Telemetry of acoustically tagged bigheaded carp (i.e., bighead carp Hypophthalmichthys nobilis and silver carp H. molitrix) and surrogate fish species has become an invaluable tool in management for these species in the upper Illinois Waterway Systems (i.e., upper Illinois River, lower Des Plaines River, and Chicago Area Waterway System). For example, movement probabilities between adjacent navigation pools need to be estimated to parameterize the Spatially Explicit Asian Carp Population Model (SEAcarP). SEAcarP is a population model used in scenario planning by the Monitoring and Response Workgroup (MRWG) to evaluate alternative management actions. These movement probabilities are estimated from the telemetry data obtained from a longitudinal network of strategically placed receivers that detect bigheaded carp that have been implanted with acoustic transmitters. In addition, fish removal by contracted fishers has become the primary method of controlling bigheaded carp in the upper Illinois and lower Des Plaines Rivers. Variable patterns in bigheaded carp distribution, habitat, and movement, influenced by seasonal and environmental conditions, make targeting bigheaded carp for removal and containment challenging and costly. Understanding these movement patterns for bigheaded carp through modeling and real-time telemetry applications informs removal efforts and facilitates monitoring and contingency actions based on fish movements.
To develop a better understanding of fish movement dynamics to meet management objectives, an existing network of real-time and data-logging acoustic receivers in the upper Illinois Waterway Systems is collaboratively managed by a multi-agency team (see Participating Agencies section above). A Telemetry Workgroup has been established by the MRWG to ensure that the multi-agency telemetry efforts are coordinated to efficiently and effectively meet the MRWG goals. This workgroup plans and executes the placement of receivers, tagging of bigheaded carp with acoustic tags, and management of the telemetry data. Three primary objectives to meet MRWG goals identified by the Telemetry Workgroup included (1) development of a common standardized telemetry database with visualization and analysis tools, (2) transitioning from Program MARK (http://www.phidot.org/software/mark/) to a custom Bayesian multi-state model for estimating movement probabilities needed for SEAcarP and (3) deploying, maintaining, and serving data from real-time acoustic receivers to inform contingency planning and fish removal.
A telemetry database and visualization tools (FishTracks) will facilitate standardization, archiving, sharing, quality assurance, visualization and analysis of the telemetry data needed for management. Modifications and additions to FishTracks will facilitate more problem-free use of the database and associated applications, as well as useful extraction of information to meet management goals. The transition to a custom Bayesian multi-state model to estimate movement probabilities will support more efficient, effective, and robust population modeling with SEAcarP by overcoming short comings of Program MARK for this purpose. These shortcomings include lack of customizability and extensibility, problems of singularities and poor-convergence, software crashes, parameter exclusion from models, an inability to consistently generate estimates of movement probability, and a lack of uncertainty estimates for movement probabilities. A real-time receiver network that is maintained and tested annually will ensure reliability and accuracy of the real-time alerts to bigheaded carp movements that can be used by management to plan contingency actions.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Interim summary report 2020","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Knights, B.C., Brey, M.K., Stanton, J.C., Harrison, T.J., Appel, D., Hlavacek, E., and Duncker, J.J., 2020, USGS Telemetry Project: Interim Summary Report, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-128212","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391250,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"},{"id":426889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"upper Illinois Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.34655836479192,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 42.36183831544582\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appel, Douglas 0000-0001-8775-1058","orcid":"https://orcid.org/0000-0001-8775-1058","contributorId":268159,"corporation":false,"usgs":true,"family":"Appel","given":"Douglas","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826143,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826144,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826145,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228600,"text":"70228600 - 2020 - Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis)","interactions":[],"lastModifiedDate":"2022-02-14T16:32:52.568381","indexId":"70228600","displayToPublicDate":"2020-11-30T09:53:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis)","title":"Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis)","docAbstract":"1) Movement has been studied extensively in stream salmonids, and most data suggest that population-level behavior is best described by a leptokurtic distribution. This distribution emphasizes the large proportion of sedentary individuals in a population, which can implicitly lead to assumptions of low population connectivity and overlook the ecological significance of rare individuals with more mobile phenotypes. 2) We report findings of a multi-season radio telemetry study conducted on four adjacent populations of wild brook trout (Salvelinus fontinalis) connected by Loyalsock Creek in northcentral Pennsylvania. We used these data to investigate temporal and spatial patterns in movement and fitness tradeoffs associated with behavioral phenotype. 3) Similar to previous studies, we found that 59 of the 120 radio-tagged individuals (49%) were sedentary and moved less than 200 m. Only 18% of individuals dispersed more than 1 km, but the maximum distanced moved exceeded 13 km. We also found that mobile individuals had significantly higher summer and fall survival than did sedentary fish, which could indicate that there are fitness benefits associated with vagility. 4) Most long-distance movements were the result of fish migrating from small tributaries into a larger mainstem river in the days after spawning. Therefore, even though mobility was only expressed for a short duration and by relatively few individuals in the population, the behavior appears to maintain metapopulation connectivity throughout the watershed. 5) Our study highlights the ecological significance of rare phenotypes for population demography across large spatial scales and the need to understand movement across multiple temporal and spatial scales to ensure adequate conservation of critical forms of cryptic life history diversity.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13637","usgsCitation":"Wagner, T., and White, S., 2020, Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis): Freshwater Biology, v. 66, no. 2, p. 278-285, https://doi.org/10.1111/fwb.13637.","productDescription":"8 p.","startPage":"278","endPage":"285","ipdsId":"IP-118703","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":454727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fwb.13637","text":"Publisher Index Page"},{"id":395892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Double Creek, East Branch Creek, Loyalsock Creek, Pole Bridge Creek, Shanerburg Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.93528175354004,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.254193933121606\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Shannon","contributorId":276311,"corporation":false,"usgs":false,"family":"White","given":"Shannon","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834736,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216731,"text":"70216731 - 2020 - An analysis of streamflow trends in the southern and southeastern US from 1950-2015","interactions":[],"lastModifiedDate":"2020-12-03T13:55:04.548935","indexId":"70216731","displayToPublicDate":"2020-11-29T07:46:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"An analysis of streamflow trends in the southern and southeastern US from 1950-2015","docAbstract":"| The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of color palettes based on the HCL (hue-chroma-luminance) color space. The three HCL dimensions have been shown to match those of the human visual system very well, thus facilitating intuitive selection of color palettes through trajectories in this space. Using the HCL color model, general strategies for three types of palettes are implemented: (1) Qualitative for coding categorical information, i.e., where no particular ordering of categories is available. (2) Sequential for coding ordered/numeric information, i.e., going from high to low (or vice versa). (3) Diverging for coding ordered/numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes. To aid selection and application of these palettes, the package also contains scales for use with ggplot2, shiny and tcltk apps for interactive exploration, visualizations of palette properties, accompanying manipulation utilities (like desaturation and lighten/darken), and emulation of color vision deficiencies. The shiny apps are also hosted online at http://hclwizard.org/. |
The Landsat Collection-2 distribution introduces a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and most accurate freely available DEM sources and will include reprocessed Shuttle Radar Topographic Mission (SRTM) data (called NASADEM), high-resolution stereo optical data (ArcticDEM), a new National Elevation Dataset (NED) and various publicly available national datasets including the Canadian Digital Elevation Model (CDEM) and DEMs for Sweden, Norway and Finland (SNF). The new DEM will be available world-wide with few exceptions. It is anticipated that the transition from the Collection-1 DEM at 3 arcsecond to the new DEM will be seamless because processing methods to maintain a seamless transition were employed, void filling techniques were used, where persistent gaps were found, and the pixel spacing is the same between the two collections. Improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the new DEM. The greatest improvement occurred where ArcticDEM data were used, where an improvement of 35 m was measured. By using theses improved vertical values in a line of sight algorithm, horizontal improvements were noted in some of the most mountainous regions over multiple 30-m Landsat pixels. This new DEM will be used to process all of the scenes from Landsat 1-8 in Collection-2 processing and will be made available to the public by the end of 2020.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12233909","usgsCitation":"Franks, S., Storey, J., and Rengarajan, R., 2020, The new Landsat Collection-2 Digital Elevation Model: Remote Sensing, v. 12, no. 23, 3909, 24 p., https://doi.org/10.3390/rs12233909.","productDescription":"3909, 24 p.","ipdsId":"IP-123106","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":454735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12233909","text":"Publisher Index Page"},{"id":381037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"23","noUsgsAuthors":false,"publicationDate":"2020-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":245457,"corporation":false,"usgs":false,"family":"Franks","given":"Shannon","email":"","affiliations":[{"id":49197,"text":"KBR, Contractor to NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":806216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storey, James C. 0000-0002-6664-7232","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":242015,"corporation":false,"usgs":false,"family":"Storey","given":"James C.","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":806217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":806218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217099,"text":"70217099 - 2020 - Lateral carbon exports from drained peatlands: An understudied carbon pathway in the Sacramento-San Joaquin Delta, California","interactions":[],"lastModifiedDate":"2021-01-06T13:29:43.897973","indexId":"70217099","displayToPublicDate":"2020-11-27T07:24:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Lateral carbon exports from drained peatlands: An understudied carbon pathway in the Sacramento-San Joaquin Delta, California","docAbstract":"Degradation of peatlands via drainage is increasing globally and destabilizing peat carbon (C) stores. The effects of drainage on the timing and magnitude of lateral C losses from degraded peatlands remains understudied. We measured spatial and temporal variability in lateral C exports from three drained peat islands in the Sacramento‐San Joaquin Delta in California across the 2017 and 2018 water years using measurements of dissolved inorganic C (DIC), dissolved organic C (DOC), and suspended particulate organic C (POC) concentration combined with discharge. These measurements were supplemented with stable isotope data (δ13C‐DIC, δ13C‐POC, δ15N‐PON, and δ2H‐H2O values) to provide insight into hydrological and biogeochemical controls on lateral C exports from drained peatlands. Drainage DOC and DIC concentrations were seasonally variable with the highest values in the winter rainy season, when discharge was also elevated. Seasonal differences in the mobilization of dissolved C appeared to result from changing water sources and water table levels. Peat island drainage C contributions to surrounding waterways were also greatest during the winter. Although temporal variability in C cycling processes and trends were generally similar across islands, baseline drainage DIC, DOC, and POC concentrations were spatially variable, likely a result of sub‐island‐scale differences in soil organic matter content and hydrology. This spatial variability complicates system‐wide assessments of C budgets. Net lateral C exports were water year dependent and comparable to previously published vertical C emission rates for this system. This work highlights the importance of including lateral C exports from drained peatlands in local and regional C budgets.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005883","usgsCitation":"Richardson, C.M., Fackrell, J.K., Kraus, T.E., Young, M.B., and Paytan, A., 2020, Lateral carbon exports from drained peatlands: An understudied carbon pathway in the Sacramento-San Joaquin Delta, California: Journal of Geophysical Research: Biogeosciences, v. 125, no. 12, e2020JG005883, 21 p., https://doi.org/10.1029/2020JG005883.","productDescription":"e2020JG005883, 21 p.","ipdsId":"IP-119742","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":467269,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/37323","text":"External Repository"},{"id":381943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.200927734375,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 37.98100996893789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Christina M. 0000-0003-0597-8836","orcid":"https://orcid.org/0000-0003-0597-8836","contributorId":147438,"corporation":false,"usgs":false,"family":"Richardson","given":"Christina","email":"","middleInitial":"M.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":807604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fackrell, Joseph K. 0000-0001-8148-3734","orcid":"https://orcid.org/0000-0001-8148-3734","contributorId":225515,"corporation":false,"usgs":true,"family":"Fackrell","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Megan B. 0000-0002-0229-4108 mbyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0229-4108","contributorId":3315,"corporation":false,"usgs":true,"family":"Young","given":"Megan","email":"mbyoung@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":807607,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paytan, Adina 0000-0001-8360-4712","orcid":"https://orcid.org/0000-0001-8360-4712","contributorId":193046,"corporation":false,"usgs":false,"family":"Paytan","given":"Adina","email":"","affiliations":[],"preferred":false,"id":807608,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240902,"text":"70240902 - 2020 - Evidence of spawning by lake trout Salvelinus namaycush on substrates at the base of large boulders in northern Lake Huron","interactions":[],"lastModifiedDate":"2023-03-01T12:39:03.288748","indexId":"70240902","displayToPublicDate":"2020-11-27T06:36:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of spawning by lake trout Salvelinus namaycush on substrates at the base of large boulders in northern Lake Huron","docAbstract":"Identification of lake trout spawning sites has focused on cobble substrates associated with bathymetric relief (e.g., ‘contour’ or ‘slope’ along reefs), but this ‘model’ may be narrow in scope. Previous telemetry work conducted near Drummond Island, USA, Lake Huron, identified egg presence in substrates at the base of large boulders (>1 m diameter); however, the extent of this phenomenon was unknown. Telemetry data paired with multi-beam bathymetry identified a 0.63 km2 area used by lake trout characterized by low bathymetric relief and numerous (~269) large boulders (>1 m diameter) with small-diameter substrates at their bases. Diver surveys revealed egg presence at all 40 boulders surveyed, exclusively associated with clean gravel-cobble (0.6–42 cm) substrates in undercut areas beneath overhanging edges of boulders and in narrow spaces between adjacent boulders. Egg presence was not associated with boulder or substrate physical characteristics which highlighted the possible importance of interstitial currents. Successful incubation in these habitats was inferred by capture of free embryos and post-embryos the following spring using traps and an electrofishing ROV although at lower densities than at popular spawning habitats nearby (1–3 km away). Free embryos and post-embryos were also caught where eggs were not observed the previous fall including unexpectedly on top of boulders which suggested that post-hatch stages may move more than previously thought. Extensive use of boulder-associated habitats for spawning, egg incubation, and early growth suggested this undescribed habitat type may provide an unanticipated contribution to total available lake trout spawning habitat and recruitment in the Great Lakes.
Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush (Artemisia tridentata) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass (Pseudoroegneria spicata) and Thurber's needlegrass (Achnatherum thurberianum) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.
The persistence of high nitrate concentrations in shallow groundwater has been well documented in the shallow glacial aquifer of Mason County, Illinois. Nitrates in groundwater can be a concern when concentrations exceed 10 milligrams per liter in drinking water. Additionally, nitrate in groundwater can contribute to surface water nitrogen loads that can cause increased algal growth. Algal growth increases oxygen consumption causing anoxic conditions as observed in the Gulf of Mexico Hypoxic Zone.
From March 8, 2017, to March 31, 2019, groundwater level, continuous nitrate, dissolved oxygen, specific conductance, water temperature, and pH data were collected in a monitoring well to temporally assess changes in water quality using high frequency data. During this same period, instantaneous field measurements of water quality and groundwater levels were made in surface water and groundwater in and near Quiver Creek in the presumed groundwater flow path about 0.6 mile from the continuous monitoring well. Groundwater nitrate concentrations continuously measured in the aquifer during this time ranged from 14.7 to 23.2 milligrams per liter, whereas instantaneously measured nitrate concentrations in Quiver Creek ranged from 0.9 to 6.4 milligrams per liter. Nitrate concentrations measured by piezometer varied laterally and vertically in the Quiver Creek floodplain and beneath the stream. Irrigation and fertigation for agriculture is widely practiced in Mason County. This may seasonally affect the groundwater flow and movement as well as the persistence of nitrate in this area. Continuously and instantaneously measured nitrate concentrations and groundwater levels indicate that during the irrigation season, discharge to Quiver Creek from the shallow groundwater system may be limited. During and following periods when estimated irrigation use is highest, the low-nitrate deeper groundwater may be the dominant contributor to the Quiver Creek surface water, whereas during recharge events and when the system is not under the stress of irrigation, there is more contribution from the local and higher-nitrate shallow groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205108","collaboration":"Prepared in cooperation with the Illinois Environmental Protection Agency","usgsCitation":"Gruhn, L.R., and Morrow, W.S., 2020, Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5108, 26 p., https://doi.org/10.3133/sir20205108.","productDescription":"viii, 26 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-108958","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5108/sir20205108.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Whitebark pine (Pinus albicaulis) is a long-lived tree found in high-elevation forests of western North America that is declining due to the non-native white pine blister rust (Cronartium ribicola) and climate-driven outbreaks of mountain pine beetle (Dendroctonus ponderosae; MPB). The National Park Service established a monitoring program for whitebark pine in seven parks, including Sequoia & Kings Canyon, Yosemite, Lassen Volcanic, Crater Lake, Mount Rainier, Olympic, and North Cascades National Parks. Using these data, we summarized stand structure, presence of blister rust, and MPB prevalence to provide a baseline for future monitoring. Next, we used a stochastic, size-structured population model to speculate on future trends in the seven national park populations under conditions of increased MPB activity and ongoing blister rust infection observed in Crater Lake. We found that blister rust infected 29 to 54% of whitebark pine in all the parks except the two southernmost, Sequoia & Kings Canyon and Yosemite, where infections rates were 0.3% and 0.2%, respectively. The proportion of dead trees in Sequoia & Kings Canyon and Yosemite was low (0 to 1%), while they ranged from 10 to 43% in the other parks. Model projections suggested an average population decline of 25% in the parks over the next century using Crater Lake conditions, declines which are possible if blister rust continues to spread and climate change results in a significant increase in the frequency or severity of MPB outbreaks. Overall, our study describes conditions at seven western parks and illustrates potential rates of whitebark pine decline if pest outbreaks and/or blister rust infections worsen.
Thirty water wells were sampled in 2018 to understand the geochemistry and age of groundwater in the Williston Basin and assess potential effects of shale-oil production from the Three Forks-Bakken petroleum system (TBPS) on groundwater quality. Two geochemical groups are identified using hierarchical cluster analysis. Group 1 represents the younger (median 4He = 21.49 × 10−8 cm3 STP/g), less chemically evolved water. Group 2 represents the older (median 4He = 1389 × 10−8 cm3 STP/g), more chemically evolved water. At least two samples from each group contain elevated Cl concentrations (>70 mg/L). Br/Cl, B/Cl, and Li/Cl ratios indicate multiple sources account for the elevated Cl concentrations: septic-system leachate/road deicing salt, lignite beds in the aquifers, Pierre Shale beneath the aquifers, and water associated with the TBPS (one sample). 3H and 14C data indicate that 10.8, 21.6, and 67.6% of the samples are modern (post-1952), mixed age, and premodern (pre-1953), respectively. Lumped-parameter modeling of 3H, SF6, 3He, and 14C concentrations indicates mean ages of the modern and premodern fractions range from ~1 to 30 years and 1300 to >30,000 years, respectively. Group 2 contains the highest CH4 concentrations (0.0018–32 mg/L). δ13C–CH4 and C1/C2+C3 data in groundwater (−91.7 to −70.0‰ and 1280 to 13,600) indicate groundwater CH4 is biogenic in origin and not from thermogenic shale gas. Four volatile organic compounds (VOCs) were detected in two samples. One mixed-age sample contains chloroform (0.25 μg/L) and dichloromethane (0.05 μg/L), which are probably associated with septic leachate. One premodern sample contains butane (0.082 μg/L) and n-pentane (0.032 μg/L), which are probably associated with thermogenic gas from a nearby oil well. The data indicate hydrocarbon production activities do not currently (2018) widely affect Cl, CH4, and VOC concentrations in groundwater. The predominance of premodern recharge in the aquifers indicates the groundwater moves relatively slowly, which could inhibit widespread chemical movement in groundwater overlying the TBPS. Comparison of groundwater-age data from five major unconventional hydrocarbon-production areas indicates aquifer zones used for water supply in the TBPS area have a lower risk of widespread chemical movement in groundwater than similar aquifer zones in the Fayetteville (Arkansas) and Marcellus (Pennsylvania) Shale production areas, but have a higher risk than similar aquifer zones in the Eagle Ford (Texas) and Haynesville (Texas, Louisiana) Shale production areas.
Riparian restoration in the southwestern United States frequently involves planting cottonwood (Populus spp.) and willow (Salix spp.). In the absence of flooding and gap-forming disturbance, planted forests often senesce without further young tree recruitment. This has largely been the case in California riparian systems that historically supported state-endangered western Yellow-billed Cuckoo (Coccyzus americanus; Cuckoo). A decline in Cuckoo population numbers in the past 30 years has been associated with forest maturation. Other riparian species of concern show a concomitant decline, indicating the problem is not specific to Cuckoos. Although varying hypotheses exist for recent decline, alternative management practices have not been sufficiently explored to rule out breeding ground habitat quality as a major contributing factor. Few intensive Cuckoo datasets exist to test hypotheses about breeding habitat quality due to extremely low populations in the remaining occupied sites. We used a historical (1986–1996) spot mapping dataset from the South Fork Kern River Valley, CA to identify vegetation characteristics related to Cuckoo and five other sensitive riparian bird territory densities. We found Cuckoo densities were positively associated with increased vertical vegetative structure 1–5 m above ground with a threshold for mean tree height. Sensitive species densities were also related to vertical structure and started to decline with stand height greater than 6–8 m. Naturally regenerated sites had higher densities of most sensitive bird species than planted sites. We provide ideas for restoring mature forest with little vertical structure.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13331","usgsCitation":"Wohner, P., Laymon, S., Stanek, J., King, S.L., and Cooper, R., 2020, Challenging our understanding of western Yellow-billed Cuckoo habitat needs and accepted management practices: Restoration Ecology, v. 29, no. 3, e13331, https://doi.org/10.1111/rec.13331.","productDescription":"e13331","ipdsId":"IP-122363","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South Fork Kern River 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P.J.","contributorId":287172,"corporation":false,"usgs":false,"family":"Wohner","given":"P.J.","affiliations":[{"id":61497,"text":"Cuckoo Conservation Initiative","active":true,"usgs":false}],"preferred":false,"id":836538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laymon, S.A.","contributorId":287173,"corporation":false,"usgs":false,"family":"Laymon","given":"S.A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":836539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanek, J.E.","contributorId":287174,"corporation":false,"usgs":false,"family":"Stanek","given":"J.E.","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":836540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, R.J.","contributorId":287175,"corporation":false,"usgs":false,"family":"Cooper","given":"R.J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":836542,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216529,"text":"sir20205088 - 2020 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018","interactions":[],"lastModifiedDate":"2020-11-25T12:58:22.191418","indexId":"sir20205088","displayToPublicDate":"2020-11-24T16:52:31","publicationYear":"2020","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":"2020-5088","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri and Mississippi Rivers on the Periphery of Missouri, July–August 2018","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 7 bridges at 6 highway crossings of the Missouri and Mississippi Rivers on the periphery of the State of Missouri from July 16 to August 13, 2018. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were present at most piers for which bathymetry could be obtained, except those on banks, on bedrock, or surrounded by riprap. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although partial exposure of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 12 and 13 at structure L0135 on State Highway 51 at Chester, Illinois, at which the bedrock material was fully exposed around the piers.
The presence of riprap blankets, pier size and nose shape, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers at which no rock or riprap were present. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several of the structures had piers that were skewed to primary approach flow, and scour holes near these piers generally displayed deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205088","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018: U.S. Geological Survey Scientific Investigations Report 2020–5088, 100 p., https://doi.org/10.3133/sir20205088.","productDescription":"Report: vii, 100 p.; Data Release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-115831","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5088/coverthb.jpg"},{"id":380761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5088/sir20205088.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5088"},{"id":380762,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"}],"country":"United States","state":"Missouri","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68017578125,\n 39.257778150283364\n ],\n [\n -94.72412109375,\n 39.07890809706475\n ],\n [\n -94.603271484375,\n 38.993572058209466\n ],\n [\n -94.28466796874999,\n 39.00211029922515\n ],\n [\n -93.88916015625,\n 39.00211029922515\n ],\n [\n -93.526611328125,\n 39.06184913429154\n ],\n [\n -93.09814453125,\n 39.155622393423215\n ],\n [\n -93.05419921875,\n 39.00211029922515\n ],\n [\n -92.92236328125,\n 38.87392853923629\n ],\n [\n -92.65869140625,\n 38.805470223177466\n ],\n [\n -92.59277343749999,\n 38.659777730712534\n ],\n [\n -92.30712890625,\n 38.53097889440024\n ],\n [\n -92.054443359375,\n 38.46219172306828\n ],\n [\n -91.42822265625,\n 38.51378825951165\n ],\n [\n -91.01074218749999,\n 38.436379603\n ],\n [\n -90.802001953125,\n 38.41916639395372\n ],\n [\n -90.3515625,\n 38.66835610151506\n ],\n [\n -90.164794921875,\n 38.77978137804918\n ],\n [\n -90.054931640625,\n 38.950865400919994\n ],\n [\n -90.19775390625,\n 38.95940879245423\n ],\n [\n -90.54931640625,\n 38.84826438869913\n ],\n [\n -90.90087890624999,\n 38.69408504756833\n ],\n [\n -91.16455078125,\n 38.831149809348744\n ],\n [\n -91.593017578125,\n 38.85682013474361\n ],\n [\n -92.10937499999999,\n 38.7283759182398\n ],\n [\n -92.373046875,\n 38.96795115401593\n ],\n [\n -92.7685546875,\n 39.27478966170308\n ],\n [\n -93.09814453125,\n 39.470125122358176\n ],\n [\n -93.955078125,\n 39.317300373271024\n ],\n [\n -94.22973632812499,\n 39.33429742980725\n ],\n [\n -94.68017578125,\n 39.257778150283364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
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Regional aquifers in the Mississippi embayment are the principal sources of water used for public and domestic supply, irrigation, and industrial uses throughout the region. An understanding of how water quality varies spatially, temporally, and with depth are critical aspects to ensuring long-term sustainable use of these resources. A boosted regression tree (BRT) model was used by the U.S. Geological Survey (USGS) to map water quality in the three regional aquifers with the largest groundwater withdrawals in the embayment: the Mississippi River Valley alluvial (MRVA) aquifer, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ).
The BRT model was used to predict pH to 1-kilometer raster grid cells for seven aquifer layers (one MRVA, four MCAQ, two LCAQ) following the hydrogeologic framework of the Mississippi embayment aquifer system regional MODFLOW model. The methods and approach used for pH predictions are the same as those used recently by the USGS to predict specific conductance and chloride in the aquifers. Explanatory variables for the BRT models included variables describing well location and construction, surficial variables such as soil properties and land use, and variables extracted from the groundwater flow model, such as groundwater levels and ages. The primary source of pH data was the USGS National Water Information System database. Additional data from State ambient groundwater monitoring programs and the Safe Drinking Water Information System also were used. For wells sampled multiple times, the most recent sample was used. Because groundwater residence times are long (greater than 100 years) throughout much of the study area, the possible effects of changes in water quality over time were considered small compared to the improvement in overall model accuracy by using available historical data. Values of pH from 3,362 wells for samples collected between 1960 and 2018 were used as training data for the BRT model. An additional 839 samples were used as holdout data to evaluate model performance. The predictive performance of the pH model is lower than for the training dataset, as indicated by an r-squared value of 0.89 for the training data and an r-squared of 0.71 for the holdout data. The root mean squared errors for the training and holdout data are 0.32 and 0.50 standard pH units, respectively. Data generated during this study and the model output are available from the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3465","usgsCitation":"Kingsbury, J.A., Knierim, K.J., and Haugh, C.J., 2020, Predicted pH of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, South-Central United States: U.S. Geological Survey Scientific Investigations Map 3465, 1 sheet, https://doi.org/10.3133/sim3465.","productDescription":"1 Sheet: 34.60 x 28.70 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-111848","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380668,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXX7LN","text":"USGS data release","linkHelpText":"Prediction grids of pH for the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":380666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3465/coverthb2.jpg"},{"id":380667,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3465/sim3465.pdf","text":"Report","size":"3.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3465"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi River Valley alluvial, Claiborne aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.296875,\n 37.020098201368114\n ],\n [\n -90.1318359375,\n 36.66841891894786\n ],\n [\n -91.93359375,\n 35.28150065789119\n ],\n [\n -93.33984375,\n 33.65120829920497\n ],\n [\n -94.04296874999999,\n 33.100745405144245\n ],\n [\n -93.91113281249999,\n 31.952162238024975\n ],\n [\n -93.1640625,\n 31.090574094954192\n ],\n [\n -91.7578125,\n 30.939924331023445\n ],\n [\n -91.0986328125,\n 31.952162238024975\n ],\n [\n -90.703125,\n 32.24997445586331\n ],\n [\n -89.3408203125,\n 32.175612478499325\n ],\n [\n -88.0224609375,\n 31.57853542647338\n ],\n [\n -87.4951171875,\n 31.80289258670676\n ],\n [\n -86.748046875,\n 32.99023555965106\n ],\n [\n -87.4072265625,\n 33.211116472416855\n ],\n [\n -88.9892578125,\n 33.94335994657882\n ],\n [\n -89.7802734375,\n 34.74161249883172\n ],\n [\n -90,\n 35.24561909420681\n ],\n [\n -89.56054687499999,\n 36.13787471840729\n ],\n [\n -89.3408203125,\n 36.421282443649496\n ],\n [\n -89.2529296875,\n 36.84446074079564\n ],\n [\n -89.296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2017). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley groundwater basin adjacent to the North and South Belridge oil fields, about 7 miles southwest of Lost Hills, California. Data collected at the Belridge multiple-well monitoring site (BWSD) provide information about the geology, hydrology, geophysical properties, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the North and South Belridge oil fields in an area where there are few groundwater data. This report presents construction information for the BWSD and initial hydrogeologic data collected from the site. A similar site installed to the east of the Lost Hills oil field, 11.5 miles to the north of the BWSD site, was described by Everett and others (2020a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201116","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., Brown, A.A., Gillespie, J.M., Kjos, A., and Fenton, N.C., 2020, Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California: U.S. Geological Survey Open-File Report 2020-1116, 10 p., https://doi.org/10.3133/ofr20201116.","productDescription":"Report: 10 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-112077","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":380658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1116/ofr20201116.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1116"},{"id":380659,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WITX5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquifer test data for the Belridge multiple-well monitoring site (BWSD), Kern County, California"},{"id":380657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1116/coverthb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-120.1945,35.788],[-120.1842,35.789],[-120.1655,35.7891],[-120.1474,35.7887],[-120.0816,35.7886],[-119.9688,35.7896],[-119.852,35.7891],[-119.7618,35.7906],[-119.6472,35.7895],[-119.5395,35.79],[-119.4301,35.7905],[-119.3308,35.7899],[-119.2169,35.7906],[-119.1182,35.7903],[-118.9027,35.789],[-118.6504,35.7897],[-118.6441,35.7896],[-118.5885,35.7897],[-118.5233,35.7892],[-118.4785,35.7915],[-118.4706,35.7919],[-118.4502,35.7908],[-118.2716,35.7896],[-118.2562,35.7894],[-118.2387,35.7897],[-118.2137,35.7894],[-118.1956,35.7896],[-118.1632,35.7893],[-118.0839,35.7865],[-118.0697,35.7859],[-118.009,35.7861],[-117.9234,35.7863],[-117.9249,35.7986],[-117.9005,35.7983],[-117.8738,35.7988],[-117.8523,35.7985],[-117.6362,35.7958],[-117.6355,35.7086],[-117.6537,35.7085],[-117.6527,35.6776],[-117.6176,35.6775],[-117.6166,35.6493],[-117.6353,35.6487],[-117.6354,35.6233],[-117.6352,35.5807],[-117.6356,35.5666],[-117.6351,35.5639],[-117.6346,35.4472],[-117.6352,35.3755],[-117.6353,35.3464],[-117.6351,35.3319],[-117.6343,35.3174],[-117.6341,35.3028],[-117.6345,35.2874],[-117.6343,35.2742],[-117.6341,35.2588],[-117.6339,35.2447],[-117.6342,35.2302],[-117.634,35.2157],[-117.6338,35.2011],[-117.6336,35.1861],[-117.6334,35.1707],[-117.6338,35.1562],[-117.6336,35.1417],[-117.6333,35.1271],[-117.6331,35.1126],[-117.6329,35.098],[-117.6352,35.0981],[-117.636,35.0872],[-117.6358,35.0727],[-117.6356,35.0581],[-117.6357,35.0295],[-117.6361,35.015],[-117.6357,34.985],[-117.6351,34.8233],[-117.6519,34.8227],[-117.6704,34.8221],[-117.7757,34.8229],[-118.1408,34.8195],[-118.1493,34.8195],[-118.5995,34.8175],[-118.8946,34.8181],[-118.8945,34.818],[-118.8825,34.791],[-118.9772,34.7902],[-118.9771,34.8126],[-119.2462,34.8147],[-119.2461,34.857],[-119.2797,34.858],[-119.2779,34.8793],[-119.3844,34.8794],[-119.385,34.884],[-119.3849,34.899],[-119.4382,34.8999],[-119.4438,34.8999],[-119.4544,34.8999],[-119.4571,34.9],[-119.4746,34.9004],[-119.4746,34.9005],[-119.4746,34.9136],[-119.474,34.9367],[-119.474,34.9499],[-119.474,34.9576],[-119.474,34.9721],[-119.4746,35.0184],[-119.4746,35.0325],[-119.4745,35.077],[-119.4908,35.077],[-119.4914,35.092],[-119.5004,35.0915],[-119.5088,35.0906],[-119.5628,35.0883],[-119.5583,35.1369],[-119.5566,35.1601],[-119.5549,35.1791],[-119.5769,35.1787],[-119.6095,35.1773],[-119.6675,35.1749],[-119.6675,35.1908],[-119.6675,35.2049],[-119.6688,35.2617],[-119.7397,35.2629],[-119.7572,35.2633],[-119.7746,35.2633],[-119.8113,35.2641],[-119.8122,35.3508],[-119.8815,35.3501],[-119.8824,35.41],[-119.8824,35.4246],[-119.8831,35.4377],[-119.9999,35.4396],[-120.0007,35.4695],[-120.0171,35.469],[-120.0194,35.4835],[-120.0358,35.4834],[-120.0359,35.497],[-120.0523,35.4974],[-120.053,35.5124],[-120.0699,35.5128],[-120.0711,35.5268],[-120.0875,35.5276],[-120.0876,35.6139],[-120.1951,35.6151],[-120.1947,35.7481],[-120.1942,35.7626],[-120.1945,35.788]]]},\"properties\":{\"name\":\"Kern\",\"state\":\"CA\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Fishes are known to use deep-sea coral and sponge (DSCS) species as habitat, but it is uncertain whether this relationship is facultative (circumstantial and not restricted to a particular function) or obligate (necessary to sustain fish populations). To explore whether DSCS provide essential habitats for demersal fishes, we analyzed 10 years of submersible survey video transect data, documenting the locations and abundance of DSCS and demersal fishes in the Southern California Bight (SCB). We first classified the different habitats in which fishes and DSCS taxa occurred using cluster analysis, which revealed four distinct DSCS assemblages based on depth and substratum. We then used logistic regression and gradient forest analysis to identify the ecological correlates most associated with the presence of rockfish taxa (Sebastes spp.) and biodiversity. After accounting for spatial autocorrelation, the factors most related to the presence of rockfishes were depth, coral height, and the abundance of a few key DSCS taxa. Of particular interest, we found that young-of-the-year rockfishes were more likely to be present in locations with taller coral and increased densities of Plumarella longispina, Lophelia pertusa, and two sponge taxa. This suggests these DSCS taxa may serve as important rearing habitat for rockfishes. Similarly, the gradient forest analysis found the most important ecological correlates for fish biodiversity were depth, coral cover, coral height, and a subset of DSCS taxa. Of the 10 top-ranked DSCS taxa in the gradient forest (out of 39 potential DSCS taxa), 6 also were associated with increased probability of fish presence in the logistic regression. The weight of evidence from these multiple analytical methods suggests that this subset of DSCS taxa are important fish habitats. In this paper we describe methods to characterize demersal communities and highlight which DSCS taxa provide habitat to demersal fishes, which is valuable information to fisheries agencies tasked to manage these fishes and their essential habitats.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.593844","usgsCitation":"Henderson, M., Huff, D., and Yoklavich, M., 2020, Deep-sea coral and sponge taxa increase demersal fish diversity and the probability of fish presence: Frontiers in Marine Science, v. 7, 593844, 19 p., https://doi.org/10.3389/fmars.2020.593844.","productDescription":"593844, 19 p.","ipdsId":"IP-102115","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454762,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.593844","text":"Publisher Index Page"},{"id":388395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.025390625,\n 32.879587173066305\n ],\n [\n -117.66357421875,\n 32.879587173066305\n ],\n [\n -117.66357421875,\n 34.43409789359469\n ],\n [\n -121.025390625,\n 34.43409789359469\n ],\n [\n -121.025390625,\n 32.879587173066305\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2020-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":821760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huff, D.D.","contributorId":264617,"corporation":false,"usgs":false,"family":"Huff","given":"D.D.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":821761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yoklavich, M.M","contributorId":264618,"corporation":false,"usgs":false,"family":"Yoklavich","given":"M.M","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":821762,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216390,"text":"sir20205081 - 2020 - Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area","interactions":[],"lastModifiedDate":"2024-03-04T19:37:36.850638","indexId":"sir20205081","displayToPublicDate":"2020-11-23T10:50:00","publicationYear":"2020","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":"2020-5081","displayTitle":"Assessment of Ambystomatid Salamander Populations and Their Breeding Habitats in the Delaware Water Gap National Recreation Area","title":"Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area","docAbstract":"This report presents abundance and occurrence data for three species of ambystomad salamanders (Ambystoma maculatum, A. jeffersonianum, and A. opacum) collected over a 3-year period (2000, 2001, and 2002) at 200 potentional breeding sies within the Delaware Water Gap National Recreation Area (DEWA). In addition, numerous measures of inpond, near-pond, and landscape attributes were measured and used to inform statistical models to determine species-habitat relationships in the DEWA.
The results of a 3-year study of ambystomatid salamander breeding habits and habitats in the (DEWA) that was conducted by the U.S. Geological Survey, in cooperation with the National Park Service, are described in the report. The objectives of the study were to document the population status and critical breeding habitats of the three species of ambystomatid salamanders known to be present in the DEWA—Ambystoma maculatum (spotted salamander), A. opacum (marbled salamander), and A. jeffersonianum (Jefferson salamander). DEWA managers are interested in ecological information on these species for several reasons. First, at the time the study began, there was little known regarding the status of pond-breeding amphibians and their habitats in the DEWA. Second, because they require undegraded habitats in both terrestrial and aquatic habitats to successfully complete their life cycles, the status of ambystomatid salamanders is widely viewed as indicative of overall ecosystem health. Third, because ambystomatid salamanders and other pond-breeding amphibians have been observed in numerous artificial impoundments with the DEWA, park managers would like to assess whether dismantling or discontinuing maintenance of artificial impoundments could affect pond-breeding amphibians and possibly other species that use pond or wetland habitats in the Park.
In 2001, 2002, and 2003, the size and location of 200 wetlands, ponds, and artificial impoundments, and related landscape positions (Ridge versus Valley; Pennsylvania side versus New Jersey side of the Delaware river) were mapped, and site habitat data relating to salamander occurrence and abundance patterns were collected. The data collected during this study provide important new baseline information on ambystomatid salamanders and wetland habitats in the DEWA that will enhance long-term inventory and monitoring efforts. In addition, breeding habitat assessments indicate that ambystomatid salamanders may be sensitive to a wide variety of stresses important in the DEWA and in the region. In particular, recent trends in development (for example, roads) in and near the DEWA, regional increases in the acidity of precipitation, and predicted long-term warming trends for the region could be detrimental to pond-breeding salamander populations because of their effects on breeding site quality and quantity, and on the integrity of migration corridors. In contrast, the results of the study indicate management plans to eliminate small impoundments are not likely to adversely affect salamanders in DEWA, at least in the short-term. However, it is possible that these small impoundments may offer stable habitats that provide a rescure effect during long-term droughts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205081","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Snyder, C.D., Young, J.A., Julian, J.T., King, T.L., and Julian, S.E., 2020, Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area: U.S. Geological Survey Scientific Investigations Report 2020–5081, 41 p., https://doi.org/10.3133/sir20205081.","productDescription":"Report: viii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113175","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":380510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5081/coverthb.jpg"},{"id":380511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5081/sir20205081.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5081"},{"id":380512,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCVHY3","text":"USGS data release","linkHelpText":"Ambystomatid salamander population and breeding pond habitat data for the Delaware Water Gap National Recreation Area (2001–2003)"}],"country":"United States","state":"New Jersey, Pennsylvania","otherGeospatial":"Delaware Water Gap National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.7564697265625,\n 41.380930388318\n ],\n [\n -74.8992919921875,\n 41.29844430929419\n ],\n [\n -74.9761962890625,\n 41.18278832811288\n ],\n [\n -75.1080322265625,\n 41.06692773019345\n ],\n [\n -75.179443359375,\n 40.992337919312305\n ],\n [\n -75.1629638671875,\n 40.93011520598305\n ],\n [\n -75.0970458984375,\n 40.93841495689795\n ],\n [\n -74.893798828125,\n 41.075210270566636\n ],\n [\n -74.6630859375,\n 41.253032440653186\n ],\n [\n -74.7564697265625,\n 41.380930388318\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
11649 Leetown Road
Kearneysville, WV 25430
Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pntd.0008868","usgsCitation":"Leach, C.B., Hoeting, J., Pepin, K.M., Eiras, A.E., Hooten, M., and Colleen T. Webb, C., 2020, Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling: PLoS Neglected Tropical Diseases, v. 14, no. 11, p. 1-20, https://doi.org/10.1371/journal.pntd.0008868.","productDescription":"e0008868, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-107662","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":454766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pntd.0008868","text":"Publisher Index Page"},{"id":393742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Espírito Santo","city":"Vitória","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -40.42968749999999,\n -20.43473423110048\n ],\n [\n -40.222320556640625,\n -20.43473423110048\n ],\n [\n -40.222320556640625,\n -20.17456745043183\n ],\n [\n -40.42968749999999,\n -20.17456745043183\n ],\n [\n -40.42968749999999,\n -20.43473423110048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Leach, Clinton B.","contributorId":270703,"corporation":false,"usgs":false,"family":"Leach","given":"Clinton","email":"","middleInitial":"B.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoeting, Jennifer A.","contributorId":270704,"corporation":false,"usgs":false,"family":"Hoeting","given":"Jennifer A.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pepin, Kim M.","contributorId":270705,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","email":"","middleInitial":"M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":829796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eiras, Alvaro E.","contributorId":270706,"corporation":false,"usgs":false,"family":"Eiras","given":"Alvaro","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":829797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":829793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Colleen T. Webb, Colleen T.","contributorId":270707,"corporation":false,"usgs":false,"family":"Colleen T. Webb","given":"Colleen T.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829798,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226612,"text":"70226612 - 2020 - Evaluation of Arctic warming in mid-Pliocene climate simulations","interactions":[],"lastModifiedDate":"2021-12-01T13:03:35.899178","indexId":"70226612","displayToPublicDate":"2020-11-23T06:57:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of Arctic warming in mid-Pliocene climate simulations","docAbstract":"Palaeoclimate simulations improve our understanding of the climate, inform us about the performance of climate models in a different climate scenario, and help to identify robust features of the climate system. Here, we analyse Arctic warming in an ensemble of 16 simulations of the mid-Pliocene Warm Period (mPWP), derived from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2).
The PlioMIP2 ensemble simulates Arctic (60–90∘ N) annual mean surface air temperature (SAT) increases of 3.7 to 11.6 ∘C compared to the pre-industrial period, with a multi-model mean (MMM) increase of 7.2 ∘C. The Arctic warming amplification ratio relative to global SAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea ice extent anomalies range from −3.0 to
Wildlife restoration often involves translocation efforts to reintroduce species and supplement small, fragmented populations. We examined the genomic consequences of bighorn sheep (Ovis canadensis) translocations and population isolation to enhance understanding of evolutionary processes that affect population genetics and inform future restoration strategies. We conducted a population genomic analysis of 511 bighorn sheep from 17 areas, including native and reintroduced populations that received 0–10 translocations. Using the Illumina High Density Ovine array, we generated datasets of 6,155 to 33,289 single nucleotide polymorphisms and completed clustering, population tree, and kinship analyses. Our analyses determined that natural gene flow did not occur between most populations, including two pairs of native herds that had past connectivity. We synthesized genomic evidence across analyses to evaluate 24 different translocation events and detected eight successful reintroductions (i.e., lack of signal for recolonization from nearby populations) and five successful augmentations (i.e., reproductive success of translocated individuals) based on genetic similarity with the source populations. A single native population founded six of the reintroduced herds, suggesting that environmental conditions did not need to match for populations to persist following reintroduction. Augmentations consisting of 18–57 animals including males and females succeeded, whereas augmentations of two males did not result in a detectable genetic signature. Our results provide insight on genomic distinctiveness of native and reintroduced herds, information on the relative success of reintroduction and augmentation efforts and their associated attributes, and guidance to enhance genetic contribution of augmentations and reintroductions to aid in bighorn sheep restoration.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6942","usgsCitation":"Flesch, E.P., Graves, T., Thomson, J., Proffitt, K., White, P., Stephenson, T.R., and Garrott, R.A., 2020, Evaluating wildlife translocations using genomics: A bighorn sheep case study: Ecology and Evolution, v. 10, no. 24, p. 13687-13704, https://doi.org/10.1002/ece3.6942.","productDescription":"18 p.","startPage":"13687","endPage":"13704","ipdsId":"IP-113330","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454775,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6942","text":"Publisher Index Page"},{"id":436715,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VMIFLP","text":"USGS data release","linkHelpText":"Bighorn sheep Ovine HD array genotypes from National Parks, 2004-2011"},{"id":382578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Idaho, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.49902343749999,\n 43.42100882994726\n ],\n [\n -108.28125,\n 43.42100882994726\n ],\n [\n -108.28125,\n 48.980216985374994\n ],\n [\n -116.49902343749999,\n 48.980216985374994\n ],\n [\n -116.49902343749999,\n 43.42100882994726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"24","noUsgsAuthors":false,"publicationDate":"2020-11-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Flesch, Elizabeth P 0000-0002-7592-8124","orcid":"https://orcid.org/0000-0002-7592-8124","contributorId":222685,"corporation":false,"usgs":false,"family":"Flesch","given":"Elizabeth","email":"","middleInitial":"P","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":809074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":809075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomson, Jennifer 0000-0003-1921-0975","orcid":"https://orcid.org/0000-0003-1921-0975","contributorId":248418,"corporation":false,"usgs":false,"family":"Thomson","given":"Jennifer","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":809076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Proffitt, Kelly 0000-0001-5528-3309","orcid":"https://orcid.org/0000-0001-5528-3309","contributorId":210093,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","email":"","affiliations":[{"id":38065,"text":"Montana Fish, Wildlife and Parks, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":809077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, P.J.","contributorId":194049,"corporation":false,"usgs":false,"family":"White","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":809078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Thomas R","contributorId":248420,"corporation":false,"usgs":false,"family":"Stephenson","given":"Thomas","email":"","middleInitial":"R","affiliations":[{"id":12939,"text":"California Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":809079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garrott, Robert A.","contributorId":171537,"corporation":false,"usgs":false,"family":"Garrott","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":809080,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217038,"text":"70217038 - 2020 - Origin and properties of hydrothermal tremor at Lone Star Geyser, Yellowstone National Park, USA","interactions":[],"lastModifiedDate":"2020-12-29T13:51:48.138349","indexId":"70217038","displayToPublicDate":"2020-11-20T07:45:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Origin and properties of hydrothermal tremor at Lone Star Geyser, Yellowstone National Park, USA","docAbstract":"Geysers are rare geologic features that intermittently discharge liquid water and steam driven by heating and decompression boiling. The cause of variability in eruptive styles and the associated seismic signals are not well understood. Data collected from five broadband seismometers at Lone Star Geyser, Yellowstone National Park are used to determine the properties, location, and temporal patterns of hydrothermal tremor. The tremor is harmonic at some stages of the eruption cycle and is caused by near‐periodic repetition of discrete seismic events. Using the polarization of ground motion, we identify the location of tremor sources throughout several eruption cycles. During preplay episodes (smaller eruptions preceding the more vigorous major eruption), tremor occurs at depths of 7–10 m and is laterally offset from the geyser's cone by ~5 m. At the onset of the main eruption, tremor sources migrate laterally and become shallower. As the eruption progresses, tremor sources migrate along the same path but in the opposite direction, ending where preplay tremor originates. The upward and then downward migration of tremor sources during eruptions are consistent with warming of the conduit followed by evacuation of water during the main eruption. We identify systematic relations among the two types of preplays, discharge, and the main eruption. A point‐source moment tensor fit to low‐frequency waveforms of an individual tremor event using half‐space velocity models indicates average VS ≳ 0.8 km/s, source depths ~4–20 m, and moment tensors with primarily positive isotropic and compensated linear vector dipole moments.
The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate.
Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (P) were used in conjunction with actual ET (ETa) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water (P – ETa) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P, ETa, and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes.
Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase.
Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa. Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R2] = 0.46). Correlations between ETa and other landscape variables were lower (R2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa, followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa: herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa, because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P, likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations.
Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa, perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa, indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water.
The best multivariate linear model for annual ETa had an R2 value of 0.62. Monthly ETa models had R2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa. Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205095","collaboration":"Prepared in cooperation with the Bernalillo County Public Works Division","usgsCitation":"Douglas-Mankin, K.R., McCutcheon, R.J., Mitchell, A.C., and Senay, G.B., 2020, Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5095, 40 p., https://doi.org/10.3133/sir20205095.","productDescription":"x, 40 p.","numberOfPages":"53","onlineOnly":"Y","ipdsId":"IP-101269","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":380594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5095/sir20205095.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5095"},{"id":380593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5095/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.65252685546875,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 34.879171662167664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The effectiveness of submerged vanes for stabilizing streamside bluffs varied over a 10-year monitoring period in a tributary to Lake Superior, United States. Submerged vanes are a river training device used to divert river flows away from eroding banks along meander bends and ultimately hold constant or reverse the direction of lateral migration. At the study site, the relatively steep slope, large substrate size, and flashy flow regime pushed the upper end of the design limitations of submerged vanes. Changes in channel location and morphology due to the vanes were monitored using repeat channel cross-section surveys along a 110-m reach. The vanes experienced 15 floods over the monitoring period. The two most damaging floods happened in the summer and fall of 2005 with annual exceedance probabilities of 7% and 6% respectively. A new data analysis method for rivers, using centroids of cross sections, was useful to track channel migration rapidly and objectively and, along with calculations of changes in bankfull channel size, provide metrics to describe channel change.