{"pageNumber":"331","pageRowStart":"8250","pageSize":"25","recordCount":41075,"records":[{"id":70204896,"text":"70204896 - 2019 - The importance of simulation assumptions when evaluating detectability in population models","interactions":[],"lastModifiedDate":"2019-08-26T09:43:48","indexId":"70204896","displayToPublicDate":"2019-07-23T14:36:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The importance of simulation assumptions when evaluating detectability in population models","docAbstract":"Population monitoring is important for investigating a variety of ecological questions, and N-mixture models are increasingly used to model population size (N) and trends (lambda) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and lambda, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system (GPS) data from greater sage-grouse (Centrocercus urophasianus) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p. We then compared estimates of N and lambda from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N-mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50% whereas N-mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of lambda from both types of models were unbiased and similar for scenarios without trends in p. When p declined systematically across years, uncorrected count models underestimated lambda whereas N-mixture models estimated lambda with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N-mixture models can distinguish between trends in p and N, but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.2791","usgsCitation":"Monroe, A., Wann, G.T., Aldridge, C.L., and Coates, P.S., 2019, The importance of simulation assumptions when evaluating detectability in population models: Ecosphere, v. 10, no. 7, e02791, 16 p., https://doi.org/10.1002/ecs2.2791.","productDescription":"e02791, 16 p.","ipdsId":"IP-097255","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467428,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2791","text":"Publisher Index Page"},{"id":437382,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91L28PG","text":"USGS data release","linkHelpText":"Simulation to evaluate response of population models to annual trends in detectability"},{"id":366805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":768938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wann, Gregory T. 0000-0001-9076-7819 wanng@usgs.gov","orcid":"https://orcid.org/0000-0001-9076-7819","contributorId":3855,"corporation":false,"usgs":true,"family":"Wann","given":"Gregory","email":"wanng@usgs.gov","middleInitial":"T.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":768939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":768940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":768941,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70205299,"text":"70205299 - 2019 - Giving ecological meaning to satellite-derived fire severity metrics across North American forests","interactions":[],"lastModifiedDate":"2026-01-21T16:28:34.513942","indexId":"70205299","displayToPublicDate":"2019-07-23T14:29:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Giving ecological meaning to satellite-derived fire severity metrics across North American forests","docAbstract":"<p><span>Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R</span><sup>2</sup><span>&nbsp;of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs11141735","usgsCitation":"Parks, S., Holsinger, L.M., Koontz, M.J., Collins, L.S., Whitman, E., Parisien, M., Loehman, R.A., Barnes, J.L., Bourdon, J., Boucher, J., Boucher, Y., Caprio, A.C., Collingwood, A., Hall, R., Park, J., Saperstein, L., Smetanka, C., Smith, R.J., and Soverel, N., 2019, Giving ecological meaning to satellite-derived fire severity metrics across North American forests: Remote Sensing, v. 11, 1735, 19 p., https://doi.org/10.3390/rs11141735.","productDescription":"1735, 19 p.","ipdsId":"IP-109412","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":460326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11141735","text":"Publisher 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National Park, PO Box 168, 22 Stable Street, Yellowstone National Park, WY, 82190, USA","active":true,"usgs":false}],"preferred":false,"id":770808,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Soverel, Nick","contributorId":218977,"corporation":false,"usgs":false,"family":"Soverel","given":"Nick","email":"","affiliations":[{"id":39948,"text":"Self-employed","active":true,"usgs":false}],"preferred":false,"id":770809,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70203800,"text":"ofr20191062 - 2019 - Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17","interactions":[],"lastModifiedDate":"2019-07-25T10:24:32","indexId":"ofr20191062","displayToPublicDate":"2019-07-23T14:26:27","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1062","displayTitle":"Monitoring Breeding and Survival of Ring-Necked Pheasant (<em>Phasianus colchicus</em>) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, Northern California—Five-Year Summary, 2013–17","title":"Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17","docAbstract":"<p class=\"p1\">The U.S. Geological Survey Western Ecological Research Center, Pheasants Forever, Mandeville Island Duck Club, and the California Department of Fish and Wildlife collaborated in a reconnaissance study to monitor populations of ring-necked pheasant (<i>Phasianus colchicus</i>) using radio-telemetry in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin of northern California. The purpose of this study was to provide agencies and private landowners with a framework of decision-support tools to help manage pheasant populations in California. During winter, spring, and autumn of 2013–17, we radio- or Global Positioning System-marked 227 female pheasant across six study sites. Data collection was focused on investigating nest-site and brood-rearing habitat selection, examining avian predator composition, and estimating population vital rates to improve our understanding of pheasant population dynamics and to identify factors that may contribute to decreases in pheasant populations in California. The cumulative annual adult survival probability across all sites during 2013–17 was 27.6 percent (95-percent confidence interval [CI], 21.9–33.6), and the cumulative nest and brood survival probabilities were 34.5 percent (95-percent CI, 27.0–42.2) and 54.2 percent (95-percent CI, 43.7–63.5), respectively. Evidence from microhabitat surveys completed at nest-sites, brood locations, and random locations suggested that marked female pheasant tended to select increasing vertical cover and residual vegetation cover and tended to avoid areas of increasing bare ground cover regardless of life-history stage. However, females at nest-sites selected increasing grass cover and height, whereas brood-rearing females tended to select increasing forb cover and height. Only perennial grass cover and perennial grass height were shown to have a positive influence on nest survival, which suggests that increasing perennial grass cover in areas occupied by pheasant may increase nest survival. Analysis of environmental factors linked to vital rate information are ongoing and will continue with investigations at increased spatial scales (that is, macro-habitat) to develop integrated population models that can&nbsp;incorporate abundance estimates from crow count data with vital rates from telemetry data. This report includes results from 5 years of data collection and should be interpreted with caution, as these findings are preliminary.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191062","collaboration":"Prepared in cooperation with the California Department of Fish and Wildlife and Pheasants Forever","usgsCitation":"=Dwight, I.A., Coates, P.S., Vogt, J.H., Atkinson, J.L., Fleskes, J.P., Connelly, D.P., Meshriy, M.C., Gardner, S.C., Stoute, S.T., and Pitesky M.E., 2019, Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17: U.S. Geological Survey Open-File Report 2019–1062, 90 p., https://doi.org/10.3133/ofr20191062.","productDescription":"90 p.","onlineOnly":"Y","ipdsId":"IP-099248","costCenters":[{"id":651,"text":"Western Ecological Research 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P.","affiliations":[],"preferred":false,"id":764177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Connelly, Daniel P.","contributorId":192079,"corporation":false,"usgs":false,"family":"Connelly","given":"Daniel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":764178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meshriy, Matt G.","contributorId":192080,"corporation":false,"usgs":false,"family":"Meshriy","given":"Matt G.","affiliations":[],"preferred":false,"id":764179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":764180,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stoute, Simone T.","contributorId":202770,"corporation":false,"usgs":false,"family":"Stoute","given":"Simone","email":"","middleInitial":"T.","affiliations":[{"id":36526,"text":"California Animal Health and Food Safety Laboratory","active":true,"usgs":false}],"preferred":false,"id":764181,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pitesky, Maurice E.","contributorId":176920,"corporation":false,"usgs":false,"family":"Pitesky","given":"Maurice","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":764182,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","interactions":[{"subject":{"id":70147329,"text":"sir20155066 - 2015 - Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats","indexId":"sir20155066","publicationYear":"2015","noYear":false,"title":"Preliminary methodology to assess the national and regional impact of U.S. wind energy development on birds and bats"},"predicate":"SUPERSEDED_BY","object":{"id":70200937,"text":"sir20185157 - 2019 - A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","indexId":"sir20185157","publicationYear":"2019","noYear":false,"title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats"},"id":1}],"lastModifiedDate":"2019-07-23T13:03:08","indexId":"sir20185157","displayToPublicDate":"2019-07-23T14:04:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5157","displayTitle":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","title":"A Methodology to Assess the National and Regional Impacts of U.S. Wind Energy Development on Birds and Bats","docAbstract":"<p>This scientific investigations report describes an effort by the U.S. Geological Survey (USGS) that used research, monitoring data, and modeling to develop a methodology to assess both the current and future population-level consequences of wind energy development on species of birds and bats that are present in the United States during any part of their life cycle. The methodology is currently applicable to birds and bats, focuses primarily on the effects of collisions with turbines, and can be applied to any species that breeds in, migrates through, or otherwise uses any part of the United States. The methodology assesses species at the national and regional scales and identifies those species potentially in need of more detailed study, as well as those species that are likely at low risk from wind energy development. This approach is fundamentally different from existing methods focusing on impacts at individual facilities.</p><p>This report supersedes USGS Scientific Investigations Report 2015–5066 by the same authors, which described a preliminary version of the methodology. Following reviews of the preliminary methodology by a panel of external experts, public comments, and additional internal review, the methodology was revised and finalized.</p><p>The three components of the refined methodology described in this new report rely on publicly available fatality information, population estimates, species range maps, turbine location data, biological characteristics of species, and population models. First, three metrics are combined to determine direct and indirect relative effects from wind energy facilities to generate a list of species scores. Second, a generic population model estimates the expected change in population trend caused by the additive mortality from collisions with wind turbines. Third, the methodology combines an estimate of observed fatalities and an estimate of potential biological removal to assess the possibility of a decrease in population size. The latter two components are quantitative. In a test case, the methodology was used to analyze data for six bird species and three bat species.</p><p>Components of the methodology are based on simplifying assumptions and require information that, for many species, may be sparse or unreliable or may require further study. These assumptions should be carefully considered when using outputs from the methodology. Increases in the quality of data for fatalities from collisions with wind turbines, species distributions, abundance, and demography will likely improve results for uses of the methodology.</p><p>The methodology’s design identifies and prioritizes a subset of the bird and bat species that may experience population-level impacts from collisions with wind turbines, both currently and from future wind energy development in the United States. Results of an assessment using this methodology could focus future research to improve our understanding of those impacts and to guide avoidance and minimization strategies. In addition, this methodology can be used to identify species for more intensive demographic modeling or to highlight those species that may not require any additional research because effects of wind energy development on their populations are projected to be small. The effects of wind energy facilities on nine unidentified species used in the test case described in this report have not been assessed. Their data were simply used to show the application of the methodology to real-world data and the types of outputs it would produce.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185157","usgsCitation":"Diffendorfer, J.E., Beston, J.A., Merrill, M.D., Stanton, J.C., Corum, M.D., Loss, S.R., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2019, A methodology to assess the national and regional impacts of U.S. wind energy development on birds and bats: U.S. Geological Survey Scientific Investigations Report 2018–5157, 45 p., https://doi.org/10.3133/sir20185157. [Supersedes USGS Scientific Investigations Report 2015–5066.]","productDescription":"ix, 45 p.","onlineOnly":"Y","ipdsId":"IP-079749","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":365690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5157/coverthb.jpg"},{"id":365691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5157/sir20185157.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5157"}],"publicComments":"Scientific Investigations Report 2018-5157 supersedes Scientific Investigations Report 2015-5066.","contact":"<p><a href=\"mailto:gd-energyprogram@usgs.gov\" data-mce-href=\"mailto:gd-energyprogram@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/eersc\" data-mce-href=\"https://www.usgs.gov/centers/eersc\">Eastern Energy Resources Science Center</a><br>U.S. Geological Survey<br>Mail Stop 956<br>12201 Sunrise Valley Drive<br>Reston, VA 20192<br><a href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/\" data-mce-href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/\">Energy Resources Program</a><br><a href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/science/wind-energy?qt-science_center_objects=0#qt-science_center_objects\" data-mce-href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/science/wind-energy?qt-science_center_objects=0#qt-science_center_objects\">Wind Energy</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>1.0 Introduction</li><li>2.0 Methodology</li><li>3.0 Overall Methodology Development and Validation</li><li>4.0 Test Case</li><li>5.0 Conclusions</li><li>References Cited</li><li>Glossary</li><li>Appendix 1. Alternative Modeling Approaches Considered</li><li>Appendix 2. Future Projections</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-07-23","noUsgsAuthors":false,"publicationDate":"2019-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":751395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beston, J.A.","contributorId":210657,"corporation":false,"usgs":false,"family":"Beston","given":"J.A.","affiliations":[{"id":38127,"text":"University of Wisconsin - Stout","active":true,"usgs":false}],"preferred":false,"id":751396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merrill, Matthew D. 0000-0003-3766-847X","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":205698,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":751394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":751397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corum, M.D. 0000-0002-9038-3935 mcorum@usgs.gov","orcid":"https://orcid.org/0000-0002-9038-3935","contributorId":2249,"corporation":false,"usgs":true,"family":"Corum","given":"M.D.","email":"mcorum@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":751398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, S.R.","contributorId":210658,"corporation":false,"usgs":false,"family":"Loss","given":"S.R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":751399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thogmartin, W.E. 0000-0002-2384-4279","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":210659,"corporation":false,"usgs":false,"family":"Thogmartin","given":"W.E.","affiliations":[],"preferred":false,"id":751400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, D.H. 0000-0002-7778-6641","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":210660,"corporation":false,"usgs":false,"family":"Johnson","given":"D.H.","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":751401,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Erickson, R.A. 0000-0003-4649-482X","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":210661,"corporation":false,"usgs":false,"family":"Erickson","given":"R.A.","affiliations":[],"preferred":false,"id":751402,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heist, K.W. 0000-0002-0140-861X","orcid":"https://orcid.org/0000-0002-0140-861X","contributorId":210662,"corporation":false,"usgs":false,"family":"Heist","given":"K.W.","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":751403,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70203969,"text":"sir20195054 - 2019 - The use of continuous water-quality time-series data to compute nutrient loadings for selected Iowa streams, 2008–17","interactions":[],"lastModifiedDate":"2019-07-23T14:31:04","indexId":"sir20195054","displayToPublicDate":"2019-07-23T13:34:03","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5054","displayTitle":"The Use of Continuous Water-Quality Time-Series Data to Compute Nutrient Loadings for Selected Iowa Streams, 2008–17","title":"The use of continuous water-quality time-series data to compute nutrient loadings for selected Iowa streams, 2008–17","docAbstract":"<p>In support of nutrient reduction efforts, nitrate (as nitrate plus nitrite) and phosphorus loads and yields were computed for selected streams in Iowa based on continuously monitored sensor data for 2008–17 and 2014–17, respectively. Sample data were used to assess nitrate sensor bias and to create phosphorus-turbidity surrogate models. Where needed, nitrate loads were corrected for site-specific sensor bias, which was determined to be as high as 9.25 percent. Nitrate loads presented in this report using continuous (generally 15-minute interval) data were on average 4 percent less, but as much as 38 percent less, than annual loads computed from daily mean nitrate concentrations not corrected for sensor bias. Streamflow-based phosphorus models had poorer fit (adjusted coefficient of determination values less than 0.75) than turbidity-based models (adjusted coefficient of determination approximately 0.9). However, alternate models based on streamflow were used to obtain a more complete annual phosphorus load despite seasonal and fragmentary sensor data.</p><p>Mean annual nitrate yields for 18 selected sites (96 site-years) ranged from 1.68 to 164 pounds per square mile per day (lb/mi<sup>2</sup>/d), compared to 19.4 lb/mi<sup>2</sup>/d average statewide yield needed to achieve the nitrate-reduction goal. Mean annual phosphorus yields for selected sites on the Maquoketa River, South Raccoon River, and West Nishnabotna River range from 1.57 to 7.19 lb/mi<sup>2</sup>/d, compared to 1.06 lb/mi<sup>2</sup>/d average statewide yield needed to achieve the phosphorus-reduction goal.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195054","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Garrett, J.D., 2019, The use of continuous water-quality time-series data to compute nutrient loadings for selected Iowa streams, 2008–17: U.S. Geological Survey Scientific Investigations Report 2019–5054, 31 p., https://doi.org/10.3133/sir20195054.","productDescription":"Report: viii, 31 p.; Appendixes: 2","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098143","costCenters":[{"id":351,"text":"Iowa Water Science 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 \"}}]}","contact":"<p>Director, <a data-mce-href=\"ttps://www.usgs.gov/centers/cm-water\" href=\"ttps://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br> U.S. Geological Survey<br>400 South Clinton Street, Suite 269 <br>Iowa City, IA 52240 </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods for Data Collection and Computation</li><li>Methods for Continuous Concentration Models</li><li>Methods for Generation of Time-Series Concentrations and Loads</li><li>Sample Water-Quality and Sensor Data</li><li>Continuous Water-Quality Time-Series Data to Compute Nutrient Loadings</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Model Calibration Samples</li><li>Appendix 2. Nitrate Check Samples</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-07-23","noUsgsAuthors":false,"publicationDate":"2019-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Garrett, Jessica D. 0000-0002-4466-3709 jgarrett@usgs.gov","orcid":"https://orcid.org/0000-0002-4466-3709","contributorId":4229,"corporation":false,"usgs":true,"family":"Garrett","given":"Jessica","email":"jgarrett@usgs.gov","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":765028,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70204423,"text":"70204423 - 2019 - Natural hazards and mineral commodity supply: Quantifying risk of earthquake disruption to South American copper supply","interactions":[],"lastModifiedDate":"2019-08-13T15:29:01","indexId":"70204423","displayToPublicDate":"2019-07-23T12:10:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3266,"text":"Resources Policy","active":true,"publicationSubtype":{"id":10}},"title":"Natural hazards and mineral commodity supply: Quantifying risk of earthquake disruption to South American copper supply","docAbstract":"Mineral resources, and their mining and enrichment operations, are not equally distributed across Earth. The concentration of mineral supply in certain regions, owing to the geology or geography of the mineral resource, raises the level of risk related to supply disruption. Where mineral production coincides with areas prone to natural hazards, supply may be especially at risk. However, the level of risk that natural hazards pose to mineral supply has yet to be quantified on a global or regional scale. Using copper in South America as a case study, this paper offers methods for quantifying (i) the coincidence of mineral production and seismic hazards, and (ii) the Expected Annual Disruption (EAD) of the mineral supply from earthquakes. The first of these methods indicates that, of the 101 copper producing facilities in South America considered, 76 are located within an area of high seismic hazard, taken here as the area with>85% chance of exceeding Modified Mercalli Intensity VI earthquake shaking in 50 years. Collectively, the 76 facilities comprise 82%, 87%, and 91% of the 2015 South American mine production, smelter capacity, and refinery capacity, respectively. For each of the 101 facilities, the second method calculates the EAD using a full earthquake shaking hazard forecast at the location, the annualized copper production of the facility, and models of the vulnerability of that production to shaking. The EADs are summed by country, here within South America, as a demonstration of how supply risk could eventually be quantified globally. Consideration of two illustrative vulnerability models shows that future work is needed to determine percentages of disruption to mineral production for different levels of earthquake shaking. Ultimately, the methods presented herein could be applied to other mineral commodities and/or adapted for other natural hazards, and the resulting EADs could be summed. Results from these methods could be used to focus more detailed risk assessments where the risk is highest.","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2019.101430","usgsCitation":"Schnebele, E.K., Jaiswal, K.S., Luco, N., and Nassar, N., 2019, Natural hazards and mineral commodity supply: Quantifying risk of earthquake disruption to South American copper supply: Resources Policy, v. 63, 101430, 10 p., https://doi.org/10.1016/j.resourpol.2019.101430.","productDescription":"101430, 10 p.","ipdsId":"IP-109128","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":467429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2019.101430","text":"Publisher Index Page"},{"id":365864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365837,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0301420718306676?via%3Dihub"}],"volume":"63","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schnebele, Emily K. 0000-0002-0245-3156 eschnebele@usgs.gov","orcid":"https://orcid.org/0000-0002-0245-3156","contributorId":217475,"corporation":false,"usgs":true,"family":"Schnebele","given":"Emily","email":"eschnebele@usgs.gov","middleInitial":"K.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":766842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":766843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":766844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":177175,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal T.","email":"nnassar@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":766845,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204470,"text":"70204470 - 2019 - Global observational needs and resources for marine biodiversity","interactions":[],"lastModifiedDate":"2019-07-26T11:26:02","indexId":"70204470","displayToPublicDate":"2019-07-23T11:07:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Global observational needs and resources for marine biodiversity","docAbstract":"<p><span>The diversity of life in the sea is critical to the health of ocean ecosystems that support living resources and therefore essential to the economic, nutritional, recreational, and health needs of billions of people. Yet there is evidence that the biodiversity of many marine habitats is being altered in response to a changing climate and human activity. Understanding this change, and forecasting where changes are likely to occur, requires monitoring of organism diversity, distribution, abundance, and health. It requires a minimum of measurements including productivity and ecosystem function, species composition, allelic diversity, and genetic expression. These observations need to be complemented with metrics of environmental change and socio-economic drivers. However, existing global ocean observing infrastructure and programs often do not explicitly consider observations of marine biodiversity and associated processes. Much effort has focused on physical, chemical and some biogeochemical measurements. Broad partnerships, shared approaches, and best practices are now being organized to implement an integrated observing system that serves information to resource managers and decision-makers, scientists and educators, from local to global scales. This integrated observing system of ocean life is now possible due to recent developments among satellite, airborne, and&nbsp;</span><i>in situ</i><span>&nbsp;sensors in conjunction with increases in information system capability and capacity, along with an improved understanding of marine processes represented in new physical, biogeochemical, and biological models.</span></p>","language":"English","publisher":"Frontiers in Marine Science","doi":"10.3389/fmars.2019.00367","usgsCitation":"Canonico, G., Buttigieg, P.L., Montes, E., Muller-Karger, F.E., Stepien, C., Wright, D.J., Benson, A., Helmuth, B., Costello, M.J., Sousa-Pinto, I., Saeedi, H., Newton, J., Appeltans, W., Bednarsek, N., Bodrossy, L., Best, B.D., Brandt, A., Goodwin, K.D., Iken, K., Marquez, A.C., Miloslavich, P., Ostrowski, M., Turner, W., Achterberg, E.P., Barry, T., DeFeo, O., Bigatti, G., Henry, L., Ramiro-Sanchez, B., Duran, P., Morato, T., Roberts, J.M., Garcia-Alegre, A., Sacau Cuadrado, M., and Murton, B., 2019, Global observational needs and resources for marine biodiversity: Frontiers in Marine Science, v. 6, 367, 20 p., https://doi.org/10.3389/fmars.2019.00367.","productDescription":"367, 20 p.","ipdsId":"IP-106362","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":467430,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2019.00367","text":"Publisher Index Page"},{"id":365981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Canonico, Gabrielle","contributorId":217563,"corporation":false,"usgs":false,"family":"Canonico","given":"Gabrielle","email":"","affiliations":[{"id":39659,"text":"National Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":767076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttigieg, Pier Luigi","contributorId":217564,"corporation":false,"usgs":false,"family":"Buttigieg","given":"Pier","email":"","middleInitial":"Luigi","affiliations":[{"id":39660,"text":"Alfred-Wegener-Institut für Polar- und Meeresforschung, Am Handelshafen 12, 27570, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":767077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montes, Enrique","contributorId":217565,"corporation":false,"usgs":false,"family":"Montes","given":"Enrique","email":"","affiliations":[{"id":39661,"text":"University of South Florida, St Petersburg, FL USA","active":true,"usgs":false}],"preferred":false,"id":767078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muller-Karger, Frank E.","contributorId":206626,"corporation":false,"usgs":false,"family":"Muller-Karger","given":"Frank","email":"","middleInitial":"E.","affiliations":[{"id":37356,"text":"University of South Florida, Saint Petersburg, FL","active":true,"usgs":false}],"preferred":false,"id":767079,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stepien, Carol","contributorId":217566,"corporation":false,"usgs":false,"family":"Stepien","given":"Carol","affiliations":[{"id":39662,"text":"NOAA Pacific Marine Environmental Lab, Seattle, WA USA","active":true,"usgs":false}],"preferred":false,"id":767080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Dawn J.","contributorId":191639,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","email":"","middleInitial":"J.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. 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,{"id":70204401,"text":"70204401 - 2019 - Preliminary report on engineering and geological effects of the July 2019 Ridgecrest earthquake sequence","interactions":[],"lastModifiedDate":"2019-07-23T09:45:32","indexId":"70204401","displayToPublicDate":"2019-07-23T09:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Preliminary report on engineering and geological effects of the July 2019 Ridgecrest earthquake sequence","docAbstract":"The Ridgecrest Earthquake sequence included a foreshock event on July 4 2019 (M6.4) and a M7.1 mainshock event on July 5 2019. These events occurred in the Eastern California Shear Zone, near Indian Wells Valley, south of China Lake and west of Searles Valley. GEER has partnered with several organizations to collect perishable data and document the important impacts of these events, including the US Geological Survey, the California Geological Survey, the US Navy, the Southern California Earthquake Center, and local utilities. Critical geotechnical features of this event are extensive left-lateral (M6.4 event) and right-lateral (M7.1 event) surface ruptures over fault segments of variable complexity and width as well as across extensional and compressive step-over zones. We also document lifeline performance at fault crossings (gas, water, electrical), mainshock slip and afterslip, liquefaction and lateral spreading features, and liquefaction effects on structures. These effects are documented using field (ground) mapping and aerial imagery that will support subsequent development of high-resolution digital elevation models. Over 750 ground motions were recorded from the foreshock and mainshock alone, with many additional aftershock records. The data demonstrate significant impacts of site response and rupture directivity on ground motion attributes.","language":"English","publisher":"Geotechnical Extreme Event Reconnaissance Association","doi":"10.18118/G6H66K","collaboration":"Naval Air Weapons Station, China Lake; City of Ridgecrest Police; GEER, EERI, SCEC, UCLA, USC, UNR and many others","usgsCitation":"Brandenberg, S.J., Wang, P., Nweke, C.C., Hudson, K., Mazzoni, S., Bozorgnia, Y., Hudnut, K.W., Davis, C.A., Ahdi, S.K., Zareian, F., Fayaz, J., Koehler, R.D., Chupik, C., Pierce, I., Williams, A., Akciz, S., Hudson, M.B., Kishida, T., Brooks, B.A., Gold, R.D., Ponti, D.J., Scharer, K., McPhillips, D., DuRoss, C., Ericksen, T., Hernandez, J., Patton, J., Olson, B., Dawson, T.E., Treiman, J., Blake, K., Buchhuber, J., Madugo, C.L., Sun, J., Donnellan, A., Lyzenga, G., and Conway, E., 2019, Preliminary report on engineering and geological effects of the July 2019 Ridgecrest earthquake sequence, 69 p., 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,{"id":70204414,"text":"70204414 - 2019 - Science needs for continued development of total nitrogen deposition budgets in the United States","interactions":[],"lastModifiedDate":"2019-07-23T09:43:24","indexId":"70204414","displayToPublicDate":"2019-07-23T09:42:35","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Science needs for continued development of total nitrogen deposition budgets in the United States","docAbstract":"The objectives of this white paper are to describe the state of the science with respect to total Nr deposition budgets in North America and the research needed to improve these budgets from both a measurement and modeling standpoint.  The document is intended to serve as a plan for TDep research activities but also, more broadly, to provide program managers, natural resource managers, policy makers and scientists with an understanding of the need for complete and accurate Nr deposition budgets to protect ecosystem health and human welfare, and the linkages between the underlying policy-relevant science questions and the specific knowledge and data gaps needed to improve Nr deposition budgets.","language":"English","publisher":"NADP Program Office, University of Wisconsin, Madison","collaboration":"USEPA, National Atmospheric Deposition Program","usgsCitation":"Wetherbee, G., Templar, P.H., Pouyat, R.V., Decina, S.M., Kerschner, B.M., Whitlow, T.H., Padgett, P.E., Donna B. Schwede, Baron, J., Clow, D., Mast, M.A., Sexstone, G., and Morris, K.H., 2019, Science needs for continued development of total nitrogen deposition budgets in the United States, 13 p.","productDescription":"13 p.","startPage":"68","endPage":"90","ipdsId":"IP-087545","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":365838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365821,"type":{"id":11,"text":"Document"},"url":"https://nadp.slh.wisc.edu/committees/tdep/reports/NADP_TDep_Nr_Deposition_White_Paper_v3.pdf"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":202919,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":766775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Templar, Pamela H.","contributorId":217438,"corporation":false,"usgs":false,"family":"Templar","given":"Pamela","email":"","middleInitial":"H.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":766776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pouyat, Richard V.","contributorId":217439,"corporation":false,"usgs":false,"family":"Pouyat","given":"Richard","email":"","middleInitial":"V.","affiliations":[{"id":39630,"text":"U.S. Forest Service - Retired","active":true,"usgs":false}],"preferred":false,"id":766777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Decina, Stephen M. 0000-0003-0694-681X","orcid":"https://orcid.org/0000-0003-0694-681X","contributorId":217440,"corporation":false,"usgs":false,"family":"Decina","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":766778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kerschner, Brian M. 0000-0001-8718-5850","orcid":"https://orcid.org/0000-0001-8718-5850","contributorId":217441,"corporation":false,"usgs":false,"family":"Kerschner","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":39631,"text":"Prairie Research Institute, Illinois State Water Survey","active":true,"usgs":false}],"preferred":false,"id":766779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitlow, Thomas H.","contributorId":217442,"corporation":false,"usgs":false,"family":"Whitlow","given":"Thomas","email":"","middleInitial":"H.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":766780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Padgett, Pamela E.","contributorId":217443,"corporation":false,"usgs":false,"family":"Padgett","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":766781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Donna B. 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,{"id":70204252,"text":"sir20195049 - 2019 - Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","interactions":[],"lastModifiedDate":"2019-12-30T11:37:00","indexId":"sir20195049","displayToPublicDate":"2019-07-22T11:20:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5049","displayTitle":"Water-Budget Analysis of the Upper Big Sandy Designated Groundwater Basin Alluvial Aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","title":"Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","docAbstract":"<p>The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District carried out a study in 2016 to evaluate potential groundwater storage changes within the Upper Big Sandy Designated Groundwater Basin (UBSDGB) alluvial aquifer, including groundwater flow between the UBSDGB alluvial aquifer and the Denver Basin bedrock aquifers. The UBSDGB alluvial aquifer is located along the ephemeral Big Sandy Creek on the east-central edge of the Denver Basin aquifer system and covers an area of about 66,560 acres within the UBSDGB. The UBSDGB alluvial aquifer consists of unconsolidated Quaternary sand and gravel deposits that contain an unconfined (water table) groundwater system. The western three-fourths of the UBSDGB alluvial aquifer overlies the Tertiary and Cretaceous bedrock formations that compose the Denver Basin aquifer system. The updated water budget for the UBSDGB alluvial aquifer, including annual change in groundwater storage in 2016, was determined by combining water-budget information from an existing Denver Basin model for about three-fourths of the study area with best estimates for the major water-budget components for the area outside the Denver Basin aquifer system. The western part of the UBSDGB was included in the Denver Basin model (modeled area), whereas the eastern part of the UBSDGB was not included in the Denver Basin model (unmodeled area). The water-budget components were first estimated for the modeled area using outputs from the Denver Basin model, which uses the modular finite-difference groundwater flow computer model MODFLOW-2000 with 1-mile grid cells. For this study, the Denver Basin model was updated with additional data from 2004 through 2016 to generate current (2016) estimates of water consumption in the UBSDGB alluvial aquifer. A basin-specific water budget for the UBSDGB alluvial aquifer from the Denver Basin model was computed using a modeling tool called ZONEBUDGET. The modeled area groundwater budget, along with previous studies, was used to estimate a groundwater budget for the unmodeled area, and results for the modeled and unmodeled areas were combined for an overall water-budget estimate for the entire UBSDGB alluvial aquifer.</p><p>The net groundwater flow into the basin from adjacent alluvial aquifers was positive with flow entering the UBSDGB alluvial aquifer. Combining the total inflow from adjacent alluvial and the total outflow to adjacent alluvial aquifers resulted in a net flow from adjacent alluvial aquifers to UBSDGB alluvial aquifer of 5,125 acre-feet (ac-ft) in 2016. The net flow between the underlying bedrock aquifers and the UBSDGB alluvial aquifer was positive with flow entering the UBSDGB alluvial aquifer from the bedrock aquifers. The net flow from the bedrock aquifers to the UBSDGB alluvial aquifer was 347 ac-ft in 2016. Net recharge (precipitation and irrigation return flows minus evaporation) into the UBSDGB alluvial aquifer was negative with groundwater being removed from the UBSDGB alluvial aquifer over the total area of the basin. Combining the total inflow from recharge to the UBSDGB alluvial aquifer of 11,153 ac-ft in 2016 and the total evapo-transpiration of −11,656 ac-ft from the UBSDGB alluvial aquifer in 2016 resulted in a net recharge from UBSDGB alluvial aquifer of −503 ac-ft in 2016. Combining the modeled and unmodeled well pumping resulted in a total well pumping volume of −3,735 ac-ft in 2016 from the UBSDGB alluvial aquifer. The net groundwater flow to the stream network in the basin was negative with flow discharging from the UBSDGB alluvial aquifer into streams. Combining the total inflow from streams and the total outflow to streams for the UBSDGB alluvial aquifer resulted in −1,032 ac-ft in 2016 that was lost to the stream network in the UBSDGB. The net groundwater flow out of the UBSDGB was negative with flow leaving the UBSDGB alluvial aquifer. Combining the total area inflow to the basin from upgradient areas and the total area outflow from the basin for the UBSDGB alluvial aquifer resulted in a net flow out of the basin of −2,300 ac-ft. In the annual groundwater budget for 2016, groundwater storage in the UBSDGB alluvial aquifer system was removed because annual groundwater outflows from storage exceeded groundwater inflows to storage; in other words, water was removed from storage to balance the annual water budget. Combining the net flow from storage for the modeled area of 73 ac-ft and the inflow from storage for the unmodeled area of 2,025 ac-ft resulted in a net positive flow from storage of the UBSDGB alluvial aquifer of 2,098 ac-ft.</p><p>Increased pumping since 1958 in the Denver and upper Arapahoe aquifers, not necessarily in the UBSDGB, has caused a change in flow from bedrock units, which were minor or non-contributors of inflow to the UBSDGB alluvial aquifer, to receiving outflow from the UBSDGB alluvial aquifer. Since 2000, aquifer storage has been an inflow component of the water budget, which means that outflow from the modeled area exceeded inflow for the UBSDGB alluvial aquifer. Increased recharge from wetter than average years could replenish the UBSDGB alluvial aquifer. From 2003 through 2016, 13 of the 25 observation wells completed in the UBSDGB alluvial aquifer had a decline in the groundwater-level elevation with an average decline of −2.21 feet, and 12 of the 25 observation wells had an increase in the groundwater-level elevation with an average increase of 1.54 feet. In general, wells at the eastern and western edges of the UBSDGB showed an increase in groundwater-level elevation that appears related to areas of groundwater discharge from the lower Dawson and Laramie-Fox Hills bedrock aquifers to the UBSDGB alluvial aquifer. The remaining wells exhibited water-level declines. Future work could include the development of a basin-specific model to serve as a basin management tool for modeling changes in groundwater levels and storage under various future groundwater recharge and withdrawal scenarios.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195049","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District","usgsCitation":"Kohn, M.S., Oden, J.H., and Arnold, L.R., 2019, Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016: U.S. Geological Survey Scientific Investigations Report 2019-5049, 25 p., https://dx.doi.org/10.3133/sir20195049.","productDescription":"Report: vi, 25 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091541","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":365584,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5049/sir20195049.pdf","text":"Report","size":"7.12 M","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5049"},{"id":365581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5049/coverthb.jpg"},{"id":365748,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DEOYGZ","text":"USGS data release","linkHelpText":"MODFLOW2000 model and ZONEBUDGET computer program used to simulate the Upper Big Sandy Designated Groundwater Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016"}],"country":"United States","state":"Colorado","county":"Elbert County, El Paso County, Lincoln County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.054,38.523],[-104.1629,38.5215],[-104.2759,38.5204],[-104.2794,38.5205],[-104.2836,38.5201],[-104.3759,38.52],[-104.4971,38.5192],[-104.6071,38.5187],[-104.7171,38.5186],[-104.736,38.5183],[-104.8295,38.5183],[-104.943,38.5175],[-104.9432,38.5479],[-104.943,38.5624],[-104.9429,38.6041],[-104.9427,38.6186],[-104.9429,38.6467],[-104.9429,38.6503],[-104.9427,38.6621],[-104.9427,38.6648],[-104.9428,38.6938],[-104.9399,38.6938],[-104.9386,38.7808],[-104.939,38.7949],[-105.0671,38.7946],[-105.0674,38.8666],[-105.0502,38.8665],[-105.0296,38.8668],[-105.026,39.0413],[-105.032,39.1311],[-104.9371,39.1312],[-104.9175,39.131],[-104.8303,39.1311],[-104.6642,39.1308],[-104.6638,39.2165],[-104.664,39.3026],[-104.663,39.3892],[-104.6626,39.4762],[-104.6627,39.5665],[-104.6054,39.5663],[-104.5374,39.5655],[-104.4927,39.5636],[-104.4891,39.5636],[-104.4742,39.5629],[-104.3841,39.5627],[-104.3763,39.5631],[-104.2695,39.5639],[-104.2647,39.5638],[-104.1602,39.5646],[-104.1543,39.565],[-104.0468,39.5652],[-104.0427,39.5651],[-103.9305,39.5646],[-103.9293,39.5646],[-103.8189,39.5646],[-103.8129,39.5649],[-103.7126,39.5649],[-103.7066,39.5648],[-103.6004,39.5646],[-103.595,39.5645],[-103.4882,39.5647],[-103.4804,39.5645],[-103.3748,39.5651],[-103.3658,39.5654],[-103.2631,39.5659],[-103.253,39.5657],[-103.1533,39.5657],[-103.1539,39.475],[-103.1537,39.3879],[-103.1542,39.3009],[-103.154,39.2147],[-103.1527,39.1258],[-103.161,39.1255],[-103.1615,39.0376],[-103.1626,38.9492],[-103.163,38.863],[-103.1634,38.7765],[-103.1638,38.6912],[-103.1709,38.6909],[-103.1705,38.6837],[-103.1731,38.6796],[-103.1716,38.6111],[-103.1714,38.5236],[-103.2809,38.5224],[-103.3897,38.5239],[-103.5086,38.5236],[-103.5089,38.5159],[-103.6118,38.5171],[-103.6116,38.5225],[-103.7228,38.5223],[-103.8328,38.523],[-103.9411,38.523],[-104.054,38.523]]]},\"properties\":{\"name\":\"Elbert\",\"state\":\"CO\"}}]}","contact":"<p>Director, <a href=\"http://co.water.usgs.gov/\" data-mce-href=\"http://co.water.usgs.gov/\">Colorado Water Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-415<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Water-Budget Analysis</li><li>Possible Future Work</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-07-22","noUsgsAuthors":false,"publicationDate":"2019-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oden, Jeannette H. 0000-0002-6473-1553 jhoden@usgs.gov","orcid":"https://orcid.org/0000-0002-6473-1553","contributorId":1152,"corporation":false,"usgs":true,"family":"Oden","given":"Jeannette","email":"jhoden@usgs.gov","middleInitial":"H.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnold, L. R. 0000-0002-5110-9642 lrarnold@usgs.gov","orcid":"https://orcid.org/0000-0002-5110-9642","contributorId":1307,"corporation":false,"usgs":true,"family":"Arnold","given":"L.","email":"lrarnold@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70226679,"text":"70226679 - 2019 - Distribution of contaminants in the environment and wildlife habitat use: A case study with lead and waterfowl on the Upper Texas Coast","interactions":[],"lastModifiedDate":"2021-12-03T12:59:16.19523","indexId":"70226679","displayToPublicDate":"2019-07-19T06:51:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of contaminants in the environment and wildlife habitat use: A case study with lead and waterfowl on the Upper Texas Coast","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>The magnitude and distribution of lead contamination remain unknown in wetland systems. Anthropogenic deposition of lead may be contributing to negative population-level effects in waterfowl and other organisms that depend on dynamic wetland habitats, particularly if they are unable to detect and differentiate levels of environmental contamination by lead. Detection of lead and behavioral response to elevated lead levels by waterfowl is poorly understood, but necessary to characterize the risk of lead-contaminated habitats. We measured the relationship between lead contamination of wetland soils and habitat use by mottled ducks (<i>Anas fulvigula</i>) on the Upper Texas Coast, USA. Mottled ducks have historically experienced disproportionate negative effects from lead exposure, and exhibit a unique nonmigratory life history that increases risk of exposure when inhabiting contaminated areas. We used spatial interpolation to estimate lead in wetland soils of the Texas Chenier Plain National Wildlife Refuge Complex. Soil lead levels varied across the refuge complex (0.01–1085.51 ppm), but greater lead concentrations frequently corresponded to areas with high densities of transmittered mottled ducks. We used soil lead concentration data and MaxENT species distribution models to quantify relationships among various habitat factors and locations of mottled ducks. Use of habitats with greater lead concentration increased during years of a major disturbance. Because mottled ducks use habitats with high concentrations of lead during periods of stress, have greater risk of exposure following major disturbance to the coastal marsh system, and no innate mechanism for avoiding the threat of lead exposure, we suggest the potential presence of an ecological trap of quality habitat that warrants further quantification at a population scale for mottled ducks.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10646-019-02079-1","usgsCitation":"Kearns, B., McDowell, S.K., Moon, J.A., Rigby, E.A., Conway, W.C., and Haukos, D.A., 2019, Distribution of contaminants in the environment and wildlife habitat use: A case study with lead and waterfowl on the Upper Texas Coast: Ecotoxicology, v. 28, p. 809-824, https://doi.org/10.1007/s10646-019-02079-1.","productDescription":"16 p.","startPage":"809","endPage":"824","ipdsId":"IP-106225","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":392430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Unied States","state":"Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.536865234375,\n              28.536274512989916\n            ],\n            [\n              -96.0260009765625,\n              28.507315578441784\n            ],\n            [\n              -95.284423828125,\n              28.62310355452992\n            ],\n            [\n              -94.207763671875,\n              29.52567042617583\n            ],\n            [\n              -94.317626953125,\n              30.90222470517144\n            ],\n            [\n              -96.536865234375,\n              30.90222470517144\n            ],\n            [\n              -96.536865234375,\n              28.536274512989916\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2019-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kearns, Brian","contributorId":198470,"corporation":false,"usgs":false,"family":"Kearns","given":"Brian","email":"","affiliations":[],"preferred":false,"id":827633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDowell, Stephen K.","contributorId":171603,"corporation":false,"usgs":false,"family":"McDowell","given":"Stephen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":827634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":827635,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rigby, Elizabeth A.","contributorId":171479,"corporation":false,"usgs":false,"family":"Rigby","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":827636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":827637,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":827638,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204092,"text":"sir20195065 - 2019 - Assessment of polycyclic aromatic hydrocarbon concentrations in southern Lake Powell, Glen Canyon National Recreation Area, Arizona and Utah, 2016–17","interactions":[],"lastModifiedDate":"2019-07-26T12:33:09","indexId":"sir20195065","displayToPublicDate":"2019-07-18T14:25:35","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5065","displayTitle":"Assessment of Polycyclic Aromatic Hydrocarbon Concentrations in Southern Lake Powell, Glen Canyon National Recreation Area, Arizona and Utah, 2016–17","title":"Assessment of polycyclic aromatic hydrocarbon concentrations in southern Lake Powell, Glen Canyon National Recreation Area, Arizona and Utah, 2016–17","docAbstract":"<p>Polycyclic aromatic hydrocarbon contamination related to boat use is one of the most important water-quality issues affecting Lake Powell. High concentrations of polycyclic aromatic hydrocarbons in water are common around marinas and other areas with extensive motorboat activity because of releases of uncombusted or partially combusted oil and gasoline from boat engines. The fate of these compounds in Lake Powell is of serious environmental concern because of their toxicity and carcinogenicity and their moderate persistence once they enter the aquatic ecosystem. In 2016–17, the U.S. Geological Survey (USGS) assessed the presence and concentrations of polycyclic aromatic hydrocarbons in Lake Powell at seven sites where concentrations have historically been elevated and one site where concentrations have historically been relatively low. Semipermeable membrane devices were used to collect samples that represent time-weighted averages of polycyclic aromatic hydrocarbon concentrations in water over one-month deployment periods. Samples were collected from the epilimnion of the lake at each of the eight sampling sites during two periods of relatively high boat use (summer), and one period of relatively low boat use (spring). Twenty-eight out of 33 polycyclic aromatic hydrocarbons analyzed were detected in Lake Powell during the three sampling events. During the two summer sampling events, concentrations were generally higher, and more compounds were detected, than during the spring sampling event. Twenty-two of the polycyclic aromatic hydrocarbons analyzed in 2016–17 had previously been analyzed by the USGS in the summer of 2010 at the same 8 sites using the same collection method. Eleven of 22 compounds were detected in the summer 2010, summer 2016, and summer 2017 sampling events, and 1 was detected in the summer 2010 and summer 2017 samplings, but not in the summer 2016 sampling event. During both the current and previous sampling events, concentrations were generally higher, and more compounds were detected, at the high-use marina sites located most downstream on the lake. The consistent presence of a wide range of polycyclic aromatic hydrocarbons in the water of Lake Powell indicates chronic and (or) recent anthropogenic sources of contamination. The results from this study will provide the National Park Service with information necessary to determine if the current regulations on emission standards for personal watercraft used on Lake Powell are effective in lowering polycyclic aromatic hydrocarbon concentrations in the lake.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195065","collaboration":"Prepared in cooperation with the National Park Service and the Bureau of Reclamation","usgsCitation":"Coes, A.L., Paretti, N.V., Alvarez, D.A., and Macy, J.P., 2019, Assessment of polycyclic aromatic hydrocarbon concentrations in southern Lake Powell, Glen Canyon National Recreation Area, Arizona and Utah, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5065, 26 p., https://doi.org/10.3133/sir20195065.","productDescription":"v, 26 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-097642","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":365700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5065/coverthb.jpg"},{"id":365701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5065/sir20195065.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5065"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon National Recreation Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.1044921875,\n              36.54494944148322\n            ],\n            [\n              -110.3466796875,\n              36.54494944148322\n            ],\n            [\n              -110.3466796875,\n              37.579412513438385\n            ],\n            [\n              -112.1044921875,\n              37.579412513438385\n            ],\n            [\n              -112.1044921875,\n              36.54494944148322\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:leenhout@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:leenhout@usgs.gov\">Director</a>,<br><a href=\"https://az.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://az.water.usgs.gov/\">Arizona Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Sampling Conditions</li><li>Data Quality Assessment</li><li>Polycyclic Aromatic Hydrocarbon Concentrations</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-07-18","noUsgsAuthors":false,"publicationDate":"2019-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Coes, Alissa L. 0000-0001-6682-5417 alcoes@usgs.gov","orcid":"https://orcid.org/0000-0001-6682-5417","contributorId":4231,"corporation":false,"usgs":true,"family":"Coes","given":"Alissa","email":"alcoes@usgs.gov","middleInitial":"L.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David A. 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":1369,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":765446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Macy, Jamie P. 0000-0003-3443-0079 jpmacy@usgs.gov","orcid":"https://orcid.org/0000-0003-3443-0079","contributorId":2173,"corporation":false,"usgs":true,"family":"Macy","given":"Jamie","email":"jpmacy@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765444,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208461,"text":"70208461 - 2019 - Seismic loss and damage in light-frame wood buildings from sequences of induced earthquakes","interactions":[],"lastModifiedDate":"2020-02-11T07:58:54","indexId":"70208461","displayToPublicDate":"2019-07-18T07:56:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1434,"text":"Earthquake Engineering and Structural Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Seismic loss and damage in light-frame wood buildings from sequences of induced earthquakes","docAbstract":"Activities related to oil and gas production, especially deep disposal of wastewater, have led to sequences of induced earthquakes in the central U.S. This study aims to quantify damage to and seismic losses for light-frame wood buildings when subjected to sequences of induced, small to moderate magnitude, events. To conduct this investigation, one and two-story multifamily wood frame buildings are designed, and their seismic response dynamically simulated using three-dimensional (3D) nonlinear models, subjected to ground motion sequences recorded in induced events. Damage is quantified through seismic losses, which are estimated using the FEMA P-58 methodology. Results show that at levels of shaking experienced in recent earthquakes, minor damage, consisting of cracking of interior finishes and nonstructural damage to plumbing and HVAC systems is expected, which is consistent with observed damage in these events. The study also examines how expected losses and building fragility will accumulate and/or change over a sequence of earthquakes. Results indicate that damage quantified in terms of absorbed hysteretic energy tended to accumulate over the sequences; this damage corresponds to elongation or widening of cracks. However, fragility is not significantly altered by damage in a preceding event, meaning structures are not becoming more vulnerable due to existing damage. In addition, sequences of events do not change losses if the building is only repaired once at the end of the sequence, as the worsening of damage does not alter repair actions. If repairs are conducted after each event, though, total seismic losses can increase greatly from the sequence.","language":"English","publisher":"Wiley","doi":"10.1002/eqe.3189","usgsCitation":"Chase, R.E., Liel, A.B., Luco, N., and Baird, B.W., 2019, Seismic loss and damage in light-frame wood buildings from sequences of induced earthquakes: Earthquake Engineering and Structural Dynamics, v. 48, no. 12, p. 1365-1383, https://doi.org/10.1002/eqe.3189.","productDescription":"19 p.","startPage":"1365","endPage":"1383","ipdsId":"IP-107935","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":372210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma 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,{"id":70205950,"text":"70205950 - 2019 - Twenty-first century California, USA, wildfires: Fuel-dominated vs. wind-dominated fires","interactions":[],"lastModifiedDate":"2019-10-11T06:44:55","indexId":"70205950","displayToPublicDate":"2019-07-18T06:43:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Twenty-first century California, USA, wildfires: Fuel-dominated vs. wind-dominated fires","docAbstract":"Since the beginning of the twenty-first century California, USA, has experienced a substantial increase in the frequency of large wildfires, often with extreme impacts on people and property. Due to the size of the state, it is not surprising that the factors driving these changes differ across this region. Although there are always multiple factors driving wildfire behavior, we believe a helpful model for understanding fires in the state is to frame the discussion in terms of bottom-up vs. top-down controls on fire behavior; that is, fires that are clearly dominated by anomalously high fuel loads from those dominated by extreme wind events. Of course, this distinction is somewhat artificial in that all fires are controlled by multiple factors involving fuels, winds, and topography. However, we believe that fires clearly recognizable as fuel-dominated vs. wind-dominated provide interesting case studies of factors behind these two extremes. These two types of fires differ greatly in their (1) geographical distribution in the state, (2) past fire history, (3) prominent sources of ignition, (4) seasonal timing, (5) resources most at risk, and (6) requirement for different management responses.","language":"English","publisher":"Springer","doi":"10.1186/s42408-019-0041-0","usgsCitation":"Keeley, J., and Syphard, A.D., 2019, Twenty-first century California, USA, wildfires: Fuel-dominated vs. wind-dominated fires: Fire Ecology, v. 15, 24, 15 p., https://doi.org/10.1186/s42408-019-0041-0.","productDescription":"24, 15 p.","ipdsId":"IP-104751","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-019-0041-0","text":"Publisher Index 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 \"}}]}","volume":"15","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":773019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":773020,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227969,"text":"70227969 - 2019 - Linking demographic and food-web models to understand management trade-offs","interactions":[],"lastModifiedDate":"2022-02-03T18:33:36.936323","indexId":"70227969","displayToPublicDate":"2019-07-17T12:25:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Linking demographic and food-web models to understand management trade-offs","docAbstract":"Alternatives in ecosystem-based management often differ with respect to trade-offs between ecosystem values. Ecosystem or food-web models and demographic models are typically employed to evaluate alternatives, but the approaches are rarely integrated to uncover conflicts between values. We applied multi-state models to a capture-recapture dataset on common guillemots Uria aalge breeding in the Baltic Sea to identify factors influencing survival. The estimated relationships were employed together with Ecopath-with-Ecosim food-web model simulations to project guillemot survival under six future scenarios incorporating climate change. The scenarios were based on management alternatives for eutrophication and cod fisheries, issues considered top priority for regional management, but without known direct effects on the guillemot population. Our demographic models identified prey quantity (abundance and biomass of sprat Sprattus sprattus) as the main factor influencing guillemot survival. Most scenarios resulted in projections of increased survival, in the near (2016-2040) and distant (2060-2085) future. However, in the scenario of reduced nutrient input and precautionary cod fishing, guillemot survival was projected to be lower in both future periods due to lower sprat stocks. Matrix population models suggested a substantial decline of the guillemot population in the near future, 24% per 10 years; and a smaller reduction, 1.1% per 10 years, in the distant future. To date, many stakeholders and Baltic Sea governments have supported reduced nutrient input and precautionary cod fishing and implementation is underway. Negative effects on non-focal-species have previously not been uncovered, but our results show that the scenario is likely to negatively impact the guillemot population. Linking model results allowed identifying trade-offs associated with management alternatives. This information is critical to thorough evaluation by decision-makers, but not easily obtained by food-web models or demographic models in isolation. Appropriate datasets are often available, making it feasible to apply a linked approach for better-informed decisions in ecosystem-based management.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5385","usgsCitation":"Kadin, M., Frederiksen, M., Niiranen, S., and Converse, S.J., 2019, Linking demographic and food-web models to understand management trade-offs: Ecology and Evolution, v. 9, no. 15, p. 8587-8600, https://doi.org/10.1002/ece3.5385.","productDescription":"14 p.","startPage":"8587","endPage":"8600","ipdsId":"IP-095999","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467445,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5385","text":"Publisher Index Page"},{"id":395382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Baltic Sea","volume":"9","issue":"15","noUsgsAuthors":false,"publicationDate":"2019-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kadin, Martina","contributorId":274509,"corporation":false,"usgs":false,"family":"Kadin","given":"Martina","email":"","affiliations":[],"preferred":false,"id":833082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frederiksen, Morten","contributorId":96404,"corporation":false,"usgs":true,"family":"Frederiksen","given":"Morten","affiliations":[],"preferred":false,"id":833083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niiranen, Susa","contributorId":274510,"corporation":false,"usgs":false,"family":"Niiranen","given":"Susa","email":"","affiliations":[],"preferred":false,"id":833084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":832833,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215402,"text":"70215402 - 2019 - Developing and testing physically based triggering thresholds for runoff‐generated debris flows","interactions":[],"lastModifiedDate":"2020-10-18T15:11:44.549573","indexId":"70215402","displayToPublicDate":"2019-07-17T10:08:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Developing and testing physically based triggering thresholds for runoff‐generated debris flows","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Runoff in steep channels is capable of transitioning into debris flows with hazardous implications for downstream communities and infrastructure, particularly in alpine landscapes with minimal vegetation and areas recently disturbed by wildfire. Here, we derive thresholds for the initiation of runoff‐generated debris flows based on critical values of dimensionless discharge and Shields stress. These thresholds are derived by using a numerical model to estimate the hydrodynamic conditions coinciding with the timing of debris flow activity in a recently burned basin. A benefit of hydrodynamic thresholds is that they can be used to assess debris flow likelihood based on measurable hydrologic and geomorphic parameters and therefore provide more universal criteria for quantifying the runoff‐to‐debris flow transition in landscape evolution studies and hazard assessments. We then demonstrate how hydrodynamic thresholds can be used to estimate rainfall intensity‐duration thresholds for runoff‐generated debris flows without the need for historic debris flow observations.</p></div></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL083623","usgsCitation":"Tang, H., McGuire, L.A., Rengers, F.K., Kean, J.W., Staley, D.M., and Smith, J.B., 2019, Developing and testing physically based triggering thresholds for runoff‐generated debris flows: Geophysical Research Letters, v. 46, no. 15, p. 8830-8839, https://doi.org/10.1029/2019GL083623.","productDescription":"10 p.","startPage":"8830","endPage":"8839","ipdsId":"IP-109482","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467446,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl083623","text":"Publisher Index Page"},{"id":437384,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3YTBP","text":"USGS data release","linkHelpText":"Post-wildfire debris-flow monitoring data, Las Lomas, 2016 Fish Fire, Los Angeles County, California, November 2016 to February 2017"},{"id":379501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Gabriel Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.553466796875,\n              34.116352469972746\n            ],\n            [\n              -117.454833984375,\n              34.116352469972746\n            ],\n            [\n              -117.454833984375,\n              34.58347505599177\n            ],\n            [\n              -118.553466796875,\n              34.58347505599177\n            ],\n            [\n              -118.553466796875,\n              34.116352469972746\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"46","issue":"15","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Tang, Hui","contributorId":215352,"corporation":false,"usgs":false,"family":"Tang","given":"Hui","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":802041,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":802042,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":802043,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":802044,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":802045,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":802046,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204409,"text":"70204409 - 2019 - FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions","interactions":[],"lastModifiedDate":"2020-04-06T20:55:10.925221","indexId":"70204409","displayToPublicDate":"2019-07-17T08:57:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions","docAbstract":"<p><span>This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH</span><sub>4</sub><span>) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH</span><sub>4</sub><span>&nbsp;flux measurements globally, initial results comparing CH</span><sub>4</sub><span>&nbsp;fluxes across the sites, and future research directions and needs. Annual estimates of net CH</span><sub>4</sub><span>&nbsp;fluxes across sites ranged from −0.2 ± 0.02 g C m</span><sup>–2</sup><span>&nbsp;yr</span><sup>–1</sup><span>&nbsp;for an upland forest site to 114.9 ± 13.4 g C m</span><sup>–2</sup><span>&nbsp;yr</span><sup>–1</sup><span>&nbsp;for an estuarine freshwater marsh, with fluxes exceeding 40 g C m</span><sup>–2</sup><span>&nbsp;yr</span><sup>–1</sup><span>&nbsp;at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH</span><sub>4</sub><span>&nbsp;flux across wetland sites globally. Water table position was positively correlated with annual CH</span><sub>4</sub><span>&nbsp;emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH</span><sub>4</sub><span>&nbsp;fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH</span><sub>4</sub><span>&nbsp;estimates due to gap-filling and random errors were on average ±1.6 g C m</span><sup>–2</sup><span>&nbsp;yr</span><sup>–1</sup><span>&nbsp;at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH</span><sub>4</sub><span>&nbsp;flux database, the controls on ecosystem CH</span><sub>4</sub><span>&nbsp;fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH</span><sub>4</sub><span>&nbsp;emissions.</span></p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0268.1","usgsCitation":"Knox, S.H., Jackson, R.B., Poulter, B., McNicol, G., Fluet-Chouinard, E., Zhang, Z., Hugelius, G., Bousquet, P., Canadell, J.G., Saunois, M., Papale, D., Chu, H., Keenan, T.F., Baldocchi, D., Torn, M.S., Mammarella, I., Trotta, C., Aurela, M., Bohrer, G., Campbell, D.I., Cescatti, A., Chamberlain, S.D., Chen, J., Chen, W., Dengel, S., Desai, A.R., Euskirchen, E.S., Friborg, T., Gasbarra, D., Goded, I., Goeckede, M., Heimann, M., Helbig, M., Hirano, T., Hollinger, D.Y., Iwata, H., Kang, M., Klatt, J., Krauss, K., Kutzbach, L., Lohila, A., Mitra, B., Morin, T., Nilsson, M.B., Niu, S., Noormets, A., Oechel, W.C., Peichl, M., Peltola, O., Reba, M.L., Richardson, A.D., Runkle, B.R., Ryu, Y., Sachs, T., Schafer, K.V., Schmid, H.P., Shurpali, N., Sonnentag, O., Tang, A., Ueyama, M., Vargas, R., Vesala, T., Ward, E., Windham-Myers, L., Wohlfahrt, G., and Zona, D., 2019, FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions: Bulletin of the American Meteorological Society, v. 100, no. 12, p. 2607-2632, https://doi.org/10.1175/BAMS-D-18-0268.1.","productDescription":"26 p.","startPage":"2607","endPage":"2632","ipdsId":"IP-102319","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":467447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70204580,"text":"70204580 - 2019 - The Aleutian Low – Beaufort Sea Anticyclone: A climate index for predicting the timing of springtime melt in the Pacific Arctic cryosphere","interactions":[],"lastModifiedDate":"2019-08-07T09:00:42","indexId":"70204580","displayToPublicDate":"2019-07-16T12:10:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The Aleutian Low – Beaufort Sea Anticyclone: A climate index for predicting the timing of springtime melt in the Pacific Arctic cryosphere","docAbstract":"Early and late extremes in the timing of snowmelt have recently been observed in the Pacific Arctic. Subseasonal-to-seasonal forecasts of this timing are important for industry, environmental management and Arctic communities. In northern Alaska, the timing is influenced by the advection of marine air from the north Pacific by the Aleutian Low, modulated by high pressure centered in the Beaufort Sea. A new climate index that integrates their interaction could advance melt predictions. We define this index based on 850 hPa geopotential height at four fixed locations referred to as the Aleutian Low –Beaufort Sea Anticyclone (ALBSA). During positive ALBSA in May, advection of +0.5-1.5 K/day is observed through the Bering Strait. ALBSA is correlated with both snowmelt in northern Alaska and the onset of sea ice melt over the adjacent seas. ALBSA, therefore, may be suitable for monitoring the relevant circulation patterns and for developing predictive tools.","language":"English","publisher":"AGU","doi":"10.1029/2019GL083306","usgsCitation":"Cox, C.J., Stone, R.S., Douglas, D., Stanitski, D., and Gallagher, M., 2019, The Aleutian Low – Beaufort Sea Anticyclone: A climate index for predicting the timing of springtime melt in the Pacific Arctic cryosphere: Geophysical Research Letters, v. 46, no. 13, p. 7464-7473, https://doi.org/10.1029/2019GL083306.","productDescription":"10 p.","startPage":"7464","endPage":"7473","ipdsId":"IP-101278","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":467451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl083306","text":"Publisher Index Page"},{"id":366304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"13","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, Christopher J.","contributorId":199259,"corporation":false,"usgs":false,"family":"Cox","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":767624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Robert S.","contributorId":199260,"corporation":false,"usgs":false,"family":"Stone","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":767625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":150115,"corporation":false,"usgs":true,"family":"Douglas","given":"David C.","email":"ddouglas@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":767623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanitski, Diane","contributorId":199261,"corporation":false,"usgs":false,"family":"Stanitski","given":"Diane","email":"","affiliations":[],"preferred":false,"id":767626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallagher, Michael","contributorId":217833,"corporation":false,"usgs":false,"family":"Gallagher","given":"Michael","email":"","affiliations":[{"id":39697,"text":"Cooperative Institute for Research in Environmental Sciences, NOAA Physical Sciences Division, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":767627,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206023,"text":"70206023 - 2019 - Geographic context affects the landscape change and fragmentation caused by wind energy facilities","interactions":[],"lastModifiedDate":"2020-01-20T12:11:36","indexId":"70206023","displayToPublicDate":"2019-07-16T11:31:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Geographic context affects the landscape change and fragmentation caused by wind energy facilities","docAbstract":"Wind energy generation causes transformation of landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change and fragmentation caused by these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities’ geographic context on changes in landscape patterns. We digitized the footprints of 39 wind facilities and the surrounding land cover using high-resolution imagery. We then measured landscape pattern before and after facility construction using 3 metrics associated with species responses to habitat loss and fragmentation (amount, core area, and connectivity of undeveloped land) within 1km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land, on average, by just 1.8% while changes in metrics of landscape pattern were relatively much larger (50 to 140%). Statistical models indicated levels of pre-construction development were a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Density of new roads was higher in places with little pre-existing development and may be influenced by facility design. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.","language":"English","doi":"10.7717/peerj.7129","collaboration":"None","usgsCitation":"Diffendorfer, J., Dorning, M., Keen, J., Kramer, L., and Taylor, R., 2019, Geographic context affects the landscape change and fragmentation caused by wind energy facilities: PeerJ, v. 7, e7129, 23p., https://doi.org/10.7717/peerj.7129.","productDescription":"e7129, 23p.","ipdsId":"IP-092054","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467452,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.7129","text":"Publisher Index Page"},{"id":437385,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7639NZB","text":"USGS data release","linkHelpText":"Data release for Geographic context affects the 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             -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n        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]\n}","volume":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":773320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorning, Monica 0000-0002-7576-1256 mdorning@usgs.gov","orcid":"https://orcid.org/0000-0002-7576-1256","contributorId":191772,"corporation":false,"usgs":true,"family":"Dorning","given":"Monica","email":"mdorning@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keen, Jolene 0000-0003-4962-6613","orcid":"https://orcid.org/0000-0003-4962-6613","contributorId":219835,"corporation":false,"usgs":false,"family":"Keen","given":"Jolene","email":"","affiliations":[{"id":37814,"text":"Former USGS","active":true,"usgs":false}],"preferred":false,"id":773322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kramer, Louisa 0000-0002-6776-9768","orcid":"https://orcid.org/0000-0002-6776-9768","contributorId":204878,"corporation":false,"usgs":true,"family":"Kramer","given":"Louisa","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Robert 0000-0001-5137-6874","orcid":"https://orcid.org/0000-0001-5137-6874","contributorId":219836,"corporation":false,"usgs":true,"family":"Taylor","given":"Robert","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70248318,"text":"70248318 - 2019 - Influences of potential oil and gas development and future climate on Sage-grouse declines and redistribution","interactions":[],"lastModifiedDate":"2024-05-16T15:31:39.363715","indexId":"70248318","displayToPublicDate":"2019-07-16T06:52:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Influences of potential oil and gas development and future climate on Sage-grouse declines and redistribution","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Multiple environmental stressors impact wildlife populations, but we often know little about their cumulative and combined influences on population outcomes. We generally know more about past effects than potential future impacts, and direct influences such as changes of habitat footprints than indirect, long-term responses in behavior, distribution, or abundance. Yet, an understanding of all these components is needed to plan for future landscapes that include human activities and wildlife. We developed a case study to assess how spatially explicit individual-based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater Sage-grouse in southwest Wyoming, USA, we projected oil and gas development footprints and climate-induced vegetation changes 50 years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we recalculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term Sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered a useful means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. The inclusion of movement and demographic responses to oil and gas infrastructure resulted in substantive changes in distribution and abundance when cumulated over several decades and throughout the regional population. When combined, additive development and climate-induced vegetation changes reduced abundance by up to half of the original size. In our example, the consideration of only a single population stressor the final possible population size by as much as 50%. Multiple stressors and their cumulative impacts need to be broadly considered through space and time to avoid underestimating the impacts of multiple gradual changes and overestimating the ability of populations to withstand change.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/eap.1912","usgsCitation":"Heinrichs, J.A., O’Donnell, M.S., Aldridge, C.L., Garman, S.L., and Homer, C.G., 2019, Influences of potential oil and gas development and future climate on Sage-grouse declines and redistribution: Ecological Applications, v. 29, no. 6, e01912, 16 p., https://doi.org/10.1002/eap.1912.","productDescription":"e01912, 16 p.","ipdsId":"IP-101540","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437386,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GRF34E","text":"USGS data release","linkHelpText":"Influences of Potential Oil and Gas Development and Future Climate on Sage-Grouse Declines and Redistribution"},{"id":420612,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.19262705318685,\n              43.34205527106326\n            ],\n            [\n              -111.19262705318685,\n              40.99843719341089\n            ],\n            [\n              -106.18500996030774,\n              40.99843719341089\n            ],\n            [\n              -106.18500996030774,\n              43.34205527106326\n            ],\n            [\n              -111.19262705318685,\n              43.34205527106326\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"29","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":882536,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garman, Steven L. 0000-0002-9032-9074 slgarman@usgs.gov","orcid":"https://orcid.org/0000-0002-9032-9074","contributorId":3741,"corporation":false,"usgs":true,"family":"Garman","given":"Steven","email":"slgarman@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":882537,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","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":882538,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70204498,"text":"70204498 - 2019 - Spatial capture-recapture reveals age- and sex-specific survival and movement in stream amphibians","interactions":[],"lastModifiedDate":"2019-08-29T11:58:54","indexId":"70204498","displayToPublicDate":"2019-07-15T15:17:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Spatial capture-recapture reveals age- and sex-specific survival and movement in stream amphibians","docAbstract":"Life history information sets the foundation for our understanding of ecology and conservation requirements. For many species, this information is lacking even for basic demographic rates such as survival and movement. When survival and movement estimates are available, they are often derived from mixed demographic groups and do not consider differences among life stages or sexes, which is critical because life stages and sexes often contribute differentially to population dynamics. We used hierarchical models informed with spatial capture-mark-recapture data of Ascaphus montanus (Rocky Mountain tailed frog) in 5 streams and A. truei (coastal tailed frog) in 1 stream to estimate variation in survival and movement by sex and age, represented by size. By incorporating survival and movement into a single model, we were able to estimate both parameters with limited bias. Annual survival was similar between sexes of A. montanus (females = 0.885 [95% CI: 0.614–1], males = 0.901 [0.657–1]), but was slightly higher for female A. truei (0.836 [0.560–0.993]) than for males (0.664 [0.354–0.962]). Survival of A. montanus peaked at mid-age, suggesting that lower survival of young and actuarial senescence may influence population demographics. Our models suggest that younger A. montanus moved farther than older individuals, and that females moved farther than males in both species. Our results provide uncommon insight into age- and sex-specific rates of survival and movement that are crucial elements of life-history strategies and are important for modeling population growth and prescribing conservation actions.","language":"English","publisher":"Springer","doi":"10.1007/s00442-019-04464-3","usgsCitation":"Honeycutt, R.K., Garwood, J.M., Lowe, W.H., and Hossack, B.R., 2019, Spatial capture-recapture reveals age- and sex-specific survival and movement in stream amphibians: Oecologia, v. 190, no. 4, p. 821-833, https://doi.org/10.1007/s00442-019-04464-3.","productDescription":"13 p.","startPage":"821","endPage":"833","ipdsId":"IP-086212","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":366004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366002,"type":{"id":15,"text":"Index Page"},"url":"https://link.springer.com/content/pdf/10.1007%2Fs00442-019-04464-3.pdf"}],"volume":"190","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Honeycutt, R. Ken 0000-0002-7157-7195 rhoneycutt@usgs.gov","orcid":"https://orcid.org/0000-0002-7157-7195","contributorId":156282,"corporation":false,"usgs":true,"family":"Honeycutt","given":"R.","email":"rhoneycutt@usgs.gov","middleInitial":"Ken","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":767262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garwood, Justin M","contributorId":217674,"corporation":false,"usgs":false,"family":"Garwood","given":"Justin","email":"","middleInitial":"M","affiliations":[{"id":39681,"text":"California Dept fish wildlife","active":true,"usgs":false}],"preferred":false,"id":767263,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Winsor H.","contributorId":126722,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor","email":"","middleInitial":"H.","affiliations":[{"id":6577,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, 59812, USA.","active":true,"usgs":false}],"preferred":false,"id":767264,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":767265,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204134,"text":"fs20193002 - 2019 - Assessment of continuous oil resources in the Eastern Great Basin Province of Nevada, Utah, and Idaho, 2018","interactions":[],"lastModifiedDate":"2019-07-16T09:12:25","indexId":"fs20193002","displayToPublicDate":"2019-07-15T13:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3002","displayTitle":"Assessment of Continuous Oil Resources in the Eastern Great Basin Province of Nevada, Utah, and Idaho, 2018","title":"Assessment of continuous oil resources in the Eastern Great Basin Province of Nevada, Utah, and Idaho, 2018","docAbstract":"<p>The U.S. Geological Survey (USGS) quantitatively assessed the&nbsp;potential for undiscovered, technically recoverable continuous oil and gas&nbsp;resources in the Eastern Great Basin Province (Anna and others, 2007) of&nbsp;Nevada, Utah, and Idaho (fig. 1). The assessment focused on the area of&nbsp;the province between the Roberts Mountains and Sevier thrust systems&nbsp;(Peterson, 1994). The major petroleum source rocks within this area are the&nbsp;Upper Devonian–Lower Mississippian Pilot Shale and the Mississippian&nbsp;Chainman Formation (Gutschick and Rodriquez, 1979; Poole and Claypool,&nbsp;1984; Giles, 1994; Trexler and others, 1995). The geologic model applied&nbsp;to the Pilot Shale and shales in the Chainman Formation is for these shales&nbsp;to have achieved generative maturity for oil by burial to at least 8,700 feet&nbsp;(2,652 meters) within some of the Neogene extensional basins (Grabb, 1994;&nbsp;Anna and others, 2007). Areas that satisfy this depth requirement were&nbsp;defined using modeled gravity data that were calibrated to the petroleum&nbsp;system in Railroad Valley and Pine Valley in Nevada (Barker and Peterson,&nbsp;1991; Ïnan and Davis, 1994; Meissner, 1995; Anna and others, 2007).&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193002","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Finn, T.M., Gaswirth, S.B., Marra, K.R., Le, P.A., Brownfield, M.E., Leathers-Miller, H.M., Drake, R.M., II, and Kinney, S.A., 2019, Assessment of continuous oil resources in the Eastern Great Basin Province of Nevada, Utah, and Idaho, 2018: U.S. Geological Survey Fact Sheet 2019–3002, 2 p., https://doi.org/10.3133/fs20193002.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-101431","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":365379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3002/coverthb.jpg"},{"id":365364,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3002/fs20193002.pdf","text":"Report","size":"660 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3002"}],"country":"United States","state":"Idaho, Nevada, Utah","otherGeospatial":"Eastern Great Basin Province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.02636718749999,\n              42.00032514831621\n            ],\n            [\n              -117.0703125,\n              36.77409249464195\n            ],\n            [\n              -114.60937499999999,\n              34.994003757575776\n    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PSC"},"publishedDate":"2019-07-15","noUsgsAuthors":false,"publicationDate":"2019-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Schenk, Christopher J. 0000-0002-0248-7305 schenk@usgs.gov","orcid":"https://orcid.org/0000-0002-0248-7305","contributorId":826,"corporation":false,"usgs":true,"family":"Schenk","given":"Christopher","email":"schenk@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":765657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mercier, Tracey J. 0000-0002-8232-525X tmercier@usgs.gov","orcid":"https://orcid.org/0000-0002-8232-525X","contributorId":2847,"corporation":false,"usgs":true,"family":"Mercier","given":"Tracey","email":"tmercier@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodall, Cheryl A. 0000-0002-4844-5768 cwoodall@usgs.gov","orcid":"https://orcid.org/0000-0002-4844-5768","contributorId":194924,"corporation":false,"usgs":true,"family":"Woodall","given":"Cheryl","email":"cwoodall@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finn, Thomas M. 0000-0001-6396-9351 finn@usgs.gov","orcid":"https://orcid.org/0000-0001-6396-9351","contributorId":778,"corporation":false,"usgs":true,"family":"Finn","given":"Thomas","email":"finn@usgs.gov","middleInitial":"M.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaswirth, Stephanie B. 0000-0001-5821-6347 sgaswirth@usgs.gov","orcid":"https://orcid.org/0000-0001-5821-6347","contributorId":150417,"corporation":false,"usgs":true,"family":"Gaswirth","given":"Stephanie","email":"sgaswirth@usgs.gov","middleInitial":"B.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marra, Kristen R. 0000-0001-8027-5255 kmarra@usgs.gov","orcid":"https://orcid.org/0000-0001-8027-5255","contributorId":4844,"corporation":false,"usgs":true,"family":"Marra","given":"Kristen","email":"kmarra@usgs.gov","middleInitial":"R.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765662,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Le, Phuong A. 0000-0003-2477-509X ple@usgs.gov","orcid":"https://orcid.org/0000-0003-2477-509X","contributorId":150418,"corporation":false,"usgs":true,"family":"Le","given":"Phuong","email":"ple@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765663,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brownfield, Michael E. 0000-0003-3633-1138 mbrownfield@usgs.gov","orcid":"https://orcid.org/0000-0003-3633-1138","contributorId":1548,"corporation":false,"usgs":true,"family":"Brownfield","given":"Michael","email":"mbrownfield@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765664,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leathers-Miller, Heidi M. 0000-0001-5208-9906 hleathers@usgs.gov","orcid":"https://orcid.org/0000-0001-5208-9906","contributorId":150419,"corporation":false,"usgs":true,"family":"Leathers-Miller","given":"Heidi","email":"hleathers@usgs.gov","middleInitial":"M.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765665,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Drake, Ronald M. II 0000-0002-1770-4667","orcid":"https://orcid.org/0000-0002-1770-4667","contributorId":206291,"corporation":false,"usgs":true,"family":"Drake","given":"Ronald M.","suffix":"II","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765666,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kinney, Scott A. 0000-0001-5008-5813 skinney@usgs.gov","orcid":"https://orcid.org/0000-0001-5008-5813","contributorId":1395,"corporation":false,"usgs":true,"family":"Kinney","given":"Scott","email":"skinney@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765689,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70204603,"text":"70204603 - 2019 - Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks","interactions":[],"lastModifiedDate":"2019-08-07T09:02:37","indexId":"70204603","displayToPublicDate":"2019-07-15T11:10:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks","docAbstract":"Estimation of low flows in rivers continues to be a vexing problem despite advances\nin statistical and process‐based hydrological models. We develop a method to\nestimate minimum streamflow at seasonal to annual timescales from measured\nstreamflow based on regional similarity in the deviations of daily streamflow from\nminimum streamflow for a period of interest. The method is applied to 1,019 gauged\nsites in the Western United States for June to December 2015. The gauges were\nclustered into six regions with distinct timing and magnitude of low flows. A gamma\ndistribution was fit each day to the deviations in specific discharge (daily streamflow\ndivided by drainage area) from minimum specific discharge for gauges in each region.\nThe Kolmogorov–Smirnov test identified days when the gamma distribution was\nadequate to represent the distribution of deviations in a region. The performance\nof the gamma distribution was evaluated at gauges by comparing daily estimates of\nminimum streamflow with estimates from area‐based regression relations for minimum\nstreamflow. Each region had at least 8 days during the period when streamflow\nmeasurements would provide better estimates than the regional regression equation,\nbut the number of such days varied by region depending on aridity and homogeneity\nof streamflow within the region. Synoptic streamflow measurements at ungauged\nsites have value for estimating minimum streamflow and improving the spatial\nresolution of hydrological model in regions with streamflow‐gauging networks.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13452","usgsCitation":"Konrad, C., 2019, Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks: Hydrological Processes, v. 33, no. 15, p. 2057-2067, https://doi.org/10.1002/hyp.13452.","productDescription":"11 p.","startPage":"2057","endPage":"2067","ipdsId":"IP-094109","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":366292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366279,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.13452"}],"volume":"33","issue":"15","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Konrad, Christopher P. 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":217885,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767745,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70199738,"text":"ofr20181158 - 2019 - Offshore shallow structure and sediment distribution, Point Sur to Point Arguello, central California","interactions":[],"lastModifiedDate":"2019-07-17T11:29:18","indexId":"ofr20181158","displayToPublicDate":"2019-07-15T09:47:02","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1158","displayTitle":"Offshore Shallow Structure and Sediment Distribution, Point Sur to Point Arguello, Central California","title":"Offshore shallow structure and sediment distribution, Point Sur to Point Arguello, central California","docAbstract":"<p>This publication consists of three map sheets that display shallow geologic structure, along with sediment distribution and thickness, for an about 225-km-long offshore section of the central California coast between Point Sur and Point Arguello. Each map sheet includes three maps, at scales of either 1:150,000 or 1:200,000, as well as a set of figures that contain representative high-resolution seismic-reflection profiles. The maps and seismic-reflection surveys cover most of the continental shelf in this region. In addition, the maps show the locations of the shelf break and the 3-nautical-mile limit of California’s State Waters. <br></p><p>The seismic-reflection data, which are the primary dataset used to develop the maps, were collected to support the California Seafloor Mapping Program and U.S. Geological Survey Offshore Geologic Hazards projects. In addition to the three map sheets, this publication includes geographic information system data files of interpreted faults, folds, sediment thicknesses, and depths-to-base of sediment. The faults and folds shown on the maps have been locally simplified as appropriate for the map scales. <br></p><p>The right-lateral San Gregorio–Hosgri Fault (SGHF) is the most significant structure in the map area. On a regional scale, the SGHF is part of a 400-km-long, right-lateral fault system that extends northwestward from Point Arguello to the area offshore of San Francisco, where it merges with the San Andreas Fault. From north to south in this part of central California, the SGHF lies offshore between the south flank of Point Sur and the north flank of Point Piedras Blancas, then comes onshore at Point Piedras Blancas, before heading offshore again between the south flank of Point Piedras Blancas and Point Arguello. Cumulative fault offset along the SGHF is as much as 150 to 160 km, decreasing to the south by transferring slip on to northwest-striking faults that converge with the SGHF both onland and offshore from the east. In the map area, the offshore-converging faults include the Los Osos Fault, the Shoreline–Point Buchon Fault, the Casmalia Fault, and the Lions Head Fault. <br></p><p>Quaternary sediments and bedrock underlie the shelf. On the seismic-reflection profiles, we divide Quaternary shelf sediments into two units. Characterizing the younger, upper unit is a focus of this publication. This unit is inferred to have been deposited on the shelf in the last about 21,000 years during the sea-level rise that followed the last major lowstand and the Last Glacial Maximum (LGM). This upper unit overlies a transgressive surface of erosion, a commonly angular, wave-cut unconformity, and is generally characterized by low-amplitude, continuous to moderately continuous, diffuse, subparallel, generally flat reflections. Maps in this publication show both the thickness of this upper sediment unit and the depth to the base of the sediment unit. Within the map region, 11 different “domains” of post-LGM shelf sediment are delineated on the basis of sediment thickness and coastal geomorphology. Maximum sediment thickness is in the southern part of the region, offshore of the mouths of the Santa Ynez and Santa Maria Rivers. Minimum sediment thickness is found offshore of prominent rocky points, including Point Buchon and Piedras Blancas. Mean sediment thickness for the entire shelf in the map area between Point Sur and Point Arguello is 12.2 m, and total sediment volume is 24.7 million cubic meters.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181158","usgsCitation":"Johnson, S.Y., Hartwell, S.R., Watt, J.T., Beeson, J.W., and Dartnell, P., 2019, Offshore shallow structure and sediment distribution, Point Sur to Point Arguello, central California: U.S. Geological Survey Open-File Report 2018–1158, 3 sheets, scales 1:150,000 and 1:200,000, https://doi.org/10.3133/ofr20181158.","productDescription":"3 Sheets: 75.25 x 37.00 inches or smaller; Data catalog; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-093009","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":362224,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161110","text":"Open-File Report 2016–1110","linkHelpText":" - California State Waters Map Series—Offshore of Monterey, California"},{"id":362225,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20181024","text":"Open-File Report 2018–1024","linkHelpText":" - California State Waters Map Series—Offshore of Point Conception, California"},{"id":362219,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1158/coverthb.jpg"},{"id":362220,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1158/ofr20181158_sheet1.pdf","text":"Sheet 1","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1158 Sheet 1","linkHelpText":" - Offshore Shallow Structure and Sediment Distribution, Point Sur to Point Arguello, Central California"},{"id":362227,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1158/ofr20181158_metadata.html"},{"id":362221,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1158/ofr20181158_sheet2.pdf","text":"Sheet 2","size":"30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1158 Sheet 2","linkHelpText":" - Offshore Shallow Structure and Sediment Distribution, Point Sur to Point Arguello, Central California"},{"id":362223,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/781/","text":"Data Series 781","linkHelpText":" - California State Waters Map Series Data Catalog"},{"id":362222,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1158/ofr20181158_sheet3.pdf","text":"Sheet 3","size":"27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1158 Sheet 3","linkHelpText":" - Offshore Shallow Structure and Sediment Distribution, Point Sur to Point Arguello, Central California"},{"id":362226,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://cmgds.marine.usgs.gov/data/csmp/PointSurToPointArguello/data_catalog_PointSurToPointArguello.html","text":" Data Catalog","linkHelpText":" - The GIS data layers for this map are accessible from “Offshore Shallow Structure and Sediment Distribution, Point Sur to Point Arguello, Central California” which is part of California State Waters Map Series Data Catalog. Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."}],"country":"United States","state":"California","otherGeospatial":"Point Arguello, Point Sur","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.62850952148436,\n              34.56199029762806\n            ],\n            [\n              -120.59555053710938,\n              34.83043208639951\n            ],\n            [\n              -120.59967041015624,\n              34.86480634950137\n            ],\n            [\n              -120.65151214599608,\n              34.91380708793209\n            ],\n            [\n              -120.62988281249999,\n              34.95180476488174\n            ],\n            [\n              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-120.96084594726564,\n              35.36105611877928\n            ],\n            [\n              -121.01165771484375,\n              35.247862157399915\n            ],\n            [\n              -120.70541381835938,\n              35.01987504801111\n            ],\n            [\n              -120.72052001953125,\n              34.8971951696173\n            ],\n            [\n              -120.68206787109375,\n              34.784483415461345\n            ],\n            [\n              -120.68344116210938,\n              34.555204483709474\n            ],\n            [\n              -120.65872192382812,\n              34.558597459864096\n            ],\n            [\n              -120.62850952148436,\n              34.56199029762806\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"http://walrus.wr.usgs.gov/infobank/programs/html/staff2html/staff.html\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"http://walrus.wr.usgs.gov/infobank/programs/html/staff2html/staff.html\">Contact Information</a><br><a data-mce-href=\"https://walrus.wr.usgs.gov/\" href=\"https://walrus.wr.usgs.gov/\" target=\"_blank\" rel=\"noopener\">Pacific Coastal &amp; Marine Science Center</a><br><a data-mce-href=\"https://usgs.gov/\" href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\">U.S. Geological Survey</a><br>Pacific Science Center<br>2885 Mission St.<br>Santa Cruz, CA 95060<br></p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-07-15","noUsgsAuthors":false,"publicationDate":"2019-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Samuel Y. 0000-0001-7972-9977","orcid":"https://orcid.org/0000-0001-7972-9977","contributorId":208205,"corporation":false,"usgs":true,"family":"Johnson","given":"Samuel Y.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartwell, Stephen R. 0000-0002-3522-7526","orcid":"https://orcid.org/0000-0002-3522-7526","contributorId":208206,"corporation":false,"usgs":true,"family":"Hartwell","given":"Stephen R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watt, Janet T. 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":208207,"corporation":false,"usgs":true,"family":"Watt","given":"Janet T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beeson, Jeffrey W. 0000-0002-7396-237X","orcid":"https://orcid.org/0000-0002-7396-237X","contributorId":194964,"corporation":false,"usgs":false,"family":"Beeson","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":746417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dartnell, Peter 0000-0002-9554-729X","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":208208,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746418,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70215332,"text":"70215332 - 2019 - Cohesive framework for modeling plant cover class data","interactions":[],"lastModifiedDate":"2020-10-16T13:48:06.231673","indexId":"70215332","displayToPublicDate":"2019-07-13T08:45:20","publicationYear":"2019","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":"Cohesive framework for modeling plant cover class data","docAbstract":"<ol class=\"\"><li>The study of plant distribution and abundance is a fundamental pursuit in ecology and conservation biology. Measuring plant abundance by visually assessing percent cover and recording a cover class is a common field method that yields ordinal data. Statistical models for ordinal data exist but entail cumbersome interpretations and sometimes restrictive assumptions.</li><li>We propose a Bayesian hierarchical framework for analysing cover class data that allows for linking ordinal observations to a latent beta distribution and accounts for zero inflation. Harnessing a latent beta distribution supports interpreting changes in abundance in terms of mean percent cover rather than odds ratios of cumulative cover classes as for cumulative link models. The zero augmentation allows for simultaneous inferences on both occurrence (distribution) and abundance. We show how our model can account for true and false zeros, misclassification of cover classes, multiple species and hierarchical sampling designs, using empirical examples and simulations.</li><li>Simulated observation errors, when ignored, led to models overestimating abundance and underestimating occurrence. Based on simulations, we found no substantial difference between mean percent cover estimates when analyzing ordinal cover classes versus continuous percent cover as the response. Our empirical datasets displayed high probability of detection (&gt;0.85 on average for all species), likely due to the sampling design used and training of observers. Probability of occurrence was slightly underestimated for bare ground,<span>&nbsp;</span><i>Artemisia tridentata</i>,<span>&nbsp;</span><i>Elycap medusae</i>, and<span>&nbsp;</span><i>Poa secunda</i><span>&nbsp;</span>using a model that ignored imperfect detection. Estimated mean percent cover was not substantially impacted by ignoring measurement error for five plant species and bare ground.</li><li>Our modelling framework for cover class data allows for an explicit separation of distribution from abundance and, importantly, allows for interpreting species–environment relationships in terms of variation in mean percent cover as compared to cumulative odds ratios. The beta distribution inherently accommodates heteroscedasticity and skewness, statistical properties that are a consequence of spatially aggregated patterns common to plant survey data. Recording cover classes provides a reliable, efficient way to measure plants and our simulations suggest little loss of information compared to assuming continuous percent cover. We provide JAGS and Stan model code for implementation.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13262","usgsCitation":"Irvine, K., Wright, W.J., Shanahan, E.K., and Rodhouse, T., 2019, Cohesive framework for modeling plant cover class data: Methods in Ecology and Evolution, v. 10, no. 10, p. 1749-1760, https://doi.org/10.1111/2041-210X.13262.","productDescription":"12 p.","startPage":"1749","endPage":"1760","ipdsId":"IP-105684","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467456,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13262","text":"Publisher Index Page"},{"id":437387,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99JX2ND","text":"USGS data release","linkHelpText":"Supplement S1: Code Release for fitting OZAB and OZABE to Empirical Datasets in &amp;amp;quot;Cohesive Framework for Modeling Plant Cover Class Data&amp;amp;quot;"},{"id":379462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"10","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":801748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Wilson J.","contributorId":192867,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":801749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanahan, Erin K.","contributorId":217938,"corporation":false,"usgs":false,"family":"Shanahan","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":801750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodhouse, Thomas J.","contributorId":127378,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas J.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":801751,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
]}