Chen J.H., Gao M., Chen G.C., Zhu H. and Ye Y. 2022. Biomass accumulation and organic carbon stocks of Kandelia obovata mangrove vegetation under different simulated sea levels. Acta Oceanologica Sinica 41(8):78-86.

Mangrove forests are vulnerably threatened by sea level rise (SLR). Vegetation organic carbon (OC) stocks are important for mangrove ecosystem carbon cycle. It is critical to understand how SLR affects vegetation OC stocks for evaluating mangrove blue carbon budget and global climate change. In this study, biomass accumulation and OC stocks of mangrove vegetation were compared among three 10 year-old Kandelia obovata (a common species in China) mangrove forests under three intertidal elevations through species-specific allometric equations. This study simulated mangrove forests with SLR values of 0 cm, 40 cm and 80 cm, respectively, representing for the current, future ~100 a and future ~200 a SLR of mangrove forests along the Jiulong River Estuary, China. SLR directly decreased mangrove individual density and inhibited the growth of mangrove vegetation. The total vegetation biomasses were (12.86±0.95) kg/m2 , (7.97±0.90) kg/m2 and (3.89±0.63) kg/m2 at Sites SLR 0 cm, SLR 40 cm and SLR 80 cm, respectively. The total vegetation OC stock decreased by approximately 3.85 kg/m2 (in terms of C) from Site SLR 0 cm to Site SLR 80 cm. Significantly lower vegetation biomass and OC stock of various components (stem, branch, leaf and root) were found at Site SLR 80 cm. Annual increments of vegetation biomass and OC stock also decreased with SLR increase. Moreover, significant lower sedimentation rate was found at Site SLR 80 cm. These indicated that SLR will decrease mangrove vegetation biomass and OC stock, which may reduce global blue carbon sink by mangroves, exacerbate global warming and give positive feedback to SLR.

Annual organic carbon (OC) stock increments and vegetation OC stocks of different components (stem, branch, leaf, aboveground (AB), root and total) of three Kandelia obovata forests (different letters for each parameter indicate a significant difference at P<0.05).