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  • 1
    Publication Date: 2015-08-01
    Description: The impacts of climate extremes on terrestrial ecosystems are poorly understood but important for predicting carbon cycle feedbacks to climate change. Coupled climate-carbon cycle models typically assume that vegetation recovery from extreme drought is immediate and complete, which conflicts with the understanding of basic plant physiology. We examined the recovery of stem growth in trees after severe drought at 1338 forest sites across the globe, comprising 49,339 site-years, and compared the results with simulated recovery in climate-vegetation models. We found pervasive and substantial "legacy effects" of reduced growth and incomplete recovery for 1 to 4 years after severe drought. Legacy effects were most prevalent in dry ecosystems, among Pinaceae, and among species with low hydraulic safety margins. In contrast, limited or no legacy effects after drought were simulated by current climate-vegetation models. Our results highlight hysteresis in ecosystem-level carbon cycling and delayed recovery from climate extremes.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Anderegg, W R L -- Schwalm, C -- Biondi, F -- Camarero, J J -- Koch, G -- Litvak, M -- Ogle, K -- Shaw, J D -- Shevliakova, E -- Williams, A P -- Wolf, A -- Ziaco, E -- Pacala, S -- New York, N.Y. -- Science. 2015 Jul 31;349(6247):528-32. doi: 10.1126/science.aab1833.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. Department of Biology, University of Utah, Salt Lake City, UT 84112, USA. ; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA. School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA. ; DendroLab and Graduate Program of Ecology, Evolution, and Conservation Biology, University of Nevada-Reno, Reno, NV 89557, USA. ; Instituto Pirenaico de Ecologia, Consejo Superior de Investigaciones Cientificas, Avda. Montanana 1005, 50192 Zaragoza, Spain. ; Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA. ; Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA. ; School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, USA. ; Rocky Mountain Research Station, U.S. Forest Service, Ogden, UT 84401, USA. ; National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA. ; Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W, Palisades, NY 10964, USA. ; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26228147" target="_blank"〉PubMed〈/a〉
    Keywords: *Carbon Cycle ; *Climate Change ; *Droughts ; Europe ; *Forests ; Models, Theoretical ; Trees/*growth & development ; United States
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 2
    ISSN: 1573-8868
    Keywords: uranium ; exploration geochemistry ; data display ; mapping digital filtering ; modeling ; computer graphics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract The U.S. Department of Energy National Uranium Resource Evaluation Program is collecting stream sediment samples at a nominal density of one per 13 km2 from two-degree quadrangles throughout the United States. Each sample has been analyzed for from 14 to 43 chemical elements and other variables. We describe new methods of statistical map analysis, which we have applied to the regional distribution of multielement data from four two-degree quandrangles in the southeastern United States including parts of the southern Appalachian Mountains and the Coastal Plain, with particular reference to uranium distributions. Patterns of uranium distribution are clearly related to the geological provinces and also to individual geologic belts, plutons, and smaller stratigraphic subdivisions. Our work developed several anomalies, some of which were followed up by additional field sampling in 1979 and 1980.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 12 (1980), S. 99-114 
    ISSN: 1573-8868
    Keywords: Coefficient of variation ; sampling error ; serial correlation ; spectral analysis ; uranium
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract This paper describes a statistical analysis of reconnaissance exploration geochemical data for uranium. Three sets of data were analyzed, as they related to: (a) Charlotte-Winston-Salem and (b) Charlotte two-degree NTMS quadrangles of the south-eastern U.S.A. The coefficient of variation for uranium in each of the three sets of data was less than unity and hence no transformation of the original variable was needed. These data were subjected to correlogram analysis. A first-order Markovian model of the type: Y0 exp (-a |p|) was fit by the least-squares method to serial correlation coefficients of these data using the method proposed by Deming (1948). The model was tested by computing the variance-volume relationship for assumed individual blocks of unit length. The noise in the input (record) was eliminated by the application of an optimum bilateral exponential smoothing technique developed by Agterberg. A comparison of spectral density estimates obtained by a maximum entropy method employing Yule-Walker equations and the Burg algorithm was also made. The prediction error coefficients needed to determine the order of the autoregressive process and hence the spectral densities were determined in both cases for the three sets of data.
    Type of Medium: Electronic Resource
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