Understanding the evolution of the Colorado River system has direct implications for (1) the processes and timing of continental-scale river system integration, (2) the formation of iconic landscapes like those in and around Grand Canyon, and (3) the availability of groundwater resources. Spatial patterns in the position and type of Colorado River deposits, only discernible through geologic mapping, can be used to test models related to Colorado River evolution. This is particularly true downstream from Grand Canyon where ancestral Colorado River deposits are well-exposed. We are principally interested in (1) regional patterns in the minimum and maximum elevation of each depositional unit, which are affected by depositional mechanism and postdepositional deformation; and (2) the volume of each unit, which reflects regional changes in erosion, transport efficiency, and accommodation space. The volume of Colorado River deposits below Grand Canyon has implications for groundwater resources, as the primary regional aquifer there is composed of those deposits. To this end, we are presently mapping Colorado River deposits and compiling and updating older mapping. This preliminary data release shows the current status of our mapping and compilation efforts. We plan to update it at regular intervals in conjunction with ongoing mapping.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181005","usgsCitation":"Crow, R.S., Block, D., Felger, T.J., House, P.K., Pearthree, P.A., Gootee, B.F., Youberg, A.M., Howard, K.A., and Beard, L.S., 2018, The Colorado River and its deposits downstream from Grand Canyon in Arizona, California, and Nevada: U.S. Geological Survey Open-File Report 2018–1005, 6 p., https://doi.org/10.3133/ofr20181005.","productDescription":"Report: iii, 6 p.; Geodatabase","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080360","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":351008,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1005/ofr20181005_gdb.zip","text":"Geodatabase","size":"4.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018-1005"},{"id":351006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1005/coverthb.jpg"},{"id":351007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1005/ofr20181005.pdf","text":"Report","size":"250 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1005"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 33.00866349457558\n ],\n [\n -114,\n 33.00866349457558\n ],\n [\n -114,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 33.00866349457558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with groundbased measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEG technical program expanded abstracts 2018","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/segam2018-2989187.1","usgsCitation":"Minsley, B.J., Foks, N.L., Kress, W., and Rigby, J., 2018, Hydrogeophysics at societally relevant scales: Airborne electromagnetic applications and model structural uncertainty quantification, in SEG technical program expanded abstracts 2018, p. 4894-4898, https://doi.org/10.1190/segam2018-2989187.1.","productDescription":"5 p.","startPage":"4894","endPage":"4898","ipdsId":"IP-096781","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":382890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foks, Nathan Leon 0000-0002-4907-3679","orcid":"https://orcid.org/0000-0002-4907-3679","contributorId":203470,"corporation":false,"usgs":true,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":809253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":196374,"corporation":false,"usgs":false,"family":"Rigby","given":"James R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227916,"text":"70227916 - 2018 - Geomorphic identification of physical habitat features in a large, altered river system","interactions":[],"lastModifiedDate":"2022-02-02T16:46:51.256361","indexId":"70227916","displayToPublicDate":"2018-02-02T10:09:54","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Geomorphic identification of physical habitat features in a large, altered river system","docAbstract":"Altered flow regimes in streams can significantly affect ecosystems and disturb ecological processes, leading to species loss and extinction. Many river management projects use stream classification and habitat assessment approaches to design practical solutions to reverse or mitigate adverse effects of flow regime alteration on stream systems. The objective of this study was to develop a methodology to provide a primary identification of physical habitats in an 80-km long segment of the Canadian River in central Oklahoma. The methodology relied on basic geomorphic Geomorphic identification of physical habitat features in a large, altered river system erial imagery and Lidar data using Geographic Information Systems. Geostatistical tests were implemented to delineate habitat units. This approach based on high resolution data and did not require in-site inspection provided a relatively refined habitat delineation, consistent with visual observations. Future efforts will focus on validation via field surveys and coupling with hydro-sedimentary modeling to provide a tool for environmental flow decisions.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"River flow 2018 - Ninth international conference on fluvial hydraulics","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"language":"English","publisherLocation":"Lyon-Villeurbance, France","doi":"10.1051/e3sconf/20184002031","usgsCitation":"Guertault, L., Fox, G., and Brewer, S.K., 2018, Geomorphic identification of physical habitat features in a large, altered river system, in River flow 2018 - Ninth international conference on fluvial hydraulics, v. 40, 02031,8 p., https://doi.org/10.1051/e3sconf/20184002031.","productDescription":"02031,8 p.","ipdsId":"IP-094756","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1051/e3sconf/20184002031","text":"Publisher Index Page"},{"id":395280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Canadian River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.92138671875,\n 34.863397850419524\n ],\n [\n -96.12487792968749,\n 34.863397850419524\n ],\n [\n -96.12487792968749,\n 35.003003395276714\n ],\n [\n -96.92138671875,\n 35.003003395276714\n ],\n [\n -96.92138671875,\n 34.863397850419524\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2018-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Guertault, L.","contributorId":273103,"corporation":false,"usgs":false,"family":"Guertault","given":"L.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":832570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, G.","contributorId":273105,"corporation":false,"usgs":false,"family":"Fox","given":"G.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":832571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":832572,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224306,"text":"70224306 - 2018 - Case study comparing multiple irrigated land datasets in Arizona and Colorado, USA","interactions":[],"lastModifiedDate":"2022-03-31T15:28:32.190561","indexId":"70224306","displayToPublicDate":"2018-02-02T07:50:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Case study comparing multiple irrigated land datasets in Arizona and Colorado, USA","docAbstract":"While there are currently a number of irrigated land datasets available for the western United States (U.S.), there is uncertainty regarding in how they relate to each other. To help understand the characteristics of available irrigated datasets, we compared (1) the Cropland Data Layer (CDL), (2) Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset (IAD), (3) Digitized Irrigated Land (DIL), and (4) Consumptive Use for Irrigation (CUI) data in Arizona and Colorado, U.S. These datasets were derived from multiple sources at various spatial resolutions and temporal scales. We found spatial and temporal trends among all of them. The datasets showed decreases in irrigated land area in Arizona during the 2000–2010 time period. The change ranges and ratios were similar in all Arizona datasets. Irrigated land in Colorado decreased in DIL and CUI but increased in IAD and CDL. The agreement within the same type of dataset during different time periods was from 60% to 80% (R2 from 0.35 to 0.72) in Arizona and from 50% to 80% (R2 from 0.23 to 0.68) in Colorado. DIL had the highest agreement (80%) in both states. The agreement among different datasets acquired at approximately the same time frame ranged from 51% to 63% (R2 from 0.14 to 0.31) in Arizona and from 47% to 69% (R2 from 0.32 to 0.40) in Colorado. The results from this study support a greater understanding of the multiresolution and multitemporal nature of these datasets for various applications.
Water quality (WQ) in many Great Lake tributaries has been degraded (increased nutrient and sediment concentrations) due to changes in their watersheds, resulting in downstream eutrophication. As part of the Great Lakes Water Quality Agreement, specific goals were established for loading of specific constituents (e.g., phosphorus). In 2010, the Great Lakes Restoration Initiative was launched to identify problem areas, accelerate restoration efforts, and track their progress. In 2011, the U.S. Geological Survey established a monitoring program on 30 tributaries to the lakes, representing ~ 46% of the U.S. draining area and the spectrum of land uses. Discrete measurements of nutrients and suspended sediment, and continuous measurements of flow and WQ surrogates (turbidity, temperature, specific conductance, pH, and dissolved oxygen) are being collected in these tributaries to document their WQ and estimate continuous (5-min) loading. To estimate loadings, two regression models were developed for each constituent for each site: one using continuous flow and a seasonality factor; and one using flow, seasonality, and continuous surrogates. Variables included in the final models for each constituent were chosen from the explanatory variables that worked “best” for all sites. In computing loads, when continuous surrogate data were unavailable for short periods, loads were computed using the flow and seasonality models. Prediction intervals for all loads were calculated using results from both models. These results provide a better understanding of short-term variability and long-term changes in loading affecting the environmental health of the Great Lakes than traditional regression techniques that employ only flow and seasonality parameters.
Hydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar‐derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidar‐derived snow data set over 3 years (2013–2015) during a prolonged drought in California, to estimate basin‐scale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short‐term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin‐mean ET. Warm‐season ET estimated from this approach is 168 (85–252 at 95% confidence), 162 (0–326) and 191 (48–334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full‐year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part‐year time period. Using streamflow and ASO snow observations, we quantify spatially‐distributed hydrologic processes otherwise difficult to observe.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR020473","usgsCitation":"Henn, B., Painter, T.H., Bormann, K.J., McGurk, B., Flint, A.L., Flint, L.E., White, V., and Lundquist, J., 2018, High‐elevation evapotranspiration estimates during drought: Using streamflow and NASA Airborne Snow Observatory SWE observations to vlose the upper Tuolumne River Basin eater balance: Water Resources Research, v. 54, no. 2, p. 746-766, https://doi.org/10.1002/2017WR020473.","productDescription":"21 p.","startPage":"746","endPage":"766","ipdsId":"IP-083705","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":469044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr020473","text":"Publisher Index Page"},{"id":357979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Tuolumne River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120,\n 37.5\n ],\n [\n -119,\n 37.5\n ],\n [\n -119,\n 38.25\n ],\n [\n -120,\n 38.25\n ],\n [\n -120,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-03","publicationStatus":"PW","scienceBaseUri":"5bc03033e4b0fc368eb539dc","contributors":{"authors":[{"text":"Henn, Brian","contributorId":139777,"corporation":false,"usgs":false,"family":"Henn","given":"Brian","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":746954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Painter, Thomas H.","contributorId":12378,"corporation":false,"usgs":true,"family":"Painter","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":746955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bormann, Kathryn J.","contributorId":208401,"corporation":false,"usgs":false,"family":"Bormann","given":"Kathryn","email":"","middleInitial":"J.","affiliations":[{"id":37796,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena","active":true,"usgs":false}],"preferred":false,"id":746960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGurk, Bruce","contributorId":74457,"corporation":false,"usgs":true,"family":"McGurk","given":"Bruce","affiliations":[],"preferred":false,"id":746956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746953,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746957,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"White, Vince","contributorId":208399,"corporation":false,"usgs":false,"family":"White","given":"Vince","email":"","affiliations":[{"id":37795,"text":"Southern California Edison","active":true,"usgs":false}],"preferred":false,"id":746958,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lundquist, Jessica D.","contributorId":12792,"corporation":false,"usgs":true,"family":"Lundquist","given":"Jessica D.","affiliations":[],"preferred":false,"id":746959,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202267,"text":"70202267 - 2018 - Wind River Subbasin Restoration, Annual report of U.S. Geological Survey activities, January 2016 through December 2016","interactions":[],"lastModifiedDate":"2019-02-20T11:24:57","indexId":"70202267","displayToPublicDate":"2018-02-01T11:24:50","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Wind River Subbasin Restoration, Annual report of U.S. Geological Survey activities, January 2016 through December 2016","docAbstract":"We used Passive Integrated Transponder (PIT)-tagging and a series of instream PIT-tag interrogation systems (PTISs) to investigate life-histories, populations, and efficacy of habitat restoration actions for steelhead Oncorhynchus mykiss in the Wind River subbasin, WA. Our tagging focused on parr in headwaters areas of the subbasin and our instream readers provided information on movement of these parr and other life-stages of tagged steelhead. The Wind River subbasin in southwest Washington State provides habitat for a population of wild Lower Columbia River steelhead and is an excellent watershed for long-term studies of population dynamics and responses to restoration of this wild population. No hatchery steelhead have been planted in the Wind River subbasin since 1994, and hatchery adults are estimated to be less than one percent of adults in any year (pers comm. Thomas Buehrens, Washington Department of Fish and Wildlife). Numerous restoration actions have been implemented in the subbasin, including the removal of Hemlock Dam on Trout Creek in 2009. Data from our study, and companion work by Washington Department of Fish and Wildlife (WDFW), will contribute to Bonneville Power Administration’s (BPA) Research Monitoring and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (www.cbfish.org/ProgramStrategy.mvc/ViewProgramStrategySummary/1), specifically the substrategies of: 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and to Uncertainties Research regarding differing life histories of a wild steelhead population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives.
During summer 2016, we sampled and PIT-tagged age-0 and age-1 steelhead parr in headwater areas of the Wind River subbasin to characterize population traits and investigate variable life-histories, including growth and parr movement downstream prior to smolting. Repeat sampling and smolt traps provide opportunities for recapture, and instream PTISs and Columbia River infrastructure provide opportunity for detection of PIT-tagged fish.
Throughout the year, we maintained a series of instream PTISs to monitor movement of tagged steelhead parr, smolts, and adults. During 2016, we repaired or replaced much of our instream PTIS infrastructure that had been damaged or destroyed during a large flood event in December 2015. This included moving our upper Wind River detection site (WRU) about a kilometer downstream to a location we hope to be less susceptible to damage in high flows and that will allow grid power connection for more reliable winter operations.
Detections at the instream PTISs showed trends of parr emigration during summer and fall, in addition to the expected movement of parr and smolts in spring. These data are increasing our understanding of varied life histories of juvenile steelhead; paired with other steelhead population work in the subbasin we hope to begin to understand some of the factors which may influence parr movements. Long-term monitoring of PIT-tagged fish over multiple years is providing information on contribution of various life-history strategies to smolt production and adult returns, as well as helping to identify factors influencing parr movement.
Movements of PIT-tagged adult steelhead were also tracked with our instream PTISs. These data have provided information on timing of adult movements to various parts of the watershed, which is allowing us to assess adult returns to tributary watersheds within the Wind River subbasin. Determination of adult use of tributary watersheds is providing data to contribute to evaluation of the efficacy of the removal of Hemlock Dam on Trout Creek. Hemlock Dam, located at rkm 2.0 of Trout Creek was removed in summer 2009 and had contributed to hydrologic impairment of Trout Creek
Evaluating restoration efforts is of interest to many managers and agencies so that funding and time are allocated for best results. The evaluation of various life-histories of Lower Columbia River steelhead within the Wind River subbasin will provide information to better track populations, and to direct habitat restoration and water allocation planning. Increasingly detailed Viable Salmonid Population information, such as that provided by PIT-tagging and instream PTISs networks like those we are building and operating in the Wind River subbasin, will provide data to inform policy and management, as life-history strategies and production bottlenecks are identified and understood.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I.G., and Connolly, P., 2018, Wind River Subbasin Restoration, Annual report of U.S. Geological Survey activities, January 2016 through December 2016, 54 p.","productDescription":"54 p.","ipdsId":"IP-093844","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361346,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/DocumentViewer/P161233/77688-1.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connolly, Patrick J. 0000-0001-7365-7618 pconnolly@usgs.gov","orcid":"https://orcid.org/0000-0001-7365-7618","contributorId":2920,"corporation":false,"usgs":true,"family":"Connolly","given":"Patrick J.","email":"pconnolly@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757562,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199044,"text":"70199044 - 2018 - Den phenology and reproductive success of polar bears in a changing climate","interactions":[],"lastModifiedDate":"2018-08-30T10:50:23","indexId":"70199044","displayToPublicDate":"2018-02-01T10:50:17","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Den phenology and reproductive success of polar bears in a changing climate","docAbstract":"Synchrony between reproduction and food availability is important in mammals due to the high energetic costs of gestation and lactation. Female polar bears (Ursus maritimus) must accumulate sufficient energy reserves during spring through autumn to produce and nurse cubs during the winter months in snow dens. Adequate time in a den is important to optimize cub development for withstanding harsh Arctic spring conditions and to synchronize emergence with peak prey availability, which occurs in May and June. During 1985–2013, den phenology was investigated using temperature data collected on satellite collars deployed on adult female polar bears in the southern Beaufort Sea (SB) and Chukchi Sea (CS). We examined relationships between den phenology, reproductive success (cub production and post-emergence survival), and environmental factors (weather and sea-ice conditions). Females observed with cubs emerged later and remained in dens on average 15.0 ± 7.6 (SE) days longer than females seen without cubs. Females occupying land-based dens, where estimated snowfall was greater, had higher reproductive success. Recently, female polar bears have increased land-based denning in the SB. Females in CS emerged later from dens than SB females, consistent with better female body condition and higher cub survival in the CS. During years with a greater area of autumn sea ice, reproductive success was higher at land-based versus sea-ice dens, suggesting continued decline in sea ice could negatively affect recruitment. However, further research is needed to better understand mechanistic relationships. Because females emerging later from dens had higher reproductive success, den duration could be a useful metric in population monitoring.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyx181","usgsCitation":"Rode, K.D., Olson, J., Eggett, D.L., Douglas, D., Durner, G.M., Atwood, T.C., Regehr, E.V., Wilson, R.H., Smith, T., and St. Martin, M., 2018, Den phenology and reproductive success of polar bears in a changing climate: Journal of Mammalogy, v. 99, no. 1, p. 16-26, https://doi.org/10.1093/jmammal/gyx181.","productDescription":"11 p.","startPage":"16","endPage":"26","ipdsId":"IP-088002","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469046,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://zenodo.org/record/7850258","text":"Publisher Index Page"},{"id":438030,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DF6PC9","text":"USGS data release","linkHelpText":"Denning Phenology, Den Substrate, and Reproductive Success of Female Polar Bears (Ursus maritimus) in the southern Beaufort Sea 1986-2013 and the Chukchi Sea 1987-1994"},{"id":356949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -186.328125,\n 66\n ],\n [\n -117.68554687499999,\n 66\n ],\n [\n -117.68554687499999,\n 80\n ],\n [\n -186.328125,\n 80\n ],\n [\n -186.328125,\n 66\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"99","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-11","publicationStatus":"PW","scienceBaseUri":"5b98a300e4b0702d0e84301c","contributors":{"authors":[{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"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":743846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Jay","contributorId":150116,"corporation":false,"usgs":false,"family":"Olson","given":"Jay","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":743847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggett, Dennis L.","contributorId":191388,"corporation":false,"usgs":false,"family":"Eggett","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":743848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","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":743849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"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":743850,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":743851,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":743852,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wilson, Ryan H. 0000-0001-7740-7771","orcid":"https://orcid.org/0000-0001-7740-7771","contributorId":130989,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":743853,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Tom","contributorId":207440,"corporation":false,"usgs":false,"family":"Smith","given":"Tom","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":743854,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"St. Martin, Michelle","contributorId":150114,"corporation":false,"usgs":false,"family":"St. Martin","given":"Michelle","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":743855,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70188594,"text":"sir20175064 - 2018 - Bathymetry of Ashokan, Cannonsville, Neversink, Pepacton, Rondout, and Schoharie Reservoirs, New York, 2013–15","interactions":[],"lastModifiedDate":"2018-12-06T12:02:20","indexId":"sir20175064","displayToPublicDate":"2018-02-01T09:00:00","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":"2017-5064","title":"Bathymetry of Ashokan, Cannonsville, Neversink, Pepacton, Rondout, and Schoharie Reservoirs, New York, 2013–15","docAbstract":"Drinking water for New York City is supplied from several large reservoirs, including a system of reservoirs west of the Hudson River. To provide updated reservoir capacity tables and bathymetry maps of the City’s six West of Hudson reservoirs, bathymetric surveys were conducted by the U.S. Geological Survey from 2013 to 2015. Depths were surveyed with a single-beam echo sounder and real-time kinematic global positioning system along planned transects at predetermined intervals for each reservoir. A separate quality assurance dataset of echo sounder points was collected along transects at oblique angles to the main transects for accuracy assessment. Field-survey data were combined with water surface elevations in a geographic information system to create three-dimensional surfaces in the form of triangulated irregular networks (TINs) representing the elevations of the reservoir geomorphology. The TINs were linearly enforced to better represent geomorphic features within the reservoirs. The linearly enforced TINs were then used to create raster surfaces and 2-foot-interval contour maps of the reservoirs. Elevationarea-capacity tables were calculated at 0.01-foot intervals. The results of the surveys show that the total capacity of the West of Hudson reservoirs decreased by 11.5 billion gallons (Ggal), or 2.3 percent, because of sedimentation since construction, and the useable capacity (the volume above the minimum operating level required to deliver full flow for drinking water supply) has decreased by 7.9 Ggal (1.7 percent). The available capacity (the volume between the spillway elevation and the lowest intake or sill elevation used for drinking water supply) decreased by 9.6 Ggal (2.0 percent), and dead storage (the volume below the lowest intake or sill elevation) decreased by 1.9 Ggal (11.6 percent). The elevation of the spillway at Schoharie Reservoir was changed because of reconstruction during 2015, resulting in an additional decrease of 0.1 Ggal in total, useable, and available capacity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175064","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Nystrom, E.A., 2018, Bathymetry of Ashokan, Cannonsville, Neversink, Pepacton, Rondout, and Schoharie Reservoirs, New York, 2013–15 (ver. 1.2, November 2018): U.S. Geological Survey Scientific Investigations Report 2017–5064, 29 p., https://doi.org/10.3133/sir20175064.","productDescription":"Report: ix, 29 p.; 6 Data Releases","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-080367","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":343564,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1V1W","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Neversink Reservoir, 2013 to 2014 "},{"id":343567,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J964HB","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Schoharie Reservoir, 2015 "},{"id":343563,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WM1BJK","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Cannonsville Reservoir, 2015 "},{"id":343562,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W7P","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Ashokan Reservoir, 2013 to 2014 "},{"id":343561,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5064/sir20175064.pdf","text":"Report","size":"19.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5064"},{"id":351432,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5064/versionHist.txt","size":"2.10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":343560,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5064/coverthb4.jpg"},{"id":343565,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5CSM","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Pepacton Reservoir, 2015"},{"id":343566,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7542KR6","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial bathymetry dataset and elevation-area-capacity table for Rondout Reservoir, 2013 to 2014"}],"country":"United States","state":"New York","otherGeospatial":"Ashokan, Cannonsville, Neversink, Pepacton, Rondout, and Schoharie Reservoirs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5,\n 41.75\n ],\n [\n -74,\n 41.75\n ],\n [\n -74,\n 42.5\n ],\n [\n -75.5,\n 42.5\n ],\n [\n -75.5,\n 41.75\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted February 1, 2018; Version 1.1: February 12, 2018, Version 1.2: November 21, 2018","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
Accurate estimates of the age composition of populations can inform past reproductive success and future population trajectories. We examined fall age ratios (juveniles:total birds) of black brant (Branta bernicla nigricans; brant) staging at Izembek National Wildlife Refuge near the tip of the Alaska Peninsula, southwest Alaska, USA, 1963 to 2015. We also investigated variation in fall age ratios associated with sampling location, an index of flock size, survey effort, day of season, observer, survey platform (boat‐ or land‐based) and tide stage. We analyzed data using logistic regression models implemented in a Bayesian framework. Mean predicted fall age ratio controlling for survey effort, day of year, and temporal and spatial variation was 0.24 (95% CL = 0.23, 0.25). Overall trend in age ratios was −0.6% per year (95% CL = −1.3%, 0.2%), resulting in an approximate 26% decline in the proportion of juveniles over the study period. We found evidence for variation across a range of variables implying that juveniles are not randomly distributed in space and time within Izembek Lagoon. Age ratios varied by location within the study area and were highly variable among years. They decreased with the number of birds aged (an index of flock size) and increased throughout September before leveling off in early October and declining in late October. Age ratios were similar among tide stages and observers and were lower during boat‐based (offshore) than land‐based (nearshore) surveys. Our results indicate surveys should be conducted annually during early to mid‐October to ensure the entire population is present and available for sampling, and throughout Izembek Lagoon to account for spatiotemporal variation in age ratios. Sampling should include a wide range of flock sizes representative of their distribution and occur in flocks located near and off shore. Further research evaluating the cause of declining age ratios in the fall population is necessary to inform management and predict long‐term population dynamics of brant.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21388","usgsCitation":"Ward, D.H., Amundson, C.L., Stehn, R.A., and Dau, C.P., 2018, Long‐term trends in fall age ratios of black brant: Journal of Wildlife Management, v. 82, no. 2, p. 362-373, https://doi.org/10.1002/jwmg.21388.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-082174","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461053,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21388","text":"Publisher Index Page"},{"id":438037,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13578ZF","text":"USGS data release","linkHelpText":"Brant Age Ratio Model"},{"id":438036,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QIJIU2","text":"USGS data release","linkHelpText":"Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska"},{"id":352991,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-27","publicationStatus":"PW","scienceBaseUri":"5afee741e4b0da30c1bfc1e7","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":732022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amundson, Courtney L. 0000-0002-0166-7224 camundson@usgs.gov","orcid":"https://orcid.org/0000-0002-0166-7224","contributorId":4833,"corporation":false,"usgs":true,"family":"Amundson","given":"Courtney","email":"camundson@usgs.gov","middleInitial":"L.","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":732023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehn, Robert A.","contributorId":83986,"corporation":false,"usgs":true,"family":"Stehn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":732024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dau, Christian P.","contributorId":26185,"corporation":false,"usgs":true,"family":"Dau","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":732025,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196285,"text":"70196285 - 2018 - Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter","interactions":[],"lastModifiedDate":"2018-03-30T11:11:48","indexId":"70196285","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3454,"text":"Space Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter","docAbstract":"This study aims to assess the spatial and visible/near-infrared (VNIR) colour/spectral capabilities of the 4-band Colour and Stereo Surface Imaging System (CaSSIS) aboard the ExoMars 2016 Trace Grace Orbiter (TGO). The instrument response functions for the CaSSIS imager was used to resample spectral libraries, modelled spectra and to construct spectrally (i.e., in I/F space) and spatially consistent simulated CaSSIS image cubes of various key sites of interest and for ongoing scientific investigations on Mars. Coordinated datasets from Mars Reconnaissance Orbiter (MRO) are ideal, and specifically used for simulating CaSSIS. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) provides colour information, while the Context Imager (CTX), and in a few cases the High-Resolution Imaging Science Experiment (HiRISE), provides the complementary spatial information at the resampled CaSSIS unbinned/unsummed pixel resolution (4.6 m/pixel from a 400-km altitude). The methodology used herein employs a Gram-Schmidt spectral sharpening algorithm to combine the ∼18–36 m/pixel CRISM-derived CaSSIS colours with I/F images primarily derived from oversampled CTX images. One hundred and eighty-one simulated CaSSIS 4-colour image cubes (at 18–36 m/pixel) were generated (including one of Phobos) based on CRISM data. From these, thirty-three “fully”-simulated image cubes of thirty unique locations on Mars (i.e., with 4 colour bands at 4.6 m/pixel) were made. All simulated image cubes were used to test both the colour capabilities of CaSSIS by producing standard colour RGB images, colour band ratio composites (CBRCs) and spectral parameters. Simulated CaSSIS CBRCs demonstrated that CaSSIS will be able to readily isolate signatures related to ferrous (Fe2+) iron- and ferric (Fe3+) iron-bearing deposits on the surface of Mars, ices and atmospheric phenomena. Despite the lower spatial resolution of CaSSIS when compared to HiRISE, the results of this work demonstrate that CaSSIS will not only compliment HiRISE-scale studies of various geological and seasonal phenomena, it will also enhance them by providing additional colour and geologic context through its wider and longer full-colour coverage (
On an annual basis, federal and state agencies are responsible for mapping and removing large expanses of aquatic nuisance vegetation from navigable waterways. This study set out to achieve four primary objectives: (1) utilize recent advancements in remote sensing techniques to classify the extent and distribution of aquatic vegetation in coastal ecosystems using satellite imagery, (2) assess primary aquatic vegetation growth and management efforts in coastal Louisiana, (3) statistically identify the ecological drivers that promote growth and infestation of aquatic nuisance vegetation, and (4) develop numerical models and a spatial tool to predict the probability of occurrence and growth of aquatic vegetation given ecological drivers. Moderate spatial resolution multispectral satellite imagery were used in conjunction with environmental variables from available data streams to generate regression models that predict aquatic vegetation occurrence in the eastern coastal region of south Louisiana. Geospatial tools were developed to execute the model logic using recent environmental conditions, thereby predicting aquatic vegetation occurrence and producing classified maps for end users. These products provide more efficient and enhanced capabilities for management of aquatic nuisance vegetation.
","language":"English","publisher":"Engineer Research and Development Center","doi":"10.21079/11681/26649","usgsCitation":"Suir, G.M., Suir, K.J., and Sapkota, S., 2018, Use of remote sensing to detect and predict aquatic nuisance vegetation growth in coastal Louisiana: Summary of findings: ERDC Technical Report ERDC/EL TR-18-3, xi, 87 p., https://doi.org/10.21079/11681/26649.","productDescription":"xi, 87 p.","ipdsId":"IP-079845","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":461061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21079/11681/26649","text":"Publisher Index Page"},{"id":355961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.6968994140625,\n 28.86391842622456\n ],\n [\n -88.9617919921875,\n 28.86391842622456\n ],\n [\n -88.9617919921875,\n 30.538607878854556\n ],\n [\n -93.6968994140625,\n 30.538607878854556\n ],\n [\n -93.6968994140625,\n 28.86391842622456\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5b6fc4a7e4b0f5d57878eab5","contributors":{"authors":[{"text":"Suir, Glenn M.","contributorId":206307,"corporation":false,"usgs":false,"family":"Suir","given":"Glenn","email":"","middleInitial":"M.","affiliations":[{"id":37304,"text":"U.S. Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":740035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suir, Kevin J. 0000-0003-1570-9648 suirk@usgs.gov","orcid":"https://orcid.org/0000-0003-1570-9648","contributorId":4894,"corporation":false,"usgs":true,"family":"Suir","given":"Kevin","email":"suirk@usgs.gov","middleInitial":"J.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":740034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sapkota, Sijan","contributorId":206308,"corporation":false,"usgs":false,"family":"Sapkota","given":"Sijan","affiliations":[{"id":37305,"text":"U.S. Army Medical Department Center and School","active":true,"usgs":false}],"preferred":false,"id":740036,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194987,"text":"70194987 - 2018 - Determinants of Pseudogymnoascus destructans within bat hibernacula: Implications for surveillance and management of white-nose syndrome","interactions":[],"lastModifiedDate":"2023-06-30T14:48:58.619547","indexId":"70194987","displayToPublicDate":"2018-02-01T00:00:00","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}},"displayTitle":"Determinants of Pseudogymnoascus destructans within bat hibernacula: Implications for surveillance and management of white-nose syndrome","title":"Determinants of Pseudogymnoascus destructans within bat hibernacula: Implications for surveillance and management of white-nose syndrome","docAbstract":"Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3701","usgsCitation":"Walsh, D.P., Norton, A.S., Storm, D.J., Van Deelen, T.R., and Heisy, D.M., 2018, Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality: Ecology and Evolution, v. 8, no. 1, p. 509-520, https://doi.org/10.1002/ece3.3701.","productDescription":"12 p.","startPage":"509","endPage":"520","ipdsId":"IP-090309","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":461055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3701","text":"Publisher Index Page"},{"id":350936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-30","publicationStatus":"PW","scienceBaseUri":"5a74357de4b0a9a2e9e25c72","contributors":{"authors":[{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":726489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norton, Andrew S.","contributorId":171631,"corporation":false,"usgs":false,"family":"Norton","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":726490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storm, Daniel J.","contributorId":171373,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":24576,"text":"University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":726491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Deelen, Timothy R.","contributorId":145413,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy","email":"","middleInitial":"R.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":726492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heisy, Dennis M.","contributorId":201572,"corporation":false,"usgs":false,"family":"Heisy","given":"Dennis","email":"","middleInitial":"M.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":726493,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194991,"text":"70194991 - 2018 - Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient","interactions":[],"lastModifiedDate":"2018-03-05T15:32:47","indexId":"70194991","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient","docAbstract":"A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2131","usgsCitation":"Schoolmaster, D., and Stagg, C.L., 2018, Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient: Ecology, v. 99, no. 3, p. 670-680, https://doi.org/10.1002/ecy.2131.","productDescription":"11 p.","startPage":"670","endPage":"680","ipdsId":"IP-089350","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":350913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.09765625,\n 28.459033019728043\n ],\n [\n -88.330078125,\n 28.459033019728043\n ],\n [\n -88.330078125,\n 31.11879439598953\n ],\n [\n -95.09765625,\n 31.11879439598953\n ],\n [\n -95.09765625,\n 28.459033019728043\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"99","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-30","publicationStatus":"PW","scienceBaseUri":"5a74357fe4b0a9a2e9e25c78","contributors":{"authors":[{"text":"Schoolmaster, Donald 0000-0003-0910-4458 schoolmasterd@usgs.gov","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":156350,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","email":"schoolmasterd@usgs.gov","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":726426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stagg, Camille L. 0000-0002-1125-7253 staggc@usgs.gov","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":4111,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","email":"staggc@usgs.gov","middleInitial":"L.","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":726427,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196822,"text":"70196822 - 2018 - Three-dimensional foraging habitat use and niche partitioning in two sympatric seabird species, Phalacrocorax auritus and P. penicillatus","interactions":[],"lastModifiedDate":"2018-05-03T13:44:36","indexId":"70196822","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Three-dimensional foraging habitat use and niche partitioning in two sympatric seabird species, Phalacrocorax auritus and P. penicillatus","title":"Three-dimensional foraging habitat use and niche partitioning in two sympatric seabird species, Phalacrocorax auritus and P. penicillatus","docAbstract":"Ecological theory predicts that co-existing, morphologically similar species will partition prey resources when faced with resource limitations. We investigated local movements, foraging dive behavior, and foraging habitat selection by breeding adults of 2 closely related cormorant species, double-crested cormorants Phalacrocorax auritus and Brandt’s cormorants P. penicillatus. These species nest sympatrically at East Sand Island in the Columbia River estuary at the border of Oregon and Washington states, USA. Breeding individuals of each species were tracked using GPS tags with integrated temperature and depth data-loggers. The overall foraging areas and core foraging areas (defined as the 95% and 50% kernel density estimates of dive locations, respectively) of double-crested cormorants were much larger and covered a broader range of riverine, mixed-estuarine, and nearshore marine habitats. Brandt’s cormorant foraging areas were less expansive, were exclusively marine, and mostly overlapped with double-crested cormorant foraging areas. Within these areas of overlap, Brandt’s cormorants tended to dive deeper (median depth = 6.48 m) than double-crested cormorants (median depth = 2.67 m), and selected dive locations where the water was deeper. Brandt’s cormorants also utilized a deeper, more benthic portion of the water column than did double-crested cormorants. Nevertheless, the substantial overlap in foraging habitat between the 2 cormorant species in the Columbia River estuary, particularly for Brandt’s cormorants, suggests that superabundant prey resources allow these 2 large and productive cormorant colonies to coexist on a single island near the mouth of the Columbia River.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps12407","usgsCitation":"Peck-Richardson, A.G., Lyons, D.E., Roby, D.D., Cushing, D.A., and Lerczak, J.A., 2018, Three-dimensional foraging habitat use and niche partitioning in two sympatric seabird species, Phalacrocorax auritus and P. penicillatus: Marine Ecology Progress Series, v. 586, p. 251-264, https://doi.org/10.3354/meps12407.","productDescription":"14 p.","startPage":"251","endPage":"264","ipdsId":"IP-087206","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":353941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"586","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1cd","contributors":{"authors":[{"text":"Peck-Richardson, Adam G.","contributorId":204662,"corporation":false,"usgs":false,"family":"Peck-Richardson","given":"Adam","email":"","middleInitial":"G.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":734610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, Donald E.","contributorId":204663,"corporation":false,"usgs":false,"family":"Lyons","given":"Donald","email":"","middleInitial":"E.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":734611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cushing, Daniel A.","contributorId":204664,"corporation":false,"usgs":false,"family":"Cushing","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":734612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lerczak, James A.","contributorId":204665,"corporation":false,"usgs":false,"family":"Lerczak","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12961,"text":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":734613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196739,"text":"70196739 - 2018 - Examining fluvial fish range loss with SDMs","interactions":[],"lastModifiedDate":"2018-04-27T13:24:42","indexId":"70196739","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Examining fluvial fish range loss with SDMs","docAbstract":"Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the range of the fluvial‐specialist shoal bass (Micropterus cataractae) continues to decrease, yet how perceived threats have contributed to range loss is largely unknown. We used species distribution models to determine which factors contributed most to shoal bass range loss. We estimated a potential distribution based on natural abiotic factors and a series of currently occupied distributions that incorporated variables characterizing land cover, non‐native species, and river fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). We allowed interspecific relationships between non‐native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of currently occupied distribution showed that range loss increased as fragmentation intensified. Response curves from models of currently occupied distribution indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non‐native congeners, wherein non‐natives may be favored at the highest fragmentation intensity. Response curves also suggested that >100 km of interconnected, free‐flowing stream fragments were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested that models had favorable predictive and discriminative abilities. Similar approaches that use readily available, diverse, geospatial data sets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats.
","language":"English","publisher":"Wiley","doi":"10.1111/cobi.13024","usgsCitation":"Taylor, A.T., Papes, M., and Long, J.M., 2018, Examining fluvial fish range loss with SDMs: Conservation Biology, v. 32, no. 1, p. 171-182, https://doi.org/10.1111/cobi.13024.","productDescription":"12 p.","startPage":"171","endPage":"182","ipdsId":"IP-079935","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-29","publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1d5","contributors":{"authors":[{"text":"Taylor, Andrew T.","contributorId":177197,"corporation":false,"usgs":false,"family":"Taylor","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":734169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Papes, Monica","contributorId":204496,"corporation":false,"usgs":false,"family":"Papes","given":"Monica","email":"","affiliations":[],"preferred":false,"id":734170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734168,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197788,"text":"70197788 - 2018 - Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States","interactions":[],"lastModifiedDate":"2018-06-20T10:54:13","indexId":"70197788","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3568,"text":"The Leading Edge","active":true,"publicationSubtype":{"id":10}},"title":"Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States","docAbstract":"Probabilistic seismic hazard analysis (PSHA) characterizes ground-motion hazard from earthquakes. Typically, the time horizon of a PSHA forecast is long, but in response to induced seismicity related to hydrocarbon development, the USGS developed one-year PSHA models. In this paper, we present a display of the variability in USGS hazard curves due to epistemic uncertainty in its informed submodel using a simple bootstrapping approach. We find that variability is highest in low-seismicity areas. On the other hand, areas of high seismic hazard, such as the New Madrid seismic zone or Oklahoma, exhibit relatively lower variability simply because of more available data and a better understanding of the seismicity. Comparing areas of high hazard, New Madrid, which has a history of large naturally occurring earthquakes, has lower forecast variability than Oklahoma, where the hazard is driven mainly by suspected induced earthquakes since 2009. Overall, the mean hazard obtained from bootstrapping is close to the published model, and variability increased in the 2017 one-year model relative to the 2016 model. Comparing the relative variations caused by individual logic-tree branches, we find that the highest hazard variation (as measured by the 95% confidence interval of bootstrapping samples) in the final model is associated with different ground-motion models and maximum magnitudes used in the logic tree, while the variability due to the smoothing distance is minimal. It should be pointed out that this study is not looking at the uncertainty in the hazard in general, but only as it is represented in the USGS one-year models.
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/tle37020141a1.1","usgsCitation":"Mousavi, S.M., Beroza, G.C., and Hoover, S.M., 2018, Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States: The Leading Edge, v. 37, no. 2, p. 141a1-141a9, https://doi.org/10.1190/tle37020141a1.1.","productDescription":"9 p.","startPage":"141a1","endPage":"141a9","ipdsId":"IP-093220","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":355202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 25\n ],\n [\n -65,\n 25\n ],\n [\n -65,\n 50\n ],\n [\n -115,\n 50\n ],\n [\n -115,\n 25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5d3e4b060350a15d21c","contributors":{"authors":[{"text":"Mousavi, S. Mostafa","contributorId":205790,"corporation":false,"usgs":false,"family":"Mousavi","given":"S.","email":"","middleInitial":"Mostafa","affiliations":[{"id":37167,"text":"Department of Geophysics, Stanford University, Stanford, CA","active":true,"usgs":false}],"preferred":false,"id":738494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beroza, Gregory C.","contributorId":191201,"corporation":false,"usgs":false,"family":"Beroza","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":738495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738496,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192339,"text":"70192339 - 2018 - An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions","interactions":[],"lastModifiedDate":"2018-04-02T13:53:09","indexId":"70192339","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions","docAbstract":"This paper describes a workflow for automating the extraction of elevation-derived stream lines using open source tools with parallel computing support and testing the effectiveness of procedures in various terrain conditions within the conterminous United States. Drainage networks are extracted from the US Geological Survey 1/3 arc-second 3D Elevation Program elevation data having a nominal cell size of 10 m. This research demonstrates the utility of open source tools with parallel computing support for extracting connected drainage network patterns and handling depressions in 30 subbasins distributed across humid, dry, and transitional climate regions and in terrain conditions exhibiting a range of slopes. Special attention is given to low-slope terrain, where network connectivity is preserved by generating synthetic stream channels through lake and waterbody polygons. Conflation analysis compares the extracted streams with a 1:24,000-scale National Hydrography Dataset flowline network and shows that similarities are greatest for second- and higher-order tributaries.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15230406.2017.1337524","usgsCitation":"Stanislawski, L.V., Survila, K., Wendel, J., Liu, Y., and Buttenfield, B., 2018, An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions: Cartography and Geographic Information Science, v. 45, no. 4, p. 319-328, https://doi.org/10.1080/15230406.2017.1337524.","productDescription":"10 p.","startPage":"319","endPage":"328","ipdsId":"IP-077833","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":350964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-04","publicationStatus":"PW","scienceBaseUri":"5a7586d8e4b00f54eb1d81f2","contributors":{"authors":[{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":715438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Survila, Kornelijus 0000-0003-4851-6084","orcid":"https://orcid.org/0000-0003-4851-6084","contributorId":196791,"corporation":false,"usgs":false,"family":"Survila","given":"Kornelijus","email":"","affiliations":[],"preferred":false,"id":715439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wendel, Jeffrey 0000-0003-0294-0250 jwendel@usgs.gov","orcid":"https://orcid.org/0000-0003-0294-0250","contributorId":196792,"corporation":false,"usgs":true,"family":"Wendel","given":"Jeffrey","email":"jwendel@usgs.gov","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":715441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Yan 0000-0003-2298-4728","orcid":"https://orcid.org/0000-0003-2298-4728","contributorId":196790,"corporation":false,"usgs":false,"family":"Liu","given":"Yan","email":"","affiliations":[],"preferred":false,"id":715442,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buttenfield, Barbara P.","contributorId":145538,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":715440,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196629,"text":"70196629 - 2018 - Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA","interactions":[],"lastModifiedDate":"2018-04-23T10:01:33","indexId":"70196629","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1718,"text":"GCB Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA","docAbstract":"Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.
","language":"English","publisher":"Wiley","doi":"10.1111/gcbb.12468","usgsCitation":"Gu, Y., and Wylie, B., 2018, Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA: GCB Bioenergy, v. 10, no. 2, p. 76-83, https://doi.org/10.1111/gcbb.12468.","productDescription":"8 p.","startPage":"76","endPage":"83","ipdsId":"IP-087756","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469053,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcbb.12468","text":"Publisher Index Page"},{"id":353641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 40.01078714046552\n ],\n [\n -95.30639648437499,\n 40.01078714046552\n ],\n [\n -95.30639648437499,\n 42.99661231842139\n ],\n [\n -99.1845703125,\n 42.99661231842139\n ],\n [\n -99.1845703125,\n 40.01078714046552\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-12","publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1d7","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":733834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":197161,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce K.","email":"wylie@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":733835,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196116,"text":"70196116 - 2018 - Radium mobility and the age of groundwater in public-drinking-water supplies from the Cambrian-Ordovician aquifer system, north-central USA","interactions":[],"lastModifiedDate":"2018-03-21T10:03:13","indexId":"70196116","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Radium mobility and the age of groundwater in public-drinking-water supplies from the Cambrian-Ordovician aquifer system, north-central USA","docAbstract":"High radium (Ra) concentrations in potable portions of the Cambrian-Ordovician (C-O) aquifer system were investigated using water-quality data and environmental tracers (3H, 3Hetrit, SF6, 14C and 4Herad) of groundwater age from 80 public-supply wells (PSWs). Groundwater ages were estimated by calibration of tracers to lumped parameter models and ranged from modern (<50 yr) in upgradient, regionally unconfined areas to ancient (>1 Myr) in the most downgradient, confined portions of the potable system. More than 80 and 40 percent of mean groundwater ages were older than 1000 and 50,000 yr, respectively. Anoxic, Fe-reducing conditions and increased mineralization develop with time in the aquifer system and mobilize Ra into solution resulting in the frequent occurrence of combined Ra (Rac = 226Ra + 228Ra) at concentrations exceeding the USEPA MCL of 185 mBq/L (5 pCi/L). The distribution of the three Ra isotopes comprising total Ra (Rat = 224Ra + 226Ra + 228Ra) differed across the aquifer system. The concentrations of 224Ra and 228Ra were strongly correlated and comprised a larger proportion of the Rat concentration in samples from the regionally unconfined area, where arkosic sandstones provide an enhanced source for progeny from the 232Th decay series. 226Ra comprised a larger proportion of the Ratconcentration in samples from downgradient confined regions. Concentrations of Rat were significantly greater in samples from the regionally confined area of the aquifer system because of the increase in 226Ra concentrations there as compared to the regionally unconfined area. 226Ra distribution coefficients decreased substantially with anoxic conditions and increasing ionic strength of groundwater (mineralization), indicating that Ra is mobilized to solution from solid phases of the aquifer as adsorption capacity is diminished. The amount of 226Ra released from solid phases by alpha-recoil mechanisms and retained in solution increases relative to the amount of Ra sequestered by adsorption processes or co-precipitation with barite as adsorption capacity and the concentration of Ba decreases. Although 226Ra occurred at concentrations greater than 224Ra or 228Ra, the ingestion exposure risk was greater for 228Ra owing to its greater toxicity. In addition, 224Ra added substantial alpha-particle radioactivity to potable samples from the C-O aquifer system. Thus, monitoring for Ra isotopes and gross-alpha-activity (GAA) is important in upgradient, regionally unconfined areas as downgradient, and GAA measurements made within 72 h of sample collection would best capture alpha-particle radiation from the short-lived 224Ra.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.11.002","usgsCitation":"Stackelberg, P.E., Szabo, Z., and Jurgens, B., 2018, Radium mobility and the age of groundwater in public-drinking-water supplies from the Cambrian-Ordovician aquifer system, north-central USA: Applied Geochemistry, v. 89, p. 34-48, https://doi.org/10.1016/j.apgeochem.2017.11.002.","productDescription":"15 p.","startPage":"34","endPage":"48","ipdsId":"IP-084578","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":469075,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.11.002","text":"Publisher Index Page"},{"id":438031,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QP0","text":"USGS data release","linkHelpText":"Data for Radium Mobility and the Age of Groundwater in Public-drinking-water Supplies from the Cambrian-Ordovician Aquifer System, North-Central USA"},{"id":352683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.82275390625,\n 38.54816542304656\n ],\n [\n -84.462890625,\n 38.54816542304656\n ],\n [\n -84.462890625,\n 46.66451741754235\n ],\n [\n -95.82275390625,\n 46.66451741754235\n ],\n [\n -95.82275390625,\n 38.54816542304656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee742e4b0da30c1bfc1ef","contributors":{"authors":[{"text":"Stackelberg, Paul E. 0000-0002-1818-355X pestack@usgs.gov","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":1069,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","email":"pestack@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szabo, Zoltan 0000-0002-0760-9607 zszabo@usgs.gov","orcid":"https://orcid.org/0000-0002-0760-9607","contributorId":2240,"corporation":false,"usgs":true,"family":"Szabo","given":"Zoltan","email":"zszabo@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":731427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":1503,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant C.","email":"bjurgens@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":731428,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197434,"text":"70197434 - 2018 - Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics","interactions":[],"lastModifiedDate":"2018-06-05T09:48:37","indexId":"70197434","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3832,"text":"Genome Biology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics","docAbstract":"Bivalves exhibit an astonishing diversity of sexual systems and sex-determining mechanisms. They can be gonochoric, hermaphroditic or androgenetic, with both genetic and environmental factors known to determine or influence sex. One unique sex-determining system involving the mitochondrial genome has also been hypothesized to exist in bivalves with doubly uniparental inheritance (DUI) of mtDNA. However, the link between DUI and sex determination remains obscure. In this study, we performed a comparative gonad transcriptomics analysis for two DUI-possessing freshwater mussel species to better understand the mechanisms underlying sex determination and DUI in these bivalves. We used a BLAST reciprocal analysis to identify orthologs between Venustaconcha ellipsiformis and Utterbackia peninsularis and compared our results with previously published sex-specific bivalve transcriptomes to identify conserved sex-determining genes. We also compared our data with other DUI species to identify candidate genes possibly involved in the regulation of DUI. A total of ∼12,000 orthologous relationships were found, with 2,583 genes differentially expressed in both species. Among these genes, key sex-determining factors previously reported in vertebrates and in bivalves (e.g., Sry, Dmrt1, Foxl2) were identified, suggesting that some steps of the sex-determination pathway may be deeply conserved in metazoans. Our results also support the hypothesis that a modified ubiquitination mechanism could be responsible for the retention of the paternal mtDNA in male bivalves, and revealed that DNA methylation could also be involved in the regulation of DUI. Globally, our results suggest that sets of genes associated with sex determination and DUI are similar in distantly-related DUI species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gbe/evy019","usgsCitation":"Capt, C., Renaut, S., Ghiselli, F., Milani, L., Johnson, N.A., Sietman, B.E., Stewart, D., and Breton, S., 2018, Deciphering the link between doubly uniparental inheritance of mtDNA and sex determination in bivalves: Clues from comparative transcriptomics: Genome Biology and Evolution, v. 10, no. 2, p. 577-590, https://doi.org/10.1093/gbe/evy019.","productDescription":"14 p.","startPage":"577","endPage":"590","ipdsId":"IP-092196","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gbe/evy019","text":"Publisher Index Page"},{"id":354709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-19","publicationStatus":"PW","scienceBaseUri":"5b46e5d4e4b060350a15d226","contributors":{"authors":[{"text":"Capt, Charlotte","contributorId":205385,"corporation":false,"usgs":false,"family":"Capt","given":"Charlotte","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renaut, Sébastien","contributorId":205386,"corporation":false,"usgs":false,"family":"Renaut","given":"Sébastien","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ghiselli, Fabrizio","contributorId":205387,"corporation":false,"usgs":false,"family":"Ghiselli","given":"Fabrizio","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milani, Liliana","contributorId":205388,"corporation":false,"usgs":false,"family":"Milani","given":"Liliana","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":737138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Nathan A. 0000-0001-5167-1988 najohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":4175,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"najohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":737134,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sietman, Bernard E.","contributorId":196565,"corporation":false,"usgs":false,"family":"Sietman","given":"Bernard","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737139,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stewart, Donald","contributorId":205389,"corporation":false,"usgs":false,"family":"Stewart","given":"Donald","affiliations":[{"id":37092,"text":"Acadia University","active":true,"usgs":false}],"preferred":false,"id":737140,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Breton, Sophie 0000-0002-8286-486X","orcid":"https://orcid.org/0000-0002-8286-486X","contributorId":196560,"corporation":false,"usgs":false,"family":"Breton","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":737141,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70197459,"text":"70197459 - 2018 - Response to Lisovski et al.","interactions":[],"lastModifiedDate":"2018-06-05T14:14:50","indexId":"70197459","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Response to Lisovski et al.","docAbstract":"Lisovski et al. [1] describe the widely recognized limitations of light-level geolocator data for identifying short-distance latitudinal movements, recommend that caution be used when interpreting such data, intimated that we did not use such caution and argued that environmental shading likely explained the Golden-winged Warbler (Vermivora chrysoptera) movements described in our 2015 report [2] . Lisovski et al. [1] conclude that the bird movements we reported could not be disentangled from estimation error in stationary animals caused by environmental shading. We argue that, to the contrary, these hypotheses can easily be disentangled because the premise that environmental shading caused synchronous and parallel error among geolocators is false. With their assertion that our location estimates could be biased by >3,500 km on a day with no observable local sources of shading, Lisovski et al. [1] have taken a position of incredulity toward all geolocator-based animal movement data published to date.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2017.12.025","usgsCitation":"Streby, H.M., Kramer, G.R., Peterson, S.M., Lehman, J.A., Buehler, D.A., and Andersen, D.E., 2018, Response to Lisovski et al.: Current Biology, v. 28, no. 3, p. R101-R102, https://doi.org/10.1016/j.cub.2017.12.025.","productDescription":"2 p.","startPage":"R101","endPage":"R102","ipdsId":"IP-092344","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2017.12.025","text":"Publisher Index Page"},{"id":354727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5d4e4b060350a15d222","contributors":{"authors":[{"text":"Streby, Henry M.","contributorId":11024,"corporation":false,"usgs":false,"family":"Streby","given":"Henry","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":737286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Gunnar R.","contributorId":94184,"corporation":false,"usgs":false,"family":"Kramer","given":"Gunnar","email":"","middleInitial":"R.","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":737287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sean M.","contributorId":9354,"corporation":false,"usgs":false,"family":"Peterson","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":13013,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley","active":true,"usgs":false},{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":737288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Justin A.","contributorId":166944,"corporation":false,"usgs":false,"family":"Lehman","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":737289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buehler, David A.","contributorId":176238,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":737290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737241,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196755,"text":"70196755 - 2018 - Seventy years of stream‐fish collections reveal invasions and native range contractions in an Appalachian (USA) watershed","interactions":[],"lastModifiedDate":"2018-04-30T10:24:31","indexId":"70196755","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Seventy years of stream‐fish collections reveal invasions and native range contractions in an Appalachian (USA) watershed","docAbstract":"Aim
Knowledge of expanding and contracting ranges is critical for monitoring invasions and assessing conservation status, yet reliable data on distributional trends are lacking for most freshwater species. We developed a quantitative technique to detect the sign (expansion or contraction) and functional form of range‐size changes for freshwater species based on collections data, while accounting for possible biases due to variable collection effort. We applied this technique to quantify stream‐fish range expansions and contractions in a highly invaded river system.
Location
Upper and middle New River (UMNR) basin, Appalachian Mountains, USA.
Methods
We compiled a 77‐year stream‐fish collections dataset partitioned into ten time periods. To account for variable collection effort among time periods, we aggregated the collections into 100 watersheds and expressed a species’ range size as detections per watershed (HUC) sampled (DPHS). We regressed DPHS against time by species and used an information‐theoretic approach to compare linear and nonlinear functional forms fitted to the data points and to classify each species as spreader, stable or decliner.
Results
We analysed changes in range size for 74 UMNR fishes, including 35 native and 39 established introduced species. We classified the majority (51%) of introduced species as spreaders, compared to 31% of natives. An exponential functional form fits best for 84% of spreaders. Three natives were among the most rapid spreaders. All four decliners were New River natives.
Main conclusions
Our DPHS‐based approach facilitated quantitative analyses of distributional trends for stream fishes based on collections data. Partitioning the dataset into multiple time periods allowed us to distinguish long‐term trends from population fluctuations and to examine nonlinear forms of spread. Our framework sets the stage for further study of drivers of stream‐fish invasions and declines in the UMNR and is widely transferable to other freshwater taxa and geographic regions.