{"pageNumber":"401","pageRowStart":"10000","pageSize":"25","recordCount":40807,"records":[{"id":70192505,"text":"70192505 - 2018 - A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","interactions":[],"lastModifiedDate":"2021-08-12T14:44:56.393391","indexId":"70192505","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","docAbstract":"
  1. The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.
  2. We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.
  3. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.
  4. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.
  5. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.12910","usgsCitation":"Chambert, T.A., Waddle, J.H., Miller, D., Walls, S.C., and Nichols, J.D., 2018, A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing: Methods in Ecology and Evolution, v. 9, no. 3, p. 560-570, https://doi.org/10.1111/2041-210X.12910.","productDescription":"11 p.","startPage":"560","endPage":"570","ipdsId":"IP-085481","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469175,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12910","text":"Publisher Index Page"},{"id":438082,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MP51H4","text":"USGS data release","linkHelpText":"Computer automated frog vocalization results from Picayune Strand State Forest, Florida 2011-2012"},{"id":347438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-09","publicationStatus":"PW","scienceBaseUri":"5a07e85ae4b09af898c8cb58","contributors":{"authors":[{"text":"Chambert, Thierry A. 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":5973,"corporation":false,"usgs":true,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":716088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A.W.","contributorId":198461,"corporation":false,"usgs":false,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":716090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":716092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716091,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192408,"text":"70192408 - 2018 - Novel application of explicit dynamics occupancy models to ongoing aquatic invasions","interactions":[],"lastModifiedDate":"2018-02-14T14:24:19","indexId":"70192408","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Novel application of explicit dynamics occupancy models to ongoing aquatic invasions","docAbstract":"
  1. Identification of suitable habitats, where invasive species can establish, is an important step towards controlling their spread. Accurate identification is difficult for new or slow invaders because unoccupied habitats may be suitable, given enough time for dispersal, while occupied habitats may prove to be unsuitable for establishment.
  2. To identify the suitable habitat of a recent invader, I used an explicit dynamics occupancy modelling framework to evaluate habitat covariates related to successful and failed establishments of American bullfrogs (Lithobates catesbeianus) within the Yellowstone River floodplain of Montana, USA from 2012-2016.
  3. During this five-year period, bullfrogs failed to establish at most sites they colonized. Bullfrog establishment was most likely to occur and least likely to fail at sites closest to human-modified ponds and lakes and those with emergent vegetation. These habitat covariates were generally associated with the presence of permanent water.
  4. Suitable habitat for bullfrog establishment is abundant in the Yellowstone River floodplain, though many sites with suitable habitat remain uncolonized. Thus, the maximum distribution of bullfrogs is much greater than their current distribution.
  5. Synthesis and applications. Focused control efforts on habitats with or proximate to permanent waters are most likely to reduce the potential for invasive bullfrog establishment and spread in the Yellowstone River. The novel application of explicit dynamics occupancy models is a useful and widely applicable tool for guiding management efforts towards those habitats where new or slow invaders are most likely to establish and persist.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13002","usgsCitation":"Sepulveda, A., 2018, Novel application of explicit dynamics occupancy models to ongoing aquatic invasions: Journal of Applied Ecology, v. 55, no. 2, p. 917-925, https://doi.org/10.1111/1365-2664.13002.","productDescription":"9 p.","startPage":"917","endPage":"925","ipdsId":"IP-085768","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13002","text":"Publisher Index Page"},{"id":347372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.149169921875,\n 45.556371735883125\n ],\n [\n -107.6495361328125,\n 45.556371735883125\n ],\n [\n -107.6495361328125,\n 46.12274903582433\n ],\n [\n -109.149169921875,\n 46.12274903582433\n ],\n [\n -109.149169921875,\n 45.556371735883125\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-31","publicationStatus":"PW","scienceBaseUri":"59f1a299e4b0220bbd9d9ecc","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715725,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70192340,"text":"70192340 - 2018 - Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","interactions":[],"lastModifiedDate":"2018-03-23T12:06:28","indexId":"70192340","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","docAbstract":"

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2 = 0.77, RMSE = 7%), annual grasses (R2 = 0.70, RMSE = 21%), perennial grasses (R2 = 0.36, RMSE = 12%), forbs (R2 = 0.52, RMSE = 6%), bare earth or litter (R2 = 0.49, RMSE = 19%), and the biomass of shrubs (R2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.09.034","usgsCitation":"Anderson, K.E., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R., McIlroy, S., and Derryberry, D.R., 2018, Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning: Ecological Indicators, v. 84, p. 793-802, https://doi.org/10.1016/j.ecolind.2017.09.034.","productDescription":"10 p.","startPage":"793","endPage":"802","ipdsId":"IP-066377","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469177,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2017.09.034","text":"Publisher Index Page"},{"id":347311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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A database called the “Comprehensive Resource Database” (CRD) was prepared to support U.S. Geological Survey (USGS) assessments of technically recoverable hydrocarbons that might result from the injection of miscible or immiscible carbon dioxide (CO2) for enhanced oil recovery (EOR). The CRD was designed by INTEK Inc., a consulting company under contract to the USGS. The CRD contains data on the location, key petrophysical properties, production, and well counts (number of wells) for the major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska. The CRD includes proprietary data on petrophysical properties of fields and reservoirs from the “Significant Oil and Gas Fields of the United States Database,” prepared by Nehring Associates in 2012, and proprietary production and drilling data from the “Petroleum Information Data Model Relational U.S. Well Data,” prepared by IHS Inc. in 2012. This report describes the CRD and the computer algorithms used to (1) estimate missing reservoir property values in the Nehring Associates (2012) database, and to (2) generate values of additional properties used to characterize reservoirs suitable for miscible or immiscible CO2 flooding for EOR. Because of the proprietary nature of the data and contractual obligations, the CRD and actual data from Nehring Associates (2012) and IHS Inc. (2012) cannot be presented in this report.

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Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
https://energy.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2017-10-24","revisedDate":"2018-06-12","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f0511be4b0220bbd9a1d4c","contributors":{"authors":[{"text":"Carolus, Marshall","contributorId":192606,"corporation":false,"usgs":false,"family":"Carolus","given":"Marshall","email":"","affiliations":[],"preferred":false,"id":698713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biglarbigi, Khosrow","contributorId":192607,"corporation":false,"usgs":false,"family":"Biglarbigi","given":"Khosrow","email":"","affiliations":[],"preferred":false,"id":698714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":698712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":193092,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":698715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":698716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lohr, Celeste D. 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":3866,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste D.","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":698717,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192041,"text":"70192041 - 2018 - Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","interactions":[],"lastModifiedDate":"2017-12-11T13:31:28","indexId":"70192041","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","docAbstract":"

Identifying areas where deteriorating sewer infrastructure is in close proximity to surface waterways is needed to map likely connections between sewers and streams. We present a method to estimate sewer installation year and deterioration status using historical maps of the sewer network, parcel-scale property assessment data, and pipe material. Areas where streams were likely buried into the sewer system were mapped by intersecting the historical stream network derived from a 10-m resolution digital elevation model with sewer pipe locations. Potential sewer leakage hotspots were mapped by identifying where aging sewer pipes are in close proximity (50-m) to surface waterways. Results from Pittsburgh, Pennsylvania (USA), indicated 41% of the historical stream length was lost or buried and the potential interface between sewers and streams is great. The co-location of aging sewer infrastructure (>75 years old) near stream channels suggests that 42% of existing streams are located in areas with a high potential for sewer leakage if sewer infrastructure fails. Mapping the sewer-stream interface provides an approach to better understand areas were failing sewers may contribute a disproportional amount of nutrients and other pathogens to surface waterways.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2017.04.011","usgsCitation":"Hopkins, K.G., and Bain, D., 2018, Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots: Landscape and Urban Planning, v. 170, p. 320-324, https://doi.org/10.1016/j.landurbplan.2017.04.011.","productDescription":"5 p.","startPage":"320","endPage":"324","ipdsId":"IP-077253","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469179,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landurbplan.2017.04.011","text":"Publisher Index Page"},{"id":347268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":" Allegheny 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511fe4b0220bbd9a1d6c","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bain, Daniel J.","contributorId":29276,"corporation":false,"usgs":true,"family":"Bain","given":"Daniel J.","affiliations":[],"preferred":false,"id":713959,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192178,"text":"70192178 - 2018 - Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter","interactions":[],"lastModifiedDate":"2020-09-01T14:03:04.536827","indexId":"70192178","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter","docAbstract":"

Soil organic matter (SOM) supports the Earth's ability to sustain terrestrial ecosystems, provide food and fiber, and retains the largest pool of actively cycling carbon. Over 75% of the soil organic carbon (SOC) in the top meter of soil is directly affected by human land use. Large land areas have lost SOC as a result of land use practices, yet there are compensatory opportunities to enhance productivity and SOC storage in degraded lands through improved management practices. Large areas with and without intentional management are also being subjected to rapid changes in climate, making many SOC stocks vulnerable to losses by decomposition or disturbance. In order to quantify potential SOC losses or sequestration at field, regional, and global scales, measurements for detecting changes in SOC are needed. Such measurements and soil-management best practices should be based on well established and emerging scientific understanding of processes of C stabilization and destabilization over various timescales, soil types, and spatial scales. As newly engaged members of the International Soil Carbon Network, we have identified gaps in data, modeling, and communication that underscore the need for an open, shared network to frame and guide the study of SOM and SOC and their management for sustained production and climate regulation.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13896","usgsCitation":"Harden, J.W., Hugelius, G., Ahlstrom, A., Blankinship, J.C., Bond-Lamberty, B., Lawrence, C., Loisel, J., Malhotra, A., Jackson, R.B., Ogle, S.M., Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S.E., Cotrufo, M.F., Keiluweit, M., Heckman, K., Crow, S.E., Silver, W., DeLonge, M., and Nave, L.E., 2018, Networking our science to characterize the state, vulnerabilities, and management opportunities of soil organic matter: Global Change Biology, v. 24, no. 2, p. e705-e718, https://doi.org/10.1111/gcb.13896.","productDescription":"14 p.","startPage":"e705","endPage":"e718","ipdsId":"IP-090298","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":469182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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C.","contributorId":197908,"corporation":false,"usgs":false,"family":"Blankinship","given":"Joseph","email":"","middleInitial":"C.","affiliations":[{"id":34215,"text":"University of Arizona, Tucson, Arizona","active":true,"usgs":false}],"preferred":false,"id":714566,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bond-Lamberty, Ben","contributorId":172028,"corporation":false,"usgs":false,"family":"Bond-Lamberty","given":"Ben","email":"","affiliations":[{"id":33852,"text":"Univ of Maryland, College Park, MD","active":true,"usgs":false},{"id":13566,"text":"Joint Global Change Research Institute, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":714567,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lawrence, Corey 0000-0002-2179-2436 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National Laboratory","active":true,"usgs":false}],"preferred":false,"id":714570,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jackson, Robert B. 0000-0001-8846-7147","orcid":"https://orcid.org/0000-0001-8846-7147","contributorId":34252,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert","email":"","middleInitial":"B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":714571,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ogle, Stephen M.","contributorId":187520,"corporation":false,"usgs":false,"family":"Ogle","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":6935,"text":"Natural Resources Ecology Laboratory, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":714572,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Phillips, Claire","contributorId":197912,"corporation":false,"usgs":false,"family":"Phillips","given":"Claire","email":"","affiliations":[{"id":35067,"text":"USDA-ARS Forage Seed and Cereal Research Unit, Corvallis, Oregon","active":true,"usgs":false}],"preferred":false,"id":714573,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ryals, Rebecca","contributorId":197913,"corporation":false,"usgs":false,"family":"Ryals","given":"Rebecca","email":"","affiliations":[],"preferred":false,"id":714574,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Todd-Brown, Katherine","contributorId":197914,"corporation":false,"usgs":false,"family":"Todd-Brown","given":"Katherine","affiliations":[],"preferred":false,"id":714575,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Vargas, Rodrigo","contributorId":172036,"corporation":false,"usgs":false,"family":"Vargas","given":"Rodrigo","affiliations":[],"preferred":false,"id":714576,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Vergara, Sintana E.","contributorId":197916,"corporation":false,"usgs":false,"family":"Vergara","given":"Sintana","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":714577,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Cotrufo, M. Francesca","contributorId":197917,"corporation":false,"usgs":false,"family":"Cotrufo","given":"M.","email":"","middleInitial":"Francesca","affiliations":[],"preferred":false,"id":714578,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Keiluweit, Marco","contributorId":197918,"corporation":false,"usgs":false,"family":"Keiluweit","given":"Marco","email":"","affiliations":[],"preferred":false,"id":714579,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Heckman, Katherine","contributorId":172877,"corporation":false,"usgs":false,"family":"Heckman","given":"Katherine","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":714580,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Crow, Susan E.","contributorId":197920,"corporation":false,"usgs":false,"family":"Crow","given":"Susan","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":714581,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Silver, Whendee L.","contributorId":80998,"corporation":false,"usgs":true,"family":"Silver","given":"Whendee L.","affiliations":[],"preferred":false,"id":714582,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"DeLonge, Marcia","contributorId":197922,"corporation":false,"usgs":false,"family":"DeLonge","given":"Marcia","email":"","affiliations":[],"preferred":false,"id":714583,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Nave, Lucas E.","contributorId":21065,"corporation":false,"usgs":true,"family":"Nave","given":"Lucas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":714584,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70192188,"text":"70192188 - 2018 - Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones","interactions":[],"lastModifiedDate":"2018-08-03T16:23:44","indexId":"70192188","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones","docAbstract":"

Due to cultural eutrophication and global climate change, an exponential increase in the number and extent of hypoxic zones in marine and freshwater ecosystems has been observed in the last few decades. Hypoxia, or low dissolved oxygen (DO) concentrations, can produce strong negative ecological impacts and, therefore, is a management concern. We measured biomass and densities of Dreissena in Lake Erie, as well as bottom DO in 2014 using 19 high frequency data loggers distributed throughout the central basin to validate a three-dimensional hydrodynamic-ecological lake model. We found that a deep, offshore hypoxic zone was formed by early August, restricting the Dreissena population to shallow areas of the central basin. Deeper than 20 m, where bottom hypoxia routinely develops, only young of the year mussels were found in small numbers, indicating restricted recruitment and survival of young Dreissena. We suggest that monitoring Dreissenadistribution can be an effective tool for mapping the extent and frequency of hypoxia in freshwater. In addition, our results suggest that an anticipated decrease in the spatial extent of hypoxia resulting from nutrient management has the potential to increase the spatial extent of profundal habitat in the central basin available for Dreissena expansion.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.08.001","usgsCitation":"Karatayev, A.Y., Burlakova, L.E., Mehler, K., Bocaniov, S.A., Collingsworth, P.D., Warren, G., Kraus, R.T., and Hinchey, E.K., 2018, Biomonitoring using invasive species in a large Lake: Dreissena distribution maps hypoxic zones: Journal of Great Lakes Research, v. 44, no. 4, p. 639-649, https://doi.org/10.1016/j.jglr.2017.08.001.","productDescription":"11 p.","startPage":"639","endPage":"649","ipdsId":"IP-074848","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469180,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://europepmc.org/articles/pmc6350251","text":"External Repository"},{"id":347124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.8104248046875,\n 42.779275360241904\n ],\n [\n -78.892822265625,\n 42.90413649491736\n ],\n [\n -79.1400146484375,\n 42.896088552971065\n ],\n [\n -79.716796875,\n 42.87596410238256\n ],\n [\n -80.255126953125,\n 42.80346172417078\n ],\n [\n -80.52978515625,\n 42.589488572714245\n ],\n [\n -80.8154296875,\n 42.68243539838623\n ],\n [\n -81.3702392578125,\n 42.66628070564928\n ],\n [\n -82.0513916015625,\n 42.28950073090457\n ],\n [\n -82.5732421875,\n 42.05745022024682\n ],\n [\n -82.9522705078125,\n 42.02889410108475\n ],\n [\n -83.12805175781249,\n 42.13896840458089\n ],\n [\n -83.36975097656249,\n 41.95949009892467\n ],\n [\n -83.5400390625,\n 41.68111756290652\n ],\n [\n -83.0731201171875,\n 41.430371882652814\n ],\n [\n -82.5457763671875,\n 41.372686481864655\n ],\n [\n -81.7657470703125,\n 41.45507852101139\n ],\n [\n -80.52429199218749,\n 41.93088998442502\n ],\n [\n -79.156494140625,\n 42.524748042348165\n ],\n [\n -78.8104248046875,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffa1e4b0220bbd988f52","contributors":{"authors":[{"text":"Karatayev, Alexander Y.","contributorId":150923,"corporation":false,"usgs":false,"family":"Karatayev","given":"Alexander","email":"","middleInitial":"Y.","affiliations":[{"id":18141,"text":"SUNY Buffalo State","active":true,"usgs":false}],"preferred":false,"id":714643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burlakova, Lyubov E.","contributorId":150918,"corporation":false,"usgs":false,"family":"Burlakova","given":"Lyubov","email":"","middleInitial":"E.","affiliations":[{"id":18141,"text":"SUNY Buffalo State","active":true,"usgs":false}],"preferred":false,"id":714644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mehler, Knut","contributorId":197953,"corporation":false,"usgs":false,"family":"Mehler","given":"Knut","email":"","affiliations":[],"preferred":false,"id":714645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bocaniov, Serghei A.","contributorId":197954,"corporation":false,"usgs":false,"family":"Bocaniov","given":"Serghei","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":714647,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warren, Glenn","contributorId":16375,"corporation":false,"usgs":true,"family":"Warren","given":"Glenn","affiliations":[],"preferred":false,"id":714648,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714642,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinchey, Elizabeth K.","contributorId":197957,"corporation":false,"usgs":false,"family":"Hinchey","given":"Elizabeth","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":714649,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192138,"text":"70192138 - 2018 - Groundwater development stress: Global-scale indices compared to regional modeling","interactions":[],"lastModifiedDate":"2018-09-12T16:08:09","indexId":"70192138","displayToPublicDate":"2017-10-23T00:00:00","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":"Groundwater development stress: Global-scale indices compared to regional modeling","docAbstract":"

The increased availability of global datasets and technologies such as global hydrologic models and the Gravity Recovery and Climate Experiment (GRACE) satellites have resulted in a growing number of global-scale assessments of water availability using simple indices of water stress. Developed initially for surface water, such indices are increasingly used to evaluate global groundwater resources. We compare indices of groundwater development stress for three major agricultural areas of the United States to information available from regional water budgets developed from detailed groundwater modeling. These comparisons illustrate the potential value of regional-scale analyses to supplement global hydrological models and GRACE analyses of groundwater depletion. Regional-scale analyses allow assessments of water stress that better account for scale effects, the dynamics of groundwater flow systems, the complexities of irrigated agricultural systems, and the laws, regulations, engineering, and socioeconomic factors that govern groundwater use. Strategic use of regional-scale models with global-scale analyses would greatly enhance knowledge of the global groundwater depletion problem.

","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12578","usgsCitation":"Alley, W., Clark, B.R., Ely, M., and Faunt, C., 2018, Groundwater development stress: Global-scale indices compared to regional modeling: Groundwater, v. 56, no. 2, p. 266-275, https://doi.org/10.1111/gwat.12578.","productDescription":"10 p.","startPage":"266","endPage":"275","ipdsId":"IP-088279","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":347137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-15","publicationStatus":"PW","scienceBaseUri":"59eeffa2e4b0220bbd988f5a","contributors":{"authors":[{"text":"Alley, William 0000-0001-7286-3938 walley@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3938","contributorId":140175,"corporation":false,"usgs":true,"family":"Alley","given":"William","email":"walley@usgs.gov","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":714370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Brian R. 0000-0001-6611-3807 brclark@usgs.gov","orcid":"https://orcid.org/0000-0001-6611-3807","contributorId":1502,"corporation":false,"usgs":true,"family":"Clark","given":"Brian","email":"brclark@usgs.gov","middleInitial":"R.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":714371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ely, Matt 0000-0003-3190-2907 mely@usgs.gov","orcid":"https://orcid.org/0000-0003-3190-2907","contributorId":1641,"corporation":false,"usgs":true,"family":"Ely","given":"Matt","email":"mely@usgs.gov","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faunt, Claudia C. 0000-0001-5659-7529 ccfaunt@usgs.gov","orcid":"https://orcid.org/0000-0001-5659-7529","contributorId":150147,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia C.","email":"ccfaunt@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192196,"text":"70192196 - 2018 - Fine-scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America","interactions":[],"lastModifiedDate":"2018-03-05T15:46:57","indexId":"70192196","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fine-scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America","title":"Fine-scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America","docAbstract":"

Previous studies of lake trout, Salvelinus namaycush, spawning habitat in the Laurentian Great Lakes have used time- and labour-intensive survey methods and have focused on areas with historic observations of spawning aggregations and on habitats prejudged by researchers to be suitable for spawning. As an alternative, we used fine-scale acoustic telemetry to locate, describe and compare lake trout spawning habitats. Adult lake trout were implanted with acoustic transmitters and tracked during five consecutive spawning seasons in a 19–27 km2 region of the Drummond Island Refuge, Lake Huron, using the VEMCO Positioning System. Acoustic telemetry revealed discrete areas of aggregation on at least five reefs in the study area, subsequently confirmed by divers to contain deposited eggs. Notably, several identified spawning sites would likely not have been discovered using traditional methods because either they were too small and obscure to stand out on a bathymetric map or because they did not conform to the conceptual model of spawning habitat held by many biologists. Our most unique observation was egg deposition in gravel and rubble substrates located at the base of and beneath overhanging edges of large boulders. Spawning sites typically comprised <10% of the reef area and were used consistently over the 5-year study. Evaluation of habitat selection from the perspective of fish behaviour through use of acoustic transmitters offers potential to expand current conceptual models of critical spawning habitat.

","language":"English","publisher":"Wiley","doi":"10.1111/eff.12373","usgsCitation":"Binder, T., Farha, S., Thompson, H.T., Holbrook, C., Bergstedt, R., Riley, S., Bronte, C.R., He, J., and Krueger, C., 2018, Fine-scale acoustic telemetry reveals unexpected lake trout, Salvelinus namaycush, spawning habitats in northern Lake Huron, North America: Ecology of Freshwater Fish, v. 27, no. 2, p. 594-605, https://doi.org/10.1111/eff.12373.","productDescription":"12 p.","startPage":"594","endPage":"605","ipdsId":"IP-088842","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12373","text":"Publisher Index Page"},{"id":347117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.697,\n 45.899\n ],\n [\n -83.619,\n 45.899\n ],\n [\n -83.619,\n 45.941\n ],\n [\n -83.697,\n 45.941\n ],\n [\n -83.697,\n 45.899\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-04","publicationStatus":"PW","scienceBaseUri":"59eeffa0e4b0220bbd988f50","contributors":{"authors":[{"text":"Binder, Thomas 0000-0001-9266-9120 tbinder@usgs.gov","orcid":"https://orcid.org/0000-0001-9266-9120","contributorId":4958,"corporation":false,"usgs":true,"family":"Binder","given":"Thomas","email":"tbinder@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farha, Steve A. 0000-0001-9953-6996 sfarha@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-6996","contributorId":5170,"corporation":false,"usgs":true,"family":"Farha","given":"Steve A.","email":"sfarha@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Henry T. 0000-0002-3730-9322 hthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-3730-9322","contributorId":5028,"corporation":false,"usgs":true,"family":"Thompson","given":"Henry","email":"hthompson@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714688,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714689,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergstedt, Roger A.","contributorId":190726,"corporation":false,"usgs":false,"family":"Bergstedt","given":"Roger A.","affiliations":[],"preferred":false,"id":714690,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Stephen 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":169479,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":714691,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":714692,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"He, Ji","contributorId":172649,"corporation":false,"usgs":false,"family":"He","given":"Ji","affiliations":[],"preferred":false,"id":714693,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":714694,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192073,"text":"70192073 - 2018 - Identifying species conservation strategies to reduce disease-associated declines","interactions":[],"lastModifiedDate":"2018-04-17T12:44:25","indexId":"70192073","displayToPublicDate":"2017-10-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1326,"text":"Conservation Letters","active":true,"publicationSubtype":{"id":10}},"title":"Identifying species conservation strategies to reduce disease-associated declines","docAbstract":"

Emerging infectious diseases (EIDs) are a salient threat to many animal taxa, causing local and global extinctions, altering communities and ecosystem function. The EID chytridiomycosis is a prominent driver of amphibian declines, which is caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd). To guide conservation policy, we developed a predictive decision-analytic model that combines empirical knowledge of host-pathogen metapopulation dynamics with expert judgment regarding effects of management actions, to select from potential conservation strategies. We apply our approach to a boreal toad (Anaxyrus boreas boreas) and Bd system, identifying optimal strategies that balance tradeoffs in maximizing toad population persistence and landscape-level distribution, while considering costs. The most robust strategy is expected to reduce the decline of toad breeding sites from 53% to 21% over 50 years. Our findings are incorporated into management policy to guide conservation planning. Our online modeling application provides a template for managers of other systems challenged by EIDs.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/conl.12393","usgsCitation":"Gerber, B.D., Converse, S.J., Muths, E.L., Crockett, H.J., Mosher, B.A., and Bailey, L., 2018, Identifying species conservation strategies to reduce disease-associated declines: Conservation Letters, v. 11, no. 2, p. 1-10, https://doi.org/10.1111/conl.12393.","productDescription":"e12393; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-083900","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469183,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.12393","text":"Publisher Index Page"},{"id":347003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-31","publicationStatus":"PW","scienceBaseUri":"59e9b98ee4b05fe04cd65c24","contributors":{"authors":[{"text":"Gerber, Brian D.","contributorId":187620,"corporation":false,"usgs":false,"family":"Gerber","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":714181,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":714082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crockett, Harry J.","contributorId":75417,"corporation":false,"usgs":true,"family":"Crockett","given":"Harry","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mosher, Brittany A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bailey, Larissa L.","contributorId":93183,"corporation":false,"usgs":true,"family":"Bailey","given":"Larissa L.","affiliations":[],"preferred":false,"id":714184,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192068,"text":"70192068 - 2018 - Tagging effects of passive integrated transponder and visual implant elastomer on the small-bodied white sands pupfish (Cyprinodon tularosa)","interactions":[],"lastModifiedDate":"2017-11-10T14:10:06","indexId":"70192068","displayToPublicDate":"2017-10-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tagging effects of passive integrated transponder and visual implant elastomer on the small-bodied white sands pupfish (Cyprinodon tularosa)","title":"Tagging effects of passive integrated transponder and visual implant elastomer on the small-bodied white sands pupfish (Cyprinodon tularosa)","docAbstract":"

One of the greatest limiting factors of studies designed to obtain growth, movement, and survival in small-bodied fishes is the selection of a viable tag. The tag must be relatively small with respect to body size as to impart minimal sub-lethal effects on growth and mobility, as well as be retained throughout the life of the fish or duration of the study. Thus, body size of the model species becomes a major limiting factor; yet few studies have obtained empirical evidence of the minimum fish size and related tagging effects. The probability of surviving a tagging event was quantified in White Sands pupfish (Cyprinodon tularosa) across a range of sizes (19–60 mm) to address the hypothesis that body size predicts tagging survival. We compared tagging related mortality, individual taggers, growth, and tag retention in White Sands pupfish implanted with 8-mm passive integrated transponder (PIT), visual implant elastomer (VIE), and control (handled similarly, but no tag implantation) over a 75 d period. Initial body weight was a good predictor of the probability of survival in PIT- and VIE-tagged fish. As weight increased by 1 g, the fish were 4.73 times more likely to survive PIT-tag implantation compared to the control fish with an estimated suitable tagging size at 1.1 g (TL: 39.29 ± 0.41 mm). Likewise, VIE-tagged animals were 2.27 times more likely to survive a tagging event compared to the control group for every additional 1 g with an estimated size suitable for tagging of 0.9 g (TL: 36.9 ± 0.36 mm) fish. Growth rates of PIT- and VIE-tagged White Sands pupfish were similar to the control groups. This research validated two popular tagging methodologies in the White Sands pupfish, thus providing a valuable tool for characterizing vital rates in other small-bodied fishes.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2017.08.019","usgsCitation":"Peterson, D., Trantham, R.B., Trantham, T.G., and Caldwell, C.A., 2018, Tagging effects of passive integrated transponder and visual implant elastomer on the small-bodied white sands pupfish (Cyprinodon tularosa): Fisheries Research, v. 198, p. 203-208, https://doi.org/10.1016/j.fishres.2017.08.019.","productDescription":"6 p.","startPage":"203","endPage":"208","ipdsId":"IP-082439","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469184,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2017.08.019","text":"Publisher Index Page"},{"id":346982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"198","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e9b98fe4b05fe04cd65c28","contributors":{"authors":[{"text":"Peterson, Damon","contributorId":197677,"corporation":false,"usgs":false,"family":"Peterson","given":"Damon","email":"","affiliations":[],"preferred":false,"id":714066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trantham, Randi B.","contributorId":197678,"corporation":false,"usgs":false,"family":"Trantham","given":"Randi","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":714067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trantham, Tulley G.","contributorId":197679,"corporation":false,"usgs":false,"family":"Trantham","given":"Tulley","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":714068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell, Colleen A. 0000-0002-4730-4867 ccaldwel@usgs.gov","orcid":"https://orcid.org/0000-0002-4730-4867","contributorId":3050,"corporation":false,"usgs":true,"family":"Caldwell","given":"Colleen","email":"ccaldwel@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714058,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191516,"text":"70191516 - 2018 - The effects of snow and salt on ice table stability in University Valley, Antarctica","interactions":[],"lastModifiedDate":"2018-01-24T15:56:00","indexId":"70191516","displayToPublicDate":"2017-10-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":814,"text":"Antarctic Science","onlineIssn":"1365-2079","printIssn":"0954-1020","active":true,"publicationSubtype":{"id":10}},"title":"The effects of snow and salt on ice table stability in University Valley, Antarctica","docAbstract":"

The Antarctic Dry Valleys represent a unique environment where it is possible to study dry permafrost overlaying an ice-rich permafrost. In this paper, two opposing mechanisms for ice table stability in University Valley are addressed: i) diffusive recharge via thin seasonal snow deposits and ii) desiccation via salt deposits in the upper soil column. A high-resolution time-marching soil and snow model was constructed and applied to University Valley, driven by meteorological station atmospheric measurements. It was found that periodic thin surficial snow deposits (observed in University Valley) are capable of drastically slowing (if not completely eliminating) the underlying ice table ablation. The effects of NaCl, CaCl2 and perchlorate deposits were then modelled. Unlike the snow cover, however, the presence of salt in the soil surface (but no periodic snow) results in a slight increase in the ice table recession rate, due to the hygroscopic effects of salt sequestering vapour from the ice table below. Near-surface pore ice frequently forms when large amounts of salt are present in the soil due to the suppression of the saturation vapour pressure. Implications for Mars high latitudes are discussed.

","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0954102017000402","usgsCitation":"Williams, K.E., Heldmann, J.L., McKay, C.P., and Mellon, M.T., 2018, The effects of snow and salt on ice table stability in University Valley, Antarctica: Antarctic Science, v. 30, no. 1, p. 67-78, https://doi.org/10.1017/S0954102017000402.","productDescription":"12 p.","startPage":"67","endPage":"78","ipdsId":"IP-086125","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469185,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7430506","text":"External Repository"},{"id":346627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, University Valley","volume":"30","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-13","publicationStatus":"PW","scienceBaseUri":"59e5c518e4b05fe04cd1c9c2","contributors":{"authors":[{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":712563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heldmann, Jennifer L.","contributorId":197096,"corporation":false,"usgs":false,"family":"Heldmann","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":712564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKay, Christopher P.","contributorId":197097,"corporation":false,"usgs":false,"family":"McKay","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":712565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mellon, Michael T.","contributorId":8603,"corporation":false,"usgs":false,"family":"Mellon","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7037,"text":"Southwest Research Institute, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":712566,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191455,"text":"70191455 - 2018 - Meteorological and environmental variables affect flight behaviour and decision-making of an obligate soaring bird, the California Condor Gymnogyps californianus","interactions":[],"lastModifiedDate":"2017-12-11T13:35:23","indexId":"70191455","displayToPublicDate":"2017-10-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Meteorological and environmental variables affect flight behaviour and decision-making of an obligate soaring bird, the California Condor Gymnogyps californianus","title":"Meteorological and environmental variables affect flight behaviour and decision-making of an obligate soaring bird, the California Condor Gymnogyps californianus","docAbstract":"

The movements of animals are limited by evolutionary constraints and ecological processes and are strongly influenced by the medium through which they travel. For flying animals, variation in atmospheric conditions is critically influential in movement. Obligate soaring birds depend on external sources of updraft more than do other flying species, as without that updraft they are unable to sustain flight for extended periods. These species are therefore good models for understanding how the environment can influence decisions about movement. We used meteorological and topographic variables to understand the environmental influences on the decision to engage in flight by obligate soaring and critically endangered California Condors Gymnogyps californianus. Condors were more likely to fly, soared at higher altitudes and flew over smoother terrain when weather conditions promoted either thermal or orographic updrafts, for example when turbulence and solar radiation were higher and when winds from the east and north were stronger. However, increased atmospheric stability, which is inconsistent with thermal development but may be associated with orographic updrafts, was correlated with a somewhat higher probability of being in flight at lower altitudes and over rougher terrain. The close and previously undescribed linkages between Condor flight and conditions that support development of thermal and orographic updrafts provide important insight into the behaviour of obligate soaring birds and into the environmental parameters that may define the currently expanding distribution of Condors within and outside the state of California.

","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12531","usgsCitation":"Poessel, S.A., Brandt, J., Miller, T.A., and Katzner, T., 2018, Meteorological and environmental variables affect flight behaviour and decision-making of an obligate soaring bird, the California Condor Gymnogyps californianus: Ibis, v. 160, no. 1, p. 36-53, https://doi.org/10.1111/ibi.12531.","productDescription":"18 p.","startPage":"36","endPage":"53","ipdsId":"IP-082014","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":346626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10205078125,\n 34.15272698011818\n ],\n [\n -117.861328125,\n 34.15272698011818\n ],\n [\n -117.861328125,\n 36.958671131530316\n ],\n [\n -122.10205078125,\n 36.958671131530316\n ],\n [\n -122.10205078125,\n 34.15272698011818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"160","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-06","publicationStatus":"PW","scienceBaseUri":"59e5c51be4b05fe04cd1c9cc","contributors":{"authors":[{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":712338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":712339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":712340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":712341,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191342,"text":"70191342 - 2018 - Variation in fish mercury concentrations in streams of the Adirondack region, New York: A simplified screening approach using chemical metrics","interactions":[],"lastModifiedDate":"2017-10-05T15:51:07","indexId":"70191342","displayToPublicDate":"2017-10-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Variation in fish mercury concentrations in streams of the Adirondack region, New York: A simplified screening approach using chemical metrics","docAbstract":"

Simple screening approaches for the neurotoxicant methylmercury (MeHg) in aquatic ecosystems may be helpful in risk assessments of natural resources. We explored the development of such an approach in the Adirondack Mountains of New York, USA, a region with high levels of MeHg bioaccumulation. Thirty-six perennial streams broadly representative of 1st and 2nd order streams in the region were sampled during summer low flow and analyzed for several solutes and for Hg concentrations in fish. Several landscape and chemical metrics that are typically strongly related to MeHg concentrations in aquatic biota were explored for strength of association with fish Hg concentrations. Data analyses were based on site mean length-normalized and standardized Hg concentrations (assumed to be dominantly MeHg) in whole juvenile and adult Brook Trout Salvelinus fontinalis, Creek Chub Semotilus atromaculatus, Blacknose Dace Rhinichthys atratulus, and Central Mudminnow Umbra limi, as well as on multi-species z-scores. Surprisingly, none of the landscape metrics was related significantly to regional variation in fish Hg concentrations or to z-scores across the study streams. In contrast, several chemical metrics including dissolved organic carbon (DOC) concentrations, sulfate concentrations (SO42−), pH, ultra-violet absorbance (UV254), and specific ultra-violet absorbance were significantly related to regional variation in fish Hg concentrations. A cluster analysis based on DOC, SO42−, and pH identified three distinct groups of streams: (1) high DOC, acidic streams, (2) moderate DOC, slightly acidic streams, and (3) low DOC circum-neutral streams with relatively high SO42−. Preliminary analysis indicated no significant difference in fish Hg z-scores between the moderate and high DOC groups, so these were combined for further analysis. The resulting two groups showed strong differences (p < 0.001) in DOC and SO42−concentrations as well as in pH and UV254 values. Median fish z-scores were significantly higher (p = 0.002) in the group of streams with higher DOC and UV254 and lower pH and SO42−. Screening values of DOC >6.9 mg/L, SO42− <2.8 mg/L, pH <6.6, and UV254>0.31 cm−1 were tested as thresholds to identify Adirondack stream sites likely to have higher fish Hg concentrations. By applying a combined threshold of exceedance for either pH or UV254, sites with fish Hg concentrations that exceeded a wildlife guideline of 100 ng/g were correctly identified about 75% of the time among the 36 study streams. An estimate of Hg risk applied to a data set of 391 streams based on DOC concentrations showed that about 28% were likely to pose high risk to wildlife; most of these streams were located in the western Adirondacks.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.09.031","usgsCitation":"Burns, D.A., and Riva-Murray, K., 2018, Variation in fish mercury concentrations in streams of the Adirondack region, New York: A simplified screening approach using chemical metrics: Ecological Indicators, v. 84, p. 648-661, https://doi.org/10.1016/j.ecolind.2017.09.031.","productDescription":"14 p.","startPage":"648","endPage":"661","ipdsId":"IP-086048","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":469187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2017.09.031","text":"Publisher Index Page"},{"id":346436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4376220703125,\n 42.99661231842139\n ],\n [\n -73.3172607421875,\n 42.99661231842139\n ],\n [\n -73.3172607421875,\n 44.89090425391711\n ],\n [\n -75.4376220703125,\n 44.89090425391711\n ],\n [\n -75.4376220703125,\n 42.99661231842139\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d74496e4b05fe04cc7e2d4","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riva-Murray, Karen krmurray@usgs.gov","contributorId":168654,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":712012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191264,"text":"70191264 - 2018 - Estimating carbon and showing impacts of drought using satellite data in regression-tree models","interactions":[],"lastModifiedDate":"2022-04-01T22:41:03.609362","indexId":"70191264","displayToPublicDate":"2017-10-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating carbon and showing impacts of drought using satellite data in regression-tree models","docAbstract":"

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

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Center","active":true,"usgs":true}],"preferred":true,"id":711760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176973,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":711761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":711762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilmanov, Tagir G.","contributorId":146124,"corporation":false,"usgs":false,"family":"Gilmanov","given":"Tagir","email":"","middleInitial":"G.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":711763,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191810,"text":"70191810 - 2018 - 2.3. Global-scale atmospheric dispersion of microorganisms","interactions":[],"lastModifiedDate":"2017-12-01T13:38:26","indexId":"70191810","displayToPublicDate":"2017-10-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"2.3. Global-scale atmospheric dispersion of microorganisms","docAbstract":"

This chapter addresses long-range dispersion and the survival of microorganisms across a wide range of altitudes in Earth's atmosphere. Topics include mechanisms of dispersion, survivability of microorganisms known to be associated with long-range transport, natural and artificial sources of bioaerosols, residence time estimation through the use of proxy aerosols, transport and emission models, and monitoring assays (both culture and molecular based). We conclude with a discussion of the known limits for Earth's biosphere boundary, relating aerobiology studies to planetary exploration given the large degree of overlapping requirements for in situ studies (including low biomass life detection and contamination control).

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Microbiology of aerosols","language":"English","publisher":"Wiley","doi":"10.1002/9781119132318.ch2c","usgsCitation":"Griffin, D.W., Gonzalez-Martin, C., Hoose, C., and Smith, D., 2018, 2.3. Global-scale atmospheric dispersion of microorganisms, chap. of Microbiology of aerosols, https://doi.org/10.1002/9781119132318.ch2c.","ipdsId":"IP-074805","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":349637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-22","publicationStatus":"PW","scienceBaseUri":"5a60fad8e4b06e28e9c227db","contributors":{"editors":[{"text":"Delort, Anne-Marie","contributorId":201091,"corporation":false,"usgs":false,"family":"Delort","given":"Anne-Marie","email":"","affiliations":[],"preferred":false,"id":724307,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Amato, Pierre","contributorId":201092,"corporation":false,"usgs":false,"family":"Amato","given":"Pierre","email":"","affiliations":[],"preferred":false,"id":724308,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":713213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez-Martin, Cristina","contributorId":30084,"corporation":false,"usgs":true,"family":"Gonzalez-Martin","given":"Cristina","email":"","affiliations":[],"preferred":false,"id":724304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoose, C.","contributorId":201090,"corporation":false,"usgs":false,"family":"Hoose","given":"C.","email":"","affiliations":[],"preferred":false,"id":724305,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, D.J.","contributorId":48417,"corporation":false,"usgs":true,"family":"Smith","given":"D.J.","email":"","affiliations":[],"preferred":false,"id":724306,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191177,"text":"70191177 - 2018 - Evaluation of bias associated with capture maps derived from nonlinear groundwater flow models","interactions":[],"lastModifiedDate":"2025-01-29T15:52:18.355099","indexId":"70191177","displayToPublicDate":"2017-09-28T00:00:00","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":"Evaluation of bias associated with capture maps derived from nonlinear groundwater flow models","docAbstract":"

The impact of groundwater withdrawal on surface water is a concern of water users and water managers, particularly in the arid western United States. Capture maps are useful tools to spatially assess the impact of groundwater pumping on water sources (e.g., streamflow depletion) and are being used more frequently for conjunctive management of surface water and groundwater. Capture maps have been derived using linear groundwater flow models and rely on the principle of superposition to demonstrate the effects of pumping in various locations on resources of interest. However, nonlinear models are often necessary to simulate head-dependent boundary conditions and unconfined aquifers. Capture maps developed using nonlinear models with the principle of superposition may over- or underestimate capture magnitude and spatial extent. This paper presents new methods for generating capture difference maps, which assess spatial effects of model nonlinearity on capture fraction sensitivity to pumping rate, and for calculating the bias associated with capture maps. The sensitivity of capture map bias to selected parameters related to model design and conceptualization for the arid western United States is explored. This study finds that the simulation of stream continuity, pumping rates, stream incision, well proximity to capture sources, aquifer hydraulic conductivity, and groundwater evapotranspiration extinction depth substantially affect capture map bias. Capture difference maps demonstrate that regions with large capture fraction differences are indicative of greater potential capture map bias. Understanding both spatial and temporal bias in capture maps derived from nonlinear groundwater flow models improves their utility and defensibility as conjunctive-use management tools.

","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12597","usgsCitation":"Nadler, C.A., Allander, K.K., Pohll, G., Morway, E.D., Naranjo, R.C., and Huntington, J., 2018, Evaluation of bias associated with capture maps derived from nonlinear groundwater flow models: Groundwater, v. 56, no. 3, p. 458-469, https://doi.org/10.1111/gwat.12597.","productDescription":"12 p.","startPage":"458","endPage":"469","ipdsId":"IP-083048","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":346162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381594,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/ja/70191177/70191177.pdf","text":"USGS open-access version of article","size":"1 MB","linkFileType":{"id":1,"text":"pdf"}}],"volume":"56","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-21","publicationStatus":"PW","scienceBaseUri":"59ce0a23e4b05fe04cc020e7","contributors":{"authors":[{"text":"Nadler, Cara A. 0000-0002-8711-7249 cnadler@usgs.gov","orcid":"https://orcid.org/0000-0002-8711-7249","contributorId":196757,"corporation":false,"usgs":true,"family":"Nadler","given":"Cara","email":"cnadler@usgs.gov","middleInitial":"A.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allander, Kip K. 0000-0002-3317-298X kalland@usgs.gov","orcid":"https://orcid.org/0000-0002-3317-298X","contributorId":2290,"corporation":false,"usgs":true,"family":"Allander","given":"Kip","email":"kalland@usgs.gov","middleInitial":"K.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pohll, Greg","contributorId":196758,"corporation":false,"usgs":false,"family":"Pohll","given":"Greg","email":"","affiliations":[],"preferred":false,"id":711437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huntington, Justin 0000-0002-2699-0108","orcid":"https://orcid.org/0000-0002-2699-0108","contributorId":178785,"corporation":false,"usgs":false,"family":"Huntington","given":"Justin","affiliations":[],"preferred":false,"id":711436,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191167,"text":"70191167 - 2018 - Semi-quantitative assessment of disease risks at the human, livestock, wildlife interface for the Republic of Korea using a nationwide survey of experts: A model for other countries","interactions":[],"lastModifiedDate":"2018-01-10T19:30:24","indexId":"70191167","displayToPublicDate":"2017-09-28T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Semi-quantitative assessment of disease risks at the human, livestock, wildlife interface for the Republic of Korea using a nationwide survey of experts: A model for other countries","docAbstract":"

Wildlife-associated diseases and pathogens have increased in importance; however, management of a large number of diseases and diversity of hosts is prohibitively expensive. Thus, the determination of priority wildlife pathogens and risk factors for disease emergence is warranted. We used an online questionnaire survey to assess release and exposure risks, and consequences of wildlife-associated diseases and pathogens in the Republic of Korea (ROK). We also surveyed opinions on pathways for disease exposure, and risk factors for disease emergence and spread. For the assessment of risk, we employed a two-tiered, statistical K-means clustering algorithm to group diseases into three levels (high, medium and low) of perceived risk based on release and exposure risks, societal consequences and the level of uncertainty of the experts’ opinions. To examine the experts’ perceived risk of routes of introduction of pathogens and disease amplification and spread, we used a Bayesian, multivariate normal order-statistics model. Six diseases or pathogens, including four livestock and two wildlife diseases, were identified as having high risk with low uncertainty. Similarly, 13 diseases were characterized as having high risk with medium uncertainty with three of these attributed to livestock, six associated with human disease, and the remainder having the potential to affect human, livestock and wildlife (i.e., One Health). Lastly, four diseases were described as high risk with high certainty, and were associated solely with fish diseases. Experts identified migration of wildlife, international human movement and illegal importation of wildlife as the three routes posing the greatest risk of pathogen introduction into ROK. Proximity of humans, livestock and wildlife was the most significant risk factor for promoting the spread of wildlife-associated diseases and pathogens, followed by high density of livestock populations, habitat loss and environmental degradation, and climate change. This study provides useful information to decision makers responsible for allocating resources to address disease risks. This approach provided a rapid, cost-effective method of risk assessment of wildlife-associated diseases and pathogens for which the published literature is sparse.

","language":"English","publisher":"Wiley","doi":"10.1111/tbed.12705","usgsCitation":"Hwang, J., Lee, K., Walsh, D.P., Kim, S., Sleeman, J.M., and Lee, H., 2018, Semi-quantitative assessment of disease risks at the human, livestock, wildlife interface for the Republic of Korea using a nationwide survey of experts: A model for other countries: Transboundary and Emerging Diseases, v. 65, no. 1, p. e155-e164, https://doi.org/10.1111/tbed.12705.","productDescription":"10 p.","startPage":"e155","endPage":"e164","ipdsId":"IP-084895","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":346156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Republic of Korea","volume":"65","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-22","publicationStatus":"PW","scienceBaseUri":"59ce0a27e4b05fe04cc020fa","contributors":{"authors":[{"text":"Hwang, Jusun","contributorId":175221,"corporation":false,"usgs":false,"family":"Hwang","given":"Jusun","email":"","affiliations":[{"id":27539,"text":"College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea","active":true,"usgs":false}],"preferred":false,"id":711385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Kyunglee","contributorId":175223,"corporation":false,"usgs":false,"family":"Lee","given":"Kyunglee","email":"","affiliations":[{"id":27540,"text":"Cetacean Research Institute, National Fisheries Research and Development Institute, Ulsan, Republic of Korea","active":true,"usgs":false}],"preferred":false,"id":711386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":711387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, SangWha","contributorId":196739,"corporation":false,"usgs":false,"family":"Kim","given":"SangWha","email":"","affiliations":[],"preferred":false,"id":711388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":711384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, Hang","contributorId":191778,"corporation":false,"usgs":false,"family":"Lee","given":"Hang","email":"","affiliations":[],"preferred":false,"id":711389,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191912,"text":"70191912 - 2018 - Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States","interactions":[],"lastModifiedDate":"2018-03-26T14:34:07","indexId":"70191912","displayToPublicDate":"2017-09-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States","docAbstract":"

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

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modeling","interactions":[],"lastModifiedDate":"2018-02-12T15:44:26","indexId":"70190745","displayToPublicDate":"2017-09-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling","docAbstract":"

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13863","usgsCitation":"Feng, X., Uriarte, M., Gonzalez, G., Reed, S.C., Thompson, J., Zimmerman, J.K., and Murphy, L., 2018, Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling: Global Change Biology, v. 24, no. 1, p. e213-e232, https://doi.org/10.1111/gcb.13863.","productDescription":"20 p.","startPage":"e213","endPage":"e232","ipdsId":"IP-086186","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":487993,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.13863","text":"Publisher Index Page"},{"id":345704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-21","publicationStatus":"PW","scienceBaseUri":"59ba43b6e4b091459a5629a3","contributors":{"authors":[{"text":"Feng, Xiaohui","contributorId":196416,"corporation":false,"usgs":false,"family":"Feng","given":"Xiaohui","email":"","affiliations":[],"preferred":false,"id":710300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uriarte, Maria","contributorId":196417,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","email":"","affiliations":[],"preferred":false,"id":710301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gonzalez, Grizelle","contributorId":191117,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Grizelle","email":"","affiliations":[],"preferred":false,"id":710302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":710299,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Jill","contributorId":201454,"corporation":false,"usgs":false,"family":"Thompson","given":"Jill","email":"","affiliations":[],"preferred":false,"id":710303,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zimmerman, Jess K.","contributorId":196419,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Jess","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":710304,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murphy, Lora","contributorId":196420,"corporation":false,"usgs":false,"family":"Murphy","given":"Lora","email":"","affiliations":[],"preferred":false,"id":710305,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190548,"text":"70190548 - 2018 - Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California","interactions":[],"lastModifiedDate":"2018-04-27T16:53:57","indexId":"70190548","displayToPublicDate":"2017-09-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California","title":"Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California","docAbstract":"Vast sections of the Sacramento River have been listed as critical habitat by the National Marine Fisheries Service for green sturgeon spawning (Acipenser medirostris), yet spawning is known to occur at only a few specific locations. This study reveals the range of physical habitat variables selected by adult green sturgeon during their spawning period. We integrated fine-scale fish positions, physical habitat characteristics, discharge, bathymetry, and simulated velocity and depth using a 2-dimensional hydraulic model (FaSTMECH). The objective was to create habitat suitability curves for depth, velocity, and substrate type within three known spawning locations over two years. An overall cumulative habitat suitability score was calculated that averaged the depth, velocity, and substrate scores over all fish, sites, and years. A weighted usable area (WUA) index was calculated throughout the sampling periods for each of the three sites. Cumulative results indicate that the microhabitat characteristics most preferred by green sturgeon in these three spawning locations were velocities between 1.0-1.1 m/s, depths of 8-9 m, and gravel and sand substrate. This study provides guidance for those who may in the future want to increase spawning habitat for green sturgeon within the Sacramento River.","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2017-0072","usgsCitation":"Wyman, M.T., Thomas, M.J., McDonald, R.R., Hearn, A.R., Batt, R.D., Chapman, E.D., Kinzel, P.J., Minear, J.T., Mora, E.A., Nelson, J.M., Pagel, M.D., and Klimley, A.P., 2018, Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 5, p. 779-791, https://doi.org/10.1139/cjfas-2017-0072.","productDescription":"13 p.","startPage":"779","endPage":"791","ipdsId":"IP-080132","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":501354,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/69177","text":"External Repository"},{"id":345523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.24899291992188,\n 40.027614437486655\n ],\n [\n -122.08694458007812,\n 40.027614437486655\n ],\n [\n -122.08694458007812,\n 40.35282369083777\n ],\n [\n -122.24899291992188,\n 40.35282369083777\n ],\n [\n -122.24899291992188,\n 40.027614437486655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b1092de4b020cdf7d8d9ad","contributors":{"authors":[{"text":"Wyman, Megan T.","contributorId":196239,"corporation":false,"usgs":false,"family":"Wyman","given":"Megan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":709736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Michael J.","contributorId":196240,"corporation":false,"usgs":false,"family":"Thomas","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":709737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":709735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hearn, Alexander R.","contributorId":196241,"corporation":false,"usgs":false,"family":"Hearn","given":"Alexander","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":709738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Batt, Ryan D.","contributorId":196242,"corporation":false,"usgs":false,"family":"Batt","given":"Ryan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":709739,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Eric D.","contributorId":34377,"corporation":false,"usgs":true,"family":"Chapman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":709740,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":709741,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minear, J. Tobey","contributorId":196246,"corporation":false,"usgs":false,"family":"Minear","given":"J.","email":"","middleInitial":"Tobey","affiliations":[],"preferred":false,"id":709745,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mora, Ethan A.","contributorId":196244,"corporation":false,"usgs":false,"family":"Mora","given":"Ethan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":709742,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":709744,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pagel, Matthew D.","contributorId":196247,"corporation":false,"usgs":false,"family":"Pagel","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":709746,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Klimley, A. Peter","contributorId":196245,"corporation":false,"usgs":false,"family":"Klimley","given":"A.","email":"","middleInitial":"Peter","affiliations":[],"preferred":false,"id":709743,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70190510,"text":"70190510 - 2018 - Climate variability and vadose zone controls on damping of transient recharge","interactions":[],"lastModifiedDate":"2018-06-19T09:55:21","indexId":"70190510","displayToPublicDate":"2017-09-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Climate variability and vadose zone controls on damping of transient recharge","docAbstract":"

Increasing demand on groundwater resources motivates understanding of the controls on recharge dynamics so model predictions under current and future climate may improve. Here we address questions about the nonlinear behavior of flux variability in the vadose zone that may explain previously reported teleconnections between global-scale climate variability and fluctuations in groundwater levels. We use hundreds of HYDRUS-1D simulations in a sensitivity analysis approach to evaluate the damping depth of transient recharge over a range of periodic boundary conditions and vadose zone geometries and hydraulic parameters that are representative of aquifer systems of the conterminous United States (U.S). Although the models were parameterized based on U.S. aquifers, findings from this study are applicable elsewhere that have mean recharge rates between 3.65 and 730 mm yr–1. We find that mean infiltration flux, period of time varying infiltration, and hydraulic conductivity are statistically significant predictors of damping depth. The resulting framework explains why some periodic infiltration fluxes associated with climate variability dampen with depth in the vadose zone, resulting in steady-state recharge, while other periodic surface fluxes do not dampen with depth, resulting in transient recharge. We find that transient recharge in response to the climate variability patterns could be detected at the depths of water levels in most U.S. aquifers. Our findings indicate that the damping behavior of transient infiltration fluxes is linear across soil layers for a range of texture combinations. The implications are that relatively simple, homogeneous models of the vadose zone may provide reasonable estimates of the damping depth of climate-varying transient recharge in some complex, layered vadose zone profiles.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.08.028","usgsCitation":"Corona, C.R., Gurdak, J.J., Dickinson, J.E., Ferre, T., and Maurer, E.P., 2018, Climate variability and vadose zone controls on damping of transient recharge: Journal of Hydrology, v. 561, p. 1094-1104, https://doi.org/10.1016/j.jhydrol.2017.08.028.","productDescription":"11 p.","startPage":"1094","endPage":"1104","ipdsId":"IP-087076","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":469191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.08.028","text":"Publisher Index Page"},{"id":345462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"561","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59afb79ae4b0e9bde1351123","contributors":{"authors":[{"text":"Corona, Claudia R.","contributorId":196165,"corporation":false,"usgs":false,"family":"Corona","given":"Claudia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":709517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gurdak, Jason J.","contributorId":196166,"corporation":false,"usgs":false,"family":"Gurdak","given":"Jason","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":709518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":709516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferre, T.P.A.","contributorId":196167,"corporation":false,"usgs":false,"family":"Ferre","given":"T.P.A.","email":"","affiliations":[],"preferred":false,"id":709519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maurer, Edwin P.","contributorId":196168,"corporation":false,"usgs":false,"family":"Maurer","given":"Edwin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":709520,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190480,"text":"70190480 - 2018 - A detailed risk assessment of shale gas development on headwater streams in the Pennsylvania portion of the Upper Susquehanna River Basin, U.S.A.","interactions":[],"lastModifiedDate":"2017-09-01T10:07:51","indexId":"70190480","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A detailed risk assessment of shale gas development on headwater streams in the Pennsylvania portion of the Upper Susquehanna River Basin, U.S.A.","docAbstract":"

The development of unconventional oil and gas (UOG) involves infrastructure development (well pads, roads and pipelines), well drilling and stimulation (hydraulic fracturing), and production; all of which have the potential to affect stream ecosystems. Here, we developed a fine-scaled (1:24,000) catchment-level disturbance intensity index (DII) that included 17 measures of UOG capturing all steps in the development process (infrastructure, water withdrawals, probabilistic spills) that could affect headwater streams (< 200 km2 in upstream catchment) in the Upper Susquehanna River Basin in Pennsylvania, U.S.A. The DII ranged from 0 (no UOG disturbance) to 100 (the catchment with the highest UOG disturbance in the study area) and it was most sensitive to removal of pipeline cover, road cover and well pad cover metrics. We related this DII to three measures of high quality streams: Pennsylvania State Exceptional Value (EV) streams, Class A brook trout streams and Eastern Brook Trout Joint Venture brook trout patches. Overall only 3.8% of all catchments and 2.7% of EV stream length, 1.9% of Class A streams and 1.2% of patches were classified as having medium to high level DII scores (> 50). Well density, often used as a proxy for development, only correlated strongly with well pad coverage and produced materials, and therefore may miss potential effects associated with roads and pipelines, water withdrawals and spills. When analyzed with a future development scenario, 91.1% of EV stream length, 68.7% of Class A streams and 80.0% of patches were in catchments with a moderate to high probability of development. Our method incorporated the cumulative effects of UOG on streams and can be used to identify catchments and reaches at risk to existing stressors or future development.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.07.247","usgsCitation":"Maloney, K.O., Young, J.A., Faulkner, S., Hailegiorgis, A., Slonecker, E., and Milheim, L., 2018, A detailed risk assessment of shale gas development on headwater streams in the Pennsylvania portion of the Upper Susquehanna River Basin, U.S.A.: Science of the Total Environment, v. 610-611, p. 154-166, https://doi.org/10.1016/j.scitotenv.2017.07.247.","productDescription":"13 p.","startPage":"154","endPage":"166","ipdsId":"IP-087579","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":461145,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2017.07.247","text":"Publisher Index Page"},{"id":438087,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z036NF","text":"USGS data release","linkHelpText":"Shale gas data used in development of the Disturbance Intensity Index for the Pennsylvania portion of the Upper Susquehanna River basin in Maloney et al. 2018."},{"id":345411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Upper Susquehanna River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.046630859375,\n 40.53050177574321\n ],\n [\n -75.047607421875,\n 40.53050177574321\n ],\n [\n -75.047607421875,\n 42.00848901572399\n ],\n [\n -79.046630859375,\n 42.00848901572399\n ],\n [\n -79.046630859375,\n 40.53050177574321\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"610-611","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59aa71d8e4b0e9bde130cfe4","contributors":{"authors":[{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":709393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":709394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":709395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hailegiorgis, Atesmachew","contributorId":196129,"corporation":false,"usgs":false,"family":"Hailegiorgis","given":"Atesmachew","email":"","affiliations":[],"preferred":false,"id":709396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Slonecker, E. Terrence","contributorId":20677,"corporation":false,"usgs":true,"family":"Slonecker","given":"E. Terrence","affiliations":[],"preferred":false,"id":709398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Milheim, Lesley lmilheim@usgs.gov","contributorId":168592,"corporation":false,"usgs":true,"family":"Milheim","given":"Lesley","email":"lmilheim@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":709397,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190445,"text":"70190445 - 2018 - Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","interactions":[],"lastModifiedDate":"2018-03-05T15:50:35","indexId":"70190445","displayToPublicDate":"2017-08-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","title":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","docAbstract":"Northern tamarisk beetles (Diorhabda carinulata) were released in the Upper Colorado River Basin in the United States in\r\n2004–2007 to defoliate introduced tamarisk shrubs (Tamarix spp.) in the region’s riparian zones. The primary purpose was\r\nto control the invasive shrub and reduce evapotranspiration (ET) by tamarisk in an attempt to increase stream flows. We\r\nevaluated beetle–tamarisk interactions with MODIS and Landsat imagery on 13 river systems, with vegetation indices used\r\nas indicators of the extent of defoliation and ET. Beetles are widespread and exhibit a pattern of colonize–defoliate–emigrate,\r\nso that riparian zones contain a mosaic of completely defoliated, partially defoliated, and refoliated tamarisk stands. Based\r\non satellite data and ET algorithms, mean ET before beetle release (2000–2006) was 416 mm/year compared to postrelease\r\n(2007–2015) ET of 355 mm/year (p<0.05) for a net reduction of 61 mm/year. This is lower than initial literature projections\r\nthat ET would be reduced by 300–460 mm/year. Reasons for the lower-than-expected ET reductions are because baseline ET\r\nrates are lower than initially projected, and percentage ET reduction is low because tamarisk stands tend to regrow new leaves\r\nafter defoliation and other plants help maintain canopy cover. Overall reductions in tamarisk green foliage during the study\r\nare 21%. However, ET in the Upper Basin has shown a steady decline since 2007 and equilibrium has not yet been reached.\r\nDefoliation is now proceeding from the Upper Basin into the Lower Basin at a rate of 40 km/year, much faster than initially\r\nprojected.","language":"English","publisher":"Wiley","doi":"10.1111/rec.12575","usgsCitation":"Nagler, P.L., Nguyen, U., Bateman, H.L., Jarchow, C., Glenn, E., Waugh, W.J., and van Riper, C., 2018, Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin: Restoration Ecology, v. 26, no. 2, p. 348-359, https://doi.org/10.1111/rec.12575.","productDescription":"12 p.","startPage":"348","endPage":"359","ipdsId":"IP-079583","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":345398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.78515624999999,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 33.94335994657882\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-25","publicationStatus":"PW","scienceBaseUri":"59a9203ee4b07e1a023ccd95","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nguyen, Uyen","contributorId":71863,"corporation":false,"usgs":false,"family":"Nguyen","given":"Uyen","email":"","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bateman, Heather L.","contributorId":72294,"corporation":false,"usgs":true,"family":"Bateman","given":"Heather","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":709187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":709190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":709191,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190198,"text":"70190198 - 2018 - Weather-centric rangeland revegetation planning","interactions":[],"lastModifiedDate":"2017-12-12T12:31:33","indexId":"70190198","displayToPublicDate":"2017-08-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Weather-centric rangeland revegetation planning","docAbstract":"Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2017.07.003","usgsCitation":"Hardegree, S.P., Abatzoglou, J.T., Brunson, M.W., Germino, M., Hegewisch, K.C., Moffet, C.A., Pilliod, D.S., Roundy, B.A., Boehm, A.R., and Meredith, G.R., 2018, Weather-centric rangeland revegetation planning: Rangeland Ecology and Management, v. 71, no. 1, p. 1-11, https://doi.org/10.1016/j.rama.2017.07.003.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-081305","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":498721,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/671080","text":"External Repository"},{"id":344996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599bf122e4b0b589267ed339","contributors":{"authors":[{"text":"Hardegree, Stuart P.","contributorId":195696,"corporation":false,"usgs":false,"family":"Hardegree","given":"Stuart","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":707927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":707928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brunson, Mark W.","contributorId":195697,"corporation":false,"usgs":false,"family":"Brunson","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","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":707925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hegewisch, Katherine C.","contributorId":195698,"corporation":false,"usgs":false,"family":"Hegewisch","given":"Katherine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707930,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moffet, Corey A.","contributorId":195699,"corporation":false,"usgs":false,"family":"Moffet","given":"Corey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707931,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":707926,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roundy, Bruce A.","contributorId":178261,"corporation":false,"usgs":false,"family":"Roundy","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boehm, Alex R.","contributorId":195700,"corporation":false,"usgs":false,"family":"Boehm","given":"Alex","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meredith, Gwendwr R.","contributorId":195701,"corporation":false,"usgs":false,"family":"Meredith","given":"Gwendwr","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":707934,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}