Extended Abstract
Introduction: In recent decades, concerns about climate change and unprecedented greenhouse gas emissions have highlighted the importance of carbon sequestration as a key tool for mitigating the negative effects of this phenomenon. Ecosystems, especially forests, capture atmospheric carbon through photosynthesis and store it in their tissues and soil. This process helps reduce atmospheric carbon levels and mitigate greenhouse gas emissions. However, the potential of ecosystems for carbon sequestration varies and is directly influenced by their functional diversity. Different species in more diverse forest ecosystems, perform different roles, such as nitrogen fixation, enhancing soil organic matter, and protecting the soil. Therefore, such ecosystems likely have a greater capacity to capture and conserve carbon. Understanding ecosystem services requires an understanding of functional diversity, which governs ecosystem processes through various components. Different plant species have different performance traits, resulting in varying abilities to absorb, store, and emit carbon. Thus, the relationship between functional diversity and carbon storage is important. This study aimed to evaluate the functional diversity and carbon storage of the Quercus brantii Lindl. forest in Dehdez city, Khuzestan Province.
 
Materials and methods: To study the correlation between different components of functional diversity and carbon storage, 16 plots were selected in a representative Quercus brantii forest in Dehdez, Khuzestan Province. The following parameters were measured to calculate community-weighted mean (CWM), functional divergence (FDvar), and functional dispersion (FDis) as indices of functional diversity: six plant traits (leaf nitrogen content, leaf phosphorus content, specific leaf area, plant height, wood specific gravity, and leaf dry matter content), total ecosystem carbon (TEC), aboveground biomass carbon (AGBC), aboveground litter carbon (ALC), and soil organic carbon (SOC). Principal component analysis (PCA) was used to identify the most important independent variables, which were then used in stepwise multiple linear regression analysis to determine which components best explain the variability in carbon storage.
 
Results and Discussion: The mean organic carbon amounts in TEC, AGBC, ALC, and SOC were 89.5, 11, 3, and 74.4 tons per hectare, respectively. The first PCA axis, explaining 41.9% of the variance, was characterized by the CWM of height, while the second axis, explaining 34.3% of the variance, was characterized by the CWM of specific leaf area. Results showed that litter carbon was predicted by functional divergence, while soil carbon was predicted by community-weighted mean. Both were related to the specific leaf area index, which had a negative association. In the study area, the community-weighted mean of leaf nitrogen and specific leaf area, as well as the functional divergence related to specific leaf area, were identified as the most important factors for predicting carbon storage. The final model for biomass indicated that an increase in nitrogen content leads to an increase in carbon storage. Overall, significant effects of functional diversity on some plant traits such as leaf nitrogen content and specific leaf area were observed.
Conclusion: This study found that indices based on a single trait, such as community-weighted mean (CWM) and functional divergence (FDvar), were more important for estimating carbon storage than the functional dispersion (FDis) index, which considers multiple traits. Additionally, the most important plant traits for carbon estimation were specific leaf area and leaf nitrogen content.