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Filtering by: Creator Anderegg, William R. L. Subject vegetation

Supporting Data for "Tree Carbon Allocation Explains Forest Drought-kill and Recovery Patterns"

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, vegetation model, CO2 fertilization, hydraulic-carbon coupling, and carbon metabolism

Subject:

droughts and vegetation

Creator:

Trugman, Anna T. , Detto, Matteo , Bartlett, Megan K., Medvigy, David, Anderegg, William R. L., Schwalm, Christopher, Schaffer, Ben, and Pacala, Stephen W.

Owner:

BRIAN MCBRIDE

Language:

English

Date Uploaded:

07/10/2019

Date Modified:

06/03/2024

Date Created:

Spring 2018

License:

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

Supporting Data for "Tree Carbon Allocation Explains Forest Drought-kill and Recovery Patterns"

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Supporting Data for "Tree Carbon Allocation Explains Forest Drought-kill and Recovery Patterns"

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