Functional–structural models optimize the placement of foliage units for multiple whole‐canopy functions

Abstract

I present examples of plant functional-structural models (FSMs) that are used to evaluate how foliage units affect whole-canopy functions, and I show that multi-criteria optimization is an effective tool for these models. FSMs produce plant structures through the repeated application of a set of rules for the placement of foliage units. The models are blind (rules are the same regardless of dynamic simulation conditions), sighted (rules change with interference from other foliage units) or self-regulatory (rules change depending on the conditions of the simulation, i.e., internal conditions). In the examples presented, the models are used to optimize plant morphology for one or more measures of plant performance; these measures include movement of materials and associated hydraulic functions, foliage display, light interception and net carbon, mechanical support and stability, and reproductive success. It is consistently found that no morphology is optimal for any single measure of plant performance, and the rules for plant development are not stationary in space and time. In multi-criteria optimization, alternative morphologies are compared against multiple measures of plant performance; these are optimized simultaneously using Pareto optimality, which yields the set of mutually co-dominant solutions not dominated by any other solution. Two solutions are considered to be mutually co-dominant if improvement with respect to one criterion is at the expense of another criterion. I conclude that multi-criteria optimization is an essential tool for the use of FSMs to relate processes at the foliage level to whole-canopy function and to explain the structural diversity of old-growth forests.

The role of crown architecture, leaf phenology and photosynthetic activity in promoting complementary use of light among coexisting species in temperate forests

“…Accumulation of studies evaluating the functional significance of crown architecture should lead to a unified theory of how the spatial and temporal variation in light interception among coexisting species contributes to ecosystem productivity. Although light is the primary resource for photosynthesis and growth, crown architecture reflects other important functions related to plant survival, growth, and reproduction, including hydraulic architecture, mechanical stability, efficient flower display and, in arid environments, minimizing surface evaporation (Farnsworth and Niklas 1995;Kennedy 2009). In addition, species may differ in their unit of response to environmental change (Kawamura 2009).…”

“…Similar interpretation may be possible for differences among species in their diurnal pattern of photosynthetic activity. Because environmental conditions are heterogeneous in time and space, and there is no optimal morphology that dominates all plant functions (Kennedy 2009), a single species cannot achieve full performance across the entire time/space spectrum of available resources. Thus, complementary use of light among diverse species, where one species can perform better than the other and vise versa under different conditions, leads to increased productivity.…”

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

“…Furthermore, correlation-driven methods do not allow the quantification and comparison of trade-offs in individual genotypes and under different conditions. This led us to ask whether more information can be extracted using the Pareto performance and efficiency frontiers [17][18][19][20][21] originating in engineering and the social sciences. While there can be many feasible strategies to allocate resources to contending tasks, only a few of them result in an optimal trade-off [21][22][23] .…”

Plant responses to heterogeneous environments: scaling from shoot modules and whole‐plant functions to ecosystem processes

“…Nevertheless, there is still limited knowledge on how the module-level responses to within-crown and withinstand microenvironment affect whole-plant function under intrinsic multiple environmental and intrinsic biomechanical constraints. It is thus highly relevant to synthesize the current knowledge on diverse structural controls at various hierarchical scales to understand the resultant effects on plant performance (Kawamura 2010;Kennedy 2010;Niinemets 2010).…”