What plant hydraulics can tell us about responses to climate-change droughts
© 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.
| Publié dans: | The New phytologist. - 1979. - 207(2015), 1 vom: 07. Juli, Seite 14-27 |
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| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2015
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| Accès à la collection: | The New phytologist |
| Sujets: | Journal Article Review climate-change drought drought mortality hydraulic limitation modeling climate change impacts plant drought responses plant water transport xylem cavitation xylem transport plus... |
| Résumé: | © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust. Climate change exposes vegetation to unusual drought, causing declines in productivity and increased mortality. Drought responses are hard to anticipate because canopy transpiration and diffusive conductance (G) respond to drying soil and vapor pressure deficit (D) in complex ways. A growing database of hydraulic traits, combined with a parsimonious theory of tree water transport and its regulation, may improve predictions of at-risk vegetation. The theory uses the physics of flow through soil and xylem to quantify how canopy water supply declines with drought and ceases by hydraulic failure. This transpiration 'supply function' is used to predict a water 'loss function' by assuming that stomatal regulation exploits transport capacity while avoiding failure. Supply-loss theory incorporates root distribution, hydraulic redistribution, cavitation vulnerability, and cavitation reversal. The theory efficiently defines stomatal responses to D, drying soil, and hydraulic vulnerability. Driving the theory with climate predicts drought-induced loss of plant hydraulic conductance (k), canopy G, carbon assimilation, and productivity. Data lead to the 'chronic stress hypothesis' wherein > 60% loss of k increases mortality by multiple mechanisms. Supply-loss theory predicts the climatic conditions that push vegetation over this risk threshold. The theory's simplicity and predictive power encourage testing and application in large-scale modeling |
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| Description: | Date Completed 08.04.2016 Date Revised 16.04.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.13354 |