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-21","publicationStatus":"PW","scienceBaseUri":"58f5d440e4b0f2e20545e415","contributors":{"authors":[{"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":565381,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":565382,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":145802,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":565383,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory J.","contributorId":34435,"corporation":false,"usgs":true,"family":"Husak","given":"Gregory","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":565384,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Michaelsen, J.","contributorId":12288,"corporation":false,"usgs":true,"family":"Michaelsen","given":"J.","affiliations":[],"preferred":false,"id":565385,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70175211,"text":"70175211 - 2016 - Metabolic and physiochemical responses to a whole-lake experimental increase in dissolved organic carbon in a north-temperate lake","interactions":[],"lastModifiedDate":"2016-08-02T15:45:39","indexId":"70175211","displayToPublicDate":"2015-12-21T16:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Metabolic and physiochemical responses to a whole-lake experimental increase in dissolved organic carbon in a north-temperate lake","docAbstract":"Over the last several decades, many lakes globally have increased in dissolved organic carbon (DOC), calling into question how lake functions may respond to increasing DOC. Unfortunately, our basis for making predictions is limited to spatial surveys, modeling, and laboratory experiments, which may not accurately capture important whole-ecosystem processes. In this article, we present data on metabolic and physiochemical responses of a multiyear experimental whole-lake increase in DOC concentration. Unexpectedly, we observed an increase in pelagic gross primary production, likely due to a small increase in phosphorus as well as a surprising lack of change in epilimnetic light climate. We also speculate on the importance of lake size modifying the relationship between light climate and elevated DOC. A larger increase in ecosystem respiration resulted in an increased heterotrophy for the treatment basin. The magnitude of the increase in heterotrophy was extremely close to the excess DOC load to the treatment basin, indicating that changes in heterotrophy may be predictable if allochthonous carbon loads are well-constrained. Elevated DOC concentration also reduced thermocline and mixed layer depth and reduced whole-lake temperature. Results from this experiment were quantitatively different, and sometimes even in the opposite direction, from expectations based on cross-system surveys and bottle experiments, emphasizing the importance of whole-ecosystem experiments in understanding ecosystem response to environmental change.
","language":"English","publisher":"American Society of Limnology and Oceanography","publisherLocation":"Waco, TX","doi":"10.1002/lno.10248","usgsCitation":"Zwart, J., Craig, N., Kelly, P., Sebestyen, S.D., Solomon, C.T., Weidel, B., and Jones, S., 2016, Metabolic and physiochemical responses to a whole-lake experimental increase in dissolved organic carbon in a north-temperate lake: Limnology and Oceanography, v. 61, no. 2, p. 723-734, https://doi.org/10.1002/lno.10248.","startPage":"723","endPage":"734","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068470","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471400,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://figshare.com/articles/journal_contribution/Metabolic_and_physiochemical_responses_to_a_whole-lake_experimental_increase_in_dissolved_organic_carbon_in_a_north-temperate_lake/24733230","text":"Publisher Index Page"},{"id":325984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-21","publicationStatus":"PW","scienceBaseUri":"57a1c430e4b006cb45552c2b","contributors":{"authors":[{"text":"Zwart, Jacob A.","contributorId":173345,"corporation":false,"usgs":false,"family":"Zwart","given":"Jacob A.","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":644342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Craig, Nicola","contributorId":150803,"corporation":false,"usgs":false,"family":"Craig","given":"Nicola","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":644343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Patrick T.","contributorId":69059,"corporation":false,"usgs":true,"family":"Kelly","given":"Patrick T.","affiliations":[],"preferred":false,"id":644344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sebestyen, Stephen D.","contributorId":107562,"corporation":false,"usgs":true,"family":"Sebestyen","given":"Stephen","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":644347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":644345,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644341,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Stuart E.","contributorId":22222,"corporation":false,"usgs":false,"family":"Jones","given":"Stuart E.","affiliations":[{"id":6966,"text":"Department of Biological Sciences, University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":644346,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70156787,"text":"70156787 - 2016 - Multi-scale predictions of massive conifer mortality due to chronic temperature rise","interactions":[],"lastModifiedDate":"2018-01-12T15:44:21","indexId":"70156787","displayToPublicDate":"2015-12-21T16:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale predictions of massive conifer mortality due to chronic temperature rise","docAbstract":"Global temperature rise and extremes accompanying drought threaten forests and their associated climatic feedbacks. Our ability to accurately simulate drought-induced forest impacts remains highly uncertain in part owing to our failure to integrate physiological measurements, regional-scale models, and dynamic global vegetation models (DGVMs). Here we show consistent predictions of widespread mortality of needleleaf evergreen trees (NET) within Southwest USA by 2100 using state-of-the-art models evaluated against empirical data sets. Experimentally, dominant Southwest USA NET species died when they fell below predawn water potential (Ψpd) thresholds (April–August mean) beyond which photosynthesis, hydraulic and stomatal conductance, and carbohydrate availability approached zero. The evaluated regional models accurately predicted NET Ψpd, and 91% of predictions (10 out of 11) exceeded mortality thresholds within the twenty-first century due to temperature rise. The independent DGVMs predicted ≥50% loss of Northern Hemisphere NET by 2100, consistent with the NET findings for Southwest USA. Notably, the global models underestimated future mortality within Southwest USA, highlighting that predictions of future mortality within global models may be underestimates. Taken together, the validated regional predictions and the global simulations predict widespread conifer loss in coming decades under projected global warming.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, UK","doi":"10.1038/nclimate2873","usgsCitation":"McDowell, N., Williams, A., Xu, C., Pockman, W., Dickman, L., Sevanto, S., Pangle, R., Limousin, J., Plaut, J., Mackay, D., Ogee, J., Domec, J., Allen, C.D., Fisher, R.A., Jiang, X., Muss, J., Breshears, D., Rauscher, S.A., and Koven, C., 2016, Multi-scale predictions of massive conifer mortality due to chronic temperature rise: Nature Climate Change, v. 6, p. 295-300, https://doi.org/10.1038/nclimate2873.","productDescription":"6 p.","startPage":"295","endPage":"300","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058464","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1492529","text":"External Repository"},{"id":312936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-21","publicationStatus":"PW","scienceBaseUri":"56826b46e4b0a04ef4925b86","contributors":{"authors":[{"text":"McDowell, Nathan G.","contributorId":9176,"corporation":false,"usgs":true,"family":"McDowell","given":"Nathan G.","affiliations":[],"preferred":false,"id":583294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, A.P.","contributorId":70226,"corporation":false,"usgs":true,"family":"Williams","given":"A.P.","email":"","affiliations":[],"preferred":false,"id":583295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, C.","contributorId":9781,"corporation":false,"usgs":true,"family":"Xu","given":"C.","email":"","affiliations":[],"preferred":false,"id":583296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pockman, W. 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These sites can be characterized by artificial fill overlying marine sediments overlying glacial till and bedrock, where the depth to bedrock ranges from 20 to 80 m. The marine sediments generally consist of organic silts, sand, and Boston Blue Clay. We chose these sites because they represent typical foundation conditions in the city of Boston, and the soil conditions are similar to other high impedance contrast environments. The sediment/bedrock interface in this region results in an impedance ratio on the order of ten, which in turn results in a significant amplification of the ground motion. Using stratigraphic information derived from numerous boreholes across the region paired with geologic and geomorphologic constraints, we develop a depth-to-bedrock model for the greater Boston region. Using shear-wave velocity profiles from 30 locations, we develop average velocity profiles for sites mapped as artificial fill, glaciofluvial deposits, and bedrock. By pairing the depth-to-bedrock model with the surficial geology and the average shear-wave velocity profiles, we can predict soil amplification in Boston. We compare linear and equivalent-linear site response predictions for a soil layer of varying thickness over bedrock, and assess the effects of varying the bedrock shear-wave velocity (VSb) and quality factor (Q). In a moderate seismicity region like Boston, many earthquakes will result in ground motions that can be modeled with linear site response methods. We also assess the effect of bedrock depth on soil amplification for a generic soil profile in artificial fill, using both linear and equivalent-linear site response models. Finally, we assess the accuracy of the model results by comparing the predicted (linear site response) and observed site response at the Northeastern University (NEU) vertical seismometer array during the 2011 M 5.8 Mineral, Virginia, earthquake. Site response at the NEU vertical array results in amplification on the order of 10 times at a period between 0.7-0.8 s. The results from this study provide evidence that the mean short-period and mean intermediate-period amplification used in design codes (i.e., from the Fa and Fv site coefficients) may underpredict soil amplification in strong impedance contrast environments such as Boston.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2015.12.016","usgsCitation":"Baise, L.G., Kaklamanos, J., Berry, B.M., and Thompson, E.M., 2016, Soil amplification with a strong impedance contrast: Boston, Massachusetts: Engineering Geology, v. 202, 13 p., https://doi.org/10.1016/j.enggeo.2015.12.016.","productDescription":"13 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071386","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471402,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enggeo.2015.12.016","text":"Publisher Index Page"},{"id":313151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Boston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.03193283081055,\n 42.38644427600168\n ],\n [\n -71.14574432373045,\n 42.43828063778991\n ],\n [\n -71.1665153503418,\n 42.42561066758352\n ],\n [\n -71.18385314941406,\n 42.36666166373274\n ],\n [\n -71.1697769165039,\n 42.345984712768576\n ],\n [\n -71.06986999511719,\n 42.3477609142747\n ],\n [\n -71.03382110595703,\n 42.3853031408436\n ],\n [\n -71.03193283081055,\n 42.38644427600168\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"202","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56865fc9e4b0e7594ee74cd5","chorus":{"doi":"10.1016/j.enggeo.2015.12.016","url":"http://dx.doi.org/10.1016/j.enggeo.2015.12.016","publisher":"Elsevier BV","authors":"Baise Laurie G., Kaklamanos James, Berry Bradford M., Thompson Eric M.","journalName":"Engineering Geology","publicationDate":"3/2016"},"contributors":{"authors":[{"text":"Baise, Laurie G.","contributorId":127395,"corporation":false,"usgs":false,"family":"Baise","given":"Laurie","email":"","middleInitial":"G.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":583496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaklamanos, James","contributorId":35053,"corporation":false,"usgs":true,"family":"Kaklamanos","given":"James","affiliations":[],"preferred":false,"id":583497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berry, Bradford M","contributorId":150894,"corporation":false,"usgs":false,"family":"Berry","given":"Bradford","email":"","middleInitial":"M","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":583498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":583495,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160337,"text":"70160337 - 2016 - Spatial capture-recapture models allowing Markovian transience or dispersal","interactions":[],"lastModifiedDate":"2016-01-11T11:10:49","indexId":"70160337","displayToPublicDate":"2015-12-17T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture-recapture models allowing Markovian transience or dispersal","docAbstract":"Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.
","language":"English","publisher":"Springer","doi":"10.1007/s10144-015-0524-z","usgsCitation":"Royle, J., Fuller, A.K., and Sutherland, C., 2016, Spatial capture-recapture models allowing Markovian transience or dispersal: Population Ecology, v. 58, no. 1, p. 53-62, https://doi.org/10.1007/s10144-015-0524-z.","productDescription":"10 p.","startPage":"53","endPage":"62","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069359","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471404,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s10144-015-0524-z","text":"External Repository"},{"id":312465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-21","publicationStatus":"PW","scienceBaseUri":"5673dcb3e4b0da412f4f81fd","contributors":{"authors":[{"text":"Royle, J. Andrew aroyle@usgs.gov","contributorId":138860,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":582604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":582623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutherland, Chris","contributorId":150670,"corporation":false,"usgs":false,"family":"Sutherland","given":"Chris","affiliations":[],"preferred":false,"id":582624,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160330,"text":"70160330 - 2016 - Structure and spatial patterns of macrobenthic community in Tai Lake, a large shallow lake, China","interactions":[],"lastModifiedDate":"2015-12-17T14:23:38","indexId":"70160330","displayToPublicDate":"2015-12-17T15:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Structure and spatial patterns of macrobenthic community in Tai Lake, a large shallow lake, China","docAbstract":"Tai Lake (Chinese: Taihu), the third-largest freshwater lake in China, suffers from harmful cyanobacteria blooms that are caused by economic development and population growth near the lake. Several studies have focused on phytoplankton in Tai Lake after a drinking water crisis in 2007; however, these studies primarily focused on microcystin bioaccumulation and toxicity to individual species without examining the effects of microcystin on macrobenthic community diversity. In this study, we conducted a survey of the lake to examine the effects of microcystine and other pollutants on marcobenthic community diversity. A totally of forty-nine species of macroinvertebrates were found in Tai Lake. Limnodrilus hoffmeisteri and Corbicula fluminea were the most abundant species. Cluster-analysis and one-way analysis of similarity (ANOSIM) identified three significantly different macrobenthic communities among the sample sites. More specifically, sites in the eastern bays, where aquatic macrophytes were abundant, had the highest diversity of macrobenthic communities, which were dominated by Bellamya aeruginosa, Bellamya purificata, L. hoffmeisteri, and Alocinma longicornis. Sites in Zhushan Bay contained relatively diverse communities, mainly composed of L. hoffmeisteri, C. fluminea, L. claparederanus, R. sinicus, and Cythura sp. Sites in the western region, Meiliang Bay and Wuli Bay had the lowest diversity, mainly composed ofL. hoffmeisteri, C. fluminea, Branchiura sowerbyi, and Rhyacodrilus sinicus. In addition, the relationships between macrobenthic metrics (Shannon–Wiener, Margalef, and Pielou) and environmental variables showed that community structure and spatial patterns of macrobenthos in Tai Lake were significantly influenced by chemical oxygen demand (CODCr), biochemical oxygen demand (BOD5), lead (Pb), and microcystin-LR (L for leucine and R for arginine). Our findings provide critical information that could help managers and policymakers assess and modify ecological restoration practices.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2015.08.043","usgsCitation":"Li, D., Erickson, R.A., Song Tang, Li, X., Niu, Z., Wang, X., Liu, H., and Yu, H., 2016, Structure and spatial patterns of macrobenthic community in Tai Lake, a large shallow lake, China: Ecological Indicators, v. 61, no. 2, p. 170-187, https://doi.org/10.1016/j.ecolind.2015.08.043.","productDescription":"18 p.","startPage":"170","endPage":"187","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062777","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":312468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Tai Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 119.89105224609375,\n 30.935212690426727\n ],\n [\n 119.89105224609375,\n 31.54460103811182\n ],\n [\n 120.59280395507812,\n 31.54460103811182\n ],\n [\n 120.59280395507812,\n 30.935212690426727\n ],\n [\n 119.89105224609375,\n 30.935212690426727\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"61","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5673dcb4e4b0da412f4f8201","contributors":{"authors":[{"text":"Li, Di","contributorId":150650,"corporation":false,"usgs":false,"family":"Li","given":"Di","email":"","affiliations":[{"id":18059,"text":"State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":582572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song Tang","contributorId":150651,"corporation":false,"usgs":false,"family":"Song Tang","affiliations":[{"id":18060,"text":"School of Environment and Sustainability, University of Saskatchewan, Canada","active":true,"usgs":false}],"preferred":false,"id":582574,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Xuwen","contributorId":150652,"corporation":false,"usgs":false,"family":"Li","given":"Xuwen","email":"","affiliations":[{"id":18061,"text":"Jiangsu Environmental Monitoring Center, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582575,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Niu, Zhichun","contributorId":150653,"corporation":false,"usgs":false,"family":"Niu","given":"Zhichun","email":"","affiliations":[{"id":18061,"text":"Jiangsu Environmental Monitoring Center, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582576,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Xia","contributorId":150654,"corporation":false,"usgs":false,"family":"Wang","given":"Xia","email":"","affiliations":[{"id":18061,"text":"Jiangsu Environmental Monitoring Center, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582577,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Hongling","contributorId":150655,"corporation":false,"usgs":false,"family":"Liu","given":"Hongling","email":"","affiliations":[{"id":18059,"text":"State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582578,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yu, Hongxia","contributorId":150656,"corporation":false,"usgs":false,"family":"Yu","given":"Hongxia","email":"","affiliations":[{"id":18059,"text":"State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China","active":true,"usgs":false}],"preferred":false,"id":582579,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70207052,"text":"70207052 - 2016 - Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models","interactions":[],"lastModifiedDate":"2019-12-04T15:05:24","indexId":"70207052","displayToPublicDate":"2015-12-15T14:59:21","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models","docAbstract":"Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. This work uses 10 statistically downscaled general circulation models (GCMs) under two greenhouse gas emission scenarios to evaluate the uncertainty propagated from GCMs in projecting the effects of climate change on water resources in a tropical island system. The assessment is conducted using a previously configured hydrologic model, the Precipitation Runoff Modelling System (PRMS) for Puerto Rico. Projected climate data and their modelled hydrologic variables versus historical measurements and their modelled hydrologic variables are found to have empirical distribution functions that are statistically different with less than 1 year of daily data aggregation. Thus, only annual averages of the projected hydrologic variables are employed as completely bias‐corrected model outputs. The magnitude of the projected total flow decreases in the four regions covering Puerto Rico, but with a large range of uncertainty depending on the makeup of the GCM ensemble. The multi‐model mean projected total flow decreases by 49–88% of historical amounts from the 1960s to the 2090s for the high emissions scenarios and by 39–79% for the low emissions scenarios. Subsurface flow contributions decreased the least and groundwater flow contributions decreased the most across the island. At locations critical to water supply for human use, projected streamflow is shown to decrease substantially below projected withdrawals by 2099.
","language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/joc.4560","usgsCitation":"Van Beusekom, A.E., Gould, W.A., Terando, A.J., and Collazo, J.A., 2016, Climate change and water resources in a tropical island system: Propagation of uncertainty from statistically downscaled climate models to hydrologic models: International Journal of Climatology, v. 36, no. 9, p. 3370-3383, https://doi.org/10.1002/joc.4560.","productDescription":"14 p.","startPage":"3370","endPage":"3383","ipdsId":"IP-062479","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":369912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.4066162109375,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 17.814071002942764\n ],\n [\n -65.56915283203125,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 18.609807415471877\n ],\n [\n -67.4066162109375,\n 17.814071002942764\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"9","noUsgsAuthors":false,"publicationDate":"2015-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":776637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William A.","contributorId":103535,"corporation":false,"usgs":true,"family":"Gould","given":"William","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":776638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":776639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":776640,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160100,"text":"70160100 - 2016 - The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls","interactions":[],"lastModifiedDate":"2017-10-07T08:55:53","indexId":"70160100","displayToPublicDate":"2015-12-15T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"The effects of habitat, climate, and Barred Owls on long-term demography of Northern Spotted Owls","docAbstract":"Estimates of species' vital rates and an understanding of the factors affecting those parameters over time and space can provide crucial information for management and conservation. We used mark–recapture, reproductive output, and territory occupancy data collected during 1985–2013 to evaluate population processes of Northern Spotted Owls (Strix occidentalis caurina) in 11 study areas in Washington, Oregon, and northern California, USA. We estimated apparent survival, fecundity, recruitment, rate of population change, and local extinction and colonization rates, and investigated relationships between these parameters and the amount of suitable habitat, local and regional variation in meteorological conditions, and competition with Barred Owls (Strix varia). Data were analyzed for each area separately and in a meta-analysis of all areas combined, following a strict protocol for data collection, preparation, and analysis. We used mixed effects linear models for analyses of fecundity, Cormack-Jolly-Seber open population models for analyses of apparent annual survival (ϕ), and a reparameterization of the Jolly-Seber capture–recapture model (i.e. reverse Jolly-Seber; RJS) to estimate annual rates of population change (λRJS) and recruitment. We also modeled territory occupancy dynamics of Northern Spotted Owls and Barred Owls in each study area using 2-species occupancy models. Estimated mean annual rates of population change (λ) suggested that Spotted Owl populations declined from 1.2% to 8.4% per year depending on the study area. The weighted mean estimate of λ for all study areas was 0.962 (± 0.019 SE; 95% CI: 0.925–0.999), indicating an estimated range-wide decline of 3.8% per year from 1985 to 2013. Variation in recruitment rates across the range of the Spotted Owl was best explained by an interaction between total winter precipitation and mean minimum winter temperature. Thus, recruitment rates were highest when both total precipitation (29 cm) and minimum winter temperature (−9.5°C) were lowest. Barred Owl presence was associated with increased local extinction rates of Spotted Owl pairs for all 11 study areas. Habitat covariates were related to extinction rates for Spotted Owl pairs in 8 of 11 study areas, and a greater amount of suitable owl habitat was generally associated with decreased extinction rates. We observed negative effects of Barred Owl presence on colonization rates of Spotted Owl pairs in 5 of 11 study areas. The total amount of suitable Spotted Owl habitat was positively associated with colonization rates in 5 areas, and more habitat disturbance was associated with lower colonization rates in 2 areas. We observed strong declines in derived estimates of occupancy in all study areas. Mean fecundity of females was highest for adults (0.309 ± 0.027 SE), intermediate for 2-yr-olds (0.179 ± 0.040 SE), and lowest for 1-yr-olds (0.065 ± 0.022 SE). The presence of Barred Owls and habitat covariates explained little of the temporal variation in fecundity in most study areas. Climate covariates occurred in competitive fecundity models in 8 of 11 study areas, but support for these relationships was generally weak. The fecundity meta-analysis resulted in 6 competitive models, all of which included the additive effects of geographic region and annual time variation. The 2 top-ranked models also weakly supported the additive negative effects of the amount of suitable core area habitat, Barred Owl presence, and the amount of edge habitat on fecundity. We found strong support for a negative effect of Barred Owl presence on apparent survival of Spotted Owls in 10 of 11 study areas, but found few strong effects of habitat on survival at the study area scale. Climate covariates occurred in top or competitive survival models for 10 of 11 study areas, and in most cases the relationships were as predicted; however, there was little consistency among areas regarding the relative importance of specific climate covariates. In contrast, meta-analysis results suggested that Spotted Owl survival was higher across all study areas when the Pacific Decadal Oscillation (PDO) was in a warming phase and the Southern Oscillation Index (SOI) was negative, with a strongly negative SOI indicative of El Niño events. The best model that included the Barred Owl covariate (BO) was ranked 4th and also included the PDO covariate, but the BO effect was strongly negative. Our results indicated that Northern Spotted Owl populations were declining throughout the range of the subspecies and that annual rates of decline were accelerating in many areas. We observed strong evidence that Barred Owls negatively affected Spotted Owl populations, primarily by decreasing apparent survival and increasing local territory extinction rates. However, the amount of suitable owl habitat, local weather, and regional climatic patterns also were related to survival, occupancy (via colonization rate), recruitment, and, to a lesser extent, fecundity, although there was inconsistency in regard to which covariates were important for particular demographic parameters or across study areas. In the study areas where habitat was an important source of variation for Spotted Owl demographics, vital rates were generally positively associated with a greater amount of suitable owl habitat. However, Barred Owl densities may now be high enough across the range of the Northern Spotted Owl that, despite the continued management and conservation of suitable owl habitat on federal lands, the long-term prognosis for the persistence of Northern Spotted Owls may be in question without additional management intervention. Based on our study, the removal of Barred Owls from the Green Diamond Resources (GDR) study area had rapid, positive effects on Northern Spotted Owl survival and the rate of population change, supporting the hypothesis that, along with habitat conservation and management, Barred Owl removal may be able to slow or reverse Northern Spotted Owl population declines on at least a localized scale.
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Climate warming is advancing the onset of spring snowmelt and may lengthen the summer-dry state and ultimately cause drier soil conditions. The magnitude of either response may vary across elevation and vegetation types. We situated our study at the lower boundary of persistent snow cover and the upper boundary of subalpine forest with paired treatment blocks in aspen forest and open meadow. In treatments plots, we advanced snowmelt timing by an average of 14 days by adding dust to the snow surface during spring melt. We specifically wanted to know whether early snowmelt would increase the duration of the summer-dry period and cause soils to be drier in the early-snowmelt treatments compared with control plots. We found no difference in the onset of the summer-dry state and no significant differences in soil moisture between treatments. To better understand the reasons soil moisture did not respond to early snowmelt as expected, we examined the mediating influences of soil organic matter, texture, temperature, and the presence or absence of forest. In our study, late-spring precipitation may have moderated the effects of early snowmelt on soil moisture. We conclude that landscape characteristics, including soil, vegetation, and regional weather patterns, may supersede the effects of snowmelt timing in determining growing season soil moisture, and efforts to anticipate the impacts of climate change on seasonally snow-covered ecosystems should take into account these mediating factors.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/eco.1697","usgsCitation":"Conner, L.G., Gill, R.A., and Belnap, J., 2016, Soil moisture response to experimentally altered snowmelt timing is mediated by soil, vegetation, and regional climate patterns: Ecohydrology, v. 9, no. 6, p. 1006-1016, https://doi.org/10.1002/eco.1697.","productDescription":"11 p.","startPage":"1006","endPage":"1016","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066535","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":314784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Fairview Canyon, Wasatch Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.25,\n 39.6\n ],\n [\n -111.25,\n 39.7\n ],\n [\n -111.35,\n 39.7\n ],\n [\n -111.35,\n 39.6\n ],\n [\n -111.25,\n 39.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-15","publicationStatus":"PW","scienceBaseUri":"56a7556be4b0b28f1184d883","contributors":{"authors":[{"text":"Conner, Lafe G","contributorId":152512,"corporation":false,"usgs":false,"family":"Conner","given":"Lafe","email":"","middleInitial":"G","affiliations":[{"id":18936,"text":"Department of Biology, Brigham Young University, 4102 LSB, Provo, UT 84602, USA","active":true,"usgs":false}],"preferred":false,"id":589592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gill, Richard A.","contributorId":85508,"corporation":false,"usgs":true,"family":"Gill","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":589593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":589591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70164524,"text":"70164524 - 2016 - Radiometric dating of marine-influenced coal using Re–Os geochronology","interactions":[],"lastModifiedDate":"2016-02-09T13:23:41","indexId":"70164524","displayToPublicDate":"2015-12-15T00:00:00","publicationYear":"2016","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":"Radiometric dating of marine-influenced coal using Re–Os geochronology","docAbstract":"Coal deposits are integral to understanding the structural evolution and thermal history of sedimentary basins and correlating contemporeous estuarine and fluvial delatic strata with marine sections. While marine shales may readily lend themselves to Re–Os dating due to the dominance of hydrogenous Re and Os, the lack of a chronometer for near-shore sedimentary environments hampers basinwide correlations in absolute time. Here, we employ the Re–Os geochronometer, along with total organic carbon (TOC) and Rock–Eval data, to determine the timing and conditions of a marine incursion at the top of the Matewan coal bed, Kanawha Formation, Pottsville Group, West Virginia, USA. The observed range for hydrogen index (HI: 267–290 mg hydrocarbon/gram total organic carbon) for these coal samples suggests dominance of aliphatic hydrocarbons with low carbon (<C19) chain length. Average Re (107.6±16.4 ng/g) and Os (0.52±0.09 ng/g) concentrations of the marine-influenced Matewan coal are higher by few orders of magnitude than published data for terrestrial coal. A Re–Os isochron for the Matewan coal provides an age of 325±14 Ma (Model 3; MSWD = 12; n=19; 2σ ). This is the first Re–Os age derived from coal samples; the age overlaps a new composite Re–Os age of 317±2 Ma for the immediately overlying Betsie Shale Member.
\nExternal precision for replicate Os analyses carried out for several Matewan coal samples shows a positive correlation with their HI. The HI, which is low in terrestrial organic matter, reflects the degree of marine influence. Thus, samples with the most profound marine influence also have the best analytical reproducibility. Equilibration of Os isotopes with seawater under marine conditions overwhelms variability inherited from terrestrial plant debris, decreasing scatter on the isochron. The 187Re/188Os ratios of the Matewan coal (∼3300–5135) are higher than most of those previously published for Phanerozoic black shale (mostly <2000). Mass balance calculations based on Re/TOC and Os/TOC ratios for the Matewan coal indicate that both Re and Os are primarily marine in origin, and their high 187Re/188Os ratios confirm efficient removal of both elements from a sulfidic water column into the coal. We show that Re–Os geochronology of marine-influenced coal can be a viable tool for constraining depositional ages.
\n\n
Predicting climate-driven species’ range shifts depends substantially on species’ exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species’ exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change.
\nWe demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040–2069) and end-of-century (2070–2099).
\nWe produced a 149-year dataset (1951–2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951–1980 mean and historical range of variability in CWD in individual years and 3-year moving windows.
\nClimate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change.
\nIn moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.
\nEstimates of demographic parameters such as survival and reproductive success are critical for guiding management efforts focused on species of conservation concern. Unfortunately, reliable demographic parameters are difficult to obtain for any species, but especially for rare or endangered species. Here we derived estimates of adult survival and recruitment in a community of Hawaiian forest birds, including eight native species (of which three are endangered) and two introduced species at Hakalau Forest National Wildlife Refuge, Hawaiʻi. Integrated population models (IPM) were used to link mark–recapture data (1994–1999) with long-term population surveys (1987–2008). To our knowledge, this is the first time that IPM have been used to characterize demographic parameters of a whole avian community, and provides important insights into the life history strategies of the community. The demographic data were used to test two hypotheses: 1) arthropod specialists, such as the ‘Akiapōlā‘au Hemignathus munroi, are ‘slower’ species characterized by a greater relative contribution of adult survival to population growth, i.e. lower fecundity and increased adult survival; and 2) a species’ susceptibility to environmental change, as reflected by its conservation status, can be predicted by its life history traits. We found that all species were characterized by a similar population growth rate around one, independently of conservation status, origin (native vs non-native), feeding guild, or life history strategy (as measured by ‘slowness’), which suggested that the community had reached an equilibrium. However, such stable dynamics were achieved differently across feeding guilds, as demonstrated by a significant increase of adult survival and a significant decrease of recruitment along a gradient of increased insectivory, in support of hypothesis 1. Supporting our second hypothesis, we found that slower species were more vulnerable species at the global scale than faster ones. The possible causes and conservation implications of these patterns are discussed.
","language":"English","publisher":"Wiley-Blackwell","doi":"10.1111/jav.00756","usgsCitation":"Guillaumet, A., Woodworth, B., Camp, R., and Paxton, E., 2016, Comparative demographics of a Hawaiian forest bird community: Journal of Avian Biology, v. 47, no. 2, p. 185-196, https://doi.org/10.1111/jav.00756.","productDescription":"12 p.","startPage":"185","endPage":"196","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068685","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":312034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.46630859375,\n 21.69826549685252\n ],\n [\n -158.04931640625,\n 21.238182425982313\n ],\n [\n -156.016845703125,\n 20.004322295998723\n ],\n [\n -156.236572265625,\n 19.590844152960933\n ],\n [\n -155.797119140625,\n 18.760712758499565\n ],\n [\n -154.698486328125,\n 19.46659223220761\n ],\n [\n -155.819091796875,\n 20.80747157680652\n ],\n [\n -157.1484375,\n 21.493963563064455\n ],\n [\n -159.697265625,\n 22.411028521558706\n ],\n [\n -160.59814453125,\n 21.800308050972603\n ],\n [\n -160.46630859375,\n 21.69826549685252\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-22","publicationStatus":"PW","scienceBaseUri":"5667ff38e4b06a3ea36c8e06","chorus":{"doi":"10.1111/jav.00756","url":"http://dx.doi.org/10.1111/jav.00756","publisher":"Wiley-Blackwell","authors":"Guillaumet Alban, Woodworth Bethany L., Camp Richard J., Paxton Eben H.","journalName":"Journal of Avian Biology","publicationDate":"11/22/2015","auditedOn":"3/28/2016"},"contributors":{"authors":[{"text":"Guillaumet, Alban","contributorId":150397,"corporation":false,"usgs":false,"family":"Guillaumet","given":"Alban","email":"","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":581523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodworth, Bethany L.","contributorId":66797,"corporation":false,"usgs":true,"family":"Woodworth","given":"Bethany L.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":581524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":581525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":581522,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}