{"pageNumber":"313","pageRowStart":"7800","pageSize":"25","recordCount":46706,"records":[{"id":70200672,"text":"sir20185135 - 2018 - The Connecticut Streamflow and Sustainable Water Use Estimator—A decision-support tool to estimate water availability at ungaged stream locations in Connecticut","interactions":[],"lastModifiedDate":"2018-12-14T11:12:37","indexId":"sir20185135","displayToPublicDate":"2018-12-13T15:00:00","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":"2018-5135","displayTitle":"The Connecticut Streamflow and Sustainable Water Use Estimator: A Decision-Support Tool To Estimate Water Availability at Ungaged Stream Locations in Connecticut","title":"The Connecticut Streamflow and Sustainable Water Use Estimator—A decision-support tool to estimate water availability at ungaged stream locations in Connecticut","docAbstract":"

Freshwater streams in Connecticut are subject to many competing demands, including public water supply; agricultural, commercial, and industrial water use; and ecosystem and habitat needs. In recent years, drought has further stressed Connecticut’s water resources. To sustainably allocate and manage water resources among these competing uses, Federal, State, and local water-resource managers require data and modeling tools to estimate the water availability at a variety of temporal and spatial scales for planning purposes. The Connecticut Streamflow and Sustainable Water Use Estimator (CT SSWUE), developed by the U.S. Geological Survey in cooperation with the Connecticut Department of Energy and Environmental Protection, is a decision-support tool for estimating daily unaltered streamflow and sustainable water use at ungaged sites in Connecticut.

The CT SSWUE estimates unaltered daily mean streamflow and water-use-adjusted streamflow for the period from October 1, 1960, to September 30, 2015, and the monthly sustainable net withdrawal at ungaged sites in Connecticut. Unaltered streamflow is the estimated daily mean streamflow in a drainage basin in the absence of any water withdrawals or wastewater discharges and with minimal human development. Sustainable net withdrawal is the maximum net withdrawal (withdrawal minus wastewater discharges) that can be drawn from a basin without critically depleting the water available through natural streamflow patterns. Sustainable net withdrawal is defined for this study as the difference between the unaltered daily mean streamflow and a user-defined target minimum streamflow.

Weighted least squares and Tobit regression techniques were used to develop equations for estimating streamflow at ungaged sites at 19 streamflow quantiles with exceedance probabilities ranging from 0.005 to 99.995 percent. Regressions were based on streamflow quantiles and basin characteristics from 36 reference streamgages in and around Connecticut. Four basin characteristics—drainage area, mean of the soil permeability, mean of the average annual precipitation, and ratio of the length of streams that overlay sand and gravel deposits to the total length of streams in the basin—are used as explanatory variables in the equations. At an ungaged site, interpolation between the streamflow quantiles estimated from the regression equations produces a continuous flow-duration curve. A time series of daily mean streamflow at an ungaged site is then estimated by assuming that for each day, the streamflow quantile occurs on the same date at both a reference streamgage and the ungaged site.

In a remove-one cross validation, estimated unaltered daily mean streamflow agreed well with observed values at reference streamgages, with a few exceptions. Nash Sutcliffe efficiency ranged from −0.43 to 0.97 with a median value of 0.88. The normalized root-mean-square error ranged from 16.6 to 120.4 percent with a median value of 34.5 percent.

An empirical method for estimating 95-percent prediction intervals for unaltered daily and monthly mean streamflow was developed and tested by using the cross-validation data. Prediction intervals for unaltered daily mean streamflow at the cross-validation reference streamgages performed well in most cases. Gaged streamflow values from the cross-validation data fell within the prediction intervals a median 96.6 percent of the time for daily mean time series and 93.9 percent of the time for monthly mean time series.

The CT SSWUE computes water-use-adjusted streamflow using spatially referenced water-use information provided by the Connecticut Department of Energy and Environmental Protection. Available water-use information included permitted and registered water withdrawals and permitted wastewater discharges during 1998 to 2015 for the Thames River Basin and central coastal drainage basins. Water-use information was incorporated into the U.S. Geological Survey StreamStats web application for Connecticut and can be used for computing water-use-adjusted streamflow and sustainable net withdrawal at selected points of interest. Altered daily streamflow is computed by applying average daily withdrawals and wastewater discharges to the water balance equation. Average daily surface water withdrawals and wastewater discharges are applied directly to the daily water balance equation. Time-lagged alterations on streamflow from groundwater withdrawals or wastewater discharges are estimated by using a response-coefficient method developed from results of previously published, calibrated groundwater models.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185135","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Levin, S.B., Olson, S.A., Nielsen, M.G., and Granato, G.E., 2018, The Connecticut Streamflow and Sustainable Water Use Estimator—A decision-support tool to estimate water availability at ungaged stream locations in Connecticut: U.S. Geological Survey Scientific Investigations Report 2018–5135, 34 p., https://doi.org/10.3133/sir20185135.","productDescription":"Report: vii, 34 p.; Table; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087738","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":360020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5135/sir20185135.pdf","text":"Report","size":"8.92 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release","linkHelpText":"Connecticut Streamflow and Sustainable Water Use Estimator (CT SSWUE) Application Software "},{"id":360022,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2018/5135/sir20185135_table1.csv","text":"Tabel 1 - CSV","size":"10.7 KB","linkFileType":{"id":7,"text":"csv"}}],"country":"United 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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2018-12-13","noUsgsAuthors":false,"publicationDate":"2018-12-13","publicationStatus":"PW","scienceBaseUri":"5c137dd0e4b006c4f8514867","contributors":{"authors":[{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nielsen, Martha G. 0000-0003-3038-9400 mnielsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3038-9400","contributorId":4169,"corporation":false,"usgs":true,"family":"Nielsen","given":"Martha","email":"mnielsen@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":753308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Granato, Gregory E. 0000-0002-2561-9913","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":203250,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750083,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201462,"text":"70201462 - 2018 - Accuracies achieved in classifying five leading world crop types and their growth stages using optimal Earth Observing-1 Hyperion hyperspectral narrowbands on Google Earth Engine","interactions":[],"lastModifiedDate":"2018-12-14T10:46:19","indexId":"70201462","displayToPublicDate":"2018-12-13T10:46:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Accuracies achieved in classifying five leading world crop types and their growth stages using optimal Earth Observing-1 Hyperion hyperspectral narrowbands on Google Earth Engine","docAbstract":"

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

","language":"English","publisher":"MDPI","doi":"10.3390/rs10122027","usgsCitation":"Aneece, I., and Thenkabail, P.S., 2018, Accuracies achieved in classifying five leading world crop types and their growth stages using optimal Earth Observing-1 Hyperion hyperspectral narrowbands on Google Earth Engine: Remote Sensing, v. 10, no. 12, p. 1-29, https://doi.org/10.3390/rs10122027.","productDescription":"Article 2027; 29 p.","startPage":"1","endPage":"29","ipdsId":"IP-097093","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10122027","text":"Publisher Index Page"},{"id":360295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-13","publicationStatus":"PW","scienceBaseUri":"5c14cfb7e4b006c4f8545d30","contributors":{"authors":[{"text":"Aneece, Itiya 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":211471,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":754190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":754191,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201410,"text":"70201410 - 2018 - Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure","interactions":[],"lastModifiedDate":"2018-12-13T14:59:37","indexId":"70201410","displayToPublicDate":"2018-12-12T14:59:31","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure","docAbstract":"

Elevation data are critical for assessments of sea-level rise (SLR) and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of SLR and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a SLR or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (<1 m) of SLR or with shorter planning horizons (<100 years) and thus they should not be used for such mapping, but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative SLR scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative SLR and coastal inundation assessments at high confidence levels.

","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2018.00230","usgsCitation":"Gesch, D.B., 2018, Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure: Frontiers in Earth Science, v. 6, p. 1-19, https://doi.org/10.3389/feart.2018.00230.","productDescription":"Article 230; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-099709","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468189,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00230","text":"Publisher Index Page"},{"id":360254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-12","publicationStatus":"PW","scienceBaseUri":"5c137dd3e4b006c4f851487e","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":754063,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70199787,"text":"70199787 - 2018 - Crop water productivity estimation with hyperspectral remote sensing","interactions":[],"lastModifiedDate":"2020-05-27T15:58:19.713875","indexId":"70199787","displayToPublicDate":"2018-12-11T10:48:12","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Crop water productivity estimation with hyperspectral remote sensing","docAbstract":"

Crop water productivity (CWP) is the ratio of accumulated crop biomass or yield (Y) to the water utilized to produce it, which is typically estimated using transpiration (ETC). CWP is an important metric to test and monitor water-saving strategies in agroecosystems across the globe. Red and near-infrared broadbands have been used to estimate CWP, because they capture biophysical constraints based on crop-light interaction principles at pixel level (e.g., 30-meter resolution) over large areas through time. Hyperspectral remote sensing, which allows for the more precise measurement of crop-light interactions at higher spectral resolution, should in theory provide higher accuracy in CWP estimation but has been underutilized by the remote sensing community due to computational challenges and lack of availability. In this study, a simple methodology is presented to demonstrate how CWP could be estimated using hyperspectral remote sensing. Due to a lack of hyperspectral data, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data were used for the demonstration. Landsat is a broadband sensor that provides considerable spectral information for CWP estimation. New bands were identified in the workflow outside the typical Landsat bands used to estimate CWP and its components (Y and ETC). Landsat bands 1 and 3 were the most effective at estimating CWP and Y with an R2 of 0.72 (RMSE = 0.50 kg m−3) and 0.64 (RMSE = 0.31 kg m−2), respectively. All of the bands were poor at estimating ETC, with Landsat bands 1 and 7 being the most highly correlated (R2 = 0.13, RMSE = 0.08 m). Future work should train models with multiple estimates of CWP and Y over the growing season, while ETC may be better estimated with thermal infrared bands not considered in this study. Finally, studies should also consider estimating CWP categorically, instead of continuously, if the same objectives of testing and monitoring are met.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","usgsCitation":"Marshall, M., Aneece, I.P., Foley, D., Xueliang, C., and Biggs, T., 2018, Crop water productivity estimation with hyperspectral remote sensing, chap. 5 of Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation, v. 4, p. 79-96.","productDescription":"18 p.","startPage":"79","endPage":"96","ipdsId":"IP-097174","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":375087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375086,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/books/9780429431166/chapters/10.1201/9780429431166-5"}],"volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Michael","contributorId":145855,"corporation":false,"usgs":false,"family":"Marshall","given":"Michael","affiliations":[{"id":16265,"text":"Dept. of Geography, UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":746604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xueliang, Cai","contributorId":208267,"corporation":false,"usgs":false,"family":"Xueliang","given":"Cai","email":"","affiliations":[],"preferred":false,"id":746606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggs, Trent","contributorId":208268,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":746607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201149,"text":"ds1102 - 2018 - Agricultural conservation practice implementation in the Chesapeake Bay watershed supported by the U.S. Department of Agriculture","interactions":[],"lastModifiedDate":"2019-02-27T08:34:00","indexId":"ds1102","displayToPublicDate":"2018-12-11T09:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1102","displayTitle":"Agricultural Conservation Practice Implementation in the Chesapeake Bay Watershed Supported by the U.S. Department of Agriculture","title":"Agricultural conservation practice implementation in the Chesapeake Bay watershed supported by the U.S. Department of Agriculture","docAbstract":"The U.S. Department of Agriculture (USDA) provides cost-share funding and technical assistance to support the implementation of agricultural conservation practices on farms throughout the Chesapeake Bay watershed. Conservation implementation has been substantial in the time period for which digital records are available (from 2007 through 2017). Farmer participation in USDA conservation programs is voluntary and the implementation data are privacy protected. In 2010, the U.S. Geological Survey (USGS) and USDA formed a cooperative partnership to analyze the effects of agricultural conservation on sediment, nutrient, and pesticide transport to the Chesapeake Bay. The USDA provides conservation implementation records for Chesapeake Bay farms to the USGS, with strict limitations on the use of the data to maintain confidentiality of site-specific farm data. The USGS aggregates the data to maintain farmer privacy, and subsequently provides the aggregated datasets to the public to inform conservation decision making processes. As part of that process, the USGS collaborates with the USDA to increase the understanding and quality of the USDA datasets and informs the interpretation of data records by Chesapeake Bay Program partners. The USGS obtains USDA conservation datasets in October of each year, performs data handling and quality checks as described in this document, and delivers aggregated summaries to the six Chesapeake Bay state jurisdictions for use in reporting conservation implementation to the Chesapeake Bay Partnership’s Annual Progress Review, which occurs in December of each year. The privacy protected, site-specific datasets are also used by USGS scientists to understand the effects of agricultural conservation on sediment, nutrient, and pesticide transport to the Chesapeake Bay at the small watershed scale. This publication describes the methods used to aggregate the datasets herein made available to the public at county and eight-digit hydrologic unit code watershed scales, reporting annual implementation from 2007 through 2017. It also documents the effect of geographic aggregation scale on the reportability of records and provides details regarding appropriate use and interpretation of the data records.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1102","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture","usgsCitation":"Hively, W.D., Devereux, O.H., and Keisman, J.L.D., 2018, Agricultural conservation practice implementation in the Chesapeake Bay watershed supported by the U.S. Department of Agriculture: U.S. Geological Data Series 1102, 46 p., https://doi.org/10.3133/ds1102.","productDescription":"Report: vii, 46 p.; Data release","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-094290","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":437655,"rank":4,"type":{"id":30,"text":"Data 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Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2018-12-11","noUsgsAuthors":false,"publicationDate":"2018-12-11","publicationStatus":"PW","scienceBaseUri":"5c10a8e4e4b034bf6a7e4dcc","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":210993,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":752922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devereux, Olivia H. 0000-0002-3911-3307","orcid":"https://orcid.org/0000-0002-3911-3307","contributorId":198108,"corporation":false,"usgs":false,"family":"Devereux","given":"Olivia H.","affiliations":[],"preferred":false,"id":752923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keisman, Jennifer L. D. 0000-0001-6808-9193","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":210994,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"","middleInitial":"L. D.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752924,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201540,"text":"70201540 - 2018 - Root endophytes and invasiveness: no difference between native and non‐native Phragmites in the Great Lakes Region","interactions":[],"lastModifiedDate":"2018-12-17T13:01:14","indexId":"70201540","displayToPublicDate":"2018-12-10T13:01:06","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Root endophytes and invasiveness: no difference between native and non‐native Phragmites in the Great Lakes Region","docAbstract":"

Microbial interactions could play an important role in plant invasions. If invasive plants associate with relatively more mutualists or fewer pathogens than their native counterparts, then microbial communities could foster plant invasiveness. Studies examining the effects of microbes on invasive plants commonly focus on a single microbial group (e.g., bacteria) or measure only plant response to microbes, not documenting the specific taxa associating with invaders. We surveyed root microbial communities associated with co‐occurring native and non‐native lineages of Phragmites australis, across Michigan, USA. Our aim was to determine whether (1) plant lineage was a stronger predictor of root microbial community composition than environmental variables and (2) the non‐native lineage associated with more mutualistic and/or fewer pathogenic microbes than the native lineage. We used microscopy and culture‐independent molecular methods to examine fungal colonization rate and community composition in three major microbial groups (bacteria, fungi, and oomycetes) within roots. We also used microbial functional databases to assess putative functions of the observed microbial taxa. While fungal colonization of roots was significantly higher in non‐native Phragmites than the native lineage, we found no differences in root microbial community composition or potential function between the two Phragmites lineages. Community composition did differ significantly by site, with soil saturation playing a significant role in structuring communities in all three microbial groups. The relative abundance of some specific bacterial taxa did differ between Phragmites lineages at the phylum and genus level (e.g., Proteobacteria, Firmicutes). Purported function of root fungi and respiratory mode of root bacteria also did not differ between native and non‐native Phragmites. We found no evidence that native and non‐native Phragmites harbored distinct root microbial communities; nor did those communities differ functionally. Therefore, if the trends revealed at our sites are widespread, it is unlikely that total root microbial communities are driving invasion by non‐native Phragmites plants.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2526","usgsCitation":"Bickford, W.A., Goldberg, D.E., Kowalski, K., and Zak, D.R., 2018, Root endophytes and invasiveness: no difference between native and non‐native Phragmites in the Great Lakes Region: Ecosphere, v. 9, no. 12, p. 1-14, https://doi.org/10.1002/ecs2.2526.","productDescription":"e02526; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-098851","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468192,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2526","text":"Publisher Index Page"},{"id":360369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-10","publicationStatus":"PW","scienceBaseUri":"5c18c424e4b006c4f856acd7","contributors":{"authors":[{"text":"Bickford, Wesley A. 0000-0001-7612-1325 wbickford@usgs.gov","orcid":"https://orcid.org/0000-0001-7612-1325","contributorId":5687,"corporation":false,"usgs":true,"family":"Bickford","given":"Wesley","email":"wbickford@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldberg, Deborah E.","contributorId":211585,"corporation":false,"usgs":false,"family":"Goldberg","given":"Deborah","email":"","middleInitial":"E.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":754422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zak, Donald R.","contributorId":211586,"corporation":false,"usgs":false,"family":"Zak","given":"Donald","email":"","middleInitial":"R.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":754424,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201790,"text":"70201790 - 2018 - Analysis ready data: Enabling analysis of the Landsat archive","interactions":[],"lastModifiedDate":"2021-04-02T14:39:41.314848","indexId":"70201790","displayToPublicDate":"2018-12-10T12:28:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Analysis ready data: Enabling analysis of the Landsat archive","docAbstract":"
Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.
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study","interactions":[],"lastModifiedDate":"2018-12-10T10:11:21","indexId":"70201245","displayToPublicDate":"2018-12-10T10:11:17","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5704,"text":"Cities and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Wildlife underpass use and environmental impact assessment: A southern California case study","docAbstract":"

Environmental planners often rely on transportation structures (i.e., underpasses, bridges) to provide connectivity for animals across developed landscapes. Environmental assessments of predicted environmental impacts from proposed developments often rely on literature reviews or other indirect measures to establish the importance of wildlife crossings. Literature-based evaluations of wildlife crossings may not be accurate, and result in under-estimation of impacts or establishment of inappropriate mitigation measures. To investigate the adequacy of literature-based evaluations, we monitored wildlife use of a freeway underpass that had been identified as critically important to wildlife connectivity, and which was evaluated in an environmental review document. Photographs were obtained from a network of trail cameras over 3 years. Six mid- to large-sized native mammal species used the underpass and two other mammal species were photographed near the underpass but not using it. American badger (Taxidea taxus) was photographed at a higher rate in the underpass than in the surrounding area. Gray fox (Urocyon cinereoargenteus) was rarely detected in the underpass relative to surrounding habitats, whereas the absence of mule deer (Odocoileus hemionus) in the underpass was unexpected, given relatively frequent detection in adjacent habitats. These results differed from the environmental assessment in that American badger was listed as \"potentially\" present while mule deer were expected to use the underpass. Results underscore importance of gathering data to document wildlife use of corridors, because some species do not or rarely take advantage of apparently suitable corridors, while others may be present when assumed to be absent.

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In Chapter 3, we examined how climate has changed in the Mid-Atlantic region during the past century. This chapter examines how climate is expected to change during the 21st century, including changes in extreme weather events and other climaterelated processes. General circulation models, also called global climate models (GCMs), are used to project future change at coarse spatial scales and then downscaled in order to be relevant at scales where land management decisions are made. These downscaled data can then be incorporated into forest species distribution models and process models (results are presented in Chapter 5). Chapter 2 more fully describes the models, data sources, and methods used to generate these downscaled projections, as well as the inherent uncertainty in making long-term projections. In Chapter 4, we focus on two climate scenarios for the assessment area, chosen to bracket a range of plausible changes in average annual and seasonal temperatures and precipitation totals. We note, however, that the two models selected here do not necessarily represent the bracketed range in terms of other metrics such as daily maximums and minimums, or extremes. Therefore, readers should exercise caution when interpreting future trends. Information related to future weather extremes and physical processes is drawn from published research.

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Department of Agriculture","doi":"10.2737/NRS-GTR-181","usgsCitation":"Butler-Leopold, P.R., Iverson, L.R., Thompson III, F., Brandt, L.A., Handler, S.D., Janowiak, M.K., Shannon, P.D., Swanston, C.W., Bearer, S., Bryan, A., Clark, K.L., Czarnecki, G., DeSenze, P., Dijak, W.D., Fraser, J.S., Gugger, P.F., Hille, A., Hynicka, J., Jantz, C.A., Kelly, M.C., Krause, K.M., La Puma, I.P., Landau, D., Lathrop, R.G., Leites, L.P., Madlinger, E., Matthews, S.N., Ozbay, G., Peters, M.P., Prasad, A., Schmit, D.A., Shephard, C., Shirer, R., Skowronski, N.S., Steele, A., Stout, S., Thomas-Van Gundy, M., Thompson, J., Turcotte, R.M., Weinstein, D.A., and Yanez, A., 2018, Projected changes in climate and physical processes, chap. 4 of General Technical Report NRS-181, Mid-Atlantic forest ecosystem vulnerability assessment and synthesis: A report from the Mid-Atlantic Climate Change Response Framework project, p. 75-92, https://doi.org/10.2737/NRS-GTR-181.","productDescription":"18 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,{"id":70199657,"text":"70199657 - 2018 - New global high-resolution centerlines dataset of selected river systems","interactions":[],"lastModifiedDate":"2019-12-06T10:10:11","indexId":"70199657","displayToPublicDate":"2018-12-06T10:05:20","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5898,"text":"Data in Brief","onlineIssn":"2352-3409","active":true,"publicationSubtype":{"id":10}},"title":"New global high-resolution centerlines dataset of selected river systems","docAbstract":"We present the first high resolution (1:20,000) river centerlines shapefiles from 50 large rivers across the world. Rivers were selected based on the criteria of having more than 1000 km length and which have been reported to have a significant contribution to global fishery production. Since large rivers often span multiple countries, the degree of changes (i.e., anthropogenic or climate derived) varies from region to region. These high-resolution layers were developed to enable researchers to delineate accurate river length, from headwaters regions to their delta and assess or visualize the ongoing changes more accurately in these river systems. Further, these polylines could be used in coordination with satellite derived environmental or landscape variables for ecological research (e.g. predicting biodiversity, estimating biomass).","language":"English","publisher":"Elsevier","doi":"10.1016/j.dib.2018.09.016","collaboration":"Michigan State University","usgsCitation":"Basher, Z., Lynch, A., and Taylor, W.W., 2018, New global high-resolution centerlines dataset of selected river systems: Data in Brief, v. 20, p. 1552-1555, https://doi.org/10.1016/j.dib.2018.09.016.","productDescription":"4 p.","startPage":"1552","endPage":"1555","ipdsId":"IP-094613","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":468201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dib.2018.09.016","text":"Publisher Index Page"},{"id":370031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357654,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S2352340918310916?via%3Dihub"}],"volume":"20","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Basher, Zeenatul 0000-0002-6439-8324 zbasher@usgs.gov","orcid":"https://orcid.org/0000-0002-6439-8324","contributorId":208142,"corporation":false,"usgs":false,"family":"Basher","given":"Zeenatul","email":"zbasher@usgs.gov","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":746092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":169460,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":746091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, William W.","contributorId":166927,"corporation":false,"usgs":false,"family":"Taylor","given":"William","email":"","middleInitial":"W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":746093,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201146,"text":"sir20185129 - 2018 - Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","interactions":[],"lastModifiedDate":"2018-12-05T14:39:59","indexId":"sir20185129","displayToPublicDate":"2018-12-04T15:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5129","displayTitle":"Vibration Monitoring Results near a Bat Hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","title":"Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","docAbstract":"

Vibrations originating from construction of a new walkway in a passage of Mammoth Cave, from walking personnel simulating a bat survey, and from ambient sources were measured near a bat hibernaculum beneath Mammoth Cave National Park, Kentucky, to determine if the vibrations were disturbing the hibernating bats. Data presented indicate direction and magnitude of the vibrations. The seven sources of vibration that were recorded include hammer drill (one location), plate compactor (two locations), jackhammer (two locations), personnel simulating a bat survey near the hibernaculum (walking throughout the cave), and background levels. Vibrations were measured for approximately 10 seconds during each triggering of the source and each source was recorded 5–10 times to represent the reproducibility of the vibrations.

The plate compactor produced the largest velocity of 0.00226 inch per second on one of the longitudinal components. The simulated bat survey produced the largest value of acceleration of 0.34 inch per square second in the vertical component. Maximum vertical velocities and accelerations did not exceed literature values for human perception or visible agitation in laboratory mice.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185129","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Adams, R.F., Morrow, W.S., and Koebel, C.M., 2018, Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016: U.S. Geological Survey Scientific Investigations Report 2018–5129, 16 p., https://doi.org/10.3133/sir20185129.","productDescription":"Report: iv, 16 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074738","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359864,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LHZHR","text":"USGS data release","description":"USGS data release","linkHelpText":"Vibration Monitoring Data from a Bat Hibernaculum at Mammoth Cave National Park, Kentucky, March 2016"},{"id":359862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5129/coverthb.jpg"},{"id":359863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5129/sir20185129.pdf","text":"Report","size":"19.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5129"}],"country":"United States","state":"Kentucky","otherGeospatial":"Mammoth Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.10980987548828,\n 37.17071303242321\n ],\n [\n -86.08234405517578,\n 37.17071303242321\n ],\n [\n -86.08234405517578,\n 37.19368966240492\n ],\n [\n -86.10980987548828,\n 37.19368966240492\n ],\n [\n -86.10980987548828,\n 37.17071303242321\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, Kentucky 40299

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2018-12-04","noUsgsAuthors":false,"publicationDate":"2018-12-04","publicationStatus":"PW","scienceBaseUri":"5c07a060e4b0815414cee773","contributors":{"authors":[{"text":"Adams, Ryan F. 0000-0001-7299-329X rfadams@usgs.gov","orcid":"https://orcid.org/0000-0001-7299-329X","contributorId":5499,"corporation":false,"usgs":true,"family":"Adams","given":"Ryan","email":"rfadams@usgs.gov","middleInitial":"F.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":752905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrow, William S. 0000-0002-2250-3165 wsmorrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2250-3165","contributorId":1886,"corporation":false,"usgs":true,"family":"Morrow","given":"William","email":"wsmorrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koebel, Carolyn M. 0000-0003-0501-2572 ckoebel@usgs.gov","orcid":"https://orcid.org/0000-0003-0501-2572","contributorId":173836,"corporation":false,"usgs":true,"family":"Koebel","given":"Carolyn","email":"ckoebel@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752907,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201006,"text":"ofr20181185 - 2018 - Interactive tool to estimate groundwater elevations in central and eastern North Dakota","interactions":[],"lastModifiedDate":"2018-12-05T14:44:37","indexId":"ofr20181185","displayToPublicDate":"2018-12-04T15:39:45","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":"2018-1185","displayTitle":"Interactive Tool to Estimate Groundwater Elevations in Central and Eastern North Dakota","title":"Interactive tool to estimate groundwater elevations in central and eastern North Dakota","docAbstract":"

This report describes an interactive tool (NDakGWtool) in which a statistical model is developed using locally weighted regression to estimate monthly mean groundwater elevations for a specified latitude and longitude, referred to as the “user-specified location.” For each user-specified location, seven models are developed for each month from April through October. Localized, high spatial-resolution maps of estimated monthly mean groundwater surface elevations are produced from the models. The tool was evaluated for glacial drift aquifers of the 32-county study area in central and eastern North Dakota. Although groundwater elevations from 1960 to 2017 were available to develop the tool, groundwater elevations from 1995 to 2015 were used for model testing and development of the model domain. There are 413 grid cells of 0.1-degree latitude by 0.1-degree longitude size in the model domain, and the tool produces maps of estimated monthly mean groundwater surface elevations for the cell containing the user-specified location. Additionally, the NDakGWtool produces maps of estimated groundwater depth below land surface and ArcGIS files of estimated groundwater surface elevations and groundwater depth below land surface. The tool is composed of four main components: data input, statistical model, output, and user-interactive process.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181185","collaboration":"Prepared in cooperation with Natural Resources Conservation Service","usgsCitation":"Nustad, R.A., Damschen, W.C., and Vecchia, A.V., 2018, Interactive tool to estimate groundwater elevations in central and eastern North Dakota: U.S. Geological Survey Open-File Report 2018–1185, 24 p., https://doi.org/10.3133/ofr20181185.","productDescription":"Report: vi, 24; Appendix","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090716","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":359877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1185/coverthb.jpg"},{"id":359878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185.pdf","text":"Report","size":"6.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1185"},{"id":359880,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185_appendix.zip","text":"Appendix","size":"27.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018–1185 Appendix","linkHelpText":"R Documentation"}],"country":"United States","state":"North Dakota","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-12-04","noUsgsAuthors":false,"publicationDate":"2018-12-04","publicationStatus":"PW","scienceBaseUri":"5c07a061e4b0815414cee775","contributors":{"authors":[{"text":"Nustad, Rochelle A. 0000-0002-4713-5944 ranustad@usgs.gov","orcid":"https://orcid.org/0000-0002-4713-5944","contributorId":1811,"corporation":false,"usgs":true,"family":"Nustad","given":"Rochelle","email":"ranustad@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":751633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Damschen, William C. 0000-0002-3770-8497 wcdamsch@usgs.gov","orcid":"https://orcid.org/0000-0002-3770-8497","contributorId":210744,"corporation":false,"usgs":true,"family":"Damschen","given":"William","email":"wcdamsch@usgs.gov","middleInitial":"C.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":751634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401 avecchia@usgs.gov","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":1173,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"avecchia@usgs.gov","middleInitial":"V.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":751635,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217043,"text":"70217043 - 2018 - Twenty-nine years of population dynamics in a small-bodied montane amphibian","interactions":[],"lastModifiedDate":"2020-12-29T13:39:05.613643","indexId":"70217043","displayToPublicDate":"2018-12-04T07:36:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Twenty-nine years of population dynamics in a small-bodied montane amphibian","docAbstract":"

Identifying population declines before they reach crisis proportions is imperative given the current global decline in vertebrate fauna and associated challenges and expense of recovery. Understanding life histories and how the environment influences demography are critical aspects of this challenge, as is determining the biological relevance of covariates that are best supported by the data. We used 29 yr of data on chorus frogs at two sites to estimate demographic parameters, examine life history, assess weather‐related covariates, and determine the magnitude of process variation in target parameters. Average estimates of survival probabilities were 0.51 (Standard Error [SE] = 0.04) and 0.43 (SE = 0.04), and average estimates of recruitment probabilities were 0.64 (SE = 0.07) and 0.44 (SE = 0.04). Process variation accounted for ≥76% of the total temporal variation in both parameters at one pond and in survival probability alone at the other, suggesting that the covariates in our top models were explaining predominantly process rather than sampling variation. Estimates of population growth rates indicated a declining population at one pond (i.e., negative population growth rates in 15 of 18 yr), and comparisons with historical estimates suggested declines in survival probability at the other. The amount of deviance explained was low, providing little support for the influence of covariates on target parameters, despite model selection support. Synthesis and applications: This analysis illustrates the value of disentangling components of variance when assessing demographic drivers and highlights the need for adequate demographic information in assigning conservation labels.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2522","usgsCitation":"Muths, E., Scherer, R., Amburgey, S.M., and Corn, P., 2018, Twenty-nine years of population dynamics in a small-bodied montane amphibian: Ecosphere, v. 9, no. 12, e02522, 15 p., https://doi.org/10.1002/ecs2.2522.","productDescription":"e02522, 15 p.","ipdsId":"IP-073404","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468206,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2522","text":"Publisher Index Page"},{"id":437659,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BZAPLB","text":"USGS data release","linkHelpText":"Demographic data from two chorus frog populations in Colorado"},{"id":381718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2018-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":245923,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":807332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scherer, R D","contributorId":245924,"corporation":false,"usgs":false,"family":"Scherer","given":"R D","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":807333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amburgey, S M 0000-0002-7100-7811","orcid":"https://orcid.org/0000-0002-7100-7811","contributorId":245926,"corporation":false,"usgs":false,"family":"Amburgey","given":"S","email":"","middleInitial":"M","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":807334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corn, PS","contributorId":245928,"corporation":false,"usgs":false,"family":"Corn","given":"PS","email":"","affiliations":[{"id":49365,"text":"Aldo Leopold Wilderness Research","active":true,"usgs":false}],"preferred":false,"id":807335,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198996,"text":"sim3413 - 2018 - Geologic map of the central Beaverhead Mountains, Lemhi County, Idaho, and Beaverhead County, Montana","interactions":[],"lastModifiedDate":"2022-04-19T20:03:20.997761","indexId":"sim3413","displayToPublicDate":"2018-12-03T13:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3413","title":"Geologic map of the central Beaverhead Mountains, Lemhi County, Idaho, and Beaverhead County, Montana","docAbstract":"

This geologic map of the central Beaverhead Mountains portrays a complex geologic history of depositional basin development interspersed with deformational events. Generalized geology for young basins, compiled from sources on both sides of the range, is combined with newly mapped bedrock geology to better integrate geologic development of the map area.

Successive extensional basins were obliquely oriented across deformed strata of each preceding basin and of the Paleoproterozoic basement. Strata deposited in these basins include (1) thick fine-grained arkosic strata of the Mesoproterozoic Lemhi basin deposited on Paleoproterozoic basement with shoreline exposed on the east side of the map, (2) siliciclastic and carbonate strata of the Late Neoproterozoic-early Paleozoic miogeocline that were deposited in deeper environments to the west and interfingered with cratonal basin deposits to the east, and (3) generally coarse deposits in several nested, fault-bounded Eocene to Holocene basins.

Syndepositional structural disruption including tilting and angular unconformities is present within strata and between stratigraphic packages formed during the different basin-filling events. Cretaceous, east-northeast-directed thrust faults inverted Mesoproterozoic and Neoproterozoic-Paleozoic basins and stacked strata from diverse stratigraphic packages and different depositional settings. The thrust plates rotated as they impinged on the Paleoproterozoic arch on the east side of the map, resulting in complex fault geometries that present as thrust faults to oblique reverse and tear (or ramp) fault along different fault segments. Cenozoic extension caused successive normal-fault basins of several orientations. Eocene volcanic rocks are preserved in fault-bounded depositional basins formed during the onset of Cenozoic extension. Eocene basins were obliquely overprinted by Oligocene-Miocene normal-fault basins. Holocene basins developed during steep normal faulting that formed the present Basin and Range topography.

This geologic map of the central Beaverhead Mountains is mapped at 1:24,000 scale and printable at 1:50,000 scale. These data were collected between 1997 and 2017 and synthesized to provide significant new stratigraphic and structural data and interpretations.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3413","usgsCitation":"Lund, K., 2018, Geologic map of the central Beaverhead Mountains, Lemhi County, Idaho, and Beaverhead County, Montana: U.S. Geological Survey Scientific Investigations Map 3413, pamphlet 27 p., scale 1:50,000, https://doi.org/10.3133/sim3413.","productDescription":"Report: iv, 27 p.; 2 Sheets: 50.0 x 46.0 inches; Read Me; Data Release","onlineOnly":"Y","ipdsId":"IP-087570","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":399121,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108200.htm"},{"id":359707,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P905PTI4","text":"USGS data release","linkHelpText":"Digital Data for the Geologic Map of the central Beaverhead Mountains, Lemhi County, Idaho, and Beaverhead County, Montana"},{"id":359706,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3413/sim3413_ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3413 Read Me"},{"id":359722,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3413/sim3413_sheet_georeferenced.pdf","text":"Georeferenced Map","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3413 Georeferenced Map"},{"id":359721,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3413/sim3413_sheet.pdf","text":"Map","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3413 Map"},{"id":359702,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3413/sim3413_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3413 Pamphlet"},{"id":359701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3413/coverthb2.jpg"}],"scale":"50000","country":"United States","state":"Idaho, Montana","county":"Beaverhead County, Lemhi County","otherGeospatial":"central Beaverhead Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.6575,\n 44.6539\n ],\n [\n -113.1736,\n 44.6539\n ],\n [\n -113.1736,\n 45.0739\n ],\n [\n -113.6575,\n 45.0739\n ],\n [\n -113.6575,\n 44.6539\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2018-12-03","noUsgsAuthors":false,"publicationDate":"2018-12-03","publicationStatus":"PW","scienceBaseUri":"5c064ee0e4b0815414cecb04","contributors":{"authors":[{"text":"Lund, Karen 0000-0002-4249-3582 klund@usgs.gov","orcid":"https://orcid.org/0000-0002-4249-3582","contributorId":1235,"corporation":false,"usgs":true,"family":"Lund","given":"Karen","email":"klund@usgs.gov","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":743706,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200811,"text":"sir20185153 - 2018 - Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon","interactions":[],"lastModifiedDate":"2018-12-04T11:02:38","indexId":"sir20185153","displayToPublicDate":"2018-12-03T12:40:58","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":"2018-5153","displayTitle":"Prioritization Framework for Ranking Riverine Ecosystem Stressors Using Example Sites from the Tualatin River Basin, Oregon","title":"Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon","docAbstract":"

As human populations increase, so does their influence over the environment. Altered terrain, degraded water quality, and threatened or endangered species are all-too-common consequences of a growing anthropogenic influence on the landscape. To help manage these effects, researchers have developed new ways to characterize current environmental conditions and help resource managers seek solutions to bring affected areas back to their best attainable health. Before an ecosystem can be improved, however, its current level of ecological stress must be determined. Characterizing environmental conditions at many sites across a landscape helps managers understand the range of current conditions and prioritize where they might focus restoration and protection efforts.

This report details the development of a prioritization framework to score riverine ecosystem stressors in a watershed based on example sites from the Tualatin River Basin in northwestern Oregon. The framework incorporated the most influential site-specific stressors throughout the basin built on a long history of data collection. These stressors were characterized with 13 metrics that were organized into 4 groups: hydrologic, water quality, physical habitat, and biological. Each stressor metric used readily accessible data and was translated to a score between 0 and 10. The higher the score, the healthier the site. This initial application of the framework used field observations and measurements to rank site conditions at two Tualatin River sites and four Tualatin River tributary sites. Given the versatility of this framework, it easily could be expanded to include more sites or new metrics, if necessary. Because stressors varied by season, all metrics for the tributary sites were scored separately during the wet season (November through April) and dry season (May through October). Water-quality data were available over a prolonged period; therefore, water-quality metrics were assessed by season and by decade (1990–99 compared to 2000–12) to evaluate long-term stressor trends.

Results for the Tualatin River Basin prioritization framework indicated that the urban tributaries demonstrated the greatest stress throughout the year, especially during the dry summer months. Spatially, the upper Tualatin River was healthier than the lower reaches of the river. Water-quality has improved in the last 10 years, mostly due to improvements in the dry period contaminant scores, but challenges remain with high water temperatures and low dissolved-oxygen conditions.

The biggest challenge with this type of research derived from inconsistencies within the available data. Both spatial and temporal data gaps must be addressed to improve the prioritization. Incorporating both discrete and continuous datasets into the prioritization framework remains a challenge because the datasets have slightly different information and criteria and are not always comparable. Regardless, this report provides guidelines for developing a prioritization framework that ranks the ecological health of sites in a watershed and provides guidance on management actions for improving conditions by targeting factors that greatly affect the health of river ecosystems.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185153","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Sobieszczyk, S., Jones, K.L., Rounds, S.A., Nilsen, E.B., and Morace, J.L., 2018, Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2018-5153, 40 p., https://doi.org/10.3133/sir20185153.","productDescription":"vii, 40 p.","onlineOnly":"Y","ipdsId":"IP-060830","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359875,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5153/sir20185153.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5153"},{"id":359874,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5153/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5,\n 45.3\n ],\n [\n -122.5,\n 45.3\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2018-12-03","noUsgsAuthors":false,"publicationDate":"2018-12-03","publicationStatus":"PW","scienceBaseUri":"5c064ee0e4b0815414cecb06","contributors":{"authors":[{"text":"Sobieszczyk, Steven 0000-0002-0834-8437 ssobie@usgs.gov","orcid":"https://orcid.org/0000-0002-0834-8437","contributorId":210445,"corporation":false,"usgs":true,"family":"Sobieszczyk","given":"Steven","email":"ssobie@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Krista L. 0000-0002-0301-4497 kljones@usgs.gov","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":4550,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","email":"kljones@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nilsen, Elena B. 0000-0002-0104-6321 enilsen@usgs.gov","orcid":"https://orcid.org/0000-0002-0104-6321","contributorId":923,"corporation":false,"usgs":true,"family":"Nilsen","given":"Elena","email":"enilsen@usgs.gov","middleInitial":"B.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morace, Jennifer L. 0000-0002-8132-4044 jlmorace@usgs.gov","orcid":"https://orcid.org/0000-0002-8132-4044","contributorId":945,"corporation":false,"usgs":true,"family":"Morace","given":"Jennifer","email":"jlmorace@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":753000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200001,"text":"sir20185132 - 2018 - Flood-inundation maps for the Salamonie River at Portland, Indiana","interactions":[],"lastModifiedDate":"2018-12-03T14:43:43","indexId":"sir20185132","displayToPublicDate":"2018-12-03T09:55:34","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":"2018-5132","displayTitle":"Flood-Inundation Maps for the Salamonie River at Portland, Indiana","title":"Flood-inundation maps for the Salamonie River at Portland, Indiana","docAbstract":"

Digital flood-inundation maps for a 6.5-mile reach of the Salamonie River at Portland, Indiana, 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 website 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 the Salamonie River at Portland, Ind. (station 03324200). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System web interface at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site PORI3) at https:/water.weather.gov/ahps/.

Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the current (2018) stage-discharge relation at the Salamonie River at Portland, Ind., streamgage.

The hydraulic model then was used to compute nine water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 10.7 ft or near bankfull to 18.7 ft, which equals the highest point on the streamgage rating curve. The simulated water-surface profiles then were combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.49-ft root mean square error and 4.9-ft horizontal resolution resampled to a 10-ft grid to delineate the area flooded at each stage. The availability of these maps, along with information regarding current stage from the USGS streamgage, 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/sir20185132","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Strauch, K.R., 2018, Flood-inundation maps for the Salamonie River at Portland, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5132, 9 p., https://doi.org/10.3133/sir20185132.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-089966","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VM4BJD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the Salamonie River at Portland, Indiana"},{"id":359798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5132/coverthb.jpg"},{"id":359799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5132/sir20185132.pdf","text":"Report","size":"996 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5132"}],"country":"United States","state":"Indiana","city":"Portland","otherGeospatial":"Salamonie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.0587272644043,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.38813537489036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Ohio Kentucky Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-12-03","noUsgsAuthors":false,"publicationDate":"2018-12-03","publicationStatus":"PW","scienceBaseUri":"5c064ee1e4b0815414cecb08","contributors":{"authors":[{"text":"Strauch, Kellan R. 0000-0002-7218-2099","orcid":"https://orcid.org/0000-0002-7218-2099","contributorId":208562,"corporation":false,"usgs":true,"family":"Strauch","given":"Kellan R.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":747701,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70201394,"text":"70201394 - 2018 - Global Earthquake Model (GEM) Risk Map","interactions":[],"lastModifiedDate":"2018-12-13T15:43:35","indexId":"70201394","displayToPublicDate":"2018-12-01T15:43:29","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Global Earthquake Model (GEM) Risk Map","docAbstract":"The Global Earthquake Risk Map (v2018.1) comprises four global maps. The main map presents the geographic distribution of average annual loss (USD) normalized by the\naverage construction costs of the respective country (USD/m2 due to ground shaking in\nthe residential, commercial and industrial building stock, considering contents, structural and non-structural components. The normalized metric allows a direct comparison of the risk between countries with widely different construction costs. It does not consider the effects of tsunamis, liquefaction, landslides, and fires following earthquakes. The loss estimates are from direct physical damage to buildings due to shaking, and thus damage to infrastructure or indirect losses due to business interruption are not included. The Global Earthquake Hazard Map depicts the geographic distribution of the Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years, computed for reference rock conditions (shear wave velocity of 760-800 m/s). The Global Exposure Map depicts the geographic distribution of residential, commercial and industrial buildings. The Global Seismic Fatalities Map depicts an estimate of average annual human losses due to earthquake-induced structural collapse of buildings. The results for human losses do not consider indirect fatalities such as those from post­ earthquake epidemics. The average annual losses and number of buildings are presented on a hexagonal grid, with a spacing of 0.30 x 0.34 decimal degrees (approximately 1,000 km2 at the equator). The average annual losses were computed using the event-based calculator of the OpenQuake engine, an open-source software for seismic hazard and risk analysis developed by the GEM Foundation. The seismic hazard, exposure and vulnerability models employed in these calculations were provided by national institutions, or developed within the scope of regional programs or bilateral collaborations. These global maps and the underlying databases are based on best available and publicly accessible datasets and models. Due to possible limitations in the model, regions portrayed with low risk may experience potentially damaging earthquakes. The GEM Risk Map is intended to be a dynamic product, which will be updated when new datasets and models become available. Updated versions of the hazard, exposure, and average annual losses will be released on a regular basis. Additional metrics for each country can be explored at globalquakemodel.org/gem.","language":"English","publisher":"Global Earthquake Model Foundation","doi":"10.13117/GEM-GLOBAL-SEISMIC-RISK-MAP-2018","usgsCitation":"Silva, V., Amo-Oduro, D., Calderon, A., Dabbeek, J., Despotaki, V., Martins, L., Rao, A., Simionato, M., Vigano, D., Yepes, C., Acevedo, A., Crowley, H., Horspool, N., Jaiswal, K.S., Journeay, M., and Pittore, M., 2018, Global Earthquake Model (GEM) Risk Map, https://doi.org/10.13117/GEM-GLOBAL-SEISMIC-RISK-MAP-2018.","ipdsId":"IP-103543","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":360265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c137dd3e4b006c4f8514887","contributors":{"authors":[{"text":"Silva, V.","contributorId":211393,"corporation":false,"usgs":false,"family":"Silva","given":"V.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amo-Oduro, D.","contributorId":211394,"corporation":false,"usgs":false,"family":"Amo-Oduro","given":"D.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Calderon, A.","contributorId":211395,"corporation":false,"usgs":false,"family":"Calderon","given":"A.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dabbeek, J.","contributorId":211396,"corporation":false,"usgs":false,"family":"Dabbeek","given":"J.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Despotaki, V.","contributorId":211397,"corporation":false,"usgs":false,"family":"Despotaki","given":"V.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martins, L.","contributorId":211398,"corporation":false,"usgs":false,"family":"Martins","given":"L.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rao, A.","contributorId":211399,"corporation":false,"usgs":false,"family":"Rao","given":"A.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simionato, M.","contributorId":211400,"corporation":false,"usgs":false,"family":"Simionato","given":"M.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753962,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vigano, D.","contributorId":211401,"corporation":false,"usgs":false,"family":"Vigano","given":"D.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753963,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yepes, C.","contributorId":211402,"corporation":false,"usgs":false,"family":"Yepes","given":"C.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753964,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Acevedo, A.","contributorId":211403,"corporation":false,"usgs":false,"family":"Acevedo","given":"A.","email":"","affiliations":[{"id":38244,"text":"Department of Civil Engineering, Universidad EAFIT, Medellin, Colombia","active":true,"usgs":false}],"preferred":false,"id":753965,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Crowley, H.","contributorId":211404,"corporation":false,"usgs":false,"family":"Crowley","given":"H.","email":"","affiliations":[{"id":38245,"text":"EUCENTRE Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":753966,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Horspool, Nick","contributorId":175114,"corporation":false,"usgs":false,"family":"Horspool","given":"Nick","email":"","affiliations":[],"preferred":false,"id":753970,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":753967,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Journeay, M.","contributorId":211405,"corporation":false,"usgs":false,"family":"Journeay","given":"M.","affiliations":[{"id":38246,"text":"Geological Survey of Canada, Vancouver Canada","active":true,"usgs":false}],"preferred":false,"id":753968,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Pittore, M.","contributorId":211406,"corporation":false,"usgs":false,"family":"Pittore","given":"M.","affiliations":[{"id":38247,"text":"GFZ Potsdam Germany","active":true,"usgs":false}],"preferred":false,"id":753969,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70202399,"text":"70202399 - 2018 - Prevalence and risk factors of Trichomonas gallinae and trichomonosis in golden eagle (Aquila chrysaetos) nestlings in western North America","interactions":[],"lastModifiedDate":"2019-02-27T15:35:57","indexId":"70202399","displayToPublicDate":"2018-12-01T15:35:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Prevalence and risk factors of Trichomonas gallinae and trichomonosis in golden eagle (Aquila chrysaetos) nestlings in western North America","title":"Prevalence and risk factors of Trichomonas gallinae and trichomonosis in golden eagle (Aquila chrysaetos) nestlings in western North America","docAbstract":"

Avian trichomonosis, caused by the protozoan Trichomonas gallinae, affects bird-eating raptors worldwide. Raptors can develop trichomonosis by feeding on infected prey, particularly Rock Pigeons (Columba livia), which are a reservoir for T. gallinae. Raptors may be particularly vulnerable to T. gallinae infection in degraded habitats, where changes in resources may cause raptors to switch from foraging on native prey to synanthropic avian species such as Rock Pigeons. Golden Eagles (Aquila chrysaetos) typically forage on mammals; however, habitat across much of their range is experiencing degradation through changes in land use, climate, and human encroachment. In 2015, we examined the prevalence of T. gallinae infection in Golden Eagle nestlings across western North America and conducted an intensive study on factors associated with T. gallinae infection and trichomonosis in southwestern Idaho. We found T. gallinaeinfection in 13% (12/96) of eagle nestlings across 10 western states and in 41% (13/32) of nestlings in southwestern Idaho. At the Idaho site, the probability of T. gallinae infection increased as the proportion of Rock Pigeons in nestling diet increased. Nestlings with diets that consisted of ≥10% Rock Pigeons had a very high probability of T. gallinae infection. We compared historical (1971–81) and recent (2014–15) diet data and incidence of trichomonosis lesions of nestling eagles in Idaho and found that the proportion of Rock Pigeons in eagle diets was higher in recent versus historical periods, as was the proportion of eagle nestlings with trichomonosis lesions. Our results suggested that localized shifts in eagle diet that result from habitat degradation and loss of historical prey resources have the potential to affect Golden Eagle nestling survival and supported the hypothesis that land use change can alter biologic communities in a way that might have consequences for disease infection and host susceptibility.

","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2017-11-271","usgsCitation":"Dudek, B.M., Kochert, M.N., Barnes, J.G., Bloom, P.H., Papp, J.M., Gerhold, R.W., Purple, K.E., Jacobson, K.V., Preston, C.R., Vennum, C.R., Watson, J.W., and Heath, J.A., 2018, Prevalence and risk factors of Trichomonas gallinae and trichomonosis in golden eagle (Aquila chrysaetos) nestlings in western North America: Journal of Wildlife Diseases, v. 54, no. 4, p. 755-764, https://doi.org/10.7589/2017-11-271.","productDescription":"10 p.","startPage":"755","endPage":"764","ipdsId":"IP-092233","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":361592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"54","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dudek, Benjamin M","contributorId":213631,"corporation":false,"usgs":false,"family":"Dudek","given":"Benjamin","email":"","middleInitial":"M","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":758189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kochert, Michael N. 0000-0002-4380-3298 mkochert@usgs.gov","orcid":"https://orcid.org/0000-0002-4380-3298","contributorId":3037,"corporation":false,"usgs":true,"family":"Kochert","given":"Michael","email":"mkochert@usgs.gov","middleInitial":"N.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":758188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnes, Joseph G.","contributorId":213632,"corporation":false,"usgs":false,"family":"Barnes","given":"Joseph","email":"","middleInitial":"G.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":758190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloom, Peter H.","contributorId":191356,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":758191,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Papp, Joseph M.","contributorId":213633,"corporation":false,"usgs":false,"family":"Papp","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":758192,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gerhold, Richard W.","contributorId":201770,"corporation":false,"usgs":false,"family":"Gerhold","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":758193,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Purple, Kathryn E.","contributorId":213634,"corporation":false,"usgs":false,"family":"Purple","given":"Kathryn","email":"","middleInitial":"E.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":758194,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jacobson, Kenneth V.","contributorId":213635,"corporation":false,"usgs":false,"family":"Jacobson","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":38831,"text":"Arizona Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":758195,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Preston, Charles R.","contributorId":198922,"corporation":false,"usgs":false,"family":"Preston","given":"Charles","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":758196,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vennum, Chris R.","contributorId":213636,"corporation":false,"usgs":false,"family":"Vennum","given":"Chris","email":"","middleInitial":"R.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":758197,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Watson, James W.","contributorId":198921,"corporation":false,"usgs":false,"family":"Watson","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":758198,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Heath, Julie A.","contributorId":192842,"corporation":false,"usgs":false,"family":"Heath","given":"Julie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":758199,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70201801,"text":"70201801 - 2018 - No flood effect on recruitment of a small Louisiana black bear population","interactions":[],"lastModifiedDate":"2019-01-30T15:05:37","indexId":"70201801","displayToPublicDate":"2018-12-01T15:02:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"No flood effect on recruitment of a small Louisiana black bear population","docAbstract":"

A flood event in 2011 had minor impacts on apparent survival and movement probabilities of a small, isolated population of Louisiana black bears (Ursus americanus luteolus) in the Upper Atchafalaya River Basin, Louisiana, USA. However, the potential effects of the flood on recruitment of juveniles into the population, then listed as threatened under the United States Endangered Species Act, were not evaluated. We used hair trapping data collected from 2007 to 2015 and Pradel temporal symmetry models in a robust‐design framework to investigate changes in per capita recruitment that could have resulted from the flood. We detected 91 bears (37 M:54 F) within the flooded area during our study period, ranging from 21 to 44 individuals/year. Models that tested for reduced recruitment resulting from the flood were not supported more than models with constant recruitment, and the population growth rate did not decline. Although we documented marginally lower recruitment following the 2011 flood, lag effects and detectability biases complicated our analysis. We suggest that wildlife managers continue monitoring recruitment and survival in this recently delisted black bear population given the potential for heightened flood frequency and severity in the future.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21399","usgsCitation":"Clark, J.D., O’Connell-Goode, K.C., Lowe, C.L., Murphy, S.M., Maehr, S.C., Davidson, M., and Laufenberg, J.S., 2018, No flood effect on recruitment of a small Louisiana black bear population: Journal of Wildlife Management, v. 82, no. 3, p. 566-572, https://doi.org/10.1002/jwmg.21399.","productDescription":"7 p.","startPage":"566","endPage":"572","ipdsId":"IP-091472","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":360831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":755409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Connell-Goode, Kaitlin C.","contributorId":211981,"corporation":false,"usgs":false,"family":"O’Connell-Goode","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":755410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Carrie L.","contributorId":187785,"corporation":false,"usgs":false,"family":"Lowe","given":"Carrie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":755411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Sean M.","contributorId":140195,"corporation":false,"usgs":false,"family":"Murphy","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":755412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maehr, Sutton C.","contributorId":211982,"corporation":false,"usgs":false,"family":"Maehr","given":"Sutton","email":"","middleInitial":"C.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":755413,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davidson, Maria M.","contributorId":187788,"corporation":false,"usgs":false,"family":"Davidson","given":"Maria M.","affiliations":[],"preferred":false,"id":755414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Laufenberg, Jared S.","contributorId":28899,"corporation":false,"usgs":false,"family":"Laufenberg","given":"Jared","email":"","middleInitial":"S.","affiliations":[{"id":7006,"text":"Department of Forestry, Wildlife and Fisheries, University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":755415,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70201740,"text":"70201740 - 2018 - How or when samples are collected affects measured arsenic concentration in new drinking water wells","interactions":[],"lastModifiedDate":"2019-01-28T14:57:43","indexId":"70201740","displayToPublicDate":"2018-12-01T14:57:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"How or when samples are collected affects measured arsenic concentration in new drinking water wells","docAbstract":"

Naturally occurring arsenic can adversely affect water quality in geologically diverse aquifers throughout the world. Chronic exposure to arsenic via drinking water is a human health concern due to risks for certain cancers, skin abnormalities, peripheral neuropathy, and other negative health effects. Statewide in Minnesota, USA, 11% of samples from new drinking water wells have arsenic concentrations exceeding 10 μg/L; in certain counties more than 35% of tested samples exceed 10 μg/L arsenic. Since 2008, Minnesota well code has required testing water from new wells for arsenic. Sample collection protocols are not specified in the well code, so among 180 well drillers there is variability in sampling methods, including sample collection point and sample collection timing. This study examines the effect of arsenic sample collection protocols on the variability of measured arsenic concentrations in water from new domestic water supply wells. Study wells were drilled between 2014 and 2016 in three regions of Minnesota that commonly have elevated arsenic concentrations in groundwater. Variability in measured arsenic concentration at a well was reduced when samples were (1) filtered, (2) collected from household plumbing instead of from the drill rig pump, or (3) collected several months after well construction (instead of within 4 weeks of well installation). Particulates and fine aquifer sediments entrained in groundwater samples, or other artifacts of drilling disturbance, can cause undesirable variability in measurements. Establishing regulatory protocols requiring sample filtration and/or collection from household plumbing could improve the reliability of information provided to well owners and to secondary data users.

","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12643","usgsCitation":"Erickson, M., Malenda, H.F., and Berquist, E.C., 2018, How or when samples are collected affects measured arsenic concentration in new drinking water wells: Groundwater, v. 56, no. 6, p. 921-933, https://doi.org/10.1111/gwat.12643.","productDescription":"13 p.","startPage":"921","endPage":"933","ipdsId":"IP-090483","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468212,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12643","text":"Publisher Index Page"},{"id":437663,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736PVK","text":"USGS data release","linkHelpText":"Arsenic and field parameter determinations for newly constructed wells in the central, northwest, and northeast regions in Minnesota"},{"id":360765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360748,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12643"}],"volume":"56","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-06","publicationStatus":"PW","scienceBaseUri":"5c5022c5e4b0708288f7e817","contributors":{"authors":[{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malenda, Helen F. 0000-0003-4143-6460","orcid":"https://orcid.org/0000-0003-4143-6460","contributorId":211885,"corporation":false,"usgs":false,"family":"Malenda","given":"Helen","email":"","middleInitial":"F.","affiliations":[{"id":38341,"text":"Colorodo School of Mines","active":true,"usgs":false}],"preferred":true,"id":755127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berquist, Emily C.","contributorId":202174,"corporation":false,"usgs":false,"family":"Berquist","given":"Emily","email":"","middleInitial":"C.","affiliations":[{"id":36357,"text":"Minnesota Department of Health","active":true,"usgs":false}],"preferred":false,"id":755128,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201802,"text":"70201802 - 2018 - 2D and 3D potential field mapping and modelling at the Fallon FORGE site, Nevada, USA","interactions":[],"lastModifiedDate":"2019-02-01T14:06:05","indexId":"70201802","displayToPublicDate":"2018-12-01T14:05:55","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"2D and 3D potential field mapping and modelling at the Fallon FORGE site, Nevada, USA","docAbstract":"Accurate geological characterization of Fallon FORGE is important for preparing the site as an EGS laboratory. As part of this effort, a 3D geologic map was constructed previously from well logs, surface geologic mapping, 2D seismic profiles, interpreted gravity & magnetic maps, and a gravity-inferred basement surface. In this study, we have conducted both 2D and 3D modelling of high-resolution gravity and magnetic data (pre-existing and new) in an effort to further refine and test this 3D geologic map at Fallon. This effort enabled a direct comparison of the 2D and 3D model results. Potential field modelling was guided by rock-property measurements of samples from drill-core and outcrop from the Fallon area. In total, five 2D potential field model profiles, up to 30 km in length, were constructed that extend across the Fallon area. The 3D gravity model volume was 8 km (N-S) x 8 km (E-W) x 4 km (thick). The majority of the 3D gravity model volume had 100 m cubic cells; but cells near the land surface were 1 m thick to adequately capture topography. Overall, the 2D & 3D geophysical modelling largely confirmed the previously constructed 3D geologic map at Fallon for three reasons: 1) lithologic boundaries in the 2D & 3D density models mostly agree with those in the 3D geologic map, 2) the rock properties used in the models lie within the range of independent measurements made on representative rock samples from the region, and 3) the match between the observed and calculated anomalies are largely within the measurement error of the observed fields. In places where the geophysical and geologic models differ, geophysical model results have revealed subsurface structural features that have helped refine geologic interpretations which, in turn, lead to adjustments to the 3D geologic map. In this paper, we present the 2D & 3D geophysical model results and discuss how they were utilized to confirm and refine our 3D geologic understanding of the Fallon FORGE site.","language":"English","publisher":"Geothermal Resources Council","usgsCitation":"Witter, J.B., Glen, J.M., Siler, D.L., and Fournier, D., 2018, 2D and 3D potential field mapping and modelling at the Fallon FORGE site, Nevada, USA: Geothermal Resources Council Transactions, v. 42.","ipdsId":"IP-099142","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":360922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360828,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1033973"}],"volume":"42","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Witter, Jeffrey B.","contributorId":211984,"corporation":false,"usgs":false,"family":"Witter","given":"Jeffrey","email":"","middleInitial":"B.","affiliations":[{"id":38376,"text":"Innovate Geothermal","active":true,"usgs":false}],"preferred":false,"id":755418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M. G. jglen@usgs.gov","contributorId":1753,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M. G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":755416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":755417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fournier, Dominique","contributorId":211985,"corporation":false,"usgs":false,"family":"Fournier","given":"Dominique","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":755419,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201731,"text":"70201731 - 2018 - Quantifying uncertainty in Sr/Ca-based estimates of SST from the coral Orbicella faveolata","interactions":[],"lastModifiedDate":"2019-01-28T13:47:59","indexId":"70201731","displayToPublicDate":"2018-12-01T13:47:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Quantifying uncertainty in Sr/Ca-based estimates of SST from the coral Orbicella faveolata","title":"Quantifying uncertainty in Sr/Ca-based estimates of SST from the coral Orbicella faveolata","docAbstract":"

The strontium to calcium ratio (Sr/Ca) in aragonitic skeletons of massive corals provides a proxy for sea surface temperature (SST) that can be used to reconstruct paleoclimates across decades, centuries, and, potentially, millennia. Determining the reproducibility of Sr/Ca records among contemporaneous coral colonies from the same region is critical to quantifying uncertainties associated with the Sr/Ca‐SST proxy. We evaluated both intracolony and intercolony variability in Sr/Ca using five modern colonies of Orbicella faveolata collected live from the Dry Tortugas National Park, FL. We regressed all available Sr/Ca‐SST data pairs from the five O. faveolata colonies against the Advanced Very High Resolution Radiometer gridded SST data set to produce a new Sr/Ca‐SST calibration equation (Sr/Ca = −0.049 × SST + 10.460), which we suggest can be applied to O. faveolatacolonies collected throughout the Gulf of Mexico/Caribbean region. We estimated total uncertainty by calculating the root‐mean‐square of the intracolony, intercolony, and analytical error terms. Our (1σ) uncertainty estimates of 0.082 mmol/mol (1.66 °C) for subannual Sr/Ca‐SST and 0.070 mmol/mol (1.43 °C) for mean annual Sr/Ca‐SST represent conservative error terms that can be applied to individual data points in single‐colony Sr/Ca‐SST reconstructions. We illustrate how these uncertainties can be significantly reduced by generating multicolony reconstructions and/or through replication of sampling within individual coral colonies. Although the uncertainties on absolute Sr/Ca‐based SST are likely too large to allow researchers to evaluate subdecadal temperature variability, we show that the O. faveolata paleothermometer can reliably detect changes of ~2 °C across decadal timescales and ~1 °C over multidecadal timescales.

","language":"English","publisher":"AGU","doi":"10.1029/2018PA003389","usgsCitation":"Flannery, J.A., Richey, J.N., Toth, L., Kuffner, I.B., and Poore, R.Z., 2018, Quantifying uncertainty in Sr/Ca-based estimates of SST from the coral Orbicella faveolata: Paleoceanography and Paleoclimatology, v. 33, no. 9, p. 958-973, https://doi.org/10.1029/2018PA003389.","productDescription":"16 p.","startPage":"958","endPage":"973","ipdsId":"IP-088292","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":360753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-15","publicationStatus":"PW","scienceBaseUri":"5c5022c5e4b0708288f7e81b","contributors":{"authors":[{"text":"Flannery, Jennifer A. 0000-0002-1692-2662 jflannery@usgs.gov","orcid":"https://orcid.org/0000-0002-1692-2662","contributorId":4317,"corporation":false,"usgs":true,"family":"Flannery","given":"Jennifer","email":"jflannery@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":755046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richey, Julie N. 0000-0002-2319-7980 jrichey@usgs.gov","orcid":"https://orcid.org/0000-0002-2319-7980","contributorId":174046,"corporation":false,"usgs":true,"family":"Richey","given":"Julie","email":"jrichey@usgs.gov","middleInitial":"N.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":755047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":755049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":755048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poore, Richard Z. rpoore@usgs.gov","contributorId":147454,"corporation":false,"usgs":true,"family":"Poore","given":"Richard","email":"rpoore@usgs.gov","middleInitial":"Z.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":755151,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202343,"text":"70202343 - 2018 - Characteristics of tropical tree species in hyperspectral and multispectral data","interactions":[],"lastModifiedDate":"2020-11-05T16:13:36.591228","indexId":"70202343","displayToPublicDate":"2018-12-01T13:41:44","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"Characteristics of tropical tree species in hyperspectral and multispectral data","docAbstract":"

Remote sensing has been hailed as a promising technology to provide spatially explicit information on tree species distribution. Such information is of high value for ecologists and forest managers, particularly in tropical environments in which it is acquired by costly field inventories performed at the plot level (∼1 ha). Over the last decade, hyperspectral sensors, usually on board airborne platforms, have been successfully employed for tropical tree species classification. Most of the studies focused on the improvement of the classification accuracy, without examining the sources of variability in canopy reflectance that contribute to discriminate among species. Trees of different species usually feature distinct crown structural and chemical characteristics defined by size, shape, leaf density, branching, pigment and nonpigment biochemical constituents, among others. Understanding the role of these characteristics on tree species discrimination is important to improve and optimize the use of hyperspectral data. Here, we show how spectral characteristics of tree species, from highly diverse tropical forests, are related to their chemical and structural properties. These spectral characteristics were retrieved by spectral mixture and feature analysis, using narrow-band data acquired in the optical domain (450–2400 nm). Finally, we assessed how the spectral resolution affects the classification accuracy and spectral separability.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Biophysical and biochemical characterization and plant species studies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9780429431180-8","usgsCitation":"Pinheiro Ferreira, M., Hummel do Amaral, C., Vaglio Laurin, G., Kokaly, R.F., Roberto de Souza Filho, C., and Edemir Shimabukuro, Y., 2018, Characteristics of tropical tree species in hyperspectral and multispectral data, chap. 8 of Biophysical and biochemical characterization and plant species studies, 21 p., https://doi.org/10.1201/9780429431180-8.","productDescription":"21 p.","ipdsId":"IP-095891","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":361655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":758615,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":758616,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Huete, Alfredo 0000-0003-2809-2376","orcid":"https://orcid.org/0000-0003-2809-2376","contributorId":208294,"corporation":false,"usgs":false,"family":"Huete","given":"Alfredo","email":"","affiliations":[],"preferred":false,"id":758617,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Pinheiro Ferreira, Matheus","contributorId":213521,"corporation":false,"usgs":false,"family":"Pinheiro Ferreira","given":"Matheus","email":"","affiliations":[{"id":38773,"text":"National Institute for Space Research, Brazil","active":true,"usgs":false}],"preferred":false,"id":757937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hummel do Amaral, Cibele","contributorId":213522,"corporation":false,"usgs":false,"family":"Hummel do Amaral","given":"Cibele","email":"","affiliations":[{"id":38774,"text":"Universidade Federal de Viçosa (UFV), Brazil","active":true,"usgs":false}],"preferred":false,"id":757938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaglio Laurin, Gaia","contributorId":213523,"corporation":false,"usgs":false,"family":"Vaglio Laurin","given":"Gaia","email":"","affiliations":[{"id":38775,"text":"Tuscia University, Italy","active":true,"usgs":false}],"preferred":false,"id":757939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101 raymond@usgs.gov","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":150717,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"raymond@usgs.gov","middleInitial":"F.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":757936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberto de Souza Filho, Carlos","contributorId":213524,"corporation":false,"usgs":false,"family":"Roberto de Souza Filho","given":"Carlos","email":"","affiliations":[{"id":38776,"text":"University of Campinas, Brazil","active":true,"usgs":false}],"preferred":false,"id":757940,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edemir Shimabukuro, Yosio","contributorId":213525,"corporation":false,"usgs":false,"family":"Edemir Shimabukuro","given":"Yosio","email":"","affiliations":[{"id":38773,"text":"National Institute for Space Research, Brazil","active":true,"usgs":false}],"preferred":false,"id":757941,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201702,"text":"70201702 - 2018 - Identifying opportunities for long-lasting habitat conservation and restoration in Hawaii’s shifting climate","interactions":[],"lastModifiedDate":"2019-01-28T13:05:02","indexId":"70201702","displayToPublicDate":"2018-12-01T13:04:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3242,"text":"Regional Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Identifying opportunities for long-lasting habitat conservation and restoration in Hawaii’s shifting climate","docAbstract":"

Conservation efforts in isolated archipelagos such as Hawaii often focus on habitat-based conservation and restoration efforts that benefit multiple species. Unfortunately, identifying locations where such efforts are safer from climatic shifts is still challenging. We aimed to provide a method to approximate these potential habitat shifts for similar data- and research-limited contexts. We modeled the relationship between climate and the potential distribution of native biomes across the Hawaiian archipelago to provide a first approximation of potential native biome shifts under end-of-century projected climate. Our correlative model circumvents the lack of data necessary for the parameterization of mechanistic vegetation models in isolated and data-poor islands. We identified locations consistently expected to remain the same in terms of the native biome compatibility by the end of the century with a robust evaluation of sources of uncertainty in our projections. Our results show that, despite large differences in climate projections considered, 35% of the areas considered are consistently projected to maintain their current compatibility to native biomes. By integrating our native biome compatibility projections with maps of current actual cover, we identified areas ideal for long-term habitat conservation and restoration. Our modeling approach can be used with relatively simple data; offers multiple forms of projection confidence estimates, model calibration, and variable selection routines; and is compatible with ensemble projections. This method is not only applicable to potential native cover, as done in this study, but to any set of vegetation classes that are related to environmental predictors available for modeling.

","language":"English","publisher":"Springer","doi":"10.1007/s10113-018-1342-6","usgsCitation":"Fortini, L., and Jacobi, J.D., 2018, Identifying opportunities for long-lasting habitat conservation and restoration in Hawaii’s shifting climate: Regional Environmental Change, v. 18, no. 8, p. 2391-2402, https://doi.org/10.1007/s10113-018-1342-6.","productDescription":"12 p.","startPage":"2391","endPage":"2402","ipdsId":"IP-080046","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":360740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","volume":"18","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-16","publicationStatus":"PW","scienceBaseUri":"5c5022c5e4b0708288f7e821","contributors":{"authors":[{"text":"Fortini, Lucas B. 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":202074,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas B.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":754903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacobi, James D. 0000-0003-2313-7862 jjacobi@usgs.gov","orcid":"https://orcid.org/0000-0003-2313-7862","contributorId":3705,"corporation":false,"usgs":true,"family":"Jacobi","given":"James","email":"jjacobi@usgs.gov","middleInitial":"D.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":754904,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201275,"text":"70201275 - 2018 - Ontogenetic changes in swimming speed of silver carp, bighead carp, and grass carp larvae: implications for larval dispersal","interactions":[],"lastModifiedDate":"2018-12-10T12:43:46","indexId":"70201275","displayToPublicDate":"2018-12-01T12:43:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Ontogenetic changes in swimming speed of silver carp, bighead carp, and grass carp larvae: implications for larval dispersal","docAbstract":"

Bighead, silver, and grass carps are invasive in the waterways of central North America, and grass carp reproduction in tributaries of the Great Lakes has now been documented. Questions about recruitment potential motivate a need for accurate models of egg and larval dispersal. Quantitative data on swimming behaviors and capabilities during early ontogeny are needed to improve these dispersal models. We measured ontogenetic changes in routine and maximum swimming speeds of bighead, grass, and silver carp larvae. Daily measurements of routine swimming speed were taken for two weeks post-hatch using a still camera and the LARVEL program, a custom image-analysis software. Larval swimming speed was calculated using larval locations in subsequent image frames and time between images. Using an endurance chamber, we determined the maximum swimming speed of larvae (post-gas bladder inflation) for four to eight weeks post-hatch. For all species, larval swimming speeds showed similar trends with respect to ontogeny: increases in maximum speed, and decreases in routine speed. Maximum speeds of bighead and grass carp larvae were similar and generally faster than silver carp larvae. Routine swimming speeds of all larvae were highest before gas bladder inflation, most likely because gas bladder inflation allowed the fish to maintain position without swimming. Downward vertical velocities of pre-gas bladder inflation fish were faster than upward velocities. Among the three species, grass carp larvae had the highest swimming speeds in the pre-gas bladder inflation period, and the lowest speeds in the post-gas bladder inflation period. Knowledge of swimming capability of these species, along with hydraulic characteristics of a river, enables further refinement of models of embryonic and larval drift.

","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.5869","usgsCitation":"George, A.E., Garcia, T., Stahlschmidt, B.H., and Chapman, D., 2018, Ontogenetic changes in swimming speed of silver carp, bighead carp, and grass carp larvae: implications for larval dispersal: PeerJ, v. 6, e5869; 18 p., https://doi.org/10.7717/peerj.5869.","productDescription":"e5869; 18 p.","ipdsId":"IP-088251","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":460799,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.5869","text":"Publisher Index Page"},{"id":437665,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WH2NW4","text":"USGS data release","linkHelpText":"Ontogenetic changes in swimming speed of silver carp, bighead carp, and grass carp larvae-Data"},{"id":360104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-02","publicationStatus":"PW","scienceBaseUri":"5c0f897be4b0c53ecb2c71fa","contributors":{"authors":[{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":753452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Tatiana 0000-0002-1979-7246 tgarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-1979-7246","contributorId":140327,"corporation":false,"usgs":true,"family":"Garcia","given":"Tatiana","email":"tgarcia@usgs.gov","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":753453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stahlschmidt, Benjamin H. 0000-0001-6197-662X","orcid":"https://orcid.org/0000-0001-6197-662X","contributorId":211250,"corporation":false,"usgs":true,"family":"Stahlschmidt","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":753454,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":753506,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}