Packing the Photosynthetic Machinery: From Leaf to Canopy

Plant canopies are characterized by extensive and interacting gradients in light, temperature, humidity and wind. As every leaf in the canopy is exposed to unique combinations of environmental variables and has distinctive suite of structural and physiological traits, modeling canopy photosynthesis is a challenging endeavor. Due to the highly non-linear response of photosynthesis to light, temperature and humidity, whole canopy photosynthesis cannot be derived from the average values of light and temperature, but complex models simulating both temporal and spatial variability in environmental drivers and photosynthetic potentials are needed to estimate canopy photosynthesis. Two fundamentally different classes of canopy models have been developed. Predictive integration algorithms that describe the actual spatial distribution of foliage elements and photosynthetic capacity have the objective to simulate whole-stand carbon uptake and other canopy processes as closely as possible. Recent advances in these models have led to complex three-dimensional (3D) models that are capable of simulating radiation interception in discontinuous canopies considering complex radiative transfer phenomena such as penumbra and light scattering. A large number of parameters needed is the disadvantage of these explicit integration algorithms. Alternatively, optimization models predict total canopy leaf area and foliage photosynthetic potentials from the assumption of maximization of canopy photosynthesis by the optimal use of available nitrogen or foliage biomass. Significantly smaller number of parameters is needed for these models as the spatial distributions of foliage and photosynthetic characteristics are determined by assumptions about optimality. However, the simple optimization models considering only light as the key environmental factor and assuming that plant canopy consists of identical individual non-competing plants result in a significant bias between simulated and measured photosynthesis profiles within the canopy, limiting the use of such models in practical scaling applications. Recently developed models considering competition between different individuals have yielded better correspondence between the data and predictions, suggesting that optimization models have a large potential for predictive purposes. More information of the functioning of plant canopies, in particular of the response of plants to multiple environmental stresses in the canopies as well as competitive interactions is still needed to define “right” optimization functions and to correctly simulate photosynthetic productivity in highly heterogeneous canopy environment.