| Popis: |
Rising carbon dioxide (CO2) levels can lead to more carbon sequestration in plant biomass, and forests’ natural ability to store carbonin long-lived woody tissue is of particular interest. However, the extent of CO2-fertilization in trees varies across age, species and the availability of other resources. Woody tissue encompasses more than just the tree’s trunk, and a critical knowledge gap lies in the allocation of carbon to the other woody components like branches and twigs. In addition, the flux of woody carbon from the tree to the forest floor (turnover) is more than events of single tree mortality. These fluxes come in the form of litterfall, breakage of whole branches or complete tree mortality. The goal of this study is to quantify biomass allocation patterns and subsequent turnover rates within the woody carbon pool of two contrasting forest FACE experiments, BIFoR FACE in Staffordshire UK, and EucFACE in Sydney, Australia and answer the following questions: how do these allocation patterns determine the potential for carbon sequestration and how do patterns shift with elevated CO2 concentrations?Terrestrial laser scanning provided the tools to determine canopy structure on a stand scale, and the use of algorithms on the resulting point cloud trees supplied data on the partitioning of biomass among twigs, branches and stems. These results were then used to test general hypotheses about canopy structure and how it changes with elevated CO2. The fluxes from the different wood components were quantified with monthly observations and collections, litter traps to collect the smallest material and transects to make an inventory of larger compartments like branches. The results of these studies will be combined with the canopy structure partitioning fractions to determine if the allocation patterns vary over time and between two contrasting forest types. It is worthwhile to increase our understanding of the dynamics of all woody components within forests and on a global scale beyond single tree mortality to improve the accuracy of predictive carbon budget models. |
