Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe

Drought-induced tree mortality has been observed globally and is expected to increase under climate change scenarios, with large potential consequences for the terrestrial carbon sink. Predicting mortality across species is crucial for assessing the effects of climate extremes on forest community bi...

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Veröffentlicht in:Proceedings of the National Academy of Sciences - PNAS 2016-05, Vol.113 (18), p.5024-5029
Hauptverfasser: Anderegg, William R. L., Klein, Tamir, Bartlett, Megan, Sack, Lawren, Pellegrini, Adam F. A., Choat, Brendan, Jansen, Steven
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Sprache:eng
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Zusammenfassung:Drought-induced tree mortality has been observed globally and is expected to increase under climate change scenarios, with large potential consequences for the terrestrial carbon sink. Predicting mortality across species is crucial for assessing the effects of climate extremes on forest community biodiversity, composition, and carbon sequestration. However, the physiological traits associated with elevated risk of mortality in diverse ecosystems remain unknown, although these traits could greatly improve understanding and prediction of tree mortality in forests. We performed a meta-analysis on species’ mortality rates across 475 species from 33 studies around the globe to assess which traits determine a species’ mortality risk. We found that species-specific mortality anomalies from community mortality rate in a given drought were associated with plant hydraulic traits. Across all species, mortality was best predicted by a low hydraulic safety margin—the difference between typical minimum xylem water potential and that causing xylem dysfunction—and xylem vulnerability to embolism. Angiosperms and gymnosperms experienced roughly equal mortality risks. Our results provide broad support for the hypothesis that hydraulic traits capture key mechanisms determining tree death and highlight that physiological traits can improve vegetation model prediction of tree mortality during climate extremes.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1525678113