Identifying post-breeding migration and wintering distributions of migratory birds is important for understanding factors that may drive population dynamics. Red-throated Loons (Gavia stellata) are widely distributed across Alaska and currently have varying population trends, including some populations with recent periods of decline. To investigate population differentiation and the location of migration pathways and wintering areas, which may inform population trend patterns, we used satellite transmitters (n = 32) to describe migration patterns of four geographically separate breeding populations of Red-throated Loons in Alaska. On average (± SD) Red-throated Loons underwent long (6,288 ± 1,825 km) fall and spring migrations predominantly along coastlines. The most northern population (Arctic Coastal Plain) migrated westward to East Asia and traveled approximately 2,000 km farther to wintering sites than the three more southerly populations (Seward Peninsula, Yukon-Kuskokwim Delta, and Copper River Delta) which migrated south along the Pacific coast of North America. These migration paths are consistent with the hypothesis that Red-throated Loons from the Arctic Coastal Plain are exposed to contaminants in East Asia. The three more southerly breeding populations demonstrated a chain migration pattern in which the more northerly breeding populations generally wintered in more northerly latitudes. Collectively, the migration paths observed in this study demonstrate that some geographically distinct breeding populations overlap in wintering distribution while others use highly different wintering areas. Red-throated Loon population trends in Alaska may therefore be driven by a wide range of effects throughout the annual cycle.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0189954","usgsCitation":"McCloskey, S., Uher-Koch, B.D., Schmutz, J.A., and Fondell, T., 2018, International migration patterns of Red-throated Loons (Gavia stellata) from four breeding populations in Alaska: PLoS ONE, v. 13, no. 1, p. 1-15, https://doi.org/10.1371/journal.pone.0189954.","productDescription":"e0189954; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-090249","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":469099,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0189954","text":"Publisher Index Page"},{"id":438057,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8KVH","text":"USGS data release","linkHelpText":" Tracking data for Red-throated Loons (Gavia stellata)"},{"id":353018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"13","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-10","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc22c","contributors":{"authors":[{"text":"McCloskey, Sarah E. smccloskey@usgs.gov","contributorId":4850,"corporation":false,"usgs":true,"family":"McCloskey","given":"Sarah E.","email":"smccloskey@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":732175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":732174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","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":732176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fondell, Thomas F. tfondell@usgs.gov","contributorId":139310,"corporation":false,"usgs":true,"family":"Fondell","given":"Thomas F.","email":"tfondell@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":732177,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237797,"text":"70237797 - 2018 - The Polar WRF downscaled historical and projected twenty-first century climate for the coast and foothills of Arctic Alaska","interactions":[],"lastModifiedDate":"2022-10-24T15:50:49.887876","indexId":"70237797","displayToPublicDate":"2018-01-09T10:44:46","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12790,"text":"Frontiers in Earth Science: Atmospheric Science","active":true,"publicationSubtype":{"id":10}},"title":"The Polar WRF downscaled historical and projected twenty-first century climate for the coast and foothills of Arctic Alaska","docAbstract":"Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research and Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979–2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950–2005), and the CESM1.0 projected (for 2006–2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-h interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP 8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2017.00111","usgsCitation":"Cai, L., Alexeev, V.A., Arp, C.D., Jones, B.M., Liljedahl, A.K., and Gadeke, A., 2018, The Polar WRF downscaled historical and projected twenty-first century climate for the coast and foothills of Arctic Alaska: Frontiers in Earth Science: Atmospheric Science, v. 5, 111, 15 p., https://doi.org/10.3389/feart.2017.00111.","productDescription":"111, 15 p.","ipdsId":"IP-091773","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":469100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2017.00111","text":"Publisher Index Page"},{"id":408650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -133.9309914494186,\n 76.57231802755655\n ],\n [\n -169.1576427848962,\n 76.57231802755655\n ],\n [\n -169.1576427848962,\n 66.53960949503181\n ],\n [\n -133.9309914494186,\n 66.53960949503181\n ],\n [\n -133.9309914494186,\n 76.57231802755655\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2018-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Cai, Lei","contributorId":173394,"corporation":false,"usgs":false,"family":"Cai","given":"Lei","email":"","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":855661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexeev, Vladimir A","contributorId":298469,"corporation":false,"usgs":false,"family":"Alexeev","given":"Vladimir","email":"","middleInitial":"A","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":855662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":855664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liljedahl, Anna K. 0000-0001-7114-6443","orcid":"https://orcid.org/0000-0001-7114-6443","contributorId":150135,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna","email":"","middleInitial":"K.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":855665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gadeke, Anne","contributorId":191059,"corporation":false,"usgs":false,"family":"Gadeke","given":"Anne","email":"","affiliations":[],"preferred":false,"id":855666,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195317,"text":"70195317 - 2018 - Managing individual nests promotes population recovery of a top predator","interactions":[],"lastModifiedDate":"2018-04-17T12:27:38","indexId":"70195317","displayToPublicDate":"2018-01-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Managing individual nests promotes population recovery of a top predator","docAbstract":"This report documents several components of the U.S. Geological Survey National Hydrologic Model of the conterminous United States for use with the Precipitation-Runoff Modeling System (PRMS). It provides descriptions of the (1) National Hydrologic Model, (2) Geospatial Fabric for National Hydrologic Modeling, (3) PRMS hydrologic simulation code, (4) parameters and estimation methods used to compute spatially and temporally distributed default values as required by PRMS, (5) National Hydrologic Model Parameter Database, and (6) model extraction tool named Bandit. The National Hydrologic Model Parameter Database contains values for all PRMS parameters used in the National Hydrologic Model. The methods and national datasets used to estimate all the PRMS parameters are described. Some parameter values are derived from characteristics of topography, land cover, soils, geology, and hydrography using traditional Geographic Information System methods. Other parameters are set to long-established default values and computation of initial values. Additionally, methods (statistical, sensitivity, calibration, and algebraic) were developed to compute parameter values on the basis of a variety of nationally-consistent datasets. Values in the National Hydrologic Model Parameter Database can periodically be updated on the basis of new parameter estimation methods and as additional national datasets become available. A companion ScienceBase resource provides a set of static parameter values as well as images of spatially-distributed parameters associated with PRMS states and fluxes for each Hydrologic Response Unit across the conterminuous United States.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface water in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B9","usgsCitation":"Regan, R.S., Markstrom, S.L., Hay, L.E., Viger, R.J., Norton, P.A., Driscoll, J.M., LaFontaine, J.H., 2018, Description of the National Hydrologic Model for use with the Precipitation-Runoff Modeling System (PRMS): U.S. Geological Survey Techniques and Methods, book 6, chap B9, 38 p., https://doi.org/10.3133/tm6B9.","productDescription":"vii, 38 p.","onlineOnly":"Y","ipdsId":"IP-084916","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":438059,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYOJKN","text":"USGS data release","linkHelpText":"National Hydrologic Model v1.0 water budget components aggregated to 10 and 12-digit Hydrologic Unit Code boundaries"},{"id":350326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b09/coverthb.jpg"},{"id":350327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b09/tm6b9.pdf","text":"Report","size":"5.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-B9"}],"publicComments":"This report is Chapter 9 of Section B: Surface Water in Book 6 Modeling Techniques.","contact":"Director, Integrated Modeling and Prediction Division
U.S. Geological Survey
Mail Stop 415
12201 Sunrise Valley Drive
Reston, VA 20192
The analysis described in this report is part of a longterm project monitoring the biological communities, habitat, and water quality of the Fountain Creek Basin. Biology, habitat, and water-quality data have been collected at 10 sites since 2003. These data include annual samples of aquatic invertebrate communities, fish communities, water quality, and quantitative riverine habitat. This report examines trends in biological communities from 2003 to 2016 and explores relationships between biological communities and abiotic variables (antecedent streamflow, physical habitat, and water quality). Six biological metrics (three invertebrate and three fish) and four individual fish species were used to examine trends in these data and how streamflow, habitat, and (or) water quality may explain these trends. The analysis of 79 trends shows that the majority of significant trends decreased over the trend period. Overall, 19 trends before adjustments for streamflow in the fish (12) and invertebrate (7) metrics were all decreasing except for the metric Invertebrate Species Richness at the most upstream site in Monument Creek. Seven of these trends were explained by streamflow and four trends were revealed that were originally masked by variability in antecedent streamflow. Only two sites (Jimmy Camp Creek at Fountain, CO and Fountain Creek near Pinon, CO) had no trends in the fish or invertebrate metrics. Ten of the streamflow-adjusted trends were explained by habitat, one was explained by water quality, and five were not explained by any of the variables that were tested. Overall, from 2003 to 2016, all the fish metric trends were decreasing with an average decline of 40 percent, and invertebrate metrics decreased on average by 9.5 percent. A potential peak streamflow threshold was identified above which there is severely limited production of age-0 flathead chub (Platygobio gracilis).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175162","collaboration":"Prepared in cooperation with the City of Colorado Springs, Water Resources Engineering Division, Public Works Department and Colorado Springs Utilities","usgsCitation":"Roberts, J.J., Bruce, J.F., and Zuellig, R.E., 2018, Changes in biological communities of the Fountain Creek Basin, Colorado, 2003–2016, in relation to antecedent streamflow, water quality, and habitat: U.S. Geological Survey Scientific Investigations Report 2017–5162, 20 p., https://doi.org/10.3133/sir20175162.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-088880","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":350235,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/f747493V","text":"USGS data release","linkHelpText":"Datasets of ecological communities (invertebrates and fish), streamflow, habitat, and water quality to examine the presence of trends in ecological communities from the Fountain Creek Basin, Colorado, USA, 2003-2016"},{"id":438060,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F747493V","text":"USGS data release","linkHelpText":"Datasets of ecological communities (invertebrates and fish), streamflow, habitat, and water quality to examine the presence of trends in ecological communities from the Fountain Creek basin, Colorado, USA, 2003-2016."},{"id":350234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5162/sir20175162.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5162"},{"id":350233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5162/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Fountain Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105,\n 38.25\n ],\n [\n -104.5,\n 38.25\n ],\n [\n -104.5,\n 39\n ],\n [\n -105,\n 39\n ],\n [\n -105,\n 38.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Fugitive dust from unpaved roads creates human health hazards, degrades road surfaces, and increases the cost of road maintenance. As a result, many different chemical treatments are applied to unpaved roads in an attempt to control dust and stabilize the wearing course. However, investigations of the effectiveness of these treatments have often been poorly planned or executed. The objective of this study was to use a combination of real-time dust monitoring and objective road condition evaluations to assess the success of two chemical treatments for a period of 19?months post-application, to provide quantitative information in support of road management decisions. Dust production from road sections treated with calcium chloride-based durablend-C™ or the synthetic fluid EnviroKleen® was monitored on five dates using a vehicle-mounted particulate matter meter. Both products reduced dust by up to 99% relative to an untreated control section during the monitoring period, and quantitative data from the meter were consistent with qualitative observations of dust conditions. Linear models of dust production indicated that road treatment and humidity explained 69% of the variation in dust over time. Road sections treated with either product developed less rutting and fewer potholes than the untreated control. Overall, the combination of real-time dust monitoring and surface condition evaluation was an effective approach for generating quantitative data on endpoints of interest to road managers.
","language":"English","publisher":"Sage Publications","doi":"10.1177/0361198118799167","usgsCitation":"Kunz, B.K., Green, N., Albers, J.L., Wildhaber, M.L., and Little, E.E., 2018, Use of real-time dust monitoring and surface condition to evaluate success of unpaved road treatments: Transportation Research Record, v. 2672, no. 52, p. 195-204, https://doi.org/10.1177/0361198118799167.","productDescription":"10 p.","startPage":"195","endPage":"204","ipdsId":"IP-092378","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":362798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Loess Bluffs National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.26691436767577,\n 40.096457912121444\n ],\n [\n -95.26811599731445,\n 40.07491896000657\n ],\n [\n -95.25678634643555,\n 40.05219060659433\n ],\n [\n -95.2488899230957,\n 40.051665005850715\n ],\n [\n -95.24940490722656,\n 40.05337319344778\n ],\n [\n -95.24614334106445,\n 40.053898781018304\n ],\n [\n -95.23086547851562,\n 40.07386810509482\n ],\n [\n -95.22897720336914,\n 40.10315461168825\n ],\n [\n -95.23721694946289,\n 40.110638378278054\n ],\n [\n -95.24288177490234,\n 40.110638378278054\n ],\n [\n -95.26691436767577,\n 40.096457912121444\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2672","issue":"52","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":760470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Nicholas S. 0000-0002-8538-4191","orcid":"https://orcid.org/0000-0002-8538-4191","contributorId":202040,"corporation":false,"usgs":true,"family":"Green","given":"Nicholas S.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":760471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Albers, Janice L. 0000-0002-6312-8269 jalbers@usgs.gov","orcid":"https://orcid.org/0000-0002-6312-8269","contributorId":3972,"corporation":false,"usgs":true,"family":"Albers","given":"Janice","email":"jalbers@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":760472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":760473,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Little, Edward E. 0000-0003-0034-3639 elittle@usgs.gov","orcid":"https://orcid.org/0000-0003-0034-3639","contributorId":1746,"corporation":false,"usgs":true,"family":"Little","given":"Edward","email":"elittle@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":760474,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194663,"text":"ofr20171162 - 2018 - Groundwater quality in the shallow aquifers of the Madera–Chowchilla and Kings subbasins, San Joaquin Valley, California","interactions":[],"lastModifiedDate":"2018-01-09T09:52:21","indexId":"ofr20171162","displayToPublicDate":"2018-01-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1162","title":"Groundwater quality in the shallow aquifers of the Madera–Chowchilla and Kings subbasins, San Joaquin Valley, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Program’s Priority Basin Project assesses the quality of groundwater resources used for drinking-water supply and increases public access to groundwater-quality information. Many households and small communities in the Madera– Chowchilla and Kings subbasins of the San Joaquin Valley rely on private domestic wells for their drinking-water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171162","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S. and Shelton, J.L., 2018, Groundwater Quality in the Shallow Aquifers of the Madera–Chowchilla and Kings Subbasins, San Joaquin Valley, California: U.S. Geological Survey Open-File Report 2017–1162, 4 p., https://doi.org/10.3133/ofr20171162.","productDescription":"4 p.","ipdsId":"IP-089766","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":350380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1162/ofr20171162.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1162"},{"id":350379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1162/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Madera– Chowchilla Subbasin, Kings Subbasin, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5,\n 36.25\n ],\n [\n -119.25,\n 36.25\n ],\n [\n -119.25,\n 37.25\n ],\n [\n -120.5,\n 37.25\n ],\n [\n -120.5,\n 36.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The river otter (Lontra canadensis) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records
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\"}}]}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Bieber, N. R.","contributorId":273158,"corporation":false,"usgs":false,"family":"Bieber","given":"N.","email":"","middleInitial":"R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":832635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, S. P.","contributorId":273159,"corporation":false,"usgs":false,"family":"Wilson","given":"S.","email":"","middleInitial":"P.","affiliations":[{"id":56368,"text":"Nebraska Game and Parks","active":true,"usgs":false}],"preferred":false,"id":832636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263443,"text":"70263443 - 2018 - River otter distribution in Nebraska","interactions":[],"lastModifiedDate":"2025-02-11T15:28:17.418219","indexId":"70263443","displayToPublicDate":"2018-01-07T09:25:34","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"River otter distribution in Nebraska","docAbstract":"The river otter (Lontra canadensis) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.843","usgsCitation":"Bieber, N., Wilson, S., and Allen, C.R., 2018, River otter distribution in Nebraska: Wildlife Society Bulletin, v. 42, no. 1, p. 136-143, https://doi.org/10.1002/wsb.843.","productDescription":"8 p.","startPage":"136","endPage":"143","ipdsId":"IP-097555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499837,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/08bde403cf22458cbb0e4cf09a97b49e","text":"External Repository"},{"id":481929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Bieber, N.R.","contributorId":350797,"corporation":false,"usgs":false,"family":"Bieber","given":"N.R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":927011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, S.P.","contributorId":341215,"corporation":false,"usgs":false,"family":"Wilson","given":"S.P.","email":"","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":927012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":927013,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194678,"text":"sir20175157 - 2018 - Simulation of hydrodynamics, water quality, and lake sturgeon habitat volumes in Lake St. Croix, Wisconsin and Minnesota, 2013","interactions":[],"lastModifiedDate":"2019-10-23T12:29:22","indexId":"sir20175157","displayToPublicDate":"2018-01-05T16:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5157","title":"Simulation of hydrodynamics, water quality, and lake sturgeon habitat volumes in Lake St. Croix, Wisconsin and Minnesota, 2013","docAbstract":"Lake St. Croix is a naturally impounded, riverine lake that makes up the last 40 kilometers of the St. Croix River. Substantial land-use changes during the past 150 years, including increased agriculture and urban development, have reduced Lake St. Croix water-quality and increased nutrient loads delivered to Lake St. Croix. A recent (2012–13) total maximum daily load phosphorus-reduction plan set the goal to reduce total phosphorus loads to Lake St. Croix by 20 percent by 2020 and reduce Lake St. Croix algal bloom frequencies. The U.S. Geological Survey, in cooperation with the National Park Service, developed a two-dimensional, carbon-based, laterally averaged, hydrodynamic and water-quality model, CE–QUAL–W2, that addresses the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of water temperature, oxygen, and chlorophyll a. Distribution is evaluated in the context of habitat for lake sturgeon, including a combination of temperature and dissolved oxygen conditions termed oxy-thermal habitat.
The Lake St. Croix CE–QUAL–W2 model successfully reproduced temperature and dissolved oxygen in the lake longitudinally (from upstream to downstream), vertically, and temporally over the seasons. The simulated water temperature profiles closely matched the measured water temperature profiles throughout the year, including the prediction of thermocline transition depths (often within 1 meter), the absolute temperature of the thermocline transitions (often within 1.0 degree Celsius), and profiles without a strong thermocline transition. Simulated dissolved oxygen profiles matched the trajectories of the measured dissolved oxygen concentrations at multiple depths over time, and the simulated concentrations matched the depth and slope of the measured concentrations.
Additionally, trends in the measured water-quality data were captured by the model simulation, gaining some potential insights into the underlying mechanisms of critical Lake St. Croix metabolic processes. The CE–QUAL–W2 model tracked nitrate plus nitrite, total nitrogen, and total phosphorus throughout the year. Inflow nutrient contributions (loads), largely dominated by upstream St. Croix River loads, were the most important controls on Lake St. Croix water quality. Close to 60 percent of total phosphorus to the lake was from phosphorus derived from organic matter, and about 89 percent of phosphorus to Lake St. Croix was delivered by St. Croix River inflows. The Lake St. Croix CE–QUAL–W2 model offered potential mechanisms for the effect of external and internal loadings on the biotic response regarding the modeled algal community types of diatoms, green algae, and blue-green algae. The model also suggested the seasonal dominance of blue-green algae in all four pools of the lake.
A sensitivity analysis was completed to test the total maximum daily load phosphorus-reduction scenario responses of total phosphorus and chlorophyll a. The modeling indicates that phosphorus reductions would result in similar Lake St. Croix reduced concentrations, although chlorophyll a concentrations did not decrease in the same proportional amounts as the total phosphorus concentrations had decreased. The smaller than expected reduction in algal growth rates highlighted that although inflow phosphorus loads are important, other constituents also can affect the algal response of the lake, such as changes in light penetration and the breakdown of organic matter releasing nutrients.
The available habitat suitable for lake sturgeon was evaluated using the modeling results to determine the total volume of good-growth habitat, optimal growth habitat, and lethal temperature habitat. Overall, with the calibrated model, the fish habitat volume in general contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer. Only brief periods of lethal oxy-thermal habitat were present in Lake St. Croix during the model simulation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175157","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Smith, E.A., Kiesling, R.L., Ziegeweid, J.R., Elliott, S.M., and Magdalene, Suzanne, 2018, Simulation of hydrodynamics, water quality, and lake sturgeon habitat volumes in Lake St. Croix, Wisconsin and Minnesota, 2013: U.S. Geological Survey Scientific Investigations Report 2017–5157, 60 p., https://doi.org/10.3133/sir20175157.","productDescription":"Report: ix, 60 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-075804","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":350333,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319V2J","text":"USGS data release","description":"USGS data release","linkHelpText":"CE–QUAL–W2 water-quality model and supporting LOADEST models for Lake St. Croix, Wisconsin and Minnesota, 2013"},{"id":350332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5157/sir20175157.pdf","text":"Report","size":"3.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5157"},{"id":350331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5157/coverthb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Lake St. Croix","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.81936645507811,\n 44.74136858658327\n ],\n [\n -92.73216247558594,\n 44.74136858658327\n ],\n [\n -92.73216247558594,\n 45.07352060670971\n ],\n [\n -92.81936645507811,\n 45.07352060670971\n ],\n [\n -92.81936645507811,\n 44.74136858658327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Auklets (Aethia spp.) are small seabirds, endemic to the North Pacific Ocean, that nest in rock crevices on islands in Alaska and Russia. Nesting habitats for least (A. pusilla) and crested (A. cristatella) auklet colonies in the southern part of their range (Aleutian and Kuril Islands) are becoming overgrown by vegetation, which is fertilized by the auklets, making rock crevices unavailable for breeding. Colonization of newly created volcanic habitats suggests that auklets are habitat-limited in the southern range. The largest colonies there of least and crested auklets exist on lava slopes <100 years old. We propose that in the south, volcanic activity is required to maintain auklet populations. In contrast, colonies in the northern Bering Sea and Sea of Okhotsk show no indication of habitat limitation. They occur in more persistent talus slope habitats maintained by weathering, slumping, frost heaving, and tumbling. Biological processes there are slower and vegetation communities not as developed. We propose a conceptual model describing the interaction of geological and biological processes that influence auklet demography. We conclude that least and crested auklets require episodic disturbance (provided by volcanoes, earthquakes, and rock fall deposits) to maintain access to nest crevices. Auklets thereby provide an example of disturbance-adapted, early successional species that self-inhibit if their habitat is not regularly disturbed.
From August 2016 to March 2017, the U.S. Geological Survey (USGS) collected representative samples of filtered and unfiltered water and suspended sediment (including the colloidal fraction) at USGS streamgage 12113390 (Duwamish River at Golf Course, at Tukwila, Washington) during 13 periods of differing flow conditions. Samples were analyzed by Washington-State-accredited laboratories for a large suite of compounds, including metals, dioxins/furans, semivolatile compounds including polycyclic aromatic hydrocarbons, butyltins, the 209 polychlorinated biphenyl (PCB) congeners, and total and dissolved organic carbon. Concurrent with the chemistry sampling, water-quality field parameters were measured, and representative water samples were collected and analyzed for river suspended-sediment concentration and particle-size distribution. The results provide new data that can be used to estimate sediment and chemical loads transported by the Green River to the Lower Duwamish Waterway.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1073","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Conn, K.E., Black, R.W., Peterson, N.T., Senter, C.A., and Chapman, E.A., 2018, Chemical concentrations in water and\nsuspended sediment, Green River to Lower Duwamish Waterway near Seattle, Washington, 2016–17: U.S. Geological\nSurvey Data Series 1073, 17 p., https://doi.org/10.3133/ds1073.","productDescription":"Report: v, 17 p.; Appendix","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-091233","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":350280,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1073/ds1073_appendixa.xlsx","text":"Appendix A","size":"259 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1073 Appendix A"},{"id":350278,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1073/coverthb.jpg"},{"id":350279,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1073/ds1073.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1073"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Green River, Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35507965087889,\n 47.50931654292719\n ],\n [\n -122.29499816894531,\n 47.50931654292719\n ],\n [\n -122.29499816894531,\n 47.572124991940015\n ],\n [\n -122.35507965087889,\n 47.572124991940015\n ],\n [\n -122.35507965087889,\n 47.50931654292719\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Maps of geoelectric amplitude covering about half the continental United States are presented that will be exceeded, on average, once per century in response to an extreme-intensity geomagnetic disturbance. These maps are constructed using an empirical parameterization of induction: convolving latitude-dependent statistical maps of extreme-value geomagnetic disturbances, obtained from decades of 1-minute magnetic observatory data, with local estimates of Earth-surface impedance obtained at discrete geographic sites from magnetotelluric surveys. Geoelectric amplitudes are estimated for geomagnetic waveforms having a 240-s (and 1200-s) sinusoidal period and amplitudes over 10 min (1 h) that exceed a once-per-century threshold. As a result of the combination of geographic differences in geomagnetic variation and Earth-surface impedance, once-per-century geoelectric amplitudes span more than two orders of magnitude and are a highly granular function of location. Specifically for north-south 240-s induction, once-per-century geoelectric amplitudes across large parts of the United States have a median value of 0.34 V/km; for east-west variation, they have a median value of 0.23 V/km. In Northern Minnesota, amplitudes exceed 14.00 V/km for north-south geomagnetic variation (23.34 V/km for east-west variation), while just over 100 km away, amplitudes are only 0.08 V/km (0.02 V/km). At some sites in the northern-central United States, once-per-century geoelectric amplitudes exceed the 2 V/km realized in Québec during the March 1989 storm.
Liming techniques are being explored as a means to accelerate the recovery of aquatic biota from decades of acid deposition in many regions. The preservation or restoration of native sportfish populations has typically been the impetus for liming programs, and as such, less attention has been given to its effects on other biological assemblages such as macroinvertebrates. Furthermore, the differing effects of various lime application strategies such as in-stream and watershed applications are not well understood. In 2012, a program was initiated using in-stream and aerial (whole-watershed) liming to improve water quality and Brook Trout (Salvelinus fontinalis) recruitment in three acidified tributaries of a high-elevation Adirondack lake in New York State. Concurrently, macroinvertebrates were sampled annually between 2013 and 2016 at 3 treated sites and 3 untreated reference sites to assess the effects of each liming technique on this community. Despite improvements in water chemistry in all three limed streams, our results generally suggest that neither liming technique succeeded in improving the condition of macroinvertebrate communities. The watershed application caused an immediate and unsustained decrease in the density of macroinvertebrates and increase in the proportion of sensitive taxa. These changes were driven primarily by a one-year 71 percent reduction of the acid-tolerant Leuctra stoneflies and likely represent an initial chemistry shock from the lime application rather than a recovery response. The in-stream applications appeared to reduce the density of macroinvertebrates, particularly in one stream where undissolved lime covered the natural substrate. The close proximity of our study sites to the in-stream application points (50 and 1230 m) may partly explain these negative effects. Our results are consistent with prior studies of in-stream liming which indicate that this technique often fails to restore macroinvertebrate communities to a pre-acidification condition, especially at distances <1.5 km downstream of the lime application point. The inability of either liming technique to improve the condition of macroinvertebrate communities may be partly explained by the persistence of acidic episodes in all three streams. This suggests that in order to be effective, liming programs should attempt to eliminate even temporary episodes of unsuitable water chemistry rather than just meeting minimal criteria the majority of the time. Because watershed liming produced a more stable water chemistry regime than in-stream liming, this technique may have greater future potential to eliminate toxic episodes and accelerate the recovery of acid-impacted macroinvertebrate communities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.11.048","usgsCitation":"George, S.D., Baldigo, B.P., Lawrence, G.B., and Fuller, R.L., 2018, Effects of watershed and in-stream liming on macroinvertebrate communities in acidified tributaries to an Adirondack lake: Ecological Indicators, v. 85, no. February 2018, p. 1058-1067, https://doi.org/10.1016/j.ecolind.2017.11.048.","productDescription":"10 p.","startPage":"1058","endPage":"1067","ipdsId":"IP-080307","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":350330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.88006591796874,\n 43.50174856516506\n ],\n [\n -74.76848602294922,\n 43.50174856516506\n ],\n [\n -74.76848602294922,\n 43.55203173091177\n ],\n [\n -74.88006591796874,\n 43.55203173091177\n ],\n [\n -74.88006591796874,\n 43.50174856516506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"February 2018","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fad1e4b06e28e9c22727","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Randall L.","contributorId":196969,"corporation":false,"usgs":false,"family":"Fuller","given":"Randall","email":"","middleInitial":"L.","affiliations":[{"id":35994,"text":"Colgate University, Hamilton, NY","active":true,"usgs":false}],"preferred":false,"id":724817,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198330,"text":"70198330 - 2018 - Nutrient dynamics in partially drained arctic thaw lakes","interactions":[],"lastModifiedDate":"2018-08-19T20:02:18","indexId":"70198330","displayToPublicDate":"2018-01-02T15:04:46","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient dynamics in partially drained arctic thaw lakes","docAbstract":"Thaw lakes are ubiquitous on arctic coastal plains (ACPs). While many thaw lakes have steep banks, stable water levels, and static surface areas, others only partially fill their basins and vary in area over the summer. These partially drained lakes (PDLs) are hydrologically connected to the wetlands immediately surrounding them. Heat and nutrient availability limit aquatic productivity on ACPs, and we hypothesized that shallow shorelines and greater hydrologic connectivity with the landscape should result in greater nutrient concentrations and biogeochemical cycling in PDLs. We tested this by monitoring water chemistry in lakes with varying levels of seasonal drainage in sandy and silty peaty lowland sites on the ACP of Alaska. One highly drained lake (N1) was significantly warmer than minimally drained lakes (minDLs) related to earlier ice off, reaching temperatures as high as 16 °C in June when minDLs still contained ice. Ammonia, total dissolved phosphorus, and dissolved organic carbon and nitrogen concentrations were higher in lakes with greater drainage, and concentrations in N1 rivaled those in the small, biologically productive ponds. Many PDLs displayed a midsummer decrease in nutrients consistent with assimilation by the aquatic ecosystem, and a late‐summer increase most likely related to runoff from drained lake margins following precipitation. N1 exported kilograms of ammonium and total dissolved phosphorus to the stream network over the summer. Given increased warming and drying in the arctic, the proportion of PDLs may be changing, which in turn may affect nutrient and organic matter availability in arctic lakes and export to downstream environments.
","language":"English","publisher":"Wiley","doi":"10.1002/2017JG004187","usgsCitation":"Koch, J.C., Fondell, T.F., Schmutz, J.A., and Laske, S.M., 2018, Nutrient dynamics in partially drained arctic thaw lakes: Journal of Geophysical Research: Biogeosciences, v. 123, no. 2, p. 440-452, https://doi.org/10.1002/2017JG004187.","productDescription":"13 p.","startPage":"440","endPage":"452","ipdsId":"IP-085121","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":469103,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004187","text":"Publisher Index Page"},{"id":438061,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XHJ","text":"USGS data release","linkHelpText":"Arctic Coastal Plain Seasonal Lake Drainage, Water Temperature, and Solute and Nutrient Concentrations, 2011 - 2014"},{"id":356006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-02-17","publicationStatus":"PW","scienceBaseUri":"5b6fc4cbe4b0f5d57878eacc","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":741071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fondell, Tom F. tfondell@usgs.gov","contributorId":3563,"corporation":false,"usgs":true,"family":"Fondell","given":"Tom","email":"tfondell@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":741072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":741073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laske, Sarah M. 0000-0002-6096-0420 slaske@usgs.gov","orcid":"https://orcid.org/0000-0002-6096-0420","contributorId":204872,"corporation":false,"usgs":true,"family":"Laske","given":"Sarah","email":"slaske@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":741074,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196764,"text":"70196764 - 2018 - Effects of sea lamprey substrate modification and carcass nutrients on macroinvertebrate assemblages in a small Atlantic coastal stream","interactions":[],"lastModifiedDate":"2018-05-01T13:33:23","indexId":"70196764","displayToPublicDate":"2018-01-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of sea lamprey substrate modification and carcass nutrients on macroinvertebrate assemblages in a small Atlantic coastal stream","docAbstract":"Aquatic macroinvertebrates respond to patch dynamics arising from interactions of physical and chemical disturbances across space and time. Anadromous fish, such as sea lamprey, Petromyzon marinus, migrate from the ocean and alter physical and chemical properties of recipient spawning streams. Sea lamprey disturb stream benthos physically through nest construction and spawning, and enrich food webs through nutrient deposition from decomposing carcasses. Sea lamprey spawning nests support greater macroinvertebrate abundance than adjacent reference areas, but concurrent effects of stream bed modification and nutrient supplementation have not been examined sequentially. We added carcasses and cleared substrate experimentally to mimic the physical disturbance and nutrient enrichment associated with lamprey spawning, and characterized effects on macroinvertebrate assemblage structure. We found that areas receiving cleared substrate and carcass nutrients were colonized largely by Simuliidae compared to upstream and downstream control areas that were colonized largely by Hydropsychidae, Philopotamidae, and Chironomidae. Environmental factors such as stream flow likely shape assemblages by physically constraining macroinvertebrate establishment and feeding. Our results indicate potential changes in macroinvertebrate assemblages from the physical and chemical changes to streams brought by spawning populations of sea lamprey.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2017.1417168","usgsCitation":"Weaver, D.M., Coghlan, S.M., and Zydlewski, J.D., 2018, Effects of sea lamprey substrate modification and carcass nutrients on macroinvertebrate assemblages in a small Atlantic coastal stream: Journal of Freshwater Ecology, v. 33, no. 1, p. 19-30, https://doi.org/10.1080/02705060.2017.1417168.","productDescription":"12 p.","startPage":"19","endPage":"30","ipdsId":"IP-087200","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2017.1417168","text":"Publisher Index Page"},{"id":353880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.7850570678711,\n 44.73411331996726\n ],\n [\n -68.7630844116211,\n 44.73411331996726\n ],\n [\n -68.7630844116211,\n 44.7703805495189\n ],\n [\n -68.7850570678711,\n 44.7703805495189\n ],\n [\n -68.7850570678711,\n 44.73411331996726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-02","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc232","contributors":{"authors":[{"text":"Weaver, Daniel M.","contributorId":145786,"corporation":false,"usgs":false,"family":"Weaver","given":"Daniel","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":734293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coghlan, Stephen M. Jr.","contributorId":169678,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen","suffix":"Jr.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":734294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":734292,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249334,"text":"70249334 - 2018 - Mapping forest change using stacked generalization: An ensemble approach","interactions":[],"lastModifiedDate":"2023-10-04T22:07:19.998682","indexId":"70249334","displayToPublicDate":"2018-01-01T16:49:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Mapping forest change using stacked generalization: An ensemble approach","docAbstract":"The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.09.029","usgsCitation":"Healey, S.P., Cohen, W., Yang, Z., Brewer, C.K., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Hughes, M.J., Kennedy, R.E., Loveland, T., Moisen, G.G., Schroeder, T.A., Stehman, S.V., Vogelmann, J., Woodcock, C.E., Yang, L., and Zhu, Z., 2018, Mapping forest change using stacked generalization: An ensemble approach: Remote Sensing of Environment, v. 204, p. 717-728, https://doi.org/10.1016/j.rse.2017.09.029.","productDescription":"12 p.","startPage":"717","endPage":"728","ipdsId":"IP-087348","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.09.029","text":"Publisher Index 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However, it is still not known how many Arctic animals will respond to a changing climate with altered trophic interactions. We studied clutch size, incubation duration and nest survival of 17 taxa of Arctic‐breeding shorebirds at 16 field sites over 7 years. We predicted that physiological benefits of higher temperatures and earlier snowmelt would increase reproductive effort and nest survival, and we expected increasing predator abundance and decreasing abundance of alternative prey (arvicoline rodents) to have a negative effect on reproduction. Although we observed wide ranges of conditions during our study, we found no effects of covariates on reproductive traits in 12 of 17 taxa. In the remaining taxa, most relationships agreed with our predictions. Earlier snowmelt increased the probability of laying a full clutch from 0.61 to 0.91 for Western Sandpipers, and shortened incubation by 1.42 days for arcticola Dunlin and 0.77 days for Red Phalaropes. Higher temperatures increased the probability of a full clutch from 0.60 to 0.93 for Western Sandpipers and from 0.76 to 0.97 for Red‐necked Phalaropes, and increased daily nest survival rates from 0.9634 to 0.9890 for Semipalmated Sandpipers and 0.9546 to 0.9880 for Western Sandpipers. Higher abundance of predators (foxes) reduced daily nest survival rates only in Western Sandpipers (0.9821–0.9031). In contrast to our predictions, the probability of a full clutch was lowest (0.83) for Semipalmated Sandpipers at moderate abundance of alternative prey, rather than low abundance (0.90). Our findings suggest that in the short‐term, climate warming may have neutral or positive effects on the nesting cycle of most Arctic‐breeding shorebirds.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12571","usgsCitation":"Weiser, E., Brown, S.C., Lanctot, R.B., Gates, H., Abraham, K., Bentzen, R., Bety, J., Boldenow, M.L., Brook, R.W., Donnelly, T.F., English, W.B., Flemming, S.A., Franks, S., Gilchrist, H.G., Giroux, M., Johnson, A.C., Kendall, S., Kennedy, L.V., Koloski, L., Kwon, E., Lamarre, J., Lank, D.B., Latty, C.J., Lecomte, N., Liebezeit, J.R., McKinnon, L., Nol, E., Perz, J., Rausch, J., Robards, M.D., Saalfeld, S.T., Senner, N.R., Smith, P., Soloviev, M., Solovyeva, D.V., Ward, D.H., Wood, P., and Sandercock, B.K., 2018, Effects of environmental conditions on reproductive effort and nest success of Arctic‐breeding shorebirds: Ibis, v. 160, p. 608-623, https://doi.org/10.1111/ibi.12571.","productDescription":"16 p.","startPage":"608","endPage":"623","ipdsId":"IP-078732","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":469106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ibi.12571","text":"External Repository"},{"id":361479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"160","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L.","contributorId":171678,"corporation":false,"usgs":false,"family":"Weiser","given":"Emily L.","affiliations":[],"preferred":false,"id":757890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Stephen C.","contributorId":38457,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":757891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":135,"text":"Biological Resources Division","active":false,"usgs":true},{"id":7029,"text":"Queen's University, Kingston, Ontario, Canada","active":true,"usgs":false},{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":757892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gates, H. 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While previous studies showed that most of the time nonlocal models are identical with correlated parameters, fundamental challenges remain in real-world applications regarding model selection and parameter definition. This study compared three popular time nonlocal transport models, including the multi-rate mass transfer (MRMT) model, the continuous time random walk (CTRW) framework, and the tempered time fractional advection–dispersion equation (tt-fADE), by focusing on their physical interpretation and feasibility in capturing non-Fickian transport. Mathematical comparison showed that these models have both related parameters defining the memory function and other basic-transport parameters (i.e., velocity v and dispersion coefficient D) with different hydrogeologic interpretations. Laboratory column transport experiments and field tracer tests were then conducted, providing data for model applicability evaluation. Laboratory and field experiments exhibited breakthrough curves with non-Fickian characteristics, which were better represented by the tt-fADE and CTRW models than the traditional advection–dispersion equation. The best-fit velocity and dispersion coefficient, however, differ significantly between the tt-fADE and CTRW. Fitting exercises further revealed that the observed late-time breakthrough curves were heavier than the MRMT solutions with no more than two mass-exchange rates and lighter than the MRMT solutions with power-law distributed mass-exchange rates. Therefore, the time nonlocal models, where some parameters are correlated and exchangeable and the others have different values, differ mainly in their quantification of pre-asymptotic transport dynamics. In all models tested above, the tt-fADE model is attractive, considering its small fitting error and the reasonable velocity close to the measured flow rate.
","language":"English","publisher":"MDPI","doi":"10.3390/w10060778","usgsCitation":"Lu, B., Zhang, Y., Zheng, C., Green, C.T., O’Neill, C., Sun, H., and Qian, J., 2018, Comparison of time nonlocal transport models for characterizing non-Fickian transport: From mathematical interpretation to laboratory application: Water, v. 10, no. 6, p. 1-28, https://doi.org/10.3390/w10060778.","productDescription":"Article 778; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-086405","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":469107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10060778","text":"Publisher Index Page"},{"id":361861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Lu, Bingqing","contributorId":214039,"corporation":false,"usgs":false,"family":"Lu","given":"Bingqing","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":758998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Yong","contributorId":214040,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":758999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zheng, Chunmiao","contributorId":214041,"corporation":false,"usgs":false,"family":"Zheng","given":"Chunmiao","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":759000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":758997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Neill, Charles","contributorId":214042,"corporation":false,"usgs":false,"family":"O’Neill","given":"Charles","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":759001,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sun, Hong-Guang 0000-0002-8422-3871","orcid":"https://orcid.org/0000-0002-8422-3871","contributorId":176581,"corporation":false,"usgs":false,"family":"Sun","given":"Hong-Guang","email":"","affiliations":[],"preferred":false,"id":759002,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Qian, Jiazhong","contributorId":214043,"corporation":false,"usgs":false,"family":"Qian","given":"Jiazhong","email":"","affiliations":[{"id":38964,"text":"Hefei University of Technology, Hefei","active":true,"usgs":false}],"preferred":false,"id":759003,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227978,"text":"70227978 - 2018 - Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","interactions":[],"lastModifiedDate":"2022-02-03T22:23:44.371866","indexId":"70227978","displayToPublicDate":"2018-01-01T16:05:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","title":"Photographs of wading bird depredation events to monitor invasion extent of Asian Swamp Eel (Monopterus albus)","docAbstract":"Several anecdotes exist of wading birds depredating invasive Monopterus albus (Asian Swamp Eel) in waterways of the conterminous US. We present photographic evidence of 4 different wading bird species depredating adult Asian Swamp Eels in Georgia and Florida herein. Photographs taken by wildlife enthusiasts could provide a means for early detection of the Asian Swamp Eel and other aquatic species that are challenging to detect in waterways.
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Although research has shown the ubiquity of small ponds globally, and in the southeastern United States in particular, their cumulative impact in terms of evaporative alteration is less well quantified. The objectives of this study are to examine the hydrologic and evaporative importance of small artificial water bodies in the Upper Oconee watershed in the northern Georgia Piedmont, USA, by mapping their locations and modelling these small reservoirs using the Soil Water Assessment Tool. Comparative Soil Water Assessment Tool models were run with and without the inclusion of small reservoir surface area and volume. The models used meteorological inputs from 1990–2013 to represent years with drought, high precipitation, and moderate precipitation for both the calibration and evaluation periods. Statistical comparison of streamflow indicated that the calibration methodology produced results where the default model simulation without reservoirs fit observed flows more closely than the modified model with small reservoirs included (e.g., Nash–Sutcliffe efficiency of 0.72 vs. 0.64, r2 of 0.73 vs. 0.66, and percent bias of 11.4 vs. 21.6). In addition, Penman–Monteith, Hargreaves, and Priestley–Taylor evapotranspiration equations were used to estimate actual evaporation from 2,219 small water bodies identified throughout the 1,936.8 km2 watershed. Depending on the evaporation equation used, water bodies evaporated an average of 0.03–0.036 km3/year for the period 2003–2013. Using Penman–Monteith further, if the reservoirs were not considered and average actual evapotranspiration rates from the rest of the basin were applied, only 0.016 km3 of water would have left the basin as a result of evapotranspiration. This finding suggests construction of small reservoirs increased evaporation by an average of 0.017 km3 per year (approximately 46,500 m3/day). As the construction of small reservoirs continues and high resolution image data used to map these water bodies becomes increasingly available, watershed models that evolve to address the cumulative impacts of small water bodies on evaporation and other hydrologic processes will have greater potential to benefit the water resource management community.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11398","usgsCitation":"Ignatius, A., and Jones, J., 2018, High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA: Hydrological Processes, v. 32, no. 1, p. 51-65, https://doi.org/10.1002/hyp.11398.","productDescription":"15 p.","startPage":"51","endPage":"65","ipdsId":"IP-073606","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11398","text":"Publisher Index Page"},{"id":358190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Upper Oconee watershed","volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-18","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539ec","contributors":{"authors":[{"text":"Ignatius, Amber R. 0000-0002-2636-836X","orcid":"https://orcid.org/0000-0002-2636-836X","contributorId":193407,"corporation":false,"usgs":false,"family":"Ignatius","given":"Amber R.","affiliations":[],"preferred":false,"id":747475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":747474,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199946,"text":"70199946 - 2018 - Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India","interactions":[],"lastModifiedDate":"2018-10-05T14:30:40","indexId":"70199946","displayToPublicDate":"2018-01-01T14:30:35","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3478,"text":"Stochastic Environmental Research and Risk Assessment","active":true,"publicationSubtype":{"id":10}},"title":"Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India","docAbstract":"The area east of Varanasi is one of numerous places along the watershed of the Ganges River with groundwater concentrations of arsenic surpassing the maximum value of 10 parts per billion (ppb) recommended by the World Health Organization in drinking water. Here we apply geostatistics and compositional data analysis for the mapping of arsenic and iron to help in understanding the conditions leading to the occurrence of elevated level of arsenic in groundwater. The methodology allows for displaying concentrations of arsenic and iron as maps consistent with the limited information from 95 water wells across an area of approximately 210 km2; visualization of the uncertainty associated with the sampling; and summary of the findings in the form of probability maps. For thousands of years, Varanasi has been on the erosional side in a meander of the river that is free of arsenic values above 10 ppb. Maps reveal two anomalies of high arsenic concentrations on the depositional side of the valley, which has started seeing urban development. The methodology using geostatistics combined with compositional data analysis is completely general, so this study could be used as a prototype for hydrochemistry mapping in other areas.
","language":"English","publisher":"Springer","doi":"10.1007/s00477-017-1390-3","usgsCitation":"Olea, R., Raju, N.J., Egozcue, J.J., Pawlowsky-Glahn, V., and Singh, S., 2018, Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India: Stochastic Environmental Research and Risk Assessment, v. 32, no. 1, p. 241-259, https://doi.org/10.1007/s00477-017-1390-3.","productDescription":"19 p.","startPage":"241","endPage":"259","ipdsId":"IP-102331","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469109,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10256/14599","text":"External Repository"},{"id":358185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Uttar Pradesh","city":"Varanasi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 82.94094085693358,\n 25.22171429348812\n ],\n [\n 83.16032409667969,\n 25.22171429348812\n ],\n [\n 83.16032409667969,\n 25.353644304321104\n ],\n [\n 82.94094085693358,\n 25.353644304321104\n ],\n [\n 82.94094085693358,\n 25.22171429348812\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-13","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539ee","contributors":{"authors":[{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":47873,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":747416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raju, N. Janardhana","contributorId":208504,"corporation":false,"usgs":false,"family":"Raju","given":"N.","email":"","middleInitial":"Janardhana","affiliations":[],"preferred":false,"id":747476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egozcue, Juan J.","contributorId":208010,"corporation":false,"usgs":false,"family":"Egozcue","given":"Juan","email":"","middleInitial":"J.","affiliations":[{"id":37677,"text":"Dept. Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":747477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pawlowsky-Glahn, Vera","contributorId":208011,"corporation":false,"usgs":false,"family":"Pawlowsky-Glahn","given":"Vera","email":"","affiliations":[{"id":37678,"text":"Dept. Informatics, Applied Matematics and Statistics, Universitat de Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":747478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Shubhra","contributorId":208505,"corporation":false,"usgs":false,"family":"Singh","given":"Shubhra","email":"","affiliations":[],"preferred":false,"id":747479,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199759,"text":"70199759 - 2018 - Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA","interactions":[],"lastModifiedDate":"2018-09-27T13:53:21","indexId":"70199759","displayToPublicDate":"2018-01-01T13:53:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA","docAbstract":"A pragmatic approach to the long‐term monitoring of rivers leverages available information with targeted field investigations to address key uncertainties relevant to management decisions. An over‐arching management issue for many rivers is how reservoir operation affects the amount and location of in‐channel sediment and the resulting distribution of aquatic habitats. We integrate remotely acquired and field‐survey morphologic data for the Cedar River, Washington, to constitute the current status of aquatic habitats and benchmarks for long‐term monitoring that will inform streamflow management. Four key habitats (river edge, side channels, riffles, and pools) are feasible to monitor with high‐resolution aerial imagery, a longitudinal profile of the river, and a side channel inventory, but full characterization of the functional differences within these habitats requires additional information. Habitat use information such as redd surveys will continue to be important for long‐term monitoring where it cannot be inferred reliably from physical habitat characteristics.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3230","usgsCitation":"Konrad, C.P., Burton, K., Little, R., Gendaszek, A.S., Munn, M.D., and Anderson, S.W., 2018, Characterizing aquatic habitats for long‐term monitoring of a fourth‐order, regulated river in the Pacific Northwest, USA: River Research and Applications, v. 34, no. 1, p. 24-33, https://doi.org/10.1002/rra.3230.","productDescription":"10 p.","startPage":"24","endPage":"33","ipdsId":"IP-084622","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":469110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3230","text":"Publisher Index Page"},{"id":357837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124,\n 46\n ],\n [\n -120,\n 46\n ],\n [\n -120,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 46\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-30","publicationStatus":"PW","scienceBaseUri":"5bc0304de4b0fc368eb539f0","contributors":{"authors":[{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burton, K.","contributorId":208244,"corporation":false,"usgs":false,"family":"Burton","given":"K.","email":"","affiliations":[],"preferred":false,"id":746516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Little, R.","contributorId":208245,"corporation":false,"usgs":false,"family":"Little","given":"R.","email":"","affiliations":[],"preferred":false,"id":746517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munn, Mark D. 0000-0002-7154-7252 mdmunn@usgs.gov","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":976,"corporation":false,"usgs":true,"family":"Munn","given":"Mark","email":"mdmunn@usgs.gov","middleInitial":"D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":107001,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":746520,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198374,"text":"70198374 - 2018 - Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","interactions":[],"lastModifiedDate":"2018-08-31T12:28:47","indexId":"70198374","displayToPublicDate":"2018-01-01T12:01:32","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","title":"Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010","docAbstract":"The study area included in the Barataria and Terrebonne Bays vignette of\nsoutheastern Louisiana spans eastward from Terrebonne Bay to Barataria Bay (Figure 1)\nand includes portions of Terrebonne, Lafourche, St. Charles, Jefferson, Orleans,\nPlaquemines, and St. Bernard Parishes. This area falls between the Mississippi River on\nthe east and northeast, extends down through the western shore of Lake Salvador and the\nDixie Delta Canal, then runs west to Houma and follows Louisiana Route 315 to the\ncoast; Barataria and Terrebonne Bays are separated from each other by Bayou Lafourche.","largerWorkTitle":"Emergent Wetlands Status and Trends in the Northern Gulf of Mexico: 1950-2010 report","conferenceTitle":"2013 Gulf of Mexico Alliance (GOMA) All Hands Meeting","conferenceDate":"June 25-27, 2013","language":"English","usgsCitation":"Lawrence Handley, Spear, K.A., Zapletal, M., Thatcher, C.A., Jones, W.R., and Wilson, S.A., 2018, Barataria and Terrebonne Bays: Chapter F in Emergent wetlands status and trends in the northern Gulf of Mexico: 1950-2010, 25 p.","productDescription":"25 p.","startPage":"1","endPage":"24","ipdsId":"IP-096196","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":357000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":356093,"type":{"id":15,"text":"Index Page"},"url":"https://gom.usgs.gov/web/documents/Chapter_F_BaratariaTerrebonneBays.pdf"}],"country":"United States","state":"Louisiana ","otherGeospatial":"Barataria Bay; Terrebonne Bay","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a317e4b0702d0e843028","contributors":{"authors":[{"text":"Lawrence Handley","contributorId":206612,"corporation":false,"usgs":false,"family":"Lawrence Handley","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":741279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spear, Kathryn A. 0000-0001-8942-2856 speark@usgs.gov","orcid":"https://orcid.org/0000-0001-8942-2856","contributorId":1949,"corporation":false,"usgs":true,"family":"Spear","given":"Kathryn","email":"speark@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":741278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zapletal, Mirka","contributorId":206613,"corporation":false,"usgs":false,"family":"Zapletal","given":"Mirka","email":"","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":741280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thatcher, Cindy A. 0000-0003-0331-071X thatcherc@usgs.gov","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":2868,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"thatcherc@usgs.gov","middleInitial":"A.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":741281,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, William R. 0000-0002-5493-4138 jonesb@usgs.gov","orcid":"https://orcid.org/0000-0002-5493-4138","contributorId":463,"corporation":false,"usgs":true,"family":"Jones","given":"William","email":"jonesb@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":741282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Scott A. 0000-0001-8055-8618 wilsons@usgs.gov","orcid":"https://orcid.org/0000-0001-8055-8618","contributorId":2360,"corporation":false,"usgs":true,"family":"Wilson","given":"Scott","email":"wilsons@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":741283,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227935,"text":"70227935 - 2018 - Resilience in environmental risk and impact assessment: Concepts and measurement","interactions":[],"lastModifiedDate":"2022-02-02T17:55:51.208195","indexId":"70227935","displayToPublicDate":"2018-01-01T11:51:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1103,"text":"Bulletin of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Resilience in environmental risk and impact assessment: Concepts and measurement","docAbstract":"Different resilience concepts have different assumptions about system dynamics, which has implications for resilience-based environmental risk and impact assessment. Engineering resilience (recovery) dominates in the risk assessment literature but this definition does not account for the possibility of ecosystems to exist in multiple regimes. In this paper we discuss resilience concepts and quantification methods. Specifically, we discuss when a system fails to show engineering resilience after disturbances, indicating a shift to a potentially undesired regime. We show quantification methods that can assess the stability of this new regime to inform managers about possibilities to transform the system to a more desired regime. We point out the usefulness of an adaptive inference, modelling and management approach that is based on reiterative testing of hypothesis. This process facilitates learning about, and reduces uncertainty arising from risk and impact.
","language":"English","publisher":"Springer","doi":"10.1007/s00128-018-2467-5","usgsCitation":"Angeler, D.G., Allen, C.R., Garmestani, A.S., Pope, K.L., Twidwell, D., and Bundschuh, M., 2018, Resilience in environmental risk and impact assessment: Concepts and measurement: Bulletin of Environmental Contamination and Toxicology, v. 101, p. 543-548, https://doi.org/10.1007/s00128-018-2467-5.","productDescription":"6 p.","startPage":"543","endPage":"548","ipdsId":"IP-095048","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00128-018-2467-5","text":"Publisher Index Page"},{"id":395286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","noUsgsAuthors":false,"publicationDate":"2018-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Angeler, David G.","contributorId":205240,"corporation":false,"usgs":false,"family":"Angeler","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":37065,"text":"Swedish University of Agricultural Sciences, Uppsala, Sweden","active":true,"usgs":false}],"preferred":false,"id":832610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garmestani, Ahjond S.","contributorId":77285,"corporation":false,"usgs":true,"family":"Garmestani","given":"Ahjond","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":832613,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":832614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bundschuh, Mirco","contributorId":205001,"corporation":false,"usgs":false,"family":"Bundschuh","given":"Mirco","email":"","affiliations":[],"preferred":false,"id":832615,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}