Understanding and modelling moss carbon dynamics in black spruce forests

Mosses play a key role in the carbon (C) budget of black spruce forests, which are widely distributed across Canada and throughout the circumboreal region. Mosses are currently not included in the forest C stock accounting of large-scale national models used by Canada to meet international greenhouse gas reporting requirements. It is therefore essential to increase our scientific understanding of the dynamics of moss-C accumulation in black spruce forest ecosystems in a national-scale context. This thesis was inspired by Canada's national-scale carbon accounting model, the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). The model was designed to provide operational forest managers with a tool to make informed management decisions based on C balance simulation of future scenarios. The model can also be scaled-up to provide regional- or national-C estimates. Previous research and inventory has shown that black spruce forests are widespread in many regions across the country, and that mosses have the potential to contribute significantly to the carbon budget of these systems. Nevertheless, the CBM-CFS3 does not account for moss-derived carbon in these forests. My overall objective is to improve scientific understanding of the C dynamics of mosses in black spruce-dominated forests from the perspective of including them in nationally-scaled models such as the CBM-CFS3. To accomplish this I: 1) investigate the potential of moss-derived C to improve the current inadequacy present in the simulation of black spruce soil carbon by CBM-CFS3, 2) collect ground plot data across several study regions in Canada to define moss-tree relationships that can be used as tools to model moss-C accumulation at the regional- or national-scale, and 3) build, test and examine a sub-model for moss-C accumulation that can be added to the CBM-CFS3 model framework to improve carbon budget accounting in black spruce forests across Canada. This research demonstrated that large differences between field-measured C stocks and CBM-CFS3 model predictions in poorly drained black spruce forests were of the same order of magnitude as would be expected if mosses could be included in the model. The field component of the research identified significant relationships between merchantable timber, canopy openness, and the relative abundance and productivity of feather moss and sphagnum moss. Together, these relationships allowed the input and loss of C via mosses to be modelled. The addition of the MOSS-C sub-model to CBM-CFS3 reduced the residual error by five fold; however, we found that the dynamics of the productivity-decomposition pathway included in the model were insufficient to account for all of variation in observed C stocks. The study suggests avenues for future research to better understand the complex interactions between decomposition, weather and fire regime on deep layer carbon storage. The key scientific merits of this thesis are: 1) the demonstration and quantification of the importance of mosses in the carbon budget of black spruce forests in national-scale models such as the CBM-CFS3, 2) the examination of the moss-tree relationships derived from field collected data that can be used to predict moss-C accumulation across several study regions in Canada, 3) the creation of a MOSS-C sub-model that can be directly applied into the current CBM-CFS3 model framework and strengthen the model's ability to predict organic soil C in black spruce forest systems nationally. Together these contributions act to provide an important first step at studying moss-C accumulation from the perspective of large-scale national or regional forest carbon budgets. They also help to demonstrate that if natural resource scientists aim to improve our ability to predict moss-C in black spruce stands at a national-scale than future work is needed in black spruce-moss ecology from a multi-scaled perspective to complement the work presented in this thesis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.