This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian‐hierarchical regression models for over 2,500 counties, 18 covariates, and three higher‐level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher‐level groups results in the best out‐of‐sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city‐level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county‐level model, the effect of each variable depends heavily on where a city is located.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR021268","usgsCitation":"Worland, S.C., Steinschneider, S., and Hornberger, G.M., 2018, Drivers of variability in public‐supply water use across the contiguous United States: Water Resources Research, v. 54, no. 3, p. 1868-1889, https://doi.org/10.1002/2017WR021268.","productDescription":"22 p.","startPage":"1868","endPage":"1889","ipdsId":"IP-087770","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":488785,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr021268","text":"Publisher Index Page"},{"id":355717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"54","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-15","publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea9c","contributors":{"authors":[{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinschneider, Scott 0000-0002-8882-1908","orcid":"https://orcid.org/0000-0002-8882-1908","contributorId":206359,"corporation":false,"usgs":false,"family":"Steinschneider","given":"Scott","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":740160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hornberger, George M.","contributorId":206360,"corporation":false,"usgs":false,"family":"Hornberger","given":"George","email":"","middleInitial":"M.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":740161,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198127,"text":"70198127 - 2018 - Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","interactions":[],"lastModifiedDate":"2018-07-17T09:55:03","indexId":"70198127","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","docAbstract":"We compare the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines, M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging, a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean streamflow with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2017.12.021","usgsCitation":"Worland, S.C., Farmer, W.H., and Kiang, J.E., 2018, Improving predictions of hydrological low-flow indices in ungaged basins using machine learning: Environmental Modelling and Software, v. 101, p. 169-182, https://doi.org/10.1016/j.envsoft.2017.12.021.","productDescription":"14 p.","startPage":"169","endPage":"182","ipdsId":"IP-080308","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":468958,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2017.12.021","text":"Publisher Index Page"},{"id":437995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5S4T","text":"USGS data release","linkHelpText":"7Q10 records and basin characteristics for 224 basins in South Carolina, Georgia, and Alabama 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\"}}]}","volume":"101","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea9e","contributors":{"authors":[{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":740154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":740155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":740156,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159602,"text":"sir20155164 - 2018 - Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models","interactions":[],"lastModifiedDate":"2023-06-08T16:40:09.114831","indexId":"sir20155164","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5164","displayTitle":"Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models","title":"Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models","docAbstract":"Hawai‘i’s aquifers have limited capacity to store fresh groundwater because each island is small and surrounded by saltwater. Saltwater also underlies much of the fresh groundwater. Fresh groundwater resources are, therefore, particularly vulnerable to human activity, short-term climate cycles, and long-term climate change. Availability of fresh groundwater for human use is constrained by the degree to which the impacts of withdrawal—such as lowering of the water table, saltwater intrusion, and reduction in the natural discharge to springs, streams, wetlands, and submarine seeps—are deemed acceptable. This report describes the hydrogeologic framework, groundwater budgets (inflows and outflows), conceptual models of groundwater occurrence and movement, and the factors limiting groundwater availability for the largest and most populated of the Hawaiian Islands—Kaua‘i, O‘ahu, Maui, and Hawai‘i Island.
The bulk of each of Hawai‘i’s islands is built of many thin lava flows erupted from shield volcanoes; the great piles of lava flows form highly permeable aquifers. In some areas, low-permeability dikes cutting across the lava flows, or low-permeability ash and soil horizons interlayered with the lava flows, can substantially alter groundwater flow. On some islands, sedimentary rocks form thick semiconfining coastal-plain deposits, locally known as caprock, that impede natural groundwater discharge to the ocean. In some regions, thick lava flows that ponded in preexisting depressions form aquifers that are much less permeable than aquifers formed by thin lava flows.
Fresh groundwater inflow to Hawai‘i’s aquifers comes from recharge. For predevelopment conditions (1870), estimates of groundwater recharge from this study are 871, 675, 1,279, and 5,291 million gallons per day (Mgal/d) for Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. Estimates of recharge for recent conditions (2010 land cover and 1978–2007 rainfall for Kaua‘i, O‘ahu, and Maui; 2008 land cover and 1916–1983 rainfall for Hawai‘i Island) are 875, 660, 1,308, and 6,595 Mgal/d for Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. Recent recharge values differ from predevelopment recharge values by only a few percent for all islands except Hawai‘i Island, where changes in forest cover affected recharge. Spatial distribution of recharge mimics the orographic rainfall pattern—recharge is high on windward slopes and mountain peaks below the top of the trade-wind inversion. Human activity such as irrigation also contributes to recharge in some areas.
Outflows from Hawai‘i’s aquifers include withdrawals from wells and natural groundwater discharge to springs, streams, wetlands, and submarine seeps. Under predevelopment conditions, groundwater withdrawal is assumed to be negligible and natural groundwater discharge probably was equal, or close, to recharge. Under recent conditions (2000–2010), groundwater withdrawal averaged 19, 209, 104, and 103 Mgal/d on Kaua‘i, O‘ahu, Maui, and Hawai‘i Island, respectively. If recent withdrawal and recharge rates are maintained until steady state is achieved, natural groundwater discharge will be reduced by an amount equal to the withdrawal rate. Total recent withdrawal for the four islands is only about 5 percent of total recharge, but about half of the withdrawal comes from O‘ahu, whereas O‘ahu receives only 7 percent of the total recharge. Effects of high withdrawals on O‘ahu cannot be mitigated by the lower withdrawals on other islands because no freshwater flows between islands. Even within an island, high withdrawals from one area cannot be completely mitigated by recharge in another area. Water-level, saltwater/freshwater-transition-zone, spring, and stream base-flow data indicate an overall reduction in storage for most areas where groundwater has been developed.
Groundwater occurrence and movement in Hawai‘i’s volcanic aquifers can be described in terms of four conceptual models: (1) fresh groundwater lenses in high-permeability lava-flow aquifers, (2) aquifers with groundwater impounded by dikes, (3) thickly saturated low-permeability aquifers, and (4) perched aquifers. In Hawai‘i, most fresh groundwater withdrawn for human use comes from freshwater lenses in the dike-free high-permeability lava-flow aquifers where the principal limiting factor to groundwater availability is saltwater intrusion, but impacts of reduced natural groundwater discharge may also limit availability. Dike-impounded groundwater is common near the center of Hawaiian shield volcanoes, where water moves and is stored in permeable lava flows between the dikes; groundwater availability in these aquifers is primarily limited by storage depletion and reduction of flow to adjacent aquifers and natural groundwater discharge. Thickly saturated low-permeability aquifers have been identified on Kaua‘i and Maui; groundwater availability is primarily limited by streamflow depletion and water-table decline. Perched groundwater is postulated to exist in some areas of Hawai‘i, but store much less water than other modes ofgroundwater occurrence. Limits on groundwater availability in perched aquifers include the potential of reducing inflow to other groundwater settings and reducing natural discharge and stream seepage. Some groundwater bodies in Hawai‘i are enigmatic; consequences of groundwater development in these bodies and their relation to groundwater availability are not completely understood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155164","usgsCitation":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","productDescription":"Report: ix, 158 p.; Data Releases","numberOfPages":"172","ipdsId":"IP-058142","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":351940,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72F7KH4","linkHelpText":"Mean annual water-budget components for the Island of Oahu, Hawaii, for current conditions, 2001-10 rainfall and 2001-10 land cover (ver. 2.0)"},{"id":351939,"rank":4,"type":{"id":30,"text":"Data 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\"}}]}","edition":"Version 1.0: June 13, 2016; Version 2.0: March 1, 2018","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Turbidity, a measure of water clarity, is monitored for a variety of purposes including (1) to help determine whether water is safe to drink, (2) to establish background conditions of lakes and rivers and detect pollution caused by construction projects and stormwater discharge, (3) to study sediment transport in rivers and erosion in catchments, (4) to manage siltation of water reservoirs, and (5) to establish connections with aquatic biological properties, such as primary production and predator–prey interactions. Turbidity is typically measured with an optical probe that detects light scattered from particles in the water. Probes have defined upper limits of the range of turbidity that they can measure. The general assumption is that when turbidity exceeds this upper limit, the values of turbidity will be constant, i.e., the probe is pegged
; however, this assumption is not necessarily valid. In rivers with limited variation in the physical properties of the suspended sediment, at lower suspended-sediment concentrations, an increase in suspended-sediment concentration will cause a linear increase in turbidity. When the suspended-sediment concentration in these rivers is high, turbidity levels can exceed the upper measurement limit of an optical probe and record a constant pegged
value. However, at extremely high suspended-sediment concentrations, optical turbidity probes do not necessarily stay pegged
at a constant value. Data from the Colorado River in Grand Canyon, Arizona, USA, and a laboratory experiment both demonstrate that when turbidity exceeds instrument-pegged conditions, increasing suspended-sediment concentration (and thus increasing turbidity) may cause optical probes to record decreasing false
turbidity values that appear to be within the valid measurement range of the probe. Therefore, under high-turbidity conditions, other surrogate measurements of turbidity (e.g., acoustic-attenuation measurements or suspended-sediment samples) are necessary to correct these low false turbidity measurements and accurately measure turbidity.
Nitrogen (N) and phosphorus (P) commonly stimulate phytoplankton production in lakes, but recent observations from lakes from an agricultural region suggest that nitrate may have a subsidy‐stress effect on chlorophyll a (Chl a). It is unclear, however, how generalizable this effect might be. Here, we analyzed a large water quality dataset of 2385 lakes spanning 60 regions across 17 states in the Northeastern and Midwestern U.S. to determine if N subsidy‐stress effects on phytoplankton are common and to identify regional landscape characteristics promoting N stress effects in lakes. We used a Bayesian hierarchical modeling framework to test our hypothesis that Chl a–total N (TN) threshold relationships would be common across the central agricultural region of the U.S. (“the Corn Belt”), where lake N and P concentrations are high. Data aggregated across all regions indicated that high TN concentrations had a negative effect on Chl a in lakes with concurrent high total P. This large‐scale pattern was driven by relationships within only a subset of regions, however. Eight regions were identified as having Chl a–TN threshold relationships, but only two of these regions located within the Corn Belt clearly demonstrated this subsidy‐stress relationship. N stress effects were not consistent across other intense agricultural regions, as we hypothesized. These findings suggest that interactions among regional land use and land cover, climate, and hydrogeology may be important in determining the synergistic conditions leading to N subsidy‐stress effects on lake phytoplankton.
","language":"English","publisher":"ASLO","doi":"10.1002/lno.10742","usgsCitation":"Filstrup, C.T., Wagner, T., Oliver, S., Stow, C.A., Webster, K.E., Stanley, E.H., and Downing, J., 2018, Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients: Limnology and Oceanography, v. 63, no. S1, p. S324-S339, https://doi.org/10.1002/lno.10742.","productDescription":"16 p.","startPage":"S324","endPage":"S339","ipdsId":"IP-088161","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468964,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10742","text":"Publisher Index Page"},{"id":353963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":734714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stow, Craig A.","contributorId":204103,"corporation":false,"usgs":false,"family":"Stow","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":36843,"text":"NOAA, Great Lakes Environmental Research Lab","active":true,"usgs":false}],"preferred":false,"id":734715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":734717,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downing, John A.","contributorId":70348,"corporation":false,"usgs":true,"family":"Downing","given":"John A.","affiliations":[],"preferred":false,"id":734718,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196947,"text":"70196947 - 2018 - Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling","interactions":[],"lastModifiedDate":"2018-05-17T15:21:08","indexId":"70196947","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling","title":"Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling","docAbstract":"Ixobrychus exillis (Least Bittern) is listed as a species of high concern in the North American Waterbird Conservation Plan and is a US Fish and Wildlife Service migratory bird species of conservation concern in the Northeast. Little is known about the population of Least Bitterns in the Northeast because of their low population density, tendency to nest in dense wetland vegetation, and secretive behavior. Urban and agricultural development is expected to encroach on and degrade suitable wetland habitat; however, we cannot predict the effects on Least Bittern populations without more accurate information on their abundance and distribution. We conducted surveys of wetlands in Vermont to assess the efficacy of a monitoring protocol and to establish baseline Least Bittern abundance and distribution data at a sample of 29 wetland sites. Surveys yielded detections of 31 individuals at 15 of 29 sites across 3 biophysical regions and at 5 sites where occupancy had not been previously reported. Probability of occupancy was positively related to wetland size and number of patches, though the relationships were not strong enough to conclude if these were true determinants of occupancy. Call—response broadcast surveys yielded 30 detections, while passive surveys yielded 13. Call—response broadcasts (P = 0.897) increased the rate of detection by 55% compared to passive surveys (P = 0.577). Our results suggest that call—response broadcast surveys are an effective means of assessing Least Bittern occupancy and may reduce bias in long-term monitoring programs.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/045.025.0104","usgsCitation":"Cherukuri, A., Strong, A., and Donovan, T.M., 2018, Monitoring Least Bitterns (Ixobrychis exilis) in Vermont: Detection probability and occupancy modeling: Northeastern Naturalist, v. 25, no. 1, p. 56-71, https://doi.org/10.1656/045.025.0104.","productDescription":"16 p.","startPage":"56","endPage":"71","ipdsId":"IP-084582","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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However, other factors such as species and habitat identity may have predictive ability, enabling selection of appropriate elemental signatures prior to costly, perhaps unnecessary water/age‐0 fish sampling. The goal of this study was to develop a predictive methodology for using species and habitat identity to design efficient otolith chemistry studies. Duplicate water samples and age‐0 fish were collected from 61 sites in 4 Missouri River reservoirs for walleye Sander vitreus and one impoundment (Lake Sharpe, South Dakota) for other fishes (bluegill Lepomis macrochirus, black crappie Pomoxis nigromaculatus, gizzard shad Dorosoma cepedianum, largemouth bass Micropterus salmoides, smallmouth bass M. dolomieu, white bass Morone chrysops, white crappie P. annularis, and yellow perch Perca flavescens). Water chemistry (barium:calcium [Ba:Ca], strontium:calcium [Sr:Ca]) was temporally stable, spatially variable, and highly correlated with otolith chemistry for all species except yellow perch. Classification accuracies based on bivariate Ba:Ca and Sr:Ca signatures were high (84% across species) yet varied between floodplain and main‐channel habitats in a species‐specific manner. Thus, to maximize the reliability of otolith chemistry, researchers can use species classifications presented herein to inform habitat selection (e.g., study reservoir‐oriented species such as white bass in main‐channel environments) and habitat‐based classifications to inform species selection (e.g., focus floodplain studies on littoral species such as largemouth bass). Overall, species and habitat identity are important considerations for efficient, effective otolith chemistry studies that inform and advance fisheries and aquatic resource management.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3248","usgsCitation":"Radigan, W., Carlson, A.K., Kientz, J., Chipps, S.R., Fincel, M.J., and Graeb, B.D., 2018, Species‐ and habitat‐specific otolith chemistry patterns inform riverine fisheries management: River Research and Applications, v. 34, no. 3, p. 279-287, https://doi.org/10.1002/rra.3248.","productDescription":"9 p.","startPage":"279","endPage":"287","ipdsId":"IP-092404","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.27,\n 42.49\n ],\n [\n -96.45,\n 42.49\n ],\n [\n -96.45,\n 47.39\n ],\n [\n -103.27,\n 47.39\n ],\n [\n -103.27,\n 42.49\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-30","publicationStatus":"PW","scienceBaseUri":"5b46e5ade4b060350a15d200","contributors":{"authors":[{"text":"Radigan, William","contributorId":205424,"corporation":false,"usgs":false,"family":"Radigan","given":"William","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":737249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlson, Andrew K.","contributorId":172103,"corporation":false,"usgs":false,"family":"Carlson","given":"Andrew","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":737250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kientz, Jeremy","contributorId":205425,"corporation":false,"usgs":false,"family":"Kientz","given":"Jeremy","email":"","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":737251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fincel, Mark J.","contributorId":171853,"corporation":false,"usgs":false,"family":"Fincel","given":"Mark","email":"","middleInitial":"J.","affiliations":[{"id":26957,"text":"South Dakota Game, Fish and Parks, Ft. 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Since 1978, multiple annual bottom trawl surveys have quantified prey fish dynamics to inform management relative to published Fish Community Objectives. In 2017, two whole-lake surveys collected 341 bottom trawls (spring: 204, fall: 137), at depths from 8-225m, and captured 751,350 fish from 29 species. Alewife were 90% of the total fish catch while Deepwater Sculpin, Round Goby, and Rainbow Smelt comprised the majority of the remaining total catch (3.8, 3.1, and 1.1% respectively). The adult Alewife abundance index for US waters increased in 2017 relative to 2016, however the index for Canadian waters declined. Adult Alewife condition, assessed by the predicted weight of a 165 mm fish (6.5 inches), declined in 2017 from record high values observed in spring 2016. Spring 2017 Alewife condition was slightly less than the 10-year average, but the fall value was well below the 10-year average, likely due to increased Age-1 Alewife abundance. The Age-1 Alewife abundance index was the highest observed in 40 years, and 8-times higher than the previous year. The Age-1 index estimates Alewife reproductive success the preceding year. The warm summer and winter of 2016 likely contributed to the large year class. In contrast the relatively cool 2017 spring and cold winter may result in a lower than average 2017 year class. Abundance indices for Rainbow Smelt, Cisco, and Emerald Shiner either declined or remained at low levels in 2017. Pelagic prey fish diversity continues to be low since a single species, Alewife, dominates the catch.
Deepwater Sculpin were the most abundant benthic prey fish in 2017 because Round Goby abundance declined sharply from 2016. Slimy Sculpin density continued to decline and the 2017 biomass index for US waters was the lowest ever observed. Prior to Round Goby proliferation, juvenile Slimy Sculpin comprised ~10% of the Slimy Sculpin catch, but since 2004, the percent of juveniles within the total catch is less than 0.5%, suggesting Round Goby are limiting Slimy Sculpin reproduction. Despite Slimy Sculpin declines, benthic prey fish community diversity has increased as Deepwater Sculpin and Round Goby comprise more of the community.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"NYSDEC Lake Ontario Annual Report 2017","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"NYSDEC","usgsCitation":"Weidel, B., Connerton, M., and Holden, J., 2018, Bottom trawl assessment of Lake Ontario prey fishes: NYSDEC Lake Ontario Annual Report , Section 12; 18 p.","productDescription":"Section 12; 18 p.","ipdsId":"IP-095503","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":354969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":372952,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://purl.nysed.gov/nysl/889897048"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.98596191406249,\n 43.113014204188914\n ],\n [\n -76.0418701171875,\n 43.113014204188914\n ],\n [\n -76.0418701171875,\n 44.33563634908042\n ],\n [\n -79.98596191406249,\n 44.33563634908042\n ],\n [\n -79.98596191406249,\n 43.113014204188914\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e5b9e4b060350a15d20b","contributors":{"authors":[{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":731717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connerton, Michael J.","contributorId":190416,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael J.","affiliations":[],"preferred":false,"id":731718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holden, Jeremy","contributorId":139654,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","affiliations":[{"id":12864,"text":"OMNRF","active":true,"usgs":false}],"preferred":false,"id":731719,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196711,"text":"70196711 - 2018 - Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","interactions":[],"lastModifiedDate":"2019-06-27T07:53:35","indexId":"70196711","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","docAbstract":"Large lakes provide a variety of ecological services to surrounding cities and communities. Many of these services are supported by ecological processes that are threatened by the increasing prevalence of cyanobacterial blooms which occur as aquatic ecosystems experience cultural eutrophication. Over the past 10 yr, Lake Erie experienced cyanobacterial blooms of increasing severity and frequency, which have resulted in impaired drinking water for the surrounding communities. Cyanobacterial blooms may impact ecological processes that support other services, but many of these impacts have not been documented. Secondary production (production of primary consumers) is an important process that supports economically important higher trophic levels. Cyanobacterial blooms may influence secondary production because cyanobacteria are a poor‐quality food resource and cyanotoxins may be harmful to consumers. Over 3 yr at 34 sites across the western basin of Lake Erie, we measured three indices of secondary production that focus on the dominant bivalve taxa: (1) growth of a native unionid mussel, (2) the size of young‐of‐year dreissenid mussels, and (3) the mass of colonizing animals on a Hester‐Dendy sampler. Associations between these indices and cyanobacterial data were estimated to assess whether cyanobacteria are associated with variation in secondary production in the western basin of Lake Erie. The results suggest cyanobacterial abundance alone is only weakly associated with secondary production, but that cyanotoxins have a larger effect on secondary production. Given recurring late‐summer cyanobacterial blooms, this impact on secondary production has the potential to undermine Lake Erie's ability to sustain important ecosystem services.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography (ASLO)","doi":"10.1002/lno.10733","usgsCitation":"Larson, J.H., Evans, M.A., Kennedy, R.J., Bailey, S., Loftin, K.A., Laughrey, Z.R., Femmer, R., Schaeffer, J., Richardson, W.B., Wynne, T., Nelson, J., and Duris, J.W., 2018, Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie: Limnology and Oceanography, v. 63, no. S1, p. 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Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":734122,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laughrey, Zachary R. 0000-0002-7630-2078 zlaughrey@usgs.gov","orcid":"https://orcid.org/0000-0002-7630-2078","contributorId":198516,"corporation":false,"usgs":true,"family":"Laughrey","given":"Zachary","email":"zlaughrey@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":734123,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Femmer, Robin 0000-0003-4896-918X rfemmer@usgs.gov","orcid":"https://orcid.org/0000-0003-4896-918X","contributorId":198517,"corporation":false,"usgs":true,"family":"Femmer","given":"Robin","email":"rfemmer@usgs.gov","affiliations":[],"preferred":true,"id":734124,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schaeffer, Jeff 0000-0003-3430-0872 jschaeffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3430-0872","contributorId":2041,"corporation":false,"usgs":true,"family":"Schaeffer","given":"Jeff","email":"jschaeffer@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":734125,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734126,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wynne, Timothy","contributorId":147819,"corporation":false,"usgs":false,"family":"Wynne","given":"Timothy","affiliations":[{"id":16942,"text":"National Oceanic and Atmospheric Administration, Silver Spring, Maryland","active":true,"usgs":false}],"preferred":false,"id":734127,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nelson, J. C. 0000-0002-7105-0107 jcnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-7105-0107","contributorId":459,"corporation":false,"usgs":true,"family":"Nelson","given":"J. C.","email":"jcnelson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":734128,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duris, Joseph W. 0000-0002-8669-8109 jwduris@usgs.gov","orcid":"https://orcid.org/0000-0002-8669-8109","contributorId":172426,"corporation":false,"usgs":true,"family":"Duris","given":"Joseph","email":"jwduris@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":734129,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70195768,"text":"70195768 - 2018 - Environmental risks and challenges associated with neonicotinoid insecticides","interactions":[],"lastModifiedDate":"2018-03-26T13:45:40","indexId":"70195768","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Environmental risks and challenges associated with neonicotinoid insecticides","docAbstract":"Neonicotinoid use has increased rapidly in recent years, with a global shift towards insecticide applications as seed coatings rather than aerial spraying. While the use of seed coatings can lessen the amount of overspray and drift, the near universal and prophylactic use of neonicotinoid seed coatings on major agricultural crops has led to widespread detections in the environment (pollen, soil, water, honey). Pollinators and aquatic insects appear to be especially susceptible to the effects of neonicotinoids with current research suggesting that chronic sub-lethal effects are more prevalent than acute toxicity. Meanwhile, evidence of clear and consistent yield benefits from the use of neonicotinoids remains elusive for most crops. Future decisions on neonicotinoid use will benefit from weighing crop yield benefits versus environmental impacts to non-target organisms and considering whether there are more environmentally benign alternatives.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.7b06388","usgsCitation":"Hladik, M., Main, A., and Goulson, D., 2018, Environmental risks and challenges associated with neonicotinoid insecticides: Environmental Science & Technology, v. 52, no. 6, p. 3329-3335, https://doi.org/10.1021/acs.est.7b06388.","productDescription":"7 p.","startPage":"3329","endPage":"3335","ipdsId":"IP-092573","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":468954,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Environmental_risks_and_challenges_associated_with_neonicotinoid_insecticides/23455121","text":"External Repository"},{"id":352161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0d2","contributors":{"authors":[{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":729914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Main, Anson 0000-0001-9539-760X","orcid":"https://orcid.org/0000-0001-9539-760X","contributorId":202852,"corporation":false,"usgs":false,"family":"Main","given":"Anson","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":729915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goulson, Dave 0000-0003-4421-2876","orcid":"https://orcid.org/0000-0003-4421-2876","contributorId":202853,"corporation":false,"usgs":false,"family":"Goulson","given":"Dave","email":"","affiliations":[{"id":36533,"text":"Univesity of Sussex, Brighton, U.K.","active":true,"usgs":false}],"preferred":false,"id":729916,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195432,"text":"70195432 - 2018 - Porosity of the Marcellus Shale: A contrast matching small-angle neutron scattering study","interactions":[],"lastModifiedDate":"2018-03-30T12:08:44","indexId":"70195432","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Porosity of the Marcellus Shale: A contrast matching small-angle neutron scattering study","docAbstract":"Neutron scattering techniques were used to determine the effect of mineral matter on the accessibility of water and toluene to pores in the Devonian Marcellus Shale. Three Marcellus Shale samples, representing quartz-rich, clay-rich, and carbonate-rich facies, were examined using contrast matching small-angle neutron scattering (CM-SANS) at ambient pressure and temperature. Contrast matching compositions of H2O, D2O and toluene, deuterated toluene were used to probe open and closed pores of these three shale samples. Results show that although the mean pore radius was approximately the same for all three samples, the fractal dimension of the quartz-rich sample was higher than for the clay-rich and carbonate-rich samples, indicating different pore size distributions among the samples. The number density of pores was highest in the clay-rich sample and lowest in the quartz-rich sample. Contrast matching with water and toluene mixtures shows that the accessibility of pores to water and toluene also varied among the samples. In general, water accessed approximately 70–80% of the larger pores (>80 nm radius) in all three samples. At smaller pore sizes (~5–80 nm radius), the fraction of accessible pores decreases. The lowest accessibility to both fluids is at pore throat size of ~25 nm radii with the quartz-rich sample exhibiting lower accessibility than the clay- and carbonate-rich samples. The mechanism for this behaviour is unclear, but because the mineralogy of the three samples varies, it is likely that the inaccessible pores in this size range are associated with organics and not a specific mineral within the samples. At even smaller pore sizes (~<2.5 nm radius), in all samples, the fraction of accessible pores to water increases again to approximately 70–80%. Accessibility to toluene generally follows that of water; however, in the smallest pores (~<2.5 nm radius), accessibility to toluene decreases, especially in the clay-rich sample which contains about 30% more closed pores than the quartz- and carbonate-rich samples. Results from this study show that mineralogy of producing intervals within a shale reservoir can affect accessibility of pores to water and toluene and these mineralogic differences may affect hydrocarbon storage and production and hydraulic fracturing characteristics
The Atlantic Tripletail Lobotes surinamensis is a popular sport fish for which age and growth data are scarce in general and nonexistent for Georgia (GA), USA, waters. These data are necessary to ensure that management regulations are adequate to protect this species, especially given its popularity as a sport fish. We evaluated whether otoliths and spines were suitable for determining the estimated age (hereafter, “age”) and growth rates of Atlantic Tripletails, and we ascertained whether one method was more accurate than the other. Atlantic Tripletails were sampled by angling and trawling during March 30–August 10, 2009, and March 14–August 6, 2010, in nearshore GA waters of the Atlantic Ocean. During the study, 243 Atlantic Tripletails were captured and sampled for aging structures. Sagittal otoliths and the first dorsal spine were removed from each fish and used to estimate the age and growth rate. Mean differences in TL at age for spine and otolith data were evaluated with ANOVA. Estimated ages for males and females ranged from 1 to 5 years based on otoliths and spines. Both otolith and spine mean TLs at ages 1 and 2 were significantly different from each other as well as all other age‐classes, whereas mean TLs for ages 3–5 were not significantly different. Differences in Atlantic Tripletail TL among the otolith‐ and spine‐derived age‐classes were not significant. Each method used to age Atlantic Tripletails had advantages and disadvantages. Otoliths had higher initial reader agreement than spines, although agreement between the structures was 84.1%. However, otoliths require sacrifice of the fish, whereas a spine can be taken without sacrificing the fish. The lack of concrete life history data and population estimates suggests that when feasible, nonlethal aging methods would be preferred over lethal methods to ensure the survival of Atlantic Tripletail populations.
","language":"English","publisher":"Wiley","doi":"10.1002/mcf2.10008","usgsCitation":"Parr, R.T., Bringolf, R.B., and Jennings, C.A., 2018, Efficacy of otoliths and first dorsal spines for preliminary age and growth determination in Atlantic Tripletails: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 10, no. 1, p. 71-79, https://doi.org/10.1002/mcf2.10008.","productDescription":"9 p.","startPage":"71","endPage":"79","ipdsId":"IP-088371","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10008","text":"Publisher Index Page"},{"id":353961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.47598266601562,\n 31.009394042537934\n ],\n [\n -81.30226135253906,\n 31.009394042537934\n ],\n [\n -81.30226135253906,\n 31.15934638141426\n ],\n [\n -81.47598266601562,\n 31.15934638141426\n ],\n [\n -81.47598266601562,\n 31.009394042537934\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc09c","contributors":{"authors":[{"text":"Parr, Russell T.","contributorId":204692,"corporation":false,"usgs":false,"family":"Parr","given":"Russell","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":734711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bringolf, Robert B.","contributorId":139241,"corporation":false,"usgs":true,"family":"Bringolf","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":734712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734652,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195819,"text":"70195819 - 2018 - Species distribution modeling in regions of high need and limited data: waterfowl of China","interactions":[],"lastModifiedDate":"2018-03-05T10:59:14","indexId":"70195819","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5641,"text":"Avian Research","active":true,"publicationSubtype":{"id":10}},"title":"Species distribution modeling in regions of high need and limited data: waterfowl of China","docAbstract":"Background
A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources. Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.
Methods
Faced with limited data, we built species distribution models using a habitat suitability approach for China’s breeding and non-breeding (hereafter, wintering) waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.
Results
We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps. Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China. Wintering waterfowl suitability was highest in the lowland regions of southeastern China. Validation measures indicated strong performance in predicting species presence. Comparing our model outputs to China’s protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.
Conclusions
These suitability models are the first available for many of China’s waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.
Species detection error (i.e., imperfect and variable detection probability) is an essential consideration when investigators map distributions and interpret habitat associations. When fish detection error that is due to highly variable instream environments needs to be addressed, sand‐bed streams of the Great Plains represent a unique challenge. We quantified seining detection probability for diminutive Great Plains fishes across a range of sampling conditions in two sand‐bed rivers in Oklahoma. Imperfect detection resulted in underestimates of species occurrence using naïve estimates, particularly for less common fishes. Seining detection probability also varied among fishes and across sampling conditions. We observed a quadratic relationship between water depth and detection probability, in which the exact nature of the relationship was species‐specific and dependent on water clarity. Similarly, the direction of the relationship between water clarity and detection probability was species‐specific and dependent on differences in water depth. The relationship between water temperature and detection probability was also species dependent, where both the magnitude and direction of the relationship varied among fishes. We showed how ignoring detection error confounded an underlying relationship between species occurrence and water depth. Despite imperfect and heterogeneous detection, our results support that determining species absence can be accomplished with two to six spatially replicated seine hauls per 200‐m reach under average sampling conditions; however, required effort would be higher under certain conditions. Detection probability was low for the Arkansas River Shiner Notropis girardi, which is federally listed as threatened, and more than 10 seine hauls per 200‐m reach would be required to assess presence across sampling conditions. Our model allows scientists to estimate sampling effort to confidently assess species occurrence, which maximizes the use of available resources. Increased implementation of approaches that consider detection error promote ecological advancements and conservation and management decisions that are better informed.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10030","usgsCitation":"Mollenhauer, R., Logue, D.R., and Brewer, S.K., 2018, Quantifying seining detection probability for fishes of Great Plains sand‐bed rivers: Transactions of the American Fisheries Society, v. 147, no. 2, p. 329-341, https://doi.org/10.1002/tafs.10030.","productDescription":"13 p.","startPage":"329","endPage":"341","ipdsId":"IP-090661","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Canadian River, Cimarron River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.008544921875,\n 33.701492795584365\n ],\n [\n -96.361083984375,\n 33.701492795584365\n ],\n [\n -96.361083984375,\n 36.99816565700228\n ],\n [\n -100.008544921875,\n 36.99816565700228\n ],\n [\n -100.008544921875,\n 33.701492795584365\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc098","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":734844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Logue, Daniel R.","contributorId":177014,"corporation":false,"usgs":false,"family":"Logue","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":734845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734819,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196538,"text":"70196538 - 2018 - Ecological drought: Accounting for the non-human impacts of water shortage in the Upper Missouri Headwaters Basin, Montana, USA","interactions":[],"lastModifiedDate":"2018-04-16T16:42:24","indexId":"70196538","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5507,"text":"Resources","printIssn":"2079-9276","active":true,"publicationSubtype":{"id":10}},"title":"Ecological drought: Accounting for the non-human impacts of water shortage in the Upper Missouri Headwaters Basin, Montana, USA","docAbstract":"Water laws and drought plans are used to prioritize and allocate scarce water resources. Both have historically been human-centric, failing to account for non-human water needs. In this paper, we examine the development of instream flow legislation and the evolution of drought planning to highlight the growing concern for the non-human impacts of water scarcity. Utilizing a new framework for ecological drought, we analyzed five watershed-scale drought plans in southwestern Montana, USA to understand if, and how, the ecological impacts of drought are currently being assessed. We found that while these plans do account for some ecological impacts, it is primarily through the narrow lens of impacts to fish as measured by water temperature and streamflow. The latter is typically based on the same ecological principles used to determine instream flow requirements. We also found that other resource plans in the same watersheds (e.g., Watershed Restoration Plans, Bureau of Land Management (BLM) Watershed Assessments or United States Forest Service (USFS) Forest Plans) identify a broader range of ecological drought risks. Given limited resources and the potential for mutual benefits and synergies, we suggest greater integration between various planning processes could result in a more holistic consideration of water needs and uses across the landscape.
","language":"English","publisher":"MDPI","doi":"10.3390/resources7010014","usgsCitation":"McEvoy, J., Bathke, D.J., Burkardt, N., Cravens, A.E., Haigh, T., Hall, K.R., Hayes, M., Jedd, T., Podebradska, M., and Wickham, E., 2018, Ecological drought: Accounting for the non-human impacts of water shortage in the Upper Missouri Headwaters Basin, Montana, USA: Resources, v. 7, no. 1, p. 1-16, https://doi.org/10.3390/resources7010014.","productDescription":"Article 14; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-092941","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/resources7010014","text":"Publisher Index Page"},{"id":353468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Upper Missouri Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.05957031249999,\n 44.4808302785626\n ],\n [\n -110.67626953125,\n 44.4808302785626\n ],\n [\n -110.67626953125,\n 48.99463598353405\n ],\n [\n -116.05957031249999,\n 48.99463598353405\n ],\n [\n -116.05957031249999,\n 44.4808302785626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-20","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0a8","contributors":{"authors":[{"text":"McEvoy, Jamie","contributorId":197223,"corporation":false,"usgs":false,"family":"McEvoy","given":"Jamie","affiliations":[],"preferred":false,"id":733491,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bathke, Deborah J.","contributorId":197224,"corporation":false,"usgs":false,"family":"Bathke","given":"Deborah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":733492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burkardt, Nina 0000-0002-9392-9251 burkardtn@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-9251","contributorId":2781,"corporation":false,"usgs":true,"family":"Burkardt","given":"Nina","email":"burkardtn@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":733499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":733490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haigh, Tonya","contributorId":204248,"corporation":false,"usgs":false,"family":"Haigh","given":"Tonya","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":733493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hall, Kimberly R.","contributorId":197221,"corporation":false,"usgs":false,"family":"Hall","given":"Kimberly","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":733494,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hayes, Michael J.","contributorId":197222,"corporation":false,"usgs":false,"family":"Hayes","given":"Michael J.","affiliations":[{"id":34856,"text":"National Drought Mitigation Center, Unversity of Nebraska","active":true,"usgs":false}],"preferred":false,"id":733495,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jedd, Theresa","contributorId":204249,"corporation":false,"usgs":false,"family":"Jedd","given":"Theresa","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":733496,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Podebradska, Marketa","contributorId":204250,"corporation":false,"usgs":false,"family":"Podebradska","given":"Marketa","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":733497,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wickham, Elliot","contributorId":204251,"corporation":false,"usgs":false,"family":"Wickham","given":"Elliot","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":733498,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70195704,"text":"tm9A0 - 2018 - General introduction for the “National Field Manual for the Collection of Water-Quality Data”","interactions":[],"lastModifiedDate":"2018-06-22T16:22:10","indexId":"tm9A0","displayToPublicDate":"2018-02-28T14:35:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"9-A0","title":"General introduction for the “National Field Manual for the Collection of Water-Quality Data”","docAbstract":"As part of its mission, the U.S. Geological Survey (USGS) collects data to assess the quality of our Nation’s water resources. A high degree of reliability and standardization of these data are paramount to fulfilling this mission. Documentation of nationally accepted methods used by USGS personnel serves to maintain consistency and technical quality in data-collection activities. “The National Field Manual for the Collection of Water-Quality Data” (NFM) provides documented guidelines and protocols for USGS field personnel who collect water-quality data. The NFM provides detailed, comprehensive, and citable procedures for monitoring the quality of surface water and groundwater. Topics in the NFM include (1) methods and protocols for sampling water resources, (2) methods for processing samples for analysis of water quality, (3) methods for measuring field parameters, and (4) specialized procedures, such as sampling water for low levels of mercury and organic wastewater chemicals, measuring biological indicators, and sampling bottom sediment for chemistry. Personnel who collect water-quality data for national USGS programs and projects, including projects supported by USGS cooperative programs, are mandated to use protocols provided in the NFM per USGS Office of Water Quality Technical Memorandum 2002.13. Formal training, for example, as provided in the USGS class, “Field Water-Quality Methods for Groundwater and Surface Water,” and field apprenticeships supplement the guidance provided in the NFM and ensure that the data collected are high quality, accurate, and scientifically defensible.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National Field Manual for the Collection of Water-Quality Data in Book 9: Handbooks for Water-Resources Investigations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A0","usgsCitation":"U.S. Geological Survey, 2018, General introduction for the “National Field Manual for the Collection of Water-Quality Data” (ver. 1.1, June 2018): U.S. Geological Survey Techniques and Methods, book 9, chap. A0, 4 p., https://doi.org/10.3133/tm9A0. [Supersedes USGS Techniques and Methods,\n
General introduction for the “National Field Manual for the Collection of Water-Quality Data” (ver. 1.1) supersedes version 1.0 released February 2018.
","contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
Globally, changing fire regimes due to climate is one of the greatest threats to ecosystems and society. In this paper, we present projections of future fire probability for the southcentral USA using downscaled climate projections and the Physical Chemistry Fire Frequency Model (PC2FM). Future fire probability is projected to both increase and decrease across the study region of Oklahoma, New Mexico, and Texas. Among all end-of-century projections, change in fire probabilities (CFPs) range from − 51 to + 240%. Greatest absolute increases in fire probability are shown for areas within the range of approximately 75 to 160 cm mean annual precipitation (MAP), regardless of climate model. Although fire is likely to become more frequent across the southcentral USA, spatial patterns may remain similar unless significant increases in precipitation occur, whereby more extensive areas with increased fire probability are predicted. Perhaps one of the most important results is illumination of climate changes where fire probability response (+, −) may deviate (i.e., tipping points). Fire regimes of southcentral US ecosystems occur in a geographic transition zone from reactant- to reaction-limited conditions, potentially making them uniquely responsive to different scenarios of temperature and precipitation changes. Identification and description of these conditions may help anticipate fire regime changes that will affect human health, agriculture, species conservation, and nutrient and water cycling.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-018-2156-8","usgsCitation":"Stambaugh, M.C., Guyette, R.P., Stroh, E.D., Struckhoff, M.A., and Whittier, J.B., 2018, Future southcentral US wildfire probability due to climate change: Climate Change, v. 147, no. 3-4, p. 617-631, https://doi.org/10.1007/s10584-018-2156-8.","productDescription":"15 p.","startPage":"617","endPage":"631","ipdsId":"IP-088702","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":352124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"147","issue":"3-4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee714e4b0da30c1bfc0ea","contributors":{"authors":[{"text":"Stambaugh, Michael C.","contributorId":202826,"corporation":false,"usgs":false,"family":"Stambaugh","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":13706,"text":"University of Missouri-Columbia","active":true,"usgs":false}],"preferred":false,"id":729793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guyette, Richard P.","contributorId":176595,"corporation":false,"usgs":false,"family":"Guyette","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":729794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stroh, Esther D. 0000-0003-4291-4647 estroh@usgs.gov","orcid":"https://orcid.org/0000-0003-4291-4647","contributorId":2813,"corporation":false,"usgs":true,"family":"Stroh","given":"Esther","email":"estroh@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":729792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Struckhoff, Matthew A. 0000-0002-4911-9956 mstruckhoff@usgs.gov","orcid":"https://orcid.org/0000-0002-4911-9956","contributorId":2095,"corporation":false,"usgs":true,"family":"Struckhoff","given":"Matthew","email":"mstruckhoff@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":729795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":729801,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196033,"text":"70196033 - 2018 - Overcoming equifinality: Leveraging long time series for stream metabolism estimation","interactions":[],"lastModifiedDate":"2020-09-02T13:05:49.378881","indexId":"70196033","displayToPublicDate":"2018-02-28T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Overcoming equifinality: Leveraging long time series for stream metabolism estimation","docAbstract":"The foundational ecosystem processes of gross primary production (GPP) and ecosystem respiration (ER) cannot be measured directly but can be modeled in aquatic ecosystems from subdaily patterns of oxygen (O2) concentrations. Because rivers and streams constantly exchange O2 with the atmosphere, models must either use empirical estimates of the gas exchange rate coefficient (K600) or solve for all three parameters (GPP, ER, and K600) simultaneously. Empirical measurements of K600 require substantial field work and can still be inaccurate. Three-parameter models have suffered from equifinality, where good fits to O2 data are achieved by many different parameter values, some unrealistic. We developed a new three-parameter, multiday model that ensures similar values for K600 among days with similar physical conditions (e.g., discharge). Our new model overcomes the equifinality problem by (1) flexibly relating K600 to discharge while permitting moderate daily deviations and (2) avoiding the oft-violated assumption that residuals in O2 predictions are uncorrelated. We implemented this hierarchical state-space model and several competitor models in an open-source R package, streamMetabolizer. We then tested the models against both simulated and field data. Our new model reduces error by as much as 70% in daily estimates of K600, GPP, and ER. Further, accuracy benefits of multiday data sets require as few as 3 days of data. This approach facilitates more accurate metabolism estimates for more streams and days, enabling researchers to better quantify carbon fluxes, compare streams by their metabolic regimes, and investigate controls on aquatic activity.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JG004140","usgsCitation":"Appling, A.P., Hall, R., Yackulic, C.B., and Arroita, M., 2018, Overcoming equifinality: Leveraging long time series for stream metabolism estimation: Journal of Geophysical Research: Biogeosciences, v. 123, no. 2, p. 624-645, https://doi.org/10.1002/2017JG004140.","productDescription":"22 p.","startPage":"624","endPage":"645","ipdsId":"IP-089889","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":468966,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004140","text":"Publisher Index Page"},{"id":352520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee714e4b0da30c1bfc0e6","contributors":{"authors":[{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":731078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Robert O. Jr.","contributorId":145459,"corporation":false,"usgs":false,"family":"Hall","given":"Robert O.","suffix":"Jr.","affiliations":[{"id":16121,"text":"Uni. of Wyoming, Department of Zoology and Physiology","active":true,"usgs":false}],"preferred":false,"id":731079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arroita, Maite 0000-0001-8754-7604","orcid":"https://orcid.org/0000-0001-8754-7604","contributorId":203307,"corporation":false,"usgs":false,"family":"Arroita","given":"Maite","email":"","affiliations":[{"id":36597,"text":"Flathead Lake Biological Station, University of Montana; University of the Basque Country","active":true,"usgs":false}],"preferred":false,"id":731081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195565,"text":"ofr20181029 - 2018 - Suspended-sediment transport from the Green-Duwamish River to the Lower Duwamish Waterway, Seattle, Washington, 2013–17","interactions":[],"lastModifiedDate":"2018-03-01T11:06:55","indexId":"ofr20181029","displayToPublicDate":"2018-02-28T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1029","title":"Suspended-sediment transport from the Green-Duwamish River to the Lower Duwamish Waterway, Seattle, Washington, 2013–17","docAbstract":"The Green-Duwamish River transports watershed-derived sediment to the Lower Duwamish Waterway Superfund site near Seattle, Washington. Understanding the amount of sediment transported by the river is essential to the bed sediment cleanup process. Turbidity, discharge, suspended-sediment concentration (SSC), and particle-size data were collected by the U.S. Geological Survey (USGS) from February 2013 to January 2017 at the Duwamish River, Washington, within the tidal influence at river kilometer 16.7 (USGS streamgage 12113390; Duwamish River at Golf Course at Tukwila, WA). This report quantifies the timing and magnitude of suspended-sediment transported in the Duwamish River. Regression models were developed between SSC and turbidity and SSC and discharge to estimate 15- minute SSC. Suspended-sediment loads were calculated from the computed SSC and time-series discharge data for every 15-minute interval during the study period. The 2014–16 average annual suspended-sediment load computed was 117,246 tons (106,364 metric tons), of which 73.5 percent or (86,191 tons; 78,191 metric tons) was fine particle (less than 0.0625 millimeter in diameter) suspended sediment. The seasonality of this site is apparent when you divide the year into \"wet\" (October 16– April 15) and \"dry\" (April 16–October 15) seasons. Most (97 percent) of the annual suspended sediment was transported during the wet season, when brief periods of intense precipitation from storms, large releases from the Howard Hanson Dam, or a combination of both were much more frequent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181029","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Senter, C.A., Conn, K.E., Black, R.W., Peterson, N., Vanderpool-Kimura, A., and Foreman, J.R., 2018, Suspended-sediment transport from the Green-Duwamish River to the Lower Duwamish Waterway, Seattle, Washington, 2013–17: U.S. Geological Survey Open-File Report 2018–1029, 23 p., https://doi.org/10.3133/ofr20181029.","productDescription":"Report: vi, 23 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-092733","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":352133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1029/coverthb.jpg"},{"id":352134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1029/ofr20181029.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1029"},{"id":352135,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71835Q9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for turbidity, discharge, and suspended-sediment concentrations and loads, Duwamish River, Tukwila, Washington"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Green-Duwamish River, Lower Duwanish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.39627838134766,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.458272792347074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Cost effective and safe highway bridge designs are required to ensure the long-term sustainability of Virginia’s road systems. The streamflows that, over time, scour streambed sediments from bridge piers inherently affect bridge safety and design costs. To ensure safety, bridge design must anticipate streambed scour at bridge piers over the lifespan of a bridge. Until recently Federal Highway Administration (FHWA) guidance provided only for scour estimates of granular, noncohesive, highly erosive material yielding overestimates of scour potential in instances when streambed materials offer some resistance to scour. This study seeks to estimate stream power and streambed scour for these more resistive sites, with bridge piers potentially established in cohesive soil or erodible rock. This new knowledge may provide significant construction cost savings while ensuring design and construction of safe highway bridges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183006","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2018, U.S. Geological Survey - Virginia Department of Transportation: Bridge scour pilot study: U.S. Geological Survey Fact Sheet 2018–3006, 2 p., https://doi.org/10.3133/fs20183006. ","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-093051","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":351995,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3006/coverthb.jpg"},{"id":351996,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3006/fs20183006.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3006"}],"contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Digital flood-inundation maps for a 1.9-mile reach of Cedar Creek at Auburn, Indiana (Ind.), from the First Street bridge, downstream to the streamgage at 18th Street, then ending approximately 1,100 feet (ft) downstream of the Baltimore and Ohio railroad, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Cedar Creek at 18th Street at Auburn, Ind. (station number 04179520). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, although forecasts of flood hydrographs are not available at this site (ABBI3).
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Cedar Creek at 18th Street at Auburn, Ind. streamgage and the documented high-water marks from the flood of March 11, 2009. The calibrated hydraulic model was then used to compute seven water-surface profiles for flood stages referenced to the streamgage datum and ranging from 7 ft, or near bankfull, to 13 ft, in 1-foot increments. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage at Cedar Creek at 18th Street at Auburn, Ind., and stream information from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175156","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Fowler, K.K., 2018, Flood-inundation maps for Cedar Creek at 18th Street at Auburn, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5156, 10 p., https://doi.org/10.3133/sir20175156.","productDescription":"Report: iv, 10 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087585","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":349964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5156/coverthb.jpg"},{"id":351891,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5156/sir20175156.pdf","text":"Report","size":"6.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5156"},{"id":351892,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72806GR","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Datasets and Surface-Water Hydraulic Model for Cedar Creek at Auburn, Indiana, Flood-inundation Study "}],"country":"United States","state":"Indiana","city":"Auburn","otherGeospatial":"Cedar Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.0667,\n 41.34582213380196\n ],\n [\n -85.0417,\n 41.34582213380196\n ],\n [\n -85.0417,\n 41.37057703323999\n ],\n [\n -85.0667,\n 41.37057703323999\n ],\n [\n -85.0667,\n 41.34582213380196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996
Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross-validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3764","usgsCitation":"Chambert, T., Pilliod, D.S., Goldberg, C.S., Doi, H., and Takahara, T., 2018, An analytical framework for estimating aquatic species density from environmental DNA: Ecology and Evolution, v. 8, no. 6, p. 3468-3477, https://doi.org/10.1002/ece3.3764.","productDescription":"10 p.","startPage":"3468","endPage":"3477","ipdsId":"IP-079053","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3764","text":"Publisher Index Page"},{"id":352059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-25","publicationStatus":"PW","scienceBaseUri":"5afee716e4b0da30c1bfc106","contributors":{"authors":[{"text":"Chambert, Thierry 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":191979,"corporation":false,"usgs":false,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","affiliations":[],"preferred":false,"id":729620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":729619,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Caren S.","contributorId":76879,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren","email":"","middleInitial":"S.","affiliations":[{"id":5132,"text":"Washington State University, Pullman","active":true,"usgs":false}],"preferred":false,"id":729621,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doi, Hideyuki","contributorId":202789,"corporation":false,"usgs":false,"family":"Doi","given":"Hideyuki","email":"","affiliations":[{"id":36527,"text":"University of Hyogo","active":true,"usgs":false}],"preferred":false,"id":729622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takahara, Teruhiko","contributorId":176873,"corporation":false,"usgs":false,"family":"Takahara","given":"Teruhiko","email":"","affiliations":[],"preferred":false,"id":729623,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195710,"text":"ofr20181028 - 2018 - Effects of the proposed California WaterFix North Delta Diversion on flow reversals and entrainment of juvenile Chinook salmon (Oncorhynchus tshawytscha) into Georgiana Slough and the Delta Cross Channel, northern California","interactions":[],"lastModifiedDate":"2018-02-28T10:43:13","indexId":"ofr20181028","displayToPublicDate":"2018-02-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1028","displayTitle":"Effects of the proposed California WaterFix North Delta Diversion on flow reversals and entrainment of juvenile Chinook salmon (Oncorhynchus tshawytscha) into Georgiana Slough and the Delta Cross Channel, northern California","title":"Effects of the proposed California WaterFix North Delta Diversion on flow reversals and entrainment of juvenile Chinook salmon (Oncorhynchus tshawytscha) into Georgiana Slough and the Delta Cross Channel, northern California","docAbstract":"The California Department of Water Resources and Bureau of Reclamation propose new water intake facilities on the Sacramento River in northern California that would convey some of the water for export to areas south of the Sacramento-San Joaquin River Delta (hereinafter referred to as the Delta) through tunnels rather than through the Delta. The collection of water intakes, tunnels, pumping facilities, associated structures, and proposed operations are collectively referred to as California WaterFix. The water intake facilities, hereinafter referred to as the North Delta Diversion (NDD), are proposed to be located on the Sacramento River downstream of the city of Sacramento and upstream of the first major river junction where Sutter Slough branches from the Sacramento River. The NDD can divert a maximum discharge of 9,000 cubic feet per second (ft3/s) from the Sacramento River, which reduces the amount of Sacramento River inflow into the Delta.
In this report, we conducted three analyses to investigate the effect of the NDD and its proposed operation on entrainment of juvenile Chinook salmon (Oncorhynchus tshawytscha) into Georgiana Slough and the Delta Cross Channel (DCC). Fish that enter the interior Delta (the network of channels to the south of the Sacramento River) through Georgiana Slough and the DCC survive at lower rates than fish that use other migration routes (Sacramento River, Sutter Slough, and Steamboat Slough). Therefore, fisheries managers were concerned about the extent to which operation of the NDD would increase the proportion of the population entering the interior Delta, which, all else being equal, would lower overall survival through the Delta by increasing the fraction of the population subject to lower survival rates. Operation of the NDD would reduce flow in the Sacramento River, which has the potential to increase the magnitude and duration of reverse flows of the Sacramento River downstream of Georgiana Slough.
In the first analysis, we evaluate the effect of the NDD bypass rules on flow reversals of the Sacramento River downstream of Georgiana Slough. The NDD bypass rules are a set of operational criteria designed to minimize upstream transport of fish into Georgiana Slough and the DCC, and were developed based on previous studies showing that the magnitude and duration of flow reversals increase the proportion of fish entering Georgiana Slough and the DCC. We estimated the frequency and duration of reverse-flow conditions of the Sacramento River downstream of Georgiana Slough under each of the prescribed minimum bypass flows described in the NDD bypass rules. To accommodate adaptive levels of protection during different times of year when juvenile salmon are migrating through the Delta, the NDD bypass rules prescribe a series of minimum allowable bypass flows that vary depending on (1) month of the year, and (2) progressively decreasing levels of protection following a pulse flow event.
We determined that the NDD bypass rules increased the frequency and duration of reverse flows of the Sacramento River downstream of Georgiana Slough, with the magnitude of increase varying among scenarios. Constant low-level pumping, the most protective bypass rule that limits diversion to 10 percent of the maximum diversion and is implemented following a pulse-flow event, led to the smallest increase in frequency and duration of flow reversals. In contrast, we found that some scenarios led to sizeable increases in the fraction of the day with reverse flow. The conditions under which the proportion of the day with reverse flow can increase by greater than or equal to 10 percentage points between October and June, when juvenile salmon are present in the Delta, include October–November bypass rules and level-3 post-pulse operations during December–June. These conditions would be expected to increase the proportion of juvenile salmon entering the interior Delta through Georgiana Slough.
In the second analysis, we assessed bias in Delta Simulation Model 2 (DSM2) flow predictions at the junction of the Sacramento River, DCC, and Georgiana Slough. Because DSM2 was being used to simulate California WaterFix operations, understanding the extent of bias relative to USGS streamgages was important since fish routing models were based on flow data at streamgages. We determined that river flow predicted by DSM2 was biased for Georgiana Slough and the Sacramento River. Therefore, for subsequent analysis, we bias-corrected the DSM2 flow predictions using measured stream flows as predictor variables.
In the third analysis, we evaluated the effect of the NDD on the daily probability of fish entering Georgiana Slough and the DCC. We applied an existing model to predict entrainment from 15-minute flow simulations for an 82-year time series of flows simulated by DSM2 under the Proposed Action (PA), where the North Delta Diversion is implemented under California WaterFix, and the No Action Alternative (NAA), where the diversion is not implemented. To estimate the daily fraction of fish entering each river channel, entrainment probabilities were averaged over each day. To evaluate the two scenarios, we then compared mean annual entrainment probabilities by month, water year classification, and three different assumed run timings. Overall, the probability of remaining in the Sacramento River was lower under the PA scenario, but the magnitude of the difference was small (3/s. At flows greater than 41,000 ft3/s, we hypothesize that entrainment into the interior Delta is relatively constant, which would have caused little difference between scenarios at higher flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181028","collaboration":"Prepared in cooperation with National Atmospheric and Oceanic Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., Romine, J.G., Pope, A.C., and Evans, S.D., 2018, Effects of the proposed California WaterFix North\nDelta Diversion on flow reversals and entrainment of juvenile Chinook salmon (Oncorhynchus tshawytscha) into\nGeorgiana Slough and the Delta Cross Channel, northern California: U.S. Geological Survey Open File Report\n2018-1028, 46 p., https://doi.org/10.3133/ofr20181028.","productDescription":"vi, 46 p.","onlineOnly":"Y","ipdsId":"IP-077416","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":352094,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1028/ofr20181028.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1028"},{"id":352093,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1028/coverthb.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 -121.53127670288086,\n 38.22449353550286\n ],\n [\n -121.49771690368652,\n 38.22449353550286\n ],\n [\n -121.49771690368652,\n 38.26466948704442\n ],\n [\n -121.53127670288086,\n 38.26466948704442\n ],\n [\n -121.53127670288086,\n 38.22449353550286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
New detrital zircon U-Pb data from the Farewell terrane of interior Alaska illuminate its early provenance evolution and connections with other Alaskan terranes. Five samples come from Neoproterozoic units in the central Farewell terrane. Basal “ferruginous beds” and the overlying Windy Fork Formation have prominent detrital zircon age populations between 2000 and 1800 Ma, with the Windy Fork Formation also having major age peaks between 700 and 600 Ma. Younger (Lone Formation) samples yield grains mainly between 750 and 550 Ma, with fewer older Proterozoic grains. Eleven samples come from deep-water early Paleozoic rocks (southeastern Farewell terrane). Ordovician sandstone (Post River Formation) has a major age population at ca. 490 Ma and subordinate 785–550 Ma populations that overlap age peaks in the Lone Formation. Turbidites in the overlying Terra Cotta Mountains Sandstone (Silurian) yield distinctly different spectra, with major ca. 450–420 Ma age populations and numerous grains between 2000 and 900 Ma. Devonian Barren Ridge Limestone samples have spectra like those of the Terra Cotta Mountains Sandstone, plus some Early Devonian grains. The Silurian shift in detrital zircon age spectra coincides with a major influx of siliciclastic sediment suggestive of a tectonic (collisional?) event involving the Farewell terrane. Neoproterozoic through Devonian successions in the Arctic Alaska–Chukotka and Alexander terranes show a similar up-section shift in detrital zircon spectra, supporting links between these terranes and the Farewell terrane during the early Paleozoic. Detrital zircon ages from the White Mountains and Livengood terranes, adjacent to the northern Farewell terrane, include major early Paleozoic populations that overlap those seen in partly coeval Farewell strata.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES01470.1","usgsCitation":"Dumoulin, J.A., Jones, J.V., Bradley, D., Till, A.B., Box, S.E., and O’Sullivan, P.B., 2018, Neoproterozoic–early Paleozoic provenance evolution of sedimentary rocks in and adjacent to the Farewell terrane (interior Alaska): Geosphere, v. 14, no. 2, p. 367-394, https://doi.org/10.1130/GES01470.1.","productDescription":"28 p.","startPage":"367","endPage":"394","ipdsId":"IP-093382","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":468972,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01470.1","text":"Publisher Index Page"},{"id":438000,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PV6JKZ","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Detrital Zircon Grains and Graptolite Fossil Data from Selected Rocks from the Western Alaska Range, Livengood area, and Seward Peninsula, Alaska - 2018"},{"id":358811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"14","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5c10aa07e4b034bf6a7e55cf","contributors":{"authors":[{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":749703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Till, Alison B. 0000-0002-6640-6877 atill@usgs.gov","orcid":"https://orcid.org/0000-0002-6640-6877","contributorId":210053,"corporation":false,"usgs":true,"family":"Till","given":"Alison","email":"atill@usgs.gov","middleInitial":"B.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":749706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":749707,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Sullivan, Paul B.","contributorId":193544,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749708,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}