Principles of carbon allocation in plants and ecosystems
File(s)
Author(s)
Ding, Ruijie
Type
Thesis
Abstract
Carbon (C) allocation refers to the processes by which plants distribute assimilated C among different compartments. While most ecosystem and land surface models explicitly represent C allocation, there is no consensus on how this should be done: treatment in many models remains rudimentary, especially when compared to more advanced representations of C assimilation. C allocation needs theoretical analysis, with predictions tested against experimental and large-scale observational data, to strengthen model foundations. This thesis develops robust semi-empirical models of C allocation of root:shoot biomass ratios (R:S), forest dynamics and biomass production efficiency (BPE) in order to explore how C partitioning is influenced by the availability of different resources. The predictors of R:S are selected based on eco-evolutionary optimality (EEO) principles. It is hypothesized that the demands of foliage production, and concomitant below-ground production to support that foliage, are satisfied with highest priority; and that any excess C (the net C profit, Pn) is allocated to stems in such a way as to maximize height growth, as a strategy for competitive fitness. The average diameter growth of a tree, and maximum tree height, in an even-aged forest are shown to be proportional to Pn. BPE quantifies the efficiency of assimilated C that is converted into structural growth. It reflects the balance between C gain by photosynthesis and C losses, principally autotrophic respiration (Ra). BPE is shown to decrease with growth temperature (Tg), stand age, soil C:N ratio, pH and sand content, and to increase with mean temperature of the coldest month—resolving a contradiction in the literature, about its apparent response to mean annual temperature—and to be greater for deciduous than evergreen woody plants. These findings contribute to an optimality-based theoretical framework for improved process-based C allocation modelling.
Version
Open Access
Date Issued
2025-06-24
Date Awarded
01/11/2025
License URL
Advisor
Prentice, Iain Colin
Sponsor
Schmidt Sciences LLC (Firm)
Publisher Department
Department of Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)