The thousands of hectares of prairie reconstructed each year in the tallgrass prairie biome can provide a valuable resource for evaluation of seed mixes, planting methods, and post-planting management if methods used and resulting characteristics of the prairies are recorded and compiled in a publicly accessible database. The objective of this study was to evaluate the use of such data to understand the outcomes of reconstructions over a 10-year period at two U.S. Fish and Wildlife Service refuges. Variables included number of species planted, seed source (combine-harvest or combine-harvest plus hand-collected), fire history, and planting method and season. In 2015 we surveyed vegetation on 81 reconstructions and calculated proportion of planted species observed; introduced species richness; native species richness, evenness and diversity; and mean coefficient of conservatism. We conducted exploratory analyses to learn how implied communities based on seed mix compared with observed vegetation; which seeding or management variables were influential in the outcome of the reconstructions; and consistency of responses between the two refuges. Insights from this analysis include: 1) proportion of planted species observed in 2015 declined as planted richness increased, but lack of data on seeding rate per species limited conclusions about value of added species; 2) differing responses to seeding and management between the two refuges suggest the importance of geographic variability that could be addressed using a public database; and 3) variables such as fire history are difficult to quantify consistently and should be carefully evaluated in the context of a public data repository.
","language":"English","publisher":"University of Wisconsin Press","doi":"10.3368/er.36.1.6","usgsCitation":"Larson, D.L., Ahlering, M., Drobney, P., Esser, R., Larson, J.L., and Viste-Sparkman, K., 2018, Developing a framework for evaluating tallgrass prairie reconstruction methods and management: Ecological Restoration, v. 36, no. 1, p. 6-18, https://doi.org/10.3368/er.36.1.6.","productDescription":"13 p.","startPage":"6","endPage":"18","ipdsId":"IP-082530","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":352555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0c4","contributors":{"authors":[{"text":"Larson, Diane L. 0000-0001-5202-0634 dlarson@usgs.gov","orcid":"https://orcid.org/0000-0001-5202-0634","contributorId":2120,"corporation":false,"usgs":true,"family":"Larson","given":"Diane","email":"dlarson@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":731143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahlering, Marissa 0000-0002-3913-428X","orcid":"https://orcid.org/0000-0002-3913-428X","contributorId":171943,"corporation":false,"usgs":false,"family":"Ahlering","given":"Marissa","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":731144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drobney, Pauline","contributorId":178447,"corporation":false,"usgs":false,"family":"Drobney","given":"Pauline","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":731146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esser, Rebecca","contributorId":197592,"corporation":false,"usgs":false,"family":"Esser","given":"Rebecca","affiliations":[],"preferred":false,"id":731145,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larson, Jennifer L.","contributorId":178444,"corporation":false,"usgs":false,"family":"Larson","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":731148,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Viste-Sparkman, Karen","contributorId":197593,"corporation":false,"usgs":false,"family":"Viste-Sparkman","given":"Karen","email":"","affiliations":[],"preferred":false,"id":731147,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70193856,"text":"70193856 - 2018 - Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock","interactions":[],"lastModifiedDate":"2018-03-29T15:13:48","indexId":"70193856","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock","docAbstract":"Switchgrass (Panicum virgatum) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine (Pinus taeda) plantations in Mississippi, USA.
We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species.
Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters.
Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival.
Synthesis and applications. Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1 km2), although at a local scale (50 m2) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.
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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\"}}]}","volume":"25","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ee4b0da30c1bfc096","contributors":{"authors":[{"text":"Cherukuri, Aswini","contributorId":205003,"corporation":false,"usgs":false,"family":"Cherukuri","given":"Aswini","email":"","affiliations":[],"preferred":false,"id":735719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strong, Allan","contributorId":205004,"corporation":false,"usgs":false,"family":"Strong","given":"Allan","email":"","affiliations":[],"preferred":false,"id":735720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":735110,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196265,"text":"70196265 - 2018 - Technical note: False low turbidity readings from optical probes during high suspended-sediment concentrations","interactions":[],"lastModifiedDate":"2018-03-29T10:32:21","indexId":"70196265","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Technical note: False low turbidity readings from optical probes during high suspended-sediment concentrations","docAbstract":"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.
Kelp forest communities are highly variable over space and time. Despite this complexity it has been suggested that kelp forest communities can be classified into one of 2 states: kelp dominated or sea urchin dominated. It has been further hypothesized that these represent “alternate stable states” because a site can remain in either of these states for decades before some perturbation causes a rapid shift to the other state. Our research group has maintained a subtidal community monitoring program for 38 years at San Nicolas Island consisting of twice-annual scuba-based surveys at 6 sites distributed within 4 regions around the island. Three types of perturbations are thought to be relevant to subtidal community dynamics at San Nicolas: (1) physical disturbances in the form of major storm and El Niño/Southern Oscillation (ENSO) events; (2) invertebrate diseases, which periodically decimate urchin populations; and (3) the reintroduction and subsequent increase of sea otters (Enhydra lutris nereis). These 3 perturbations differ in spatial and temporal specificity; physical disturbances and disease outbreaks occur periodically and could affect all 4 regions, while sea otter predation has been concentrated primarily at the West End sites over the last 15 years. The different types of perturbations and the duration of the time series at the kelp forests at San Nicolas make the data set ideal for testing the “alternate stable state” hypothesis. We use nonmetric multidimensional scaling (NMDS) to examine spatial and temporal patterns of community similarity at the 4 regions. In particular, we evaluate support for the existence of stable states, which are represented on NMDS plots as distinct spatial clusters. Community dynamics at each site approximated a biased random walk in NMDS space, with one or more basins of attraction and occasional jumps between basins. We found evidence for alternative stable states at some sites, and we show that transitions from one stable state to another may be influenced by interactions between multiple perturbations.
","language":"English","publisher":"Western North American Naturalist Publications","doi":"10.3398/064.078.0407","usgsCitation":"Kenner, M.C., and Tinker, M.T., 2018, Stability and change in kelp forest habitats at San Nicolas Island: Western North American Naturalist, v. 78, no. 4, p. 633-643, https://doi.org/10.3398/064.078.0407.","productDescription":"11 p.","startPage":"633","endPage":"643","ipdsId":"IP-086463","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488846,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsarchive.byu.edu/wnan/vol78/iss4/14","text":"External Repository"},{"id":352844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58824157714842,\n 33.20881849225547\n ],\n [\n -119.42893981933592,\n 33.20881849225547\n ],\n [\n -119.42893981933592,\n 33.289785856885224\n ],\n [\n -119.58824157714842,\n 33.289785856885224\n ],\n [\n -119.58824157714842,\n 33.20881849225547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee710e4b0da30c1bfc0b6","contributors":{"authors":[{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":203543,"corporation":false,"usgs":false,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":731747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":731746,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196830,"text":"70196830 - 2018 - Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients","interactions":[],"lastModifiedDate":"2018-05-04T11:41:55","indexId":"70196830","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":"Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients","docAbstract":"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. 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\"}}]}","volume":"63","issue":"S1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-14","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc09e","contributors":{"authors":[{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":734713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliver, Samantha 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":70196215,"text":"70196215 - 2018 - Bottom trawl assessment of Lake Ontario prey fishes","interactions":[],"lastModifiedDate":"2023-05-09T14:18:38.897107","indexId":"70196215","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5114,"text":"NYSDEC Lake Ontario Annual Report ","active":true,"publicationSubtype":{"id":2}},"chapter":"12","title":"Bottom trawl assessment of Lake Ontario prey fishes","docAbstract":"Managing Lake Ontario fisheries in an ecosystem-context requires prey fish community and population data. 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.
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","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Modeling methods in Book 14: Landslide and debris-flow assessment","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm14A2","usgsCitation":"Baum, R.L., Fischer, S.J., and Vigil, J.C., 2018, THRESH—Software for tracking rainfall thresholds for landslide and debris-flow occurrence, user manual: U.S. Geological Survey Techniques and Methods, book 14, chap. A2, 33 p., https://doi.org/10.3133/tm14A2.","productDescription":"Report: v, 33 p.; Software release","onlineOnly":"Y","ipdsId":"IP-087132","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437999,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23XR0","text":"USGS data release","linkHelpText":"Thresh - Software for Tracking Rainfall Thresholds for Landslide and Debris Flow Occurrence, Code Repository"},{"id":352130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/14/a2/coverthb.jpg"},{"id":352140,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F7Q23XR0","text":"Software Release","linkHelpText":"THRESH"},{"id":352131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/14/a2/tm14a2.pdf","text":"Report","size":"7.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 14-A2"}],"publicComments":"This report is Chapter 2 of Section A: Modeling methods in Book 14: Landslide and debris-flow assessment.","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225-0046
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
Agencies are increasingly called upon to implement their natural resource management programs within an adaptive management (AM) framework. This article provides the background and motivation for the R package, AMModels. AMModels was developed under R version 3.2.2. The overall goal of AMModels is simple: To codify knowledge in the form of models and to store it, along with models generated from numerous analyses and datasets that may come our way, so that it can be used or recalled in the future. AMModels facilitates this process by storing all models and datasets in a single object that can be saved to an .RData file and routinely augmented to track changes in knowledge through time. Through this process, AMModels allows the capture, development, sharing, and use of knowledge that may help organizations achieve their mission. While AMModels was designed to facilitate adaptive management, its utility is far more general. Many R packages exist for creating and summarizing models, but to our knowledge, AMModels is the only package dedicated not to the mechanics of analysis but to organizing analysis inputs, analysis outputs, and preserving descriptive metadata. We anticipate that this package will assist users hoping to preserve the key elements of an analysis so they may be more confidently revisited at a later date.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0188966","usgsCitation":"Donovan, T.M., and Katz, J., 2018, AMModels: An R package for storing models, data, and metadata to facilitate adaptive management: PLoS ONE, v. 13, no. 2, p. 1-57, https://doi.org/10.1371/journal.pone.0188966.","productDescription":"e0188966; 57","startPage":"1","endPage":"57","ipdsId":"IP-081371","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461013,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0188966","text":"Publisher Index Page"},{"id":353899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee714e4b0da30c1bfc0e4","contributors":{"authors":[{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Jonathan","contributorId":8370,"corporation":false,"usgs":true,"family":"Katz","given":"Jonathan","affiliations":[],"preferred":false,"id":734478,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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
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
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
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":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"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":70195674,"text":"70195674 - 2018 - Surrounding land cover types as predictors of palustrine wetland vegetation quality in conterminous USA","interactions":[],"lastModifiedDate":"2018-02-27T09:51:12","indexId":"70195674","displayToPublicDate":"2018-02-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Surrounding land cover types as predictors of palustrine wetland vegetation quality in conterminous USA","docAbstract":"The loss of wetland habitats and their often-unique biological communities is a major environmental concern. We examined vegetation data obtained from 380 wetlands sampled in a statistical survey of wetlands in the USA. Our goal was to identify which surrounding land cover types best predict two indices of vegetation quality in wetlands at the regional scale. We considered palustrine wetlands in four regions (Coastal Plains, North Central East, Interior Plains, and West) in which the dominant vegetation was emergent, forested, or scrub-shrub. For each wetland, we calculated weighted proportions of eight land cover types surrounding the area in which vegetation was assessed, in four zones radiating from the edge of the assessment area to 2 km. Using Akaike's Information Criterion, we determined the best 1-, 2- and 3-predictor models of the two indices, using the weighted proportions of the land cover types as potential predictors. Mean values of the two indices were generally higher in the North Central East and Coastal Plains than the other regions for forested and emergent wetlands. In nearly all cases, the best predictors of the indices were not the dominant surrounding land cover types. Overall, proportions of forest (positive effect) and agriculture (negative effect) surrounding the assessment area were the best predictors of the two indices. One or both of these variables were included as predictors in 65 of the 72 models supported by the data. Wetlands surrounding the assessment area had a positive effect on the indices, and ranked third (33%) among the predictors included in supported models. Development had a negative effect on the indices and was included in only 28% of supported models. These results can be used to develop regional management plans for wetlands, such as creating forest buffers around wetlands, or to conserve zones between wetlands to increase habitat connectivity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.11.107","usgsCitation":"Stapanian, M.A., Gara, B., and Schumacher, W., 2018, Surrounding land cover types as predictors of palustrine wetland vegetation quality in conterminous USA: Science of the Total Environment, v. 619-620, p. 366-375, https://doi.org/10.1016/j.scitotenv.2017.11.107.","productDescription":"10 p.","startPage":"366","endPage":"375","ipdsId":"IP-088234","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":352053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n\n\n","volume":"619-620","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee715e4b0da30c1bfc102","contributors":{"authors":[{"text":"Stapanian, Martin A. 0000-0001-8173-4273 mstapanian@usgs.gov","orcid":"https://orcid.org/0000-0001-8173-4273","contributorId":3425,"corporation":false,"usgs":true,"family":"Stapanian","given":"Martin","email":"mstapanian@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":729637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gara, Brian","contributorId":52061,"corporation":false,"usgs":true,"family":"Gara","given":"Brian","affiliations":[],"preferred":false,"id":729638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schumacher, William","contributorId":150060,"corporation":false,"usgs":false,"family":"Schumacher","given":"William","email":"","affiliations":[{"id":17898,"text":"Ohio Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":729639,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195320,"text":"ofr20181019 - 2018 - Automated remote cameras for monitoring alluvial sandbars on the Colorado River in Grand Canyon, Arizona","interactions":[],"lastModifiedDate":"2018-02-28T10:23:34","indexId":"ofr20181019","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":"2018-1019","title":"Automated remote cameras for monitoring alluvial sandbars on the Colorado River in Grand Canyon, Arizona","docAbstract":"Automated camera systems deployed at 43 remote locations along the Colorado River corridor in Grand Canyon National Park, Arizona, are used to document sandbar erosion and deposition that are associated with the operations of Glen Canyon Dam. The camera systems, which can operate independently for a year or more, consist of a digital camera triggered by a separate data controller, both of which are powered by an external battery and solar panel. Analysis of images for categorical changes in sandbar size show deposition at 50 percent or more of monitoring sites during controlled flood releases done in 2012, 2013, 2014, and 2016. The images also depict erosion of sandbars and show that erosion rates were highest in the first 3 months following each controlled flood. Erosion rates were highest in 2015, the year of highest annual dam release volume. Comparison of the categorical estimates of sandbar change agree with sandbar change (erosion or deposition) measured by topographic surveys in 76 percent of cases evaluated. A semiautomated method for quantifying changes in sandbar area from the remote-camera images by rectifying the oblique images and segmenting the sandbar from the rest of the image is presented. Calculation of sandbar area by this method agrees with sandbar area determined by topographic survey within approximately 8 percent and allows quantification of sandbar area monthly (or more frequently).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181019","collaboration":"Prepared in cooperation with Northern Arizona University","usgsCitation":"Grams, P.E., Tusso, R.B., and Buscombe, D., 2018, Automated remote cameras for monitoring alluvial sandbars on the Colorado River in Grand Canyon, Arizona: U.S. Geological Survey Open-File Report 2018–1019, 50 p., https://doi.org/10.3133/ofr20181019.","productDescription":"xi, 50 p.","numberOfPages":"61","onlineOnly":"Y","ipdsId":"IP-092323","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":352079,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1019/ofr20181019.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1019"},{"id":352078,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1019/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.42559814453125,\n 35.733136223133926\n ],\n [\n -111.52221679687499,\n 35.733136223133926\n ],\n [\n -111.52221679687499,\n 36.88401445049676\n ],\n [\n -113.42559814453125,\n 36.88401445049676\n ],\n [\n -113.42559814453125,\n 35.733136223133926\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
We have produced a uniformly processed database of orientation-independent (RotD50, RotD100) ground motion intensity measurements containing peak horizontal ground motions (accelerations and velocities) and 5-percent-damped pseudospectral accelerations (0.1–10 s) from more than 3,800 M ≥ 3 earthquakes in Oklahoma and Kansas that occurred between January 2009 and December 2016. Ground motion time series were collected from regional, national, and temporary seismic arrays out to 500 km. We relocated the majority of the earthquake hypocenters using a multiple-event relocation algorithm to produce a set of near-uniformly processed hypocentral locations. Ground motion processing followed standard methods, with the primary objective of reducing the effects of noise on the measurements. Regional wave-propagation features and the high seismicity rate required careful selection of signal windows to ensure that we captured the entire ground motion record and that contaminating signals from extraneous earthquakes did not contribute to the database. Processing was carried out with an automated scheme and resulted in a database comprising more than 174,000 records (https://dx.doi.org/10.5066/F73B5X8N). We anticipate that these results will be useful for improved understanding of earthquake ground motions and for seismic hazard applications.
","language":"English","publisher":"EERI","doi":"10.1193/101916EQS175DP","usgsCitation":"Rennolet, S.B., Moschetti, M.P., Thompson, E.M., and Yeck, W.L., 2018, A flatfile of ground motion intensity measurements from induced earthquakes in Oklahoma and Kansas: Earthquake Spectra, v. 34, no. 1, p. 1-20, https://doi.org/10.1193/101916EQS175DP.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-090009","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":352062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Oklahoma","volume":"34","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-01","publicationStatus":"PW","scienceBaseUri":"5afee715e4b0da30c1bfc100","contributors":{"authors":[{"text":"Rennolet, Steven B.","contributorId":197099,"corporation":false,"usgs":false,"family":"Rennolet","given":"Steven","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":729685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":729684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":146592,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":729686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":729687,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195640,"text":"70195640 - 2018 - Assessment of distribution and abundance estimates for Mariana swiftlets (Aerodramus bartschi) via examination of survey methods","interactions":[],"lastModifiedDate":"2018-04-27T16:41:14","indexId":"70195640","displayToPublicDate":"2018-02-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3784,"text":"Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Assessment of distribution and abundance estimates for Mariana swiftlets (Aerodramus bartschi) via examination of survey methods","title":"Assessment of distribution and abundance estimates for Mariana swiftlets (Aerodramus bartschi) via examination of survey methods","docAbstract":"We described past and present distribution and abundance data to evaluate the status of the endangered Mariana Swiftlet (Aerodramus bartschi), a little-known echolocating cave swiftlet that currently inhabits 3 of 5 formerly occupied islands in the Mariana archipelago. We then evaluated the survey methods used to attain these estimates via fieldwork carried out on an introduced population of Mariana Swiftlets on the island of O'ahu, Hawaiian Islands, to derive better methods for future surveys. We estimate the range-wide population of Mariana Swiftlets to be 5,704 individuals occurring in 15 caves on Saipan, Aguiguan, and Guam in the Marianas; and 142 individuals occupying one tunnel on O'ahu. We further confirm that swiftlets have been extirpated from Rota and Tinian and have declined on Aguiguan. Swiftlets have remained relatively stable on Guam and Saipan in recent years. Our assessment of survey methods used for Mariana Swiftlets suggests overestimates depending on the technique used. We suggest the use of night vision technology and other changes to more accurately reflect their distribution, abundance, and status.
","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/16-106.1","usgsCitation":"Johnson, N.C., Haig, S.M., and Mosher, S.M., 2018, Assessment of distribution and abundance estimates for Mariana swiftlets (Aerodramus bartschi) via examination of survey methods: Wilson Journal of Ornithology, v. 130, no. 1, p. 23-29, https://doi.org/10.1676/16-106.1.","productDescription":"7 p.","startPage":"23","endPage":"29","ipdsId":"IP-080248","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":438001,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79P2ZTD","text":"USGS data release","linkHelpText":"Mariana swiftlet (Aerodramus bartschi) survey data from O'ahu, Hawai'i, 2005-2006"},{"id":352001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":352000,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wjoonline.org/doi/abs/10.1676/16-106.1"}],"volume":"130","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee716e4b0da30c1bfc10e","contributors":{"authors":[{"text":"Johnson, Nathan C. ncjohnson@usgs.gov","contributorId":196296,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"ncjohnson@usgs.gov","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":false,"id":729526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":729525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mosher, Stephen M.","contributorId":202753,"corporation":false,"usgs":false,"family":"Mosher","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":36522,"text":"U.S. Navy","active":true,"usgs":false}],"preferred":false,"id":729527,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222584,"text":"70222584 - 2018 - Rare long-distance dispersal of the Island Night Lizard, Xantusia riversiana, maintains high diversity in a fragmented environment","interactions":[],"lastModifiedDate":"2021-08-05T21:06:33.373807","indexId":"70222584","displayToPublicDate":"2018-02-24T15:59:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rare long-distance dispersal of the Island Night Lizard, Xantusia riversiana, maintains high diversity in a fragmented environment","title":"Rare long-distance dispersal of the Island Night Lizard, Xantusia riversiana, maintains high diversity in a fragmented environment","docAbstract":"The Island Night Lizard (Xantusia riversiana) is endemic to three of the Channel Islands off the coast of California, USA. Introduced species such as goats, sheep, and cats have profoundly affected the fauna and flora of the islands for over 150 years, but most of these non-native species have been recently removed. We measured the distribution of genetic diversity in Island Night Lizards across San Nicolas Island using DNA microsatellites to assess the impacts of historical habitat change on effective population size, gene flow, and population divergence; to provide baseline data for future monitoring of genetic diversity; and to provide recommendations to inform the restoration of degraded habitat. Despite a history of profound anthropogenic habitat disturbance, genetic diversity was high within sites, and there was no evidence of population bottlenecks. Divergence between sites was extraordinarily high, as expected for this sedentary species. Landscape resistance modeling using circuit theory showed that unsuitable habitat is relatively permeable to gene flow compared to suitable habitat, and yet populations separated by very short geographic distances remain genetically distinct. We found no evidence of a need for short-term intervention such as artificial translocations to maintain genetic diversity. Instead, we suggest that management should focus on maintaining, improving, and increasing habitat, especially in creating patches of habitat to link existing sites.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-018-1055-x","usgsCitation":"O’Donnell, R.P., Drost, C.A., Fellers, G.M., Crabb, B.A., and Mock, K., 2018, Rare long-distance dispersal of the Island Night Lizard, Xantusia riversiana, maintains high diversity in a fragmented environment: Conservation Genetics, v. 19, p. 803-814, https://doi.org/10.1007/s10592-018-1055-x.","productDescription":"12 p.","startPage":"803","endPage":"814","ipdsId":"IP-081802","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.42962646484374,\n 33.23007250637392\n ],\n [\n -119.46086883544922,\n 33.258211766248415\n ],\n [\n -119.50000762939452,\n 33.27285208252106\n ],\n [\n -119.52953338623045,\n 33.28691595686207\n ],\n [\n -119.5528793334961,\n 33.28146288679663\n ],\n [\n -119.56523895263673,\n 33.27514838003839\n ],\n [\n -119.58103179931642,\n 33.28117587367123\n ],\n [\n -119.57210540771484,\n 33.24902443255544\n ],\n [\n -119.54635620117188,\n 33.23122122490653\n ],\n [\n -119.50035095214844,\n 33.216861158847486\n ],\n [\n -119.47048187255858,\n 33.21226543987183\n ],\n [\n -119.44061279296875,\n 33.215712251730736\n ],\n [\n -119.42962646484374,\n 33.23007250637392\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2018-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Donnell, Ryan P. 0000-0002-8710-7956 rodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-8710-7956","contributorId":4657,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Ryan","email":"rodonnell@usgs.gov","middleInitial":"P.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":820644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":820645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fellers, Gary M. 0000-0003-4092-0285 gary_fellers@usgs.gov","orcid":"https://orcid.org/0000-0003-4092-0285","contributorId":3150,"corporation":false,"usgs":true,"family":"Fellers","given":"Gary","email":"gary_fellers@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":820646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crabb, Benjamin A.","contributorId":261781,"corporation":false,"usgs":false,"family":"Crabb","given":"Benjamin","email":"","middleInitial":"A.","affiliations":[{"id":53015,"text":"Remote Sensing/Geographic Information Systems Laboratory, College of Natural Resources, 5275 Old Main Hill, Utah State University, Logan, UT 84322-5275","active":true,"usgs":false}],"preferred":false,"id":820647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mock, Karen E.","contributorId":261782,"corporation":false,"usgs":false,"family":"Mock","given":"Karen E.","affiliations":[{"id":53016,"text":"Wildland Resources Department, 5230 Old Main Hill, Utah State University, Logan, UT 84322-5230","active":true,"usgs":false}],"preferred":false,"id":820648,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208286,"text":"70208286 - 2018 - AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data","interactions":[],"lastModifiedDate":"2020-02-03T09:55:23","indexId":"70208286","displayToPublicDate":"2018-02-23T09:47:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5923,"text":"SoftwareX","active":true,"publicationSubtype":{"id":10}},"title":"AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data","docAbstract":"In this work we describe the AutoCNet library, written in Python, to support the application of Computer Vision techniques for n-image correspondence identication in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.","language":"English","publisher":"Elsevier","doi":"10.1016/j.softx.2018.02.001","usgsCitation":"Laura, J.R., Rodriguez, K., Paquette, A., and Dunn, E., 2018, AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data: SoftwareX, v. 7, p. 37-40, https://doi.org/10.1016/j.softx.2018.02.001.","productDescription":"4 p.","startPage":"37","endPage":"40","ipdsId":"IP-091361","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":488879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.softx.2018.02.001","text":"Publisher Index Page"},{"id":371913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason R. 0000-0002-1377-8159 jlaura@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":5603,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","email":"jlaura@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodriguez, Kelvin 0000-0001-7972-0235","orcid":"https://orcid.org/0000-0001-7972-0235","contributorId":222121,"corporation":false,"usgs":true,"family":"Rodriguez","given":"Kelvin","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paquette, Adam 0000-0001-5666-1105","orcid":"https://orcid.org/0000-0001-5666-1105","contributorId":222122,"corporation":false,"usgs":true,"family":"Paquette","given":"Adam","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunn, Evin 0000-0001-9379-7781","orcid":"https://orcid.org/0000-0001-9379-7781","contributorId":222123,"corporation":false,"usgs":true,"family":"Dunn","given":"Evin","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781264,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187785,"text":"sir20175051 - 2018 - Status and understanding of groundwater quality in the North San Francisco Bay Shallow Aquifer study unit, 2012; California GAMA Priority Basin Project (ver. 1.1, February 2018)","interactions":[],"lastModifiedDate":"2018-02-26T10:42:16","indexId":"sir20175051","displayToPublicDate":"2018-02-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5051","title":"Status and understanding of groundwater quality in the North San Francisco Bay Shallow Aquifer study unit, 2012; California GAMA Priority Basin Project (ver. 1.1, February 2018)","docAbstract":"Groundwater quality in the North San Francisco Bay Shallow Aquifer study unit (NSF-SA) was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in Marin, Mendocino, Napa, Solano, and Sonoma Counties and included two physiographic study areas: the Valleys and Plains area and the surrounding Highlands area. The NSF-SA focused on groundwater resources used for domestic drinking water supply, which generally correspond to shallower parts of aquifer systems than that of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included three components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply for 2012; (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources; and (3) a comparison between the groundwater resources used for domestic supply and those used for public supply.
The status assessment was based on data collected from 71 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2012. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that has concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas.
In the NSF-SA study unit as a whole, inorganic constituents with human-health benchmarks were detected at high relative concentrations (RCs) in 27 percent of the shallow aquifer system, and inorganic constituents with secondary maximum contaminant levels (SMCL) were detected at high RCs in 24 percent of the system. The inorganic constituents detected at high RCs were arsenic, boron, fluoride, manganese, nitrate, iron, sulfate, and total dissolved solids (TDS). Organic constituents with human-health benchmarks were detected at high RCs in 1 percent of the shallow aquifer system. Of the 148 organic constituents analyzed, 30 constituents were detected, although only 1, chloroform, had a detection frequency greater than 10 percent.
Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors. Groundwater age class (modern, mixed, or pre-modern), redox class (oxic or anoxic), aquifer lithology class (metamorphic, sedimentary, or volcanic), and dissolved oxygen concentrations were the explanatory factors that explained distribution patterns of most of the inorganic constituents best. Groundwater classified primarily as pre-modern or mixed in age was associated with higher concentrations of arsenic and fluoride than waters classified as modern. Anoxic or mixed redox conditions were associated with higher concentrations of boron, fluoride, and manganese. Similar patterns of association with explanatory variables were seen for inorganic constituents with aesthetic-based benchmarks detected at high concentrations. Nitrate and perchlorate had higher concentrations in oxic than in the anoxic redox class and were positively correlated with urban land use.
The NSF-SA water-quality results were compared to those of the GAMA North San Francisco Bay Public-Supply Aquifer study unit (NSF-PA). The NSF-PA was sampled in 2004 and covers much of the same area as the NSF-SA, but focused on the deeper public-supply aquifer system. The comparison of the NSF-PA to the NSF-SA showed that there were more differences between the Valleys and Plains study areas of the two study units than between the Highlands study areas of the two study units. As expected from the shallower depth of wells, the NSF-SA Valleys and Plains study area had a lesser proportion of pre-modern age groundwater and greater proportion of modern age groundwater than the NSF-PA Valleys and Plains study area. In contrast, well depths and groundwater ages were not significantly different between the two Highlands study areas. Arsenic, manganese, and nitrate were present at high RCs, and perchlorate was detected in greater proportions of the NSF-SA Valleys and Plains study area than the NSF-PA Valleys and Plains study area.
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2018","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Researchers from the U.S. Geological Survey (USGS) conducted a long-term coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April 2016, scientists from the USGS St. Petersburg Coastal and Marine Science Center conducted geophysical and sediment sampling surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets.
This report, along with the associated USGS data release, serves as an archive of ground penetrating radar (GPR) and post-processed differential global positioning system (DGPS) data collected from beach and back-barrier environments on Fire Island, April 6–13, 2016 (USGS Field Activity Number 2016-322-FA). Data products, including unprocessed GPR trace data, processed DGPS data, elevation-corrected subsurface profile images, geographic information system files, and accompanying Federal Geographic Data Committee metadata are available for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1078","usgsCitation":"Forde, A.S., Bernier, J.C., and Miselis, J.L., 2018, Ground penetrating radar and differential global positioning system data collected in April 2016 from Fire Island, New York: U.S. Geological Survey Data Series 1078, https://doi.org/10.3133/ds1078.","productDescription":"Report: HTML; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092111","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":351847,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P84B1P","text":"USGS data release","description":"USGS data release","linkHelpText":"Archive of Ground Penetrating Radar and Differential Global Positioning System Data Collected in April 2016 from Fire Island, New York\""},{"id":438004,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97YW2UL","text":"USGS data release","linkHelpText":"Ground Penetrating Radar and Global Positioning System Data Collected in 2021 From Fire Island, New York"},{"id":351845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1078/coverthb.jpg"},{"id":351846,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1078/index.html","text":"Report HTML","description":"DS 1078"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.32962036132812,\n 40.59414233212419\n ],\n [\n -72.88742065429688,\n 40.59414233212419\n ],\n [\n -72.88742065429688,\n 40.737892702684064\n ],\n [\n -73.32962036132812,\n 40.737892702684064\n ],\n [\n -73.32962036132812,\n 40.59414233212419\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Rationale
Water stable isotope ratios (δ2H and δ18O values) are widely used tracers in environmental studies; hence, accurate and precise assays are required for providing sound scientific information. We tested the analytical performance of 235 international laboratories conducting water isotope analyses using dual-inlet and continuous-flow isotope ratio mass spectrometers and laser spectrometers through a water isotope inter-comparison test.
Methods
Eight test water samples were distributed by the IAEA to international stable isotope laboratories. These consisted of a core set of five samples spanning the common δ-range of natural waters, and three optional samples (highly depleted, enriched, and saline). The fifth core sample contained unrevealed trace methanol to assess analyst vigilance to the impact of organic contamination on water isotopic measurements made by all instrument technologies.
Results
For the core and optional samples ~73 % of laboratories gave acceptable results within 0.2 ‰ and 1.5 ‰ of the reference values for δ18O and δ2H, respectively; ~27 % produced unacceptable results. Top performance for δ18O values was dominated by dual-inlet IRMS laboratories; top performance for δ2H values was led by laser spectrometer laboratories. Continuous-flow instruments yielded comparatively intermediate results. Trace methanol contamination of water resulted in extreme outlier δ-values for laser instruments, but also affected reactor-based continuous-flow IRMS systems; however, dual-inlet IRMS δ-values were unaffected.
Conclusions
Analysis of the laboratory results and their metadata suggested inaccurate or imprecise performance stemmed mainly from skill- and knowledge-based errors including: calculation mistakes, inappropriate or compromised laboratory calibration standards, poorly performing instrumentation, lack of vigilance to contamination, or inattention to unreasonable isotopic outcomes. To counteract common errors, we recommend that laboratories include 1–2 'known' control standards in all autoruns; laser laboratories should screen each autorun for spectral contamination; and all laboratories should evaluate whether derived d-excess values are realistic when both isotope ratios are measured. Combined, these data evaluation strategies should immediately inform the laboratory about fundamental mistakes or compromised samples.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.8052","usgsCitation":"Wassenaar, L.I., Terzer-Wassmuth, S., Douence, C., Araguas-Araguas, L., Aggarwal, P.K., and Coplen, T.B., 2018, Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry: Rapid Communications in Mass Spectrometry, v. 32, no. 5, p. 393-406, https://doi.org/10.1002/rcm.8052.","productDescription":"14 p.","startPage":"393","endPage":"406","ipdsId":"IP-094049","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":488646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rcm.8052","text":"Publisher Index Page"},{"id":351880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-08","publicationStatus":"PW","scienceBaseUri":"5afee725e4b0da30c1bfc13a","contributors":{"authors":[{"text":"Wassenaar, Leonard I.","contributorId":202666,"corporation":false,"usgs":false,"family":"Wassenaar","given":"Leonard","middleInitial":"I.","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terzer-Wassmuth, S.","contributorId":202667,"corporation":false,"usgs":false,"family":"Terzer-Wassmuth","given":"S.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douence, C.","contributorId":202668,"corporation":false,"usgs":false,"family":"Douence","given":"C.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Araguas-Araguas, L.","contributorId":202669,"corporation":false,"usgs":false,"family":"Araguas-Araguas","given":"L.","email":"","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aggarwal, P. K.","contributorId":202670,"corporation":false,"usgs":false,"family":"Aggarwal","given":"P.","email":"","middleInitial":"K.","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. A-1400, Austria","active":true,"usgs":false}],"preferred":false,"id":729278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":729273,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195550,"text":"70195550 - 2018 - A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length","interactions":[],"lastModifiedDate":"2018-02-23T10:51:59","indexId":"70195550","displayToPublicDate":"2018-02-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length","docAbstract":"Floodplains are among the world’s economically-most-valuable, environmentally-most-threatened, and yet conceptually-least-understood ecosystems. Drawing on concepts from existing riverine and wetland models, and empirical data from floodplains of Atlantic Coast rivers in the Southeastern US (and elsewhere when possible), we introduce a conceptual model to explain a continuum of longitudinal variation in floodplain ecosystem functions with a particular focus on biotic change. Our hypothesis maintains that major controls on floodplain ecology are either external (ecotonal interactions with uplands or stream/river channels) or internal (wetland-specific functions), and the relative importance of these controls changes progressively from headwater to mid-river to lower-river floodplains. Inputs of water, sediments, nutrients, flora, and fauna from uplands-to-floodplains decrease, while the impacts of wetland biogeochemistry and obligate wetland plants and animals within-floodplains increase, along the length of a river floodplain. Inputs of water, sediment, nutrients, and fauna from river/stream channels to floodplains are greatest mid-river, and lower either up- or down-stream. While the floodplain continuum we develop is regional in scope, we review how aspects may apply more broadly. Management of coupled floodplain-river ecosystems would be improved by accounting for how factors controlling the floodplain ecosystem progressively change along longitudinal riverine gradients.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-017-0983-4","usgsCitation":"Batzer, D.P., Noe, G.E., Lee, L., and Galatowitsch, M., 2018, A floodplain continuum for Atlantic coast rivers of the Southeastern US: Predictable changes in floodplain biota along a river's length: Wetlands, v. 38, no. 1, p. 1-13, https://doi.org/10.1007/s13157-017-0983-4.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-091974","costCenters":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"links":[{"id":351873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"38","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-04","publicationStatus":"PW","scienceBaseUri":"5afee726e4b0da30c1bfc13c","contributors":{"authors":[{"text":"Batzer, Darold P.","contributorId":202656,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold","email":"","middleInitial":"P.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":729238,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":729237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Linda","contributorId":202657,"corporation":false,"usgs":false,"family":"Lee","given":"Linda","affiliations":[{"id":36513,"text":"University of Georgia Savannah River Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":729239,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galatowitsch, Mark","contributorId":202658,"corporation":false,"usgs":false,"family":"Galatowitsch","given":"Mark","email":"","affiliations":[{"id":36514,"text":"Centre College","active":true,"usgs":false}],"preferred":false,"id":729240,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}