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In times of climate change, understanding the dependency of plant productivity on xylem hydraulic physiology is of high importance. We studied saplings of three broadleaved tree species grown under favourable and stressed conditions, and related various hydraulic traits to juvenile growth performance. Except for one species, high productivity was associated with a high hydraulic efficiency caused by large xylem vessels and a large supported leaf area. Although no safety-efficiency trade-off was observed, productivity was significantly related to xylem safety in two of the tree species but without revealing any clear pattern. Furthermore, a considerable plasticity in given traits was observed between stressed and non-stressed individuals, with safety-related traits being more static while efficiency-related traits revealed a higher intra-specific plasticity. We confirm that a high hydraulic efficiency seems to be a prerequisite for high biomass production, while our controversial results on the growth-xylem safety relationship deserve further attention., funded through the scholarship under Netaji Subhas - ICAR International Fellowship

The efficiency of the water transport system in trees sets physical limits to their productivity and water use. Although the coordination of carbon assimilation and hydraulic functions has long been documented, the mutual inter-relationships between wood anatomy, water use and productivity have not yet been jointly addressed in comprehensive field studies. Based on observational data from 99 Indonesian rainforest tree species from 37 families across 22 plots, we analyzed how wood anatomy and sap flux density relate to tree size and wood density, and tested their combined influence on aboveground biomass increment (ABI) and daily water use (DWU). Results from pairwise correlations were compared to the outcome of a structural equation model (SEM). Across species, we found a strong positive correlation between ABI and DWU. Wood hydraulic anatomy was more closely related to these indicators of plant performance than wood density. According to the SEM, the common effect of average tree size and sap flux density on the average stem increment and water use of a species was sufficient to fully explain the observed correlation between these variables. Notably, after controlling for average size, only a relatively small indirect effect of wood properties on stem increment and water use remained that was mediated by sap flux density, which was significantly higher for species with lighter and hydraulically more efficient wood. We conclude that wood hydraulic traits are mechanistically linked to water use and productivity via their influence on sap flow, but large parts of these commonly observed positive relationships can be attributed to confounding size effects. Open-Access-Publikation 2020 peerReviewed

Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r$^2$ = 0.87 for trees and r$^2$ = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration. Open-Access-Publikationsfonds 2020 peerReviewed