Carbon stock in the Sundarbans mangrove forest: spatial variations in vegetation types and salinity zones

TLDR: In this paper, the authors presented the estimation of ecosystem carbon stock in the Sundarbans using a large scale data set collected from systematic grid samples throughout the forest, and the results revealed that no matter whether the mangroves are tall or dwarf, a significant amount of carbon is stored into the sediment.

Abstract: The Sundarbans (21o30′–22o30′ N and 89o00′–89o55′ E) is the largest mangrove forest in the world. Forests are very important for sequestering atmospheric carbon and mangroves are amongst the most efficient in carbon sequestration. This study presents the estimation of ecosystem carbon (above- and belowground) stock in the Sundarbans using a large scale data sets collected from systematic grid samples throughout the forest. The variation of carbon stock in different vegetation types and in different salinity zones in Sundarbans was investigated. The relationships between carbon stock and different vegetation functional attributes (basal area, mean tree height, crown coverage etc.) were also investigated. The amount of carbon stored varied significantly among vegetation types, salinity zones and vegetation functional attributes (P < 0.05). Sundri (Heritiera fomes) dominated forest types store more ecosystem carbon (360.1 ± 22.71 Mg C ha−1) than other vegetation types. The fresh water zone shows the highest ecosystem carbon stock (336.09 ± 14.74 Mg C ha−1) followed by moderate and strong salinity zones. Salinity was found to enhance belowground carbon stock as revealed by the lowest proportion of belowground carbon stock (57.2 %) with respect to ecosystem carbon in fresh water zone and by the highest (71.9 %) in strong salinity zone. The results also reveal that no matter whether the mangroves are tall or dwarf, a significant amount of carbon is stored into the sediment. The vegetation attributes (basal area and mean tree height) of the dominant mangrove species in each vegetation type were identified as the key indicator of ecosystem carbon stock. We recommended some generalized regression equations to predict ecosystem carbon stock from basal area or mean tree height.