Conservation and management for a species requires reliable information on its status, distribution, and habitat use. We identified occupancy and distributions of king (Rallus elegans) and clapper (R. crepitans) rail populations in marsh complexes along the Pamunkey and Mattaponi Rivers in Virginia, USA by modeling data on vocalizations recorded from autonomous recording units (ARUs). Occupancy probability for both species combined was 0.64 (95% CI: 0.53, 0.75) in marshes along the Pamunkey and 0.59 (0.45, 0.72) in marshes along the Mattaponi. Occupancy probability along the Pamunkey was strongly influenced by salinity, increasing logistically by a factor of 1.62 (0.6, 2.65) per parts per thousand of salinity. In contrast, there was not a strong salinity gradient on the Mattaponi and therefore vegetative community structure determined occupancy probability on that river. Estimated detection probability across both marshes was 0.63 (0.62, 0.65), but detection rates decreased as the season progressed. Monitoring wildlife within wetlands presents unique challenges for conservation managers. Our findings provide insight not only into how rails responded to environmental variation but also into the general utility of ARUs for occupancy modeling of the distribution and habitat associations of rails within tidal marsh systems.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-018-1003-z","usgsCitation":"Stiffler, L.L., Anderson, J.T., and Katzner, T., 2018, Occupancy modeling of autonomously recorded vocalizations to predict distribution of rallids in tidal wetlands: Wetlands, v. 38, no. 3, p. 605-612, https://doi.org/10.1007/s13157-018-1003-z.","productDescription":"8 p.","startPage":"605","endPage":"612","ipdsId":"IP-088312","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":351216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Mattaponi River, Pamunkey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.96678161621094,\n 37.51190453731693\n ],\n [\n -76.75804138183594,\n 37.51190453731693\n ],\n [\n -76.75804138183594,\n 37.6359849542696\n ],\n [\n -76.96678161621094,\n 37.6359849542696\n ],\n [\n -76.96678161621094,\n 37.51190453731693\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-22","publicationStatus":"PW","scienceBaseUri":"5a7acd15e4b00f54eb20c576","contributors":{"authors":[{"text":"Stiffler, Lydia L.","contributorId":198904,"corporation":false,"usgs":false,"family":"Stiffler","given":"Lydia","email":"","middleInitial":"L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":727616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, James T.","contributorId":28071,"corporation":false,"usgs":false,"family":"Anderson","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":727617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":727615,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196342,"text":"70196342 - 2018 - Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming","interactions":[],"lastModifiedDate":"2018-04-03T11:07:44","indexId":"70196342","displayToPublicDate":"2018-02-06T00:00:00","publicationYear":"2018","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":"Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming","docAbstract":"Snow sublimation is an important component of the snow mass balance, but the spatial and temporal variability of this process is not well understood in mountain environments. This study combines a process‐based snow model (SnowModel) with eddy covariance (EC) measurements to investigate (1) the spatio‐temporal variability of simulated snow sublimation with respect to station observations, (2) the contribution of snow sublimation to the ablation of the snowpack, and (3) the sensitivity and response of snow sublimation to bark beetle‐induced forest mortality and climate warming across the north‐central Colorado Rocky Mountains. EC‐based observations of snow sublimation compared well with simulated snow sublimation at stations dominated by surface and canopy sublimation, but blowing snow sublimation in alpine areas was not well captured by the EC instrumentation. Water balance calculations provided an important validation of simulated sublimation at the watershed scale. Simulated snow sublimation across the study area was equivalent to 28% of winter precipitation on average, and the highest relative snow sublimation fluxes occurred during the lowest snow years. Snow sublimation from forested areas accounted for the majority of sublimation fluxes, highlighting the importance of canopy and sub‐canopy surface sublimation in this region. Simulations incorporating the effects of tree mortality due to bark‐beetle disturbance resulted in a 4% reduction in snow sublimation from forested areas. Snow sublimation rates corresponding to climate warming simulations remained unchanged or slightly increased, but total sublimation losses decreased by up to 6% because of a reduction in snow covered area and duration.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR021172","usgsCitation":"Sexstone, G., Clow, D.W., Fassnacht, S.R., Liston, G.E., Hiemstra, C.A., Knowles, J.F., and Penn, C.A., 2018, Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming: Water Resources Research, v. 54, no. 2, p. 1191-1211, https://doi.org/10.1002/2017WR021172.","productDescription":"21 p.","startPage":"1191","endPage":"1211","ipdsId":"IP-087155","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":469034,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr021172","text":"Publisher Index Page"},{"id":438026,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75M64QQ","text":"USGS data release","linkHelpText":"SnowModel simulations and supporting observations for the north-central Colorado Rocky Mountains during water years 2011 through 2015"},{"id":353081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-23","publicationStatus":"PW","scienceBaseUri":"5afee73fe4b0da30c1bfc1bf","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":732483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":732484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fassnacht, Steven R.","contributorId":177135,"corporation":false,"usgs":false,"family":"Fassnacht","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":732485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liston, Glen E.","contributorId":203852,"corporation":false,"usgs":false,"family":"Liston","given":"Glen","email":"","middleInitial":"E.","affiliations":[{"id":36729,"text":"Cooperative Institute for Research in the Atmosphere","active":true,"usgs":false}],"preferred":false,"id":732486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hiemstra, Christopher A.","contributorId":147379,"corporation":false,"usgs":false,"family":"Hiemstra","given":"Christopher","email":"","middleInitial":"A.","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":732487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowles, John F.","contributorId":203853,"corporation":false,"usgs":false,"family":"Knowles","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":732488,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Penn, Colin A. 0000-0002-5195-2744","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":203851,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":732489,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195031,"text":"70195031 - 2018 - Regional variability of nitrate fluxes in the unsaturated zone and groundwater, Wisconsin, USA","interactions":[],"lastModifiedDate":"2018-02-22T12:49:24","indexId":"70195031","displayToPublicDate":"2018-02-06T00:00:00","publicationYear":"2018","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":"Regional variability of nitrate fluxes in the unsaturated zone and groundwater, Wisconsin, USA","docAbstract":"Process-based modeling of regional NO3− fluxes to groundwater is critical for understanding and managing water quality, but the complexity of NO3− reactive transport processes make implementation a challenge. This study introduces a regional vertical flux method (VFM) for efficient estimation of reactive transport of NO3− in the vadose zone and groundwater. The regional VFM was applied to 443 well samples in central-eastern Wisconsin. Chemical measurements included O2, NO3−, N2 from denitrification, and atmospheric tracers of groundwater age including carbon-14, chlorofluorocarbons, tritium, and tritiogenic helium. VFM results were consistent with observed chemistry, and calibrated parameters were in-line with estimates from previous studies. Results indicated that (1) unsaturated zone travel times were a substantial portion of the transit time to wells and streams (2) since 1945 fractions of applied N leached to groundwater have increased for manure-N, possibly due to increased injection of liquid manure, and decreased for fertilizer-N, and (3) under current practices and conditions, approximately 60% of the shallow aquifer will eventually be affected by downward migration of NO3−, with denitrification protecting the remaining 40%. Recharge variability strongly affected the unsaturated zone lag times and the eventual depth of the NO3− front. Principal components regression demonstrated that VFM parameters and predictions were significantly correlated with hydrogeochemical landscape features. The diverse and sometimes conflicting aspects of N management (e.g. limiting N volatilization versus limiting N losses to groundwater) warrant continued development of large-scale holistic strategies to manage water quality and quantity.","language":"English","publisher":"AGU","doi":"10.1002/2017WR022012","usgsCitation":"Green, C., Liao, L., Nolan, B.T., Juckem, P.F., Shope, C.L., Tesoriero, A.J., and Jurgens, B.C., 2018, Regional variability of nitrate fluxes in the unsaturated zone and groundwater, Wisconsin, USA: Water Resources Research, v. 54, no. 1, p. 301-322, https://doi.org/10.1002/2017WR022012.","productDescription":"22 p.","startPage":"301","endPage":"322","ipdsId":"IP-086033","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr022012","text":"Publisher Index Page"},{"id":351039,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.3291015625,\n 46.13417004624326\n ],\n [\n -92.59277343749999,\n 45.98169518512228\n ],\n [\n -92.85644531250001,\n 45.72152152227956\n ],\n [\n -92.85644531250001,\n 45.5679096098613\n ],\n [\n -92.6806640625,\n 45.47554027158593\n ],\n [\n -92.8125,\n 45.213003555993964\n ],\n [\n -92.83447265624999,\n 44.74673324024678\n ],\n [\n -92.48291015625,\n 44.5435052132082\n ],\n [\n -92.21923828124999,\n 44.402391829093915\n ],\n [\n -91.99951171875,\n 44.33956524809713\n ],\n [\n -91.7138671875,\n 44.11914151643737\n ],\n [\n -91.2744140625,\n 43.88205730390537\n ],\n [\n -91.2744140625,\n 43.48481212891603\n ],\n [\n -91.0546875,\n 43.34116005412307\n ],\n [\n -91.25244140624999,\n 43.068887774169625\n ],\n [\n -91.16455078125,\n 42.71473218539458\n ],\n [\n -90.76904296874999,\n 42.66628070564928\n ],\n [\n -90.65917968749999,\n 42.48830197960227\n ],\n [\n -87.7587890625,\n 42.52069952914966\n ],\n [\n -87.71484375,\n 42.71473218539461\n ],\n [\n -87.802734375,\n 42.90816007196054\n ],\n [\n -87.802734375,\n 43.16512263158296\n ],\n [\n -87.8466796875,\n 43.29320031385285\n ],\n [\n -87.71484375,\n 43.42100882994726\n ],\n [\n -87.6708984375,\n 43.61221676817573\n ],\n [\n -87.6708984375,\n 43.739352079154706\n ],\n [\n -87.62695312499999,\n 43.89789239125797\n ],\n [\n -87.5390625,\n 43.99281450048989\n ],\n [\n -87.5390625,\n 44.15068115978094\n ],\n [\n -87.3193359375,\n 44.465151013519616\n ],\n [\n -87.14355468749999,\n 44.77793589631623\n ],\n [\n -86.9677734375,\n 45.089035564831036\n ],\n [\n -86.8359375,\n 45.24395342262324\n ],\n [\n -86.7041015625,\n 45.460130637921004\n ],\n [\n -87.099609375,\n 45.521743896993634\n ],\n [\n -87.3193359375,\n 45.24395342262324\n ],\n [\n -87.6708984375,\n 44.84029065139799\n ],\n [\n -88.02246093750001,\n 44.49650533109348\n ],\n [\n -87.890625,\n 44.809121700077355\n ],\n [\n -87.802734375,\n 44.902577996288876\n ],\n [\n -87.5830078125,\n 45.058001435398246\n ],\n [\n -87.71484375,\n 45.182036837015886\n ],\n [\n -87.62695312499999,\n 45.398449976304086\n ],\n [\n -87.890625,\n 45.336701909968134\n ],\n [\n -87.802734375,\n 45.49094569262732\n ],\n [\n -87.802734375,\n 45.67548217560647\n ],\n [\n -87.802734375,\n 45.79816953017263\n ],\n [\n -88.11035156249999,\n 45.79816953017263\n ],\n [\n -88.11035156249999,\n 45.98169518512228\n ],\n [\n -88.6376953125,\n 46.10370875598026\n ],\n [\n -88.9013671875,\n 46.13417004624326\n ],\n [\n -90.087890625,\n 46.34692761055673\n ],\n [\n -90.263671875,\n 46.498392258597654\n ],\n [\n -90.43945312500001,\n 46.58906908309182\n ],\n [\n -90.2197265625,\n 47.07012182383309\n ],\n [\n -90.3515625,\n 47.279229002570794\n ],\n [\n -90.966796875,\n 47.18971246448421\n ],\n [\n -91.4501953125,\n 47.010225655683485\n ],\n [\n -91.93359375,\n 46.830133640447386\n ],\n [\n -92.3291015625,\n 46.73986059969267\n ],\n [\n -92.3291015625,\n 46.13417004624326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-23","publicationStatus":"PW","scienceBaseUri":"5a7acd1de4b00f54eb20c589","contributors":{"authors":[{"text":"Green, Christopher T. ctgreen@usgs.gov","contributorId":146339,"corporation":false,"usgs":true,"family":"Green","given":"Christopher T.","email":"ctgreen@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":726652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liao, Lixia 0000-0003-2513-0680","orcid":"https://orcid.org/0000-0003-2513-0680","contributorId":201643,"corporation":false,"usgs":true,"family":"Liao","given":"Lixia","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":726653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":726654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":726655,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shope, Christopher L. cshope@usgs.gov","contributorId":5016,"corporation":false,"usgs":true,"family":"Shope","given":"Christopher","email":"cshope@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":726656,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":195265,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":726657,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":726658,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203295,"text":"70203295 - 2018 - Maturity of nearby faults influences seismic hazard from hydraulic fracturing","interactions":[],"lastModifiedDate":"2019-05-02T06:54:39","indexId":"70203295","displayToPublicDate":"2018-02-05T06:50:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Maturity of nearby faults influences seismic hazard from hydraulic fracturing","docAbstract":"Understanding the causes of human-induced earthquakes is paramount to reducing societal risk. We investigated five cases of seismicity associated with hydraulic fracturing (HF) in Ohio since 2013 that, because of their isolation from other injection activities, provide an ideal setting for studying the relations between high-pressure injection and earthquakes. Our analysis revealed two distinct groups: (i) deeper earthquakes in the Precambrian basement, with larger magnitudes (M > 2), b-values < 1, and many post–shut-in earthquakes, versus (ii) shallower earthquakes in Paleozoic rocks ∼400 m below HF, with smaller magnitudes (M < 1), b-values > 1.5, and few post–shut-in earthquakes. Based on geologic history, laboratory experiments, and fault modeling, we interpret the deep seismicity as slip on more mature faults in older crystalline rocks and the shallow seismicity as slip on immature faults in younger sedimentary rocks. This suggests that HF inducing deeper seismicity may pose higher seismic hazards. Wells inducing deeper seismicity produced more water than wells with shallow seismicity, indicating more extensive hydrologic connections outside the target formation, consistent with pore pressure diffusion influencing seismicity. However, for both groups, the 2 to 3 h between onset of HF and seismicity is too short for typical fluid pressure diffusion rates across distances of ∼1 km and argues for poroelastic stress transfer also having a primary influence on seismicity.
Understanding the evolution of the Colorado River system has direct implications for (1) the processes and timing of continental-scale river system integration, (2) the formation of iconic landscapes like those in and around Grand Canyon, and (3) the availability of groundwater resources. Spatial patterns in the position and type of Colorado River deposits, only discernible through geologic mapping, can be used to test models related to Colorado River evolution. This is particularly true downstream from Grand Canyon where ancestral Colorado River deposits are well-exposed. We are principally interested in (1) regional patterns in the minimum and maximum elevation of each depositional unit, which are affected by depositional mechanism and postdepositional deformation; and (2) the volume of each unit, which reflects regional changes in erosion, transport efficiency, and accommodation space. The volume of Colorado River deposits below Grand Canyon has implications for groundwater resources, as the primary regional aquifer there is composed of those deposits. To this end, we are presently mapping Colorado River deposits and compiling and updating older mapping. This preliminary data release shows the current status of our mapping and compilation efforts. We plan to update it at regular intervals in conjunction with ongoing mapping.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181005","usgsCitation":"Crow, R.S., Block, D., Felger, T.J., House, P.K., Pearthree, P.A., Gootee, B.F., Youberg, A.M., Howard, K.A., and Beard, L.S., 2018, The Colorado River and its deposits downstream from Grand Canyon in Arizona, California, and Nevada: U.S. Geological Survey Open-File Report 2018–1005, 6 p., https://doi.org/10.3133/ofr20181005.","productDescription":"Report: iii, 6 p.; Geodatabase","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080360","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":351008,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1005/ofr20181005_gdb.zip","text":"Geodatabase","size":"4.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018-1005"},{"id":351006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1005/coverthb.jpg"},{"id":351007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1005/ofr20181005.pdf","text":"Report","size":"250 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1005"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 33.00866349457558\n ],\n [\n -114,\n 33.00866349457558\n ],\n [\n -114,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 33.00866349457558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with groundbased measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEG technical program expanded abstracts 2018","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/segam2018-2989187.1","usgsCitation":"Minsley, B.J., Foks, N.L., Kress, W., and Rigby, J., 2018, Hydrogeophysics at societally relevant scales: Airborne electromagnetic applications and model structural uncertainty quantification, in SEG technical program expanded abstracts 2018, p. 4894-4898, https://doi.org/10.1190/segam2018-2989187.1.","productDescription":"5 p.","startPage":"4894","endPage":"4898","ipdsId":"IP-096781","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":382890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foks, Nathan Leon 0000-0002-4907-3679","orcid":"https://orcid.org/0000-0002-4907-3679","contributorId":203470,"corporation":false,"usgs":true,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":809253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":196374,"corporation":false,"usgs":false,"family":"Rigby","given":"James R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227916,"text":"70227916 - 2018 - Geomorphic identification of physical habitat features in a large, altered river system","interactions":[],"lastModifiedDate":"2022-02-02T16:46:51.256361","indexId":"70227916","displayToPublicDate":"2018-02-02T10:09:54","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geomorphic identification of physical habitat features in a large, altered river system","docAbstract":"Altered flow regimes in streams can significantly affect ecosystems and disturb ecological processes, leading to species loss and extinction. Many river management projects use stream classification and habitat assessment approaches to design practical solutions to reverse or mitigate adverse effects of flow regime alteration on stream systems. The objective of this study was to develop a methodology to provide a primary identification of physical habitats in an 80-km long segment of the Canadian River in central Oklahoma. The methodology relied on basic geomorphic Geomorphic identification of physical habitat features in a large, altered river system erial imagery and Lidar data using Geographic Information Systems. Geostatistical tests were implemented to delineate habitat units. This approach based on high resolution data and did not require in-site inspection provided a relatively refined habitat delineation, consistent with visual observations. Future efforts will focus on validation via field surveys and coupling with hydro-sedimentary modeling to provide a tool for environmental flow decisions.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"River flow 2018 - Ninth international conference on fluvial hydraulics","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"language":"English","publisherLocation":"Lyon-Villeurbance, France","doi":"10.1051/e3sconf/20184002031","usgsCitation":"Guertault, L., Fox, G., and Brewer, S.K., 2018, Geomorphic identification of physical habitat features in a large, altered river system, in River flow 2018 - Ninth international conference on fluvial hydraulics, v. 40, 02031,8 p., https://doi.org/10.1051/e3sconf/20184002031.","productDescription":"02031,8 p.","ipdsId":"IP-094756","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1051/e3sconf/20184002031","text":"Publisher Index Page"},{"id":395280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Canadian River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.92138671875,\n 34.863397850419524\n ],\n [\n -96.12487792968749,\n 34.863397850419524\n ],\n [\n -96.12487792968749,\n 35.003003395276714\n ],\n [\n -96.92138671875,\n 35.003003395276714\n ],\n [\n -96.92138671875,\n 34.863397850419524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2018-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Guertault, L.","contributorId":273103,"corporation":false,"usgs":false,"family":"Guertault","given":"L.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":832570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, G.","contributorId":273105,"corporation":false,"usgs":false,"family":"Fox","given":"G.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":832571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":832572,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212316,"text":"70212316 - 2018 - Development of a species status assessment process for decisions under the U.S. Endangered Species Act","interactions":[],"lastModifiedDate":"2020-08-17T12:36:08.764155","indexId":"70212316","displayToPublicDate":"2018-02-02T10:04:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Development of a species status assessment process for decisions under the U.S. Endangered Species Act","docAbstract":"Decisions under the U.S. Endangered Species Act (ESA) require scientific input on the risk that the species will become extinct. A series of critiques on the role of science in ESA decisions have called for improved consistency and transparency in species risk assessments and clear distinctions between science input and policy application. To address the critiques and document the emerging practice of the U.S. Fish and Wildlife Service (USFWS), we outline an assessment process based on principles and practices of risk and decision analyses that results in a scientific report on species status. The species status assessment (SSA) process has three successive stages: 1) document the life history and ecological relationships of the species in question to provide the foundation for the assessment, 2) describe and hypothesize causes for the current condition of the species, and 3) forecast the species' future condition. The future condition refers to the ability of a species to sustain populations in the wild under plausible future scenarios. The scenarios help explore the species' response to future environmental stressors and to assess the potential for conservation to intervene to improve its status. The SSA process incorporates modeling and scenario planning for prediction of extinction risk and applies the conservation biology principles of representation, resiliency, and redundancy to evaluate the current and future condition. The SSA results in a scientific report distinct from policy application, which contributes to streamlined, transparent, and consistent decision-making and allows for greater technical participation by experts outside of the USFWS, for example, by state natural resource agencies. We present two case studies based on assessments of the eastern massasauga rattlesnake Sistrurus catenatus and the Sonoran Desert tortoise Gopherus morafkai to illustrate the process. The SSA builds upon the past threat-focused assessment by including systematic and explicit analyses of a species' future response to stressors and conservation, and as a result, we believe it provides an improved scientific analysis for ESA decisions.
","language":"English","publisher":"U.S. Fish & Wildlife Service","doi":"10.3996/052017-JFWM-041","usgsCitation":"Smith, D.R., Allan, N.L., McGowan, C.P., Szymankski, J.A., Oetker, S.R., and Bell, H.M., 2018, Development of a species status assessment process for decisions under the U.S. Endangered Species Act: Journal of Fish and Wildlife Management, v. 9, no. 1, p. 302-320, https://doi.org/10.3996/052017-JFWM-041.","productDescription":"19 p.","startPage":"302","endPage":"320","ipdsId":"IP-079068","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":469038,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/052017-jfwm-041","text":"Publisher Index Page"},{"id":377524,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan L.","contributorId":193025,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":796341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":796342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Szymankski, Jennifer A.","contributorId":238520,"corporation":false,"usgs":false,"family":"Szymankski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oetker, Susan R.","contributorId":238519,"corporation":false,"usgs":false,"family":"Oetker","given":"Susan","email":"","middleInitial":"R.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bell, Heather M.","contributorId":238521,"corporation":false,"usgs":false,"family":"Bell","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224306,"text":"70224306 - 2018 - Case study comparing multiple irrigated land datasets in Arizona and Colorado, USA","interactions":[],"lastModifiedDate":"2022-03-31T15:28:32.190561","indexId":"70224306","displayToPublicDate":"2018-02-02T07:50:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Case study comparing multiple irrigated land datasets in Arizona and Colorado, USA","docAbstract":"While there are currently a number of irrigated land datasets available for the western United States (U.S.), there is uncertainty regarding in how they relate to each other. To help understand the characteristics of available irrigated datasets, we compared (1) the Cropland Data Layer (CDL), (2) Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset (IAD), (3) Digitized Irrigated Land (DIL), and (4) Consumptive Use for Irrigation (CUI) data in Arizona and Colorado, U.S. These datasets were derived from multiple sources at various spatial resolutions and temporal scales. We found spatial and temporal trends among all of them. The datasets showed decreases in irrigated land area in Arizona during the 2000–2010 time period. The change ranges and ratios were similar in all Arizona datasets. Irrigated land in Colorado decreased in DIL and CUI but increased in IAD and CDL. The agreement within the same type of dataset during different time periods was from 60% to 80% (R2 from 0.35 to 0.72) in Arizona and from 50% to 80% (R2 from 0.23 to 0.68) in Colorado. DIL had the highest agreement (80%) in both states. The agreement among different datasets acquired at approximately the same time frame ranged from 51% to 63% (R2 from 0.14 to 0.31) in Arizona and from 47% to 69% (R2 from 0.32 to 0.40) in Colorado. The results from this study support a greater understanding of the multiresolution and multitemporal nature of these datasets for various applications.
The link between composition and reactivity of dissolved organic matter (DOM) is central to understanding the role aquatic systems play in the global carbon cycle; yet, unifying concepts driving molecular composition have yet to be established. We characterized 37 DOM isolates from diverse aquatic ecosystems, including their stable and radiocarbon isotopes (δ13C-dissolved organic carbon (DOC) and Δ14C-DOC), optical properties (absorbance and fluorescence), and molecular composition (ultrahigh resolution mass spectrometry). Isolates encompassed end-members of allochthonous and autochthonous DOM from sites across the United States, the Pacific Ocean, and Antarctic lakes. Modern Δ14C-DOC and optical properties reflecting increased aromaticity, such as carbon specific UV absorbance at 254 nm (SUVA254), were directly related to polyphenolic and polycyclic aromatic compounds, whereas enriched δ13C-DOC and optical properties reflecting autochthonous end-members were positively correlated to more aliphatic compounds. Furthermore, the two sets of autochthonous end-members (Pacific Ocean and Antarctic lakes) exhibited distinct molecular composition due to differences in extent of degradation. Across all sites and end-members studied, we find a consistent shift in composition with aging, highlighting the persistence of certain biomolecules concurrent with degradation time.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.7b05513","usgsCitation":"Kellerman, A.M., Guillemette, F., Podgorski, D.C., Aiken, G.R., Butler, K.D., and Spencer, R.G., 2018, Unifying concepts linking dissolved organic matter composition to persistence in aquatic ecosystems: Environmental Science & Technology, v. 52, no. 5, p. 2538-2548, https://doi.org/10.1021/acs.est.7b05513.","productDescription":"11 p.","startPage":"2538","endPage":"2548","ipdsId":"IP-090644","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":353383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-02","publicationStatus":"PW","scienceBaseUri":"5afee73fe4b0da30c1bfc1c3","contributors":{"authors":[{"text":"Kellerman, Anne M.","contributorId":204172,"corporation":false,"usgs":false,"family":"Kellerman","given":"Anne","email":"","middleInitial":"M.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":733283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guillemette, Francois","contributorId":204173,"corporation":false,"usgs":false,"family":"Guillemette","given":"Francois","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":733284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":733285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":733286,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Kenna D. 0000-0001-9604-4603 kebutler@usgs.gov","orcid":"https://orcid.org/0000-0001-9604-4603","contributorId":178885,"corporation":false,"usgs":true,"family":"Butler","given":"Kenna","email":"kebutler@usgs.gov","middleInitial":"D.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":733282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M.","contributorId":204174,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert","email":"","middleInitial":"G. M.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":733287,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263665,"text":"70263665 - 2018 - Euler-vector clustering of GPS velocities defines microplate geometry in southwest Japan","interactions":[],"lastModifiedDate":"2025-02-19T15:45:33.491099","indexId":"70263665","displayToPublicDate":"2018-02-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Euler-vector clustering of GPS velocities defines microplate geometry in southwest Japan","docAbstract":"I have used Euler-vector clustering to assign 469 GEONET stations in southwest Japan to k clusters (k = 2, 3,..., 9) so that, for any k, the velocities of stations within each cluster are most consistent with rigid-block motion on a sphere. That is, I attempt to explain the raw (i.e., uncorrected for strain accumulation), 1996–2006 velocities of those 469 Global Positioning System stations by rigid motion of k clusters on the surface of a spherical Earth. Because block geometry is maintained as strain accumulates, Euler-vector clustering may better approximate the block geometry than the values of the associated Euler vectors. The microplate solution for each k is constructed by merging contiguous clusters that have closely similar Euler vectors. The best solution consists of three microplates arranged along the Nankaido Trough-Ryukyu Trench between the Amurian and Philippine Sea Plates. One of these microplates, the South Kyushu Microplate (an extension of the Ryukyu forearc into the southeast corner of Kyushu), had previously been identified from paleomagnetic rotations. Relative to ITRF2000 the three microplates rotate at different rates about neighboring poles located close to the northwest corner of Shikoku. The microplate model is identical to that proposed in the block model of Wallace et al. (2009, https://doi.org/10.1130/G2522A.1) except in southernmost Kyushu. On Shikoku and Honshu, but not Kyushu, the microplate model is consistent with that proposed in the block models of Nishimura and Hashimoto (2006, https://doi.org/10.1016/j.tecto.2006.04.017) and Loveless and Meade (2010, https://doi.org/10.1029/2008JB006248) without the low-slip-rate boundaries proposed in the latter.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014874","usgsCitation":"Savage, J.C., 2018, Euler-vector clustering of GPS velocities defines microplate geometry in southwest Japan: JGR Solid Earth, v. 123, no. 2, p. 1954-1968, https://doi.org/10.1002/2017JB014874.","productDescription":"15 p.","startPage":"1954","endPage":"1968","ipdsId":"IP-088924","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","otherGeospatial":"southwest Japan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[134.63843,34.14923],[134.76638,33.80633],[134.20342,33.20118],[133.79295,33.52199],[133.28027,33.28957],[133.01486,32.70457],[132.36311,32.98938],[132.37118,33.46364],[132.92437,34.0603],[133.49297,33.94462],[133.90411,34.36493],[134.63843,34.14923]]],[[[140.97639,37.14207],[140.59977,36.34398],[140.77407,35.84288],[140.25328,35.13811],[138.97553,34.6676],[137.2176,34.60629],[135.79298,33.46481],[135.12098,33.84907],[135.07943,34.59654],[133.34032,34.37594],[132.15677,33.90493],[130.98614,33.88576],[132.00004,33.14999],[131.33279,31.45035],[130.68632,31.02958],[130.20242,31.41824],[130.44768,32.31947],[129.81469,32.61031],[129.40846,33.29606],[130.35394,33.60415],[130.87845,34.23274],[131.88423,34.74971],[132.61767,35.43339],[134.6083,35.73162],[135.67754,35.52713],[136.72383,37.30498],[137.39061,36.82739],[138.8576,37.82748],[139.4264,38.21596],[140.05479,39.43881],[139.88338,40.56331],[140.30578,41.19501],[141.36897,41.37856],[141.91426,39.99162],[141.8846,39.18086],[140.95949,38.174],[140.97639,37.14207]]],[[[143.91016,44.1741],[144.61343,43.96088],[145.32083,44.38473],[145.54314,43.26209],[144.05966,42.98836],[143.18385,41.99521],[141.61149,42.67879],[141.06729,41.58459],[139.95511,41.56956],[139.81754,42.56376],[140.31209,43.33327],[141.38055,43.38882],[141.67195,44.77213],[141.96764,45.55148],[143.14287,44.51036],[143.91016,44.1741]]]]},\"properties\":{\"name\":\"Japan\"}}]}","volume":"123","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Savage, James C. 0000-0002-5114-7673 jasavage@usgs.gov","orcid":"https://orcid.org/0000-0002-5114-7673","contributorId":2412,"corporation":false,"usgs":true,"family":"Savage","given":"James","email":"jasavage@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927729,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70194993,"text":"70194993 - 2018 - Patterns of circulating corticosterone in a population of rattlesnakes afflicted with snake fungal disease: Stress hormones as a potential mediator of seasonal cycles in disease severity and outcomes","interactions":[],"lastModifiedDate":"2018-02-02T13:44:57","indexId":"70194993","displayToPublicDate":"2018-02-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3075,"text":"Physiological and Biochemical Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of circulating corticosterone in a population of rattlesnakes afflicted with snake fungal disease: Stress hormones as a potential mediator of seasonal cycles in disease severity and outcomes","docAbstract":"Snake fungal disease (SFD) is an emerging threat to snake populations in the United States. Fungal pathogens are often associated with a physiological stress response mediated by the hypothalamo-pituitary-adrenal axis (HPA), and afflicted individuals may incur steep coping costs. The severity of SFD can vary seasonally; however, little is known regarding (1) how SFD infection relates to HPA activity and (2) how seasonal shifts in environment, life history, or HPA activity may interact to drive seasonal patterns of infection severity and outcomes. To test the hypothesis that SFD is associated with increased HPA activity and to identify potential environmental or physiological drivers of seasonal infection, we monitored baseline corticosterone, SFD infection severity, foraging success, body condition, and reproductive status in a field-active population of pigmy rattlesnakes. Both plasma corticosterone and the severity of clinical signs of SFD peaked in the winter. Corticosterone levels were also elevated in the fall before the seasonal rise in SFD severity. Severely symptomatic snakes were in low body condition and had elevated corticosterone levels compared to moderately infected and uninfected snakes. The monthly mean severity of SFD in the population was negatively related to population-wide estimates of body condition and temperature measured in the precedent month and positively correlated with corticosterone levels measured in the precedent month. Symptomatic females were less likely to enter reproductive bouts compared to asymptomatic females. We propose the hypothesis that the seasonal interplay among environment, host energetics, and HPA activity initiates trade-offs in the fall that drive the increase in SFD prevalence, symptom severity, and decline in condition observed in the population through winter.","language":"English","publisher":"University of Chicago Press","doi":"10.1086/695747","usgsCitation":"Lind, C.M., Moore, I.T., Akcay, C., Vernasco, B.J., Lorch, J.M., and Farrell, T.M., 2018, Patterns of circulating corticosterone in a population of rattlesnakes afflicted with snake fungal disease: Stress hormones as a potential mediator of seasonal cycles in disease severity and outcomes: Physiological and Biochemical Zoology, v. 91, no. 2, p. 765-775, https://doi.org/10.1086/695747.","productDescription":"11 p.","startPage":"765","endPage":"775","ipdsId":"IP-091446","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":469041,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/99353","text":"External Repository"},{"id":350950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7586d4e4b00f54eb1d81ce","contributors":{"authors":[{"text":"Lind, Craig M.","contributorId":201569,"corporation":false,"usgs":false,"family":"Lind","given":"Craig","email":"","middleInitial":"M.","affiliations":[{"id":27623,"text":"Stetson University","active":true,"usgs":false}],"preferred":false,"id":726484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Ignacio T.","contributorId":201570,"corporation":false,"usgs":false,"family":"Moore","given":"Ignacio","email":"","middleInitial":"T.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":726485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Akcay, Caglar","contributorId":201571,"corporation":false,"usgs":false,"family":"Akcay","given":"Caglar","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":726486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vernasco, Ben J.","contributorId":166945,"corporation":false,"usgs":false,"family":"Vernasco","given":"Ben","email":"","middleInitial":"J.","affiliations":[{"id":24577,"text":"University of Minnesota, St. Paul, MN","active":true,"usgs":false}],"preferred":false,"id":726487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":726483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farrell, Terence M.","contributorId":176253,"corporation":false,"usgs":false,"family":"Farrell","given":"Terence","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":726488,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195002,"text":"70195002 - 2018 - Potential for western US seasonal snowpack prediction","interactions":[],"lastModifiedDate":"2018-02-14T14:09:54","indexId":"70195002","displayToPublicDate":"2018-02-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Potential for western US seasonal snowpack prediction","docAbstract":"Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1716760115","usgsCitation":"Kapnick, S.B., Yang, X., Vecchi, G., Delworth, T.L., Gudgel, R., Malyshev, S., Milly, P.C., Shevliakova, E., Underwood, S., and Margulis, S.A., 2018, Potential for western US seasonal snowpack prediction: Proceedings of the National Academy of Sciences of the United States of America, v. 115, no. 6, p. 1180-1185, https://doi.org/10.1073/pnas.1716760115.","productDescription":"6 p.","startPage":"1180","endPage":"1185","ipdsId":"IP-090874","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469039,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1073/pnas.1716760115","text":"External Repository"},{"id":350944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.49609375,\n 30.56226095049944\n ],\n [\n -101.162109375,\n 30.56226095049944\n ],\n [\n -101.162109375,\n 49.809631563563094\n ],\n [\n -128.49609375,\n 49.809631563563094\n ],\n [\n -128.49609375,\n 30.56226095049944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"115","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-22","publicationStatus":"PW","scienceBaseUri":"5a7586d3e4b00f54eb1d81c8","contributors":{"authors":[{"text":"Kapnick, Sarah B.","contributorId":189908,"corporation":false,"usgs":false,"family":"Kapnick","given":"Sarah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":726514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Xiaosong","contributorId":201610,"corporation":false,"usgs":false,"family":"Yang","given":"Xiaosong","email":"","affiliations":[],"preferred":false,"id":726515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vecchi, Gabriel A.","contributorId":201585,"corporation":false,"usgs":false,"family":"Vecchi","given":"Gabriel A.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":726516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Delworth, Thomas L.","contributorId":189909,"corporation":false,"usgs":false,"family":"Delworth","given":"Thomas","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":726518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gudgel, Rich","contributorId":201586,"corporation":false,"usgs":false,"family":"Gudgel","given":"Rich","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":726517,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Malyshev, Sergey","contributorId":22175,"corporation":false,"usgs":true,"family":"Malyshev","given":"Sergey","affiliations":[],"preferred":false,"id":726519,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":726513,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shevliakova, Elena","contributorId":9596,"corporation":false,"usgs":true,"family":"Shevliakova","given":"Elena","affiliations":[],"preferred":false,"id":726520,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Underwood, Seth","contributorId":201611,"corporation":false,"usgs":false,"family":"Underwood","given":"Seth","email":"","affiliations":[],"preferred":false,"id":726521,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Margulis, Steven A.","contributorId":201612,"corporation":false,"usgs":false,"family":"Margulis","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":726522,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70194997,"text":"70194997 - 2018 - Remote sensing and modeling to fill the “gap” in missing natural capital","interactions":[],"lastModifiedDate":"2018-02-02T13:52:18","indexId":"70194997","displayToPublicDate":"2018-02-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Remote sensing and modeling to fill the “gap” in missing natural capital","docAbstract":"This chapter reviews recent advances in remote sensing and environmental modeling that address the first step in ecosystem accounting: biophysical quantification of ecosystem services. The chapter focuses on those ecosystem services in which the most rapid advances are likely, including crop pollination, sediment regulation, carbon sequestration and storage, and coastal flood regulation.
The discussion highlights data sources and modeling approaches that can support wealth accounting, next steps for mapping and biophysical modeling of ecosystem services, and considerations for integrating biophysical modeling and monetary valuation. These approaches could make the inclusion of some ecosystem services increasingly feasible in future versions of wealth accounts.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The changing wealth of nations 2018 : Building a sustainable future","language":"English","publisher":"The World Bank","usgsCitation":"Bagstad, K.J., Willcock, S., and Lange, G., 2018, Remote sensing and modeling to fill the “gap” in missing natural capital, chap. of The changing wealth of nations 2018 : Building a sustainable future, p. 199-210.","productDescription":"12 p.","startPage":"199","endPage":"210","ipdsId":"IP-087781","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":350976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350934,"type":{"id":15,"text":"Index Page"},"url":"https://openknowledge.worldbank.org/handle/10986/29001"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7586d4e4b00f54eb1d81cb","contributors":{"authors":[{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":726497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willcock, Simon 0000-0001-9534-9114","orcid":"https://orcid.org/0000-0001-9534-9114","contributorId":201576,"corporation":false,"usgs":false,"family":"Willcock","given":"Simon","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":726498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lange, Glenn-Marie","contributorId":201577,"corporation":false,"usgs":false,"family":"Lange","given":"Glenn-Marie","email":"","affiliations":[{"id":36208,"text":"The World Bank","active":true,"usgs":false}],"preferred":false,"id":726499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202467,"text":"70202467 - 2018 - A surrogate regression approach for computing continuous loads for the tributary nutrient and sediment monitoring program on the Great Lakes","interactions":[],"lastModifiedDate":"2019-03-04T15:31:28","indexId":"70202467","displayToPublicDate":"2018-02-01T15:30:52","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"A surrogate regression approach for computing continuous loads for the tributary nutrient and sediment monitoring program on the Great Lakes","docAbstract":"Water quality (WQ) in many Great Lake tributaries has been degraded (increased nutrient and sediment concentrations) due to changes in their watersheds, resulting in downstream eutrophication. As part of the Great Lakes Water Quality Agreement, specific goals were established for loading of specific constituents (e.g., phosphorus). In 2010, the Great Lakes Restoration Initiative was launched to identify problem areas, accelerate restoration efforts, and track their progress. In 2011, the U.S. Geological Survey established a monitoring program on 30 tributaries to the lakes, representing ~ 46% of the U.S. draining area and the spectrum of land uses. Discrete measurements of nutrients and suspended sediment, and continuous measurements of flow and WQ surrogates (turbidity, temperature, specific conductance, pH, and dissolved oxygen) are being collected in these tributaries to document their WQ and estimate continuous (5-min) loading. To estimate loadings, two regression models were developed for each constituent for each site: one using continuous flow and a seasonality factor; and one using flow, seasonality, and continuous surrogates. Variables included in the final models for each constituent were chosen from the explanatory variables that worked “best” for all sites. In computing loads, when continuous surrogate data were unavailable for short periods, loads were computed using the flow and seasonality models. Prediction intervals for all loads were calculated using results from both models. These results provide a better understanding of short-term variability and long-term changes in loading affecting the environmental health of the Great Lakes than traditional regression techniques that employ only flow and seasonality parameters.
Hydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar‐derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidar‐derived snow data set over 3 years (2013–2015) during a prolonged drought in California, to estimate basin‐scale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short‐term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin‐mean ET. Warm‐season ET estimated from this approach is 168 (85–252 at 95% confidence), 162 (0–326) and 191 (48–334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full‐year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part‐year time period. Using streamflow and ASO snow observations, we quantify spatially‐distributed hydrologic processes otherwise difficult to observe.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR020473","usgsCitation":"Henn, B., Painter, T.H., Bormann, K.J., McGurk, B., Flint, A.L., Flint, L.E., White, V., and Lundquist, J., 2018, High‐elevation evapotranspiration estimates during drought: Using streamflow and NASA Airborne Snow Observatory SWE observations to vlose the upper Tuolumne River Basin eater balance: Water Resources Research, v. 54, no. 2, p. 746-766, https://doi.org/10.1002/2017WR020473.","productDescription":"21 p.","startPage":"746","endPage":"766","ipdsId":"IP-083705","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":469044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr020473","text":"Publisher Index Page"},{"id":357979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Tuolumne River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120,\n 37.5\n ],\n [\n -119,\n 37.5\n ],\n [\n -119,\n 38.25\n ],\n [\n -120,\n 38.25\n ],\n [\n -120,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-03","publicationStatus":"PW","scienceBaseUri":"5bc03033e4b0fc368eb539dc","contributors":{"authors":[{"text":"Henn, Brian","contributorId":139777,"corporation":false,"usgs":false,"family":"Henn","given":"Brian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":746954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Painter, Thomas H.","contributorId":12378,"corporation":false,"usgs":true,"family":"Painter","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":746955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bormann, Kathryn J.","contributorId":208401,"corporation":false,"usgs":false,"family":"Bormann","given":"Kathryn","email":"","middleInitial":"J.","affiliations":[{"id":37796,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena","active":true,"usgs":false}],"preferred":false,"id":746960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGurk, Bruce","contributorId":74457,"corporation":false,"usgs":true,"family":"McGurk","given":"Bruce","affiliations":[],"preferred":false,"id":746956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746953,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746957,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"White, Vince","contributorId":208399,"corporation":false,"usgs":false,"family":"White","given":"Vince","email":"","affiliations":[{"id":37795,"text":"Southern California Edison","active":true,"usgs":false}],"preferred":false,"id":746958,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lundquist, Jessica D.","contributorId":12792,"corporation":false,"usgs":true,"family":"Lundquist","given":"Jessica D.","affiliations":[],"preferred":false,"id":746959,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70198670,"text":"70198670 - 2018 - Life‐history tradeoffs revealed by seasonal declines in reproductive traits of Arctic‐breeding shorebirds","interactions":[],"lastModifiedDate":"2018-08-15T10:23:02","indexId":"70198670","displayToPublicDate":"2018-02-01T14:30:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Life‐history tradeoffs revealed by seasonal declines in reproductive traits of Arctic‐breeding shorebirds","docAbstract":"Seasonal declines in breeding performance are widespread in wild animals, resulting from temporal changes in environmental conditions or from individual variation. Seasonal declines might drive selection for early breeding, with implications for other stages of the annual cycle. Alternatively, selection on the phenology of nonbreeding stages could constrain timing of the breeding season and lead to seasonal changes in reproductive performance. We studied 25 taxa of migratory shorebirds (including five subspecies) at 16 arctic sites in Russia, Alaska, and Canada. We investigated seasonal changes in four reproductive traits, and developed a novel Bayesian risk‐partitioning model of daily nest survival to examine seasonal trends in two causes of nest failure. We found strong seasonal declines in reproductive traits for a subset of species. The probability of laying a full four‐egg clutch declined by 8–78% in 12 of 25 taxa tested, daily nest survival rates declined by 1–12% in eight of 22 taxa, incubation duration declined by 2.0–2.5% in two of seven taxa, and mean egg volume declined by 5% in one of 15 taxa. Temporal changes were not fully explained by individual variation. Across all species, the proportion of failed nests that were depredated declined over the season from 0.98 to 0.60, while the proportion abandoned increased from 0.01 to 0.35 and drove the seasonal declines in nest survival. An increase in abandonment of late nests is consistent with a life‐history tradeoff whereby either adult mortality increased or adults deserted the breeding attempt to maximize adult survival. In turn, seasonal declines in clutch size and incubation duration might be adaptive to hasten hatching of later nests. In other species of shorebirds, we found no seasonal patterns in breeding performance, suggesting that some species are not subject to selective pressure for early breeding.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.01531","usgsCitation":"Weiser, E., Brown, S.C., Lanctot, R.B., Gates, H., Abraham, K., Bentzen, R., Bety, J., Brook, R.W., Boldenow, M.L., Donnelly, T.F., English, W.B., Flemming, S.A., Franks, S., Gilchrist, H.G., Giroux, M., Johnson, A.C., Kennedy, L.V., Koloski, L., Kwon, E., Lamarre, J., Lank, D.B., Lecomte, N., Liebezeit, J.R., McKinnon, L., Nol, E., Perz, J., Rausch, J., Robards, M.D., Saalfeld, S., Senner, N.R., Smith, P., Soloviev, M., Solovyeva, D.V., Ward, D.H., Wood, P., and Sandercock, B.K., 2018, Life‐history tradeoffs revealed by seasonal declines in reproductive traits of Arctic‐breeding shorebirds: Journal of Avian Biology, v. 49, no. 2, p. 1-16, https://doi.org/10.1111/jav.01531.","productDescription":"jav-01531; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-076413","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":356449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-19","publicationStatus":"PW","scienceBaseUri":"5b98a300e4b0702d0e84301a","contributors":{"authors":[{"text":"Weiser, Emily L.","contributorId":171678,"corporation":false,"usgs":false,"family":"Weiser","given":"Emily L.","affiliations":[],"preferred":false,"id":742462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Stephen C.","contributorId":38457,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":742463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lanctot, Richard B.","contributorId":31894,"corporation":false,"usgs":true,"family":"Lanctot","given":"Richard","email":"","middleInitial":"B.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false},{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false},{"id":7029,"text":"Queen's University, Kingston, Ontario, Canada","active":true,"usgs":false},{"id":135,"text":"Biological Resources Division","active":false,"usgs":true}],"preferred":false,"id":742464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gates, H. River","contributorId":84256,"corporation":false,"usgs":true,"family":"Gates","given":"H. River","affiliations":[],"preferred":false,"id":742465,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abraham, Kenneth F.","contributorId":32215,"corporation":false,"usgs":true,"family":"Abraham","given":"Kenneth F.","affiliations":[],"preferred":false,"id":742466,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bentzen, Rebecca L.","contributorId":62070,"corporation":false,"usgs":true,"family":"Bentzen","given":"Rebecca L.","affiliations":[],"preferred":false,"id":742467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bêty, Joël","contributorId":169335,"corporation":false,"usgs":false,"family":"Bêty","given":"Joël","affiliations":[],"preferred":false,"id":742468,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brook, Rodney W.","contributorId":92083,"corporation":false,"usgs":false,"family":"Brook","given":"Rodney","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":742474,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Boldenow, Megan L.","contributorId":203662,"corporation":false,"usgs":false,"family":"Boldenow","given":"Megan","email":"","middleInitial":"L.","affiliations":[{"id":36677,"text":"Department of Biology and Wildlife, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":742469,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Donnelly, Tyrone F. tfdonnelly@usgs.gov","contributorId":4369,"corporation":false,"usgs":true,"family":"Donnelly","given":"Tyrone","email":"tfdonnelly@usgs.gov","middleInitial":"F.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":742475,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"English, Willow B.","contributorId":169341,"corporation":false,"usgs":false,"family":"English","given":"Willow","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":742476,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Flemming, Scott A.","contributorId":207034,"corporation":false,"usgs":false,"family":"Flemming","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":742477,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Franks, Samantha E.","contributorId":92979,"corporation":false,"usgs":true,"family":"Franks","given":"Samantha E.","affiliations":[],"preferred":false,"id":742478,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gilchrist, H. Grant","contributorId":177911,"corporation":false,"usgs":false,"family":"Gilchrist","given":"H.","email":"","middleInitial":"Grant","affiliations":[],"preferred":false,"id":742479,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Giroux, Marie-Andree","contributorId":169343,"corporation":false,"usgs":false,"family":"Giroux","given":"Marie-Andree","email":"","affiliations":[],"preferred":false,"id":742480,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Andrew C.","contributorId":169346,"corporation":false,"usgs":false,"family":"Johnson","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":742481,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Kennedy, Lisa V.","contributorId":201905,"corporation":false,"usgs":false,"family":"Kennedy","given":"Lisa","email":"","middleInitial":"V.","affiliations":[{"id":36284,"text":"Western Ontario University, London, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":742482,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Koloski, Laura","contributorId":203665,"corporation":false,"usgs":false,"family":"Koloski","given":"Laura","email":"","affiliations":[{"id":36679,"text":"Trent University","active":true,"usgs":false}],"preferred":false,"id":742483,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Kwon, Eunbi","contributorId":169349,"corporation":false,"usgs":false,"family":"Kwon","given":"Eunbi","email":"","affiliations":[],"preferred":false,"id":742484,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Lamarre, Jean-François","contributorId":169350,"corporation":false,"usgs":false,"family":"Lamarre","given":"Jean-François","affiliations":[],"preferred":false,"id":742485,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Lank, David B.","contributorId":42533,"corporation":false,"usgs":false,"family":"Lank","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":29801,"text":"Department of Biological Sciences, Simon Fraser University, Burnaby, BC","active":true,"usgs":false}],"preferred":false,"id":742486,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Lecomte, Nicolas","contributorId":131119,"corporation":false,"usgs":false,"family":"Lecomte","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":742487,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Liebezeit, Joseph R.","contributorId":127693,"corporation":false,"usgs":false,"family":"Liebezeit","given":"Joseph","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":742488,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"McKinnon, Laura","contributorId":169353,"corporation":false,"usgs":false,"family":"McKinnon","given":"Laura","email":"","affiliations":[],"preferred":false,"id":742489,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Nol, Erica","contributorId":38459,"corporation":false,"usgs":true,"family":"Nol","given":"Erica","affiliations":[],"preferred":false,"id":742490,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Perz, Johanna","contributorId":169356,"corporation":false,"usgs":false,"family":"Perz","given":"Johanna","email":"","affiliations":[],"preferred":false,"id":742491,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Rausch, Jennie","contributorId":103938,"corporation":false,"usgs":true,"family":"Rausch","given":"Jennie","affiliations":[],"preferred":false,"id":742492,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Robards, Martin D.","contributorId":40148,"corporation":false,"usgs":false,"family":"Robards","given":"Martin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":742493,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Saalfeld, Sarah T.","contributorId":41721,"corporation":false,"usgs":true,"family":"Saalfeld","given":"Sarah T.","affiliations":[],"preferred":false,"id":742494,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Senner, Nathan R.","contributorId":140465,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":742495,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Smith, Paul A.","contributorId":73477,"corporation":false,"usgs":true,"family":"Smith","given":"Paul A.","affiliations":[],"preferred":false,"id":742496,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Soloviev, Mikhail","contributorId":207035,"corporation":false,"usgs":false,"family":"Soloviev","given":"Mikhail","affiliations":[],"preferred":false,"id":742497,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Solovyeva, Diana V.","contributorId":106033,"corporation":false,"usgs":true,"family":"Solovyeva","given":"Diana","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":742498,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":742499,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Wood, Paul F.","contributorId":203707,"corporation":false,"usgs":false,"family":"Wood","given":"Paul F.","affiliations":[],"preferred":false,"id":742500,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Sandercock, Brett K.","contributorId":95816,"corporation":false,"usgs":true,"family":"Sandercock","given":"Brett","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":742501,"contributorType":{"id":1,"text":"Authors"},"rank":36}]}} ,{"id":70199376,"text":"70199376 - 2018 - Monitoring global tree mortality patterns and trends. Report from the VW symposium ‘Crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health’","interactions":[],"lastModifiedDate":"2018-09-17T14:20:18","indexId":"70199376","displayToPublicDate":"2018-02-01T14:20:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring global tree mortality patterns and trends. Report from the VW symposium ‘Crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health’","docAbstract":"While recent investigations of induced earthquakes have focused on earthquakes associated with wastewater injection and unconventional recovery methods, the potential for earthquakes to be induced by primary production has long been recognized. We use boundary element methods to quantify the predicted geometry and amplitude of stress and strain changes associated with removal of large volumes of fluids in poroelastic reservoirs, focusing on the Los Angeles Basin (LA Basin) in California. We show that significant stress perturbations (upward of 0.1 MPa), while localized, typically extended several kilometers away from production horizons by the early 1940s. By this time, production horizons in the southwestern LA Basin were 2–4 km deep; models thus predict that stress conditions would have been perturbed significantly on faults at the upper edge of the seismogenic brittle crust, typically around 6 km. Predicted stress and strain changes associated with oil fields in the southwestern LA Basin during the first half of the 20th century, combined with stress changes caused by the 1933 Long Beach earthquake, could plausibly have induced a number of moderate-to-large earthquakes between 1932 and 1944. The rate of earthquakes in the southwestern LA Basin has been significantly lower since 1945 than it was for the three decades prior to 1945. We conclude that while decreasing production and pore-pressure reduction contributed to the initial decline, the continued decline was due in part to the advent of widespread water-flooding methods that maintained subsurface reservoir pressures.
","language":"English","publisher":"Society of Exploration Geophysicsts","doi":"10.1190/tle37020108.1","usgsCitation":"Hough, S.E., and Bilham, R.G., 2018, Poroelastic stress changes associated with primary oil production in the Los Angeles Basin, California: The Leading Edge, v. 37, no. 2, p. 108-116, https://doi.org/10.1190/tle37020108.1.","productDescription":"9 p.","startPage":"108","endPage":"116","ipdsId":"IP-093213","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":357538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Los Angeles Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.5,\n 33.67\n ],\n [\n -118,\n 33.67\n ],\n [\n -118,\n 34\n ],\n [\n -118.5,\n 34\n ],\n [\n -118.5,\n 33.67\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc03033e4b0fc368eb539e2","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":745652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bilham, Roger G. 0000-0002-5547-4102","orcid":"https://orcid.org/0000-0002-5547-4102","contributorId":48200,"corporation":false,"usgs":true,"family":"Bilham","given":"Roger","email":"","middleInitial":"G.","affiliations":[],"preferred":true,"id":745653,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216333,"text":"70216333 - 2018 - Evaluating the “Gradual Entrainment Lake Inverter” (GELI) artificial mixing technology for lake and reservoir management","interactions":[],"lastModifiedDate":"2020-11-12T14:38:59.020532","indexId":"70216333","displayToPublicDate":"2018-02-01T08:35:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5096,"text":"Land and Reservoir Management","onlineIssn":" 2151-553","printIssn":"1040-2381","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the “Gradual Entrainment Lake Inverter” (GELI) artificial mixing technology for lake and reservoir management","docAbstract":"Thermal stratification is important to the structure and function of lake and reservoir ecosystems. Yet when lakes undergo eutrophication, thermal stratification can exacerbate water quality problems. As a result, lake management has sometimes involved artificial mixing and destratification, though the available technologies are few and costly. It is therefore important to test the efficacy of new technologies when they arise. Here, we evaluate a lake mixing technology, the “Gradual Entrainment Lake Inverter” (GELI), which was used to mix Crystal Lake, Wisconsin, a 34 ha, 21 m deep, dimictic lake during the summer months of 2012 and 2013. To assess the effect of the GELI treatment on thermal regime, we used DYRESM to model thermal conditions in the 2 treatment years but in the absence of treatment. We found GELI treatment slowly reduced stratification and the temperature range of the lake to 4.2 and 5.3 C in each treatment year, on average. Full destratification and oxygenation of the water column prevented fall hypoxia and anoxia. We found efficiency of the GELI averaged 1.9% during treatment, which is higher than efficiencies reported from field applications of bubble plume aeration. We used DYRESM to simulate bubble plume aeration to match the observed destratification from our GELI treatment and estimate aeration would have required 1.4–1.8 times the airflow and power costs of the GELI. Though considerable limitations of the current iteration of this technology exist, these may be reduced in future versions, possibly leading to a practical lake and reservoir management tool.
Much of our understanding of en route landbird habitat use comes from research performed at local scales, ignoring effects at larger spatial scales. We used a multiscale approach to investigate stopover habitat use by landbirds using transect surveys in 68 forested sites in southwestern Michigan, USA, during the springs of 2002 and 2003. We modeled relationships of bird density and arthropod abundance with broad-scale spatiotemporal factors (year, day of year, geographic location) and local landscape (forest composition and structure, presence of open water) as well as site-scale factors (bird density and arthropod abundance, which exchanged roles as predictor and response variables). We found migrant densities to be most influenced by fine-scale factors, such as the abundance of other avian taxa and substrate arthropods, followed by broader-scale factors, such as forest structure and location, within the local and broader surrounding landscape. We found that migrant habitat associations either did not directly match or were weakly associated with the availability of riparian or lacustrine water habitats at a local scale, even though our results suggested that birds using these habitat cues would have encountered more arthropods. Rather than finding indirect measures of food abundance—such as distance to a water source or forest cover at the landscape scale—important, our models best explained bird density by a direct relationship with site-scale food resources. Thus, the scale at which migrants demonstrate habitat selection appears to be influenced by proximate mechanisms such as high-quality habitat availability and the presence of large ecological features within the landscape. Not only do factors operating at multiple scales influence how birds use habitats, but scale also influences how we interpret research findings, in turn influencing conservation decisions.
","language":"English","publisher":"Oxford Academic","doi":"10.1650/CONDOR-17-33.1","usgsCitation":"Zenzal, T., Smith, R., Ewert, D.N., Diehl, R.H., and Buler, J.J., 2018, Fine-scale heterogeneity drives forest use by spring migrant landbirds across a broad, contiguous forest matrix: Ornithological Applications, v. 120, no. 1, p. 166-184, https://doi.org/10.1650/CONDOR-17-33.1.","productDescription":"19 p.","startPage":"166","endPage":"184","ipdsId":"IP-084874","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-33.1","text":"Publisher Index Page"},{"id":410461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.57971174479208,\n 42.71893219358296\n ],\n [\n -85.57971174479208,\n 42.54471942672549\n ],\n [\n -85.31477909045175,\n 42.54471942672549\n ],\n [\n -85.31477909045175,\n 42.71893219358296\n ],\n [\n -85.57971174479208,\n 42.71893219358296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zenzal, Theodore J. Jr.","contributorId":299882,"corporation":false,"usgs":false,"family":"Zenzal","given":"Theodore J.","suffix":"Jr.","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":858906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Robert J.","contributorId":299883,"corporation":false,"usgs":false,"family":"Smith","given":"Robert J.","affiliations":[{"id":64967,"text":"University of Scranton","active":true,"usgs":false}],"preferred":false,"id":858907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ewert, David N.","contributorId":299884,"corporation":false,"usgs":false,"family":"Ewert","given":"David","email":"","middleInitial":"N.","affiliations":[{"id":34601,"text":"Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":858908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Robert H. 0000-0001-9141-1734 rhdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9141-1734","contributorId":3396,"corporation":false,"usgs":true,"family":"Diehl","given":"Robert","email":"rhdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buler, Jeffrey J.","contributorId":194648,"corporation":false,"usgs":false,"family":"Buler","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":858910,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197460,"text":"70197460 - 2018 - Contaminants in tropical island streams and their biota","interactions":[],"lastModifiedDate":"2018-06-05T14:35:51","indexId":"70197460","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1561,"text":"Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"Contaminants in tropical island streams and their biota","docAbstract":"Environmental contamination is problematic for tropical islands due to their typically dense human populations and competing land and water uses. The Caribbean island of Puerto Rico (USA) has a long history of anthropogenic chemical use, and its human population density is among the highest globally, providing a model environment to study contaminant impacts on tropical island stream ecosystems. Polycyclic Aromatic Hydrocarbons, historic-use chlorinated pesticides, current-use pesticides, Polychlorinated Biphenyls (PCBs), and metals (mercury, cadmium, copper, lead, nickel, zinc, and selenium) were quantified in the habitat and biota of Puerto Rico streams and assessed in relation to land-use patterns and toxicological thresholds. Water, sediment, and native fish and shrimp species were sampled in 13 rivers spanning broad watershed land-use characteristics during 2009–2010. Contrary to expectations, freshwater stream ecosystems in Puerto Rico were not severely polluted, likely due to frequent flushing flows and reduced deposition associated with recurring flood events. Notable exceptions of contamination were nickel in sediment within three agricultural watersheds (range 123–336 ppm dry weight) and organic contaminants (PCBs, organochlorine pesticides) and mercury in urban landscapes. At an urban site, PCBs in several fish species (Mountain Mullet Agonostomus monticola [range 0.019–0.030 ppm wet weight] and American Eel Anguilla rostrata [0.019–0.031 ppm wet weight]) may pose human health hazards, with concentrations exceeding the U.S. Environmental Protection Agency (EPA) consumption limit for 1 meal/month. American Eel at the urban site also contained dieldrin (range < detection-0.024 ppm wet weight) that exceeded the EPA maximum allowable consumption limit. The Bigmouth Sleeper Gobiomorous dormitor, an important piscivorus sport fish, accumulated low levels of organic contaminants in edible muscle tissue (due to its low lipid content) and may be most suitable for human consumption island-wide; only mercury at one site (an urban location) exceeded EPA's consumption limit of 3 meals/month for this species. These results comprise the first comprehensive island-wide contaminant assessment of Puerto Rico streams and biota and provide natural resource and public health agencies here and in similar tropical islands elsewhere with information needed to guide ecosystem and fisheries conservation and management and human health risk assessment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envres.2017.11.053","usgsCitation":"Buttermore, E.N., Cope, W., Kwak, T.J., Cooney, P.B., Shea, D., and Lazaro, P.R., 2018, Contaminants in tropical island streams and their biota: Environmental Research, v. 161, p. 615-623, https://doi.org/10.1016/j.envres.2017.11.053.","productDescription":"9 p.","startPage":"615","endPage":"623","ipdsId":"IP-092384","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"161","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5d4e4b060350a15d220","contributors":{"authors":[{"text":"Buttermore, Elissa N.","contributorId":84871,"corporation":false,"usgs":true,"family":"Buttermore","given":"Elissa","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":737243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":737244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":737242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cooney, Patrick B.","contributorId":141249,"corporation":false,"usgs":false,"family":"Cooney","given":"Patrick","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":737245,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Damian","contributorId":145456,"corporation":false,"usgs":false,"family":"Shea","given":"Damian","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":737246,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lazaro, Peter R.","contributorId":205423,"corporation":false,"usgs":false,"family":"Lazaro","given":"Peter","email":"","middleInitial":"R.","affiliations":[{"id":37103,"text":"Department of Biological Sciences, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":737247,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195430,"text":"70195430 - 2018 - Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts","interactions":[],"lastModifiedDate":"2018-02-14T13:31:00","indexId":"70195430","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts","docAbstract":"Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2067","usgsCitation":"Muller, B.J., Cade, B.S., and Schwarzkoph, L., 2018, Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts: Ecosphere, v. 9, no. 1, p. 1-14, https://doi.org/10.1002/ecs2.2067.","productDescription":"Article e02067; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-092621","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2067","text":"Publisher Index Page"},{"id":351611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-05","publicationStatus":"PW","scienceBaseUri":"5afee743e4b0da30c1bfc203","contributors":{"authors":[{"text":"Muller, Benjamin J.","contributorId":202492,"corporation":false,"usgs":false,"family":"Muller","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":36457,"text":"Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Quensland, Australia","active":true,"usgs":false}],"preferred":false,"id":728565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":728564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarzkoph, Lin","contributorId":202493,"corporation":false,"usgs":false,"family":"Schwarzkoph","given":"Lin","email":"","affiliations":[{"id":36458,"text":"College of Science and Engineering, James Cook University, Townsville, Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":728566,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197893,"text":"70197893 - 2018 - Seismic hazard, risk, and design for South America","interactions":[],"lastModifiedDate":"2018-10-04T13:27:16","indexId":"70197893","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismic hazard, risk, and design for South America","docAbstract":"We calculate seismic hazard, risk, and design criteria across South America using the latest data, models, and methods to support public officials, scientists, and engineers in earthquake risk mitigation efforts. Updated continental scale seismic hazard models are based on a new seismicity catalog, seismicity rate models, evaluation of earthquake sizes, fault geometry and rate parameters, and ground‐motion models. Resulting probabilistic seismic hazard maps show peak ground acceleration, modified Mercalli intensity, and spectral accelerations at 0.2 and 1 s periods for 2%, 10%, and 50% probabilities of exceedance in 50 yrs. Ground shaking soil amplification at each site is calculated by considering uniform soil that is applied in modern building codes or by applying site‐specific factors based on