Optimal stomatal drought response shaped by competition for water and hydraulic risk can explain plant trait covariation.

The classical theory of stomatal optimization stipulates that stomata should act to maximize photosynthesis while minimizing transpiration. This theory, despite its remarkable success in reproducing empirical patterns, does not account for the fact that the available water to plants is dynamically regulated by plants themselves, and that plants compete for water in most locations. Here, we develop an alternative theory in which plants maximize the expected carbon gain under stochastic rainfall in a competitive environment. We further incorporate xylem hydraulic limitation as an additional constraint to transpiration and evaluate its impacts on stomatal optimization by incorporating the direct carbon cost of xylem recovery and the opportunity cost of reduced future photosynthesis as a result of irrecoverable xylem damage. We predict stomatal behaviour to be more conservative with a higher cost induced by xylem damage. By varying the unit carbon cost and extent of xylem recovery, characterizing the direct and opportunity cost of xylem damage, respectively, our model can reproduce several key patterns of stomatal-hydraulic trait covariations. By addressing the key elements of water limitation in plant gas exchange simultaneously, including plants' self-regulation of water availability, competition for water and hydraulic risk, our study provides a comprehensive theoretical basis for understanding stomatal behaviour.

Optimal stomatal behaviour under stochastic rainfall.

Vegetation CO2 uptake is always accompanied by water loss. The balance in this gas exchange is controlled by the stomata, through which CO2 and water vapour diffuse between the leaf and the atmosphere. The optimal stomatal behaviour theory proposes that vegetation should optimise its stomatal behaviour such that, for given water availability, photosynthesis is maximised. In this paper, we optimise stomatal conductance as a function of soil water content for the maximum expected value of photosynthesis rate. This optimisation process is considered under stochastic rainfall. The optimal solution is largely shaped by two constraints: the risks of soil water exhaustion and surface runoff, which results in an inverse S-shaped curve of stomatal conductance along the soil water gradient. We derive the optimal functional relationship between stomatal conductance and soil water content under varying rainfall frequency, mean annual precipitation and atmospheric CO2 concentration. Comparisons with large-scale observational data show that the model is able to broadly capture responses of photosynthesis, transpiration, and water use efficiency along rainfall gradients, although notable discrepancies suggest additional factors are important in shaping these responses. Our work provides a theoretical framework for analysing the vegetation gas exchange under environmental change.

Non-invasive measurement of leaf water content and pressure-volume curves using terahertz radiation.

In this paper we describe a non-invasive method of measuring leaf water content using THz radiation and combine this with psychrometry for determination of leaf pressure-volume relationships. In contrast to prior investigations using THz radiation to measure plant water status, the reported method exploits the differential absorption characteristic of THz radiation at multiple frequencies within plant leaves to determine absolute water content in real-time. By combining the THz system with a psychrometer, pressure-volume curves were generated in a completely automated fashion for the determination of leaf tissue water relations parameters including water potential at turgor loss, osmotic potential at full turgor and the relative water content at the turgor loss point. This novel methodology provides for repeated, non-destructive measurement of leaf water content and greatly increased efficiency in generation of leaf PV curves by reducing user handling time.

A Variational Bayesian Approach to Robust Identification of Switched ARX Models.

A variational Bayesian approach to robust identification of switched auto-regressive exogenous models is developed in this paper. By formulating the problem of interest under a full Bayesian identification framework, the number of local-models can be determined automatically, while accounting for the uncertainty of parameter estimates in the overall identification procedure. A set of significance coefficients is used to assign proper importance weights to local-models. By maximizing the marginal likelihood of the identification data, insignificant local-models will be suppressed and the optimal number of local-models can be determined. Considering the fact that the identification data may be contaminated with outliers, t distributions with adjustable tails are utilized to model the contaminating noise so that the proposed identification algorithm is robust. The effectiveness of the proposed Bayesian approach is demonstrated through a simulated example as well as a detailed industrial application.