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  1. Supporting data for 'Tree carbon allocation explains forest drought-kill and recovery patterns'.

    Work
    Description: The mechanisms governing tree drought mortality and recovery remain a subject of inquiry and active debate given their role in the terrestrial carbon cycle and their concomitant impact on climate change. Counter-intuitively, many trees do not die during the drought itself. Indeed, observations globally have documented that trees often grow for several years after drought before mortality. A combination of meta-analysis and tree physiological models demonstrate that optimal carbon allocation after drought explains observed patterns of delayed tree mortality and provides a predictive recovery framework. Specifically, post-drought, trees attempt to repair water transport tissue and achieve positive carbon balance through regrowing drought-damaged xylem. Further, the number of years of xylem regrowth required to recover function increases with tree size, explaining why drought mortality increases with size. These results indicate that tree resilience to drought-kill may increase in the future, provided that CO2 fertilization facilitates more rapid xylem regrowth.
    Keyword: drought, optimality theory, hydraulic-carbon coupling, CO2 fertilization, carbon metabolism, and vegetation model
    Creator: Schwalm, C., Detto, M., Bartlett, M. K., Schahher, B., Anderegg, W. R. L., Trugman, Anna T., Medvigy, D., and Pacala, S. W.
    Owner: Anna Trugman
    Date Uploaded: 08/08/2018
    Date Modified: 10/25/2018
    Date Created: Spring 2018
    Rights: CC BY NC - Allows others to use and share your data non-commercially and with attribution.
    Resource Type: Dataset
    Identifier: https://doi.org/10.7278/S5N29V4F
    Contact Email: a.trugman@utah.edu
    Funders: USDA National Institute of Food and Agriculture Postdoctoral Research Fellowship Grant No. 2017-07164
  2. Supplemental data for 'Soil moisture drought as a major driver of carbon cycle uncertainty", Geophysical Research Letters

    Work
    Description: Future projections suggest an increase in drought globally with climate change. Current vegetation models typically regulate the plant photosynthetic response to soil moisture stress through an empirical function, rather than a mechanistic response where plant water potentials respond to changes in soil water. This representation of soil moisture stress may introduce significant uncertainty into projections for the terrestrial carbon cycle. We examined the use of the soil moisture limitation function in historical and future emissions scenarios in nine Earth system models. We found that soil moisture-limited productivity across models represented a large and uncertain component of the simulated carbon cycle, comparable to 3-286% of current global productivity. Approximately 40-80% of the intermodel variability was due to the functional form of the limitation equation alone. Our results highlight the importance of implementing mechanistic water limitation schemes in models and illuminate several avenues for improving projections of the land carbon sink.
    Keyword: carbon cycle, Water limitation , drought, Gross primary productivity, soil moisture, and Earth system modeling
    Creator: Anderegg, William R.L., Mankin, Justin S., Medvigy, David, and Trugman, Anna T.
    Owner: Anna Trugman
    Date Uploaded: 06/25/2018
    Date Modified: 10/25/2018
    Date Created: Spring 2016
    Rights: CC BY – Allows others to use and share your data, even commercially, with attribution.
    Resource Type: Dataset
    Identifier: https://doi.org/10.7278/S5707ZMS
    Contact Email: a.trugman@utah.edu
    Funders: USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme, Ecosystem Services and Agro-ecosystem Management, grant no. 2017-05521, National Science Foundation grant 1714972, US Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science (TES) Program award DE-SC0014363 , National Science Foundation Award 1151102 , and USDA National Institute of Food and Agriculture Postdoctoral Research Fellowship Grant No. 2017-07164