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The conifer-curve: fast prediction of hydraulic conductivity loss and vulnerability to cavitation

Abstract : Abstract Key messageThe relationship between relative water loss (RWL) and hydraulic conductivity loss (PLC) in sapwood is robust across conifer species. We provide an empirical model (conifer-curve) for predicting PLC from simple RWL measurements. The approach is regarded as a new relevant phenotyping tool for drought sensitivity and offers reliable and fast prediction of diurnal, seasonal, or drought-induced changes in PLC. ContextFor conifer species drought is one of the main climate risks related to loss of hydraulic capacity in sapwood inducing dieback or mortality. More frequently occurring drought waves call for fast and easily applicable methods to predict drought sensitivity. AimsWe aimed at developing a fast and reliable method for determination of the percent loss of hydraulic conductivity (PLC) and eventually the drought sensitivity trait P50, i.e., the water potential that causes 50% conductivity loss. MethodsWe measured the loss of water transport capacity, defined as the relative water loss (RWL) together with PLC in trunk wood, branches, and saplings of eight different conifer species. Air injection was used to induce specific water potentials. ResultsThe relationship between RWL and PLC was robust across species, organs, and age classes. The equation established allows fast prediction of PLC from simple gravimetrical measurements and thus post hoc calculation of P50 (r2 = 0.94). ConclusionThe approach is regarded as a relevant new phenotyping tool. Future potential applications are screening conifers for drought sensitivity and a fast interpretation of diurnal, seasonal, or drought-induced changes in xylem water content upon their impact on conductivity loss.
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Sabine Rosner, Daniel M. Johnson, Klara Voggeneder, Jean-Christophe Domec. The conifer-curve: fast prediction of hydraulic conductivity loss and vulnerability to cavitation. Annals of Forest Science, Springer Nature (since 2011)/EDP Science (until 2010), 2019, 76 (3), pp.82. ⟨10.1007/s13595-019-0868-1⟩. ⟨hal-02620675⟩

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