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

Abstract

& Key message The 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. & Context For 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. & Aims We aimed at developing a fast and reliable method for determination of the percent loss of hydraulic conductivity (PLC) and eventually the drought sensitivity trait P 50 , i.e., the water potential that causes 50% conductivity loss. & Methods We 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. & Results The 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 P 50 (r 2 = 0.94). & Conclusion The 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.

Physiological and anatomical responses to drought stress differ between two larch species and their hybrid

“…From the constitutive structure-function point of view, wood of EL is designed to keep stomata open at more negative water potentials than JL or HL, because cavitation occurs at lower water potentials. Accordingly, EL had a lower conductivity loss, indicated by a tendency to higher RWC (Rosner et al 2019), compared to JL and HL.…”

“…However, during a mild drought stress, anisohydric behaviour of HL might be possible, since HL solely showed a significant decrease in relative water content in sapwood under the impact of prolonged drought stress. The observed relative water loss of 15% in drought stressed HL corresponds to more than 40% of loss in hydraulic conductivity (Rosner et al 2019), which is quite considerable for conifers. Bhusal et al 2020 recently reported that JL shows both isohydric and anisohydric drought response, however, with a stronger tendency towards isohydry.…”

Not Only Severe Events: Moderate Dry Periods Impact the Hydraulic Functioning and Survival of Planted Ponderosa Pine Seedlings

“…We then used the relationship between RWL and loss of hydraulic conductivity from Rosner et al [41] to infer seedling mortality. Rosner et al [41] determined RWL from sapwood area hydraulic conductivity under a hydraulic pressure head, and found a robust relationship (R 2 = 0.99) between RWL and hydraulic conductivity for ponderosa pines.…”

“…We then used the relationship between RWL and loss of hydraulic conductivity from Rosner et al [41] to infer seedling mortality. Rosner et al [41] determined RWL from sapwood area hydraulic conductivity under a hydraulic pressure head, and found a robust relationship (R 2 = 0.99) between RWL and hydraulic conductivity for ponderosa pines. By comparing their analysis to the relationship of p50 and p88 values (representing 50% and 88% hydraulic conductivity loss, respectively; [42]), Rosner et al [41] developed estimates of the range of RWL that correspond p50 and p88 of ponderosa pine seedlings.…”

Wood formation and tree adaptation to climate

“…Some large-scale observational studies based on tree-ring width and structure are dealing with global change impacts on forest and fire dynamics (Abrams and Nowacki 2019), as well as on permafrost and wood quality (Arzac et al 2019). More detailed observational studies focus on the adaptive response of trees to rapidly changing environments, in terms of stem cycle variations (Balducci et al 2019), wood formation phenology (Saderi et al 2019) and dynamics (Nanayakkara et al 2019), intra-annual dynamics of carbon sequestration (Andrianantenaina et al 2019), and resulting tree-ring structure and function (Rosner et al 2019). Moreover, experimental studies explore the impact of forecasted climate changes (global warming and drought events in particular) on primary and secondary growth (Vieira et al 2019), as well as on tree mortality, in interaction with biotic stress such as defoliation (Bouzidi et al 2019).…”