Carbon pool study at Lambir forest reserves in Sarawak

Carbon cycling processes in tropical forests are still poorly quantified and their relationship to environmental factors not well understood. Almost all recent advances in this field have occurred in the Neotropics (Amazonian and central America), with very few studies in Asia. However, the Malesian tropical forests are fundamentally different from Neotropical and African forests, with their dominance by dipterocarp trees, and inherently different magnitude of biomass and productivity. Variation in the pattern of allocation of carbon may explain these differences in magnitude. This study was conducted in a lowland mixed-dipterocarp forest in the Lambir Hills National Park, Miri, Sarawak to explore carbon allocation and the cycling processes. Two study plots measuring 1-ha each were established on clay and sandy loam sites following comparable biomass estimates to similar studies being undertaken in several sites across Amazonia. We estimated aboveground wood productivity using long-term forestry inventory data and incorporated consistent calculation and correction methods. In addition, all trees were outfitted with dendrometer bands to measure minor changes in circumference of existing trees and recruitment rates of new trees. We adopted a comprehensive approach to capture all major components of the belowground carbon cycle. Here we focus on an intensive approach to capture monthly soil CO2 flux. Understanding diurnal cycle of soil respiration is important to capture spatial and temporal variations. Hence, we established the long-term DIRT (Detritus Input Removal and Transfer) Experiment to partition and manipulate soil organic matter. We employed the LI-8100 automated soil CO₂ flux system (with 4 long-term chambers) to capture continuous soil CO₂ flux on soil partitions under different treatment conditions (control, mycorrhizal, root/litter-free soil, and litter-free soil). This study aims to establish estimates of above- and -belowground productivity, carbon dynamics, and its spatial variability across the research plot with new approaches and promising methods. The results provide imperative datasets to fill the gaps in understanding the global carbon dynamics and the cycle of tropical forests.