Incorporating non-stomatal limitation improves the performance of leaf and canopy models at high vapour pressure deficit.

Vapour pressure deficit (D) is projected to increase in the future as temperature rises. In response to increased D, stomatal conductance (gs) and photosynthesis (A) are reduced, which may result in significant reductions in terrestrial carbon, water and energy fluxes. It is thus important for gas exchange models to capture the observed responses of gs and A with increasing D. We tested a series of coupled A-gs models against leaf gas exchange measurements from the Cumberland Plain Woodland (Australia), where D regularly exceeds 2 kPa and can reach 8 kPa in summer. Two commonly used A-gs models were not able to capture the observed decrease in A and gs with increasing D at the leaf scale. To explain this decrease in A and gs, two alternative hypotheses were tested: hydraulic limitation (i.e., plants reduce gs and/or A due to insufficient water supply) and non-stomatal limitation (i.e., downregulation of photosynthetic capacity). We found that the model that incorporated a non-stomatal limitation captured the observations with high fidelity and required the fewest number of parameters. Whilst the model incorporating hydraulic limitation captured the observed A and gs, it did so via a physical mechanism that is incorrect. We then incorporated a non-stomatal limitation into the stand model, MAESPA, to examine its impact on canopy transpiration and gross primary production. Accounting for a non-stomatal limitation reduced the predicted transpiration by ~19%, improving the correspondence with sap flow measurements, and gross primary production by ~14%. Given the projected global increases in D associated with future warming, these findings suggest that models may need to incorporate non-stomatal limitation to accurately simulate A and gs in the future with high D. Further data on non-stomatal limitation at high D should be a priority, in order to determine the generality of our results and develop a widely applicable model.

Light interception efficiency explained by two simple variables: a test using a diversity of small- to medium-sized woody plants.

• Plant light interception efficiency is a crucial determinant of carbon uptake by individual plants and by vegetation. Our aim was to identify whole-plant variables that summarize complex crown architecture, which can be used to predict light interception efficiency. • We gathered the largest database of digitized plants to date (1831 plants of 124 species), and estimated a measure of light interception efficiency with a detailed three-dimensional model. Light interception efficiency was defined as the ratio of the hemispherically averaged displayed to total leaf area. A simple model was developed that uses only two variables, crown density (the ratio of leaf area to total crown surface area) and leaf dispersion (a measure of the degree of aggregation of leaves). • The model explained 85% of variation in the observed light interception efficiency across the digitized plants. Both whole-plant variables varied across species, with differences in leaf dispersion related to leaf size. Within species, light interception efficiency decreased with total leaf number. This was a result of changes in leaf dispersion, while crown density remained constant. • These results provide the basis for a more general understanding of the role of plant architecture in determining the efficiency of light harvesting.

Contributions of climate, leaf area index and leaf physiology to variation in gross primary production of six coniferous forests across Europe: a model-based analysis.

Gross primary production (GPP) is the primary source of all carbon fluxes in the ecosystem. Understanding variation in this flux is vital to understanding variation in the carbon sink of forest ecosystems, and this would serve as input to forest production models. Using GPP derived from eddy-covariance (EC) measurements, it is now possible to determine the most important factor to scale GPP across sites. We use long-term EC measurements for six coniferous forest stands in Europe, for a total of 25 site-years, located on a gradient between southern France and northern Finland. Eddy-derived GPP varied threefold across the six sites, peak ecosystem leaf area index (LAI) (all-sided) varied from 4 to 22 m(2) m(-2) and mean annual temperature varied from -1 to 13 degrees C. A process-based model operating at a half-hourly time-step was parameterized with available information for each site, and explained 71-96% in variation between daily totals of GPP within site-years and 62% of annual total GPP across site-years. Using the parameterized model, we performed two simulation experiments: weather datasets were interchanged between sites, so that the model was used to predict GPP at some site using data from either a different year or a different site. The resulting bias in GPP prediction was related to several aggregated weather variables and was found to be closely related to the change in the effective temperature sum or mean annual temperature. High R(2)s resulted even when using weather datasets from unrelated sites, providing a cautionary note on the interpretation of R(2) in model comparisons. A second experiment interchanged stand-structure information between sites, and the resulting bias was strongly related to the difference in LAI, or the difference in integrated absorbed light. Across the six sites, variation in mean annual temperature had more effect on simulated GPP than the variation in LAI, but both were important determinants of GPP. A sensitivity analysis of leaf physiology parameters showed that the quantum yield was the most influential parameter on annual GPP, followed by a parameter controlling the seasonality of photosynthesis and photosynthetic capacity. Overall, the results are promising for the development of a parsimonious model of GPP.

Predicting the decline in daily maximum transpiration rate of two pine stands during drought based on constant minimum leaf water potential and plant hydraulic conductance.

The effect of drought on forest water use is often estimated with models, but comprehensive models require many parameters, and simple models may not be sufficiently flexible. Many tree species, Pinus species in particular, have been shown to maintain a constant minimum leaf water potential above the critical threshold for xylem embolism during drought. In such cases, prediction of the relative decline in daily maximum transpiration rate with decreasing soil water content is relatively straightforward. We constructed a soil-plant water flow model assuming constant plant conductance and daily minimum leaf water potential, but variable conductance from soil to root. We tested this model against independent data from two sites: automatic shoot chamber data and sap flow measurements from a boreal Scots pine (Pinus sylvestris L.) stand; and sap flow measurements from a maritime pine (Pinus pinaster Ait.) stand. To focus on soil limitations to water uptake, we expressed daily maximum transpiration rate relative to the rate that would be obtained in wet soil with similar environmental variables. The comparison was successful, although the maritime pine stand showed carry-over effects of the drought that we could not explain. For the boreal Scots pine stand, daily maximum transpiration was best predicted by water content of soil deeper than 5 cm. A sensitivity analysis revealed that model predictions were relatively insensitive to the minimum leaf water potential, which can be accounted for by the importance of soil resistance of drying soil. We conclude that a model with constant plant conductance and minimum leaf water potential can accurately predict the decline in daily maximum transpiration rate during drought for these two pine stands, and that including further detail about plant compartments would add little predictive power, except in predicting recovery from severe drought.

Summary models for light interception and light-use efficiency of non-homogeneous canopies.

The application of detailed models of canopy photosynthesis rely on the estimation of attenuation of light in the canopy. This attenuation is readily estimated with the Lambert-Beer law when the canopy is homogeneous. In reality, forest canopies are far from homogeneous, and this has led to the use of detailed light extinction models that account for grouping of foliage between and within trees. Because such models require detailed parameterization and fine resolution inputs, they are impractical in larger-scale applications. Thus, there is interest in simplified models that can be readily parameterized. We developed two equations that can be used to estimate mean annual light interception by single unshaded trees and by stands of Poisson distributed trees. Interception by single trees is a function of crown surface area, the ratio of leaf area to crown surface area, the extinction coefficient in a homogeneous canopy--which can be determined separately--and one empirical parameter that depends on the mean solar angle. The summary model was tested against a detailed model of interception, and showed good agreement, although with slight bias. The results showed that crown surface area is a good summary variable for crown size and shape, because errors are independent of crown shape (ellipsoids, cones and height:width ratios). We also tested whether canopy photosynthesis is proportional to light interception across canopies differing in structure and leaf area index, and found that light-use efficiency is influenced by canopy structure. The model is useful in larger-scale applications because it can be parameterized with available data without the need for additional empirical parameters. It can also be used to study the effect of stand structure on mean annual light interception and productivity.