Forest management-mortality interactions – quantification of management effects on tree mortality and implications for carbon cycling
Forests form a major terrestrial carbon sink, but its size is subject to large uncertainties. Recent advances in the quantification of the sink have highlighted the need for accounting for the effects of forest management and tree mortality. However, management and mortality have been considered in isolation from each other, even though management practises are known have large effects on tree mortality. So far, the lack of suitable management data has hindered the realistic consideration of management effects on forest dynamics in global vegetation models. ForMMI aims to fill these gaps by (1) developing novel observation-based methodology to quantify forest management regimes from single-measurement national forest inventory data, (2) creating statistical models that allow calculation of mortality rates based on management type and intensity and (3) integrating these models into a dynamic global vegetation model LPJ-GUESS and use it to quantify the implications to carbon cycle. The methodological development will enable obtaining of observations-based management information over large areas, thus greatly improving the current use of national-level statistics and assumptions based on management guidelines. This will greatly benefit the future efforts of linking management to forest dynamics in large-scale models. The results will provide a first large-scale and consistent assessment of management impacts on natural mortality of trees and quantify the implications for carbon cycle. This will create an important basis in accounting for management-mortality interactions in global vegetation models, on which further research will be able to build on. The project will provide Dr Suvanto with the skills and expertise needed to establish a leading international role in forest management-mortality interactions, a research direction with increasing importance as current efforts on including management effects and mortality processes to large-scale models move forward.