Search

Mangrove forest plays a key role in regulating climate change, earth carbon cycling and other biogeochemical processes within blue carbon ecosystems. Therefore, mangrove forests should be incorporated into Earth system climate models with the aim of understanding future climate change. Despite multiple carbon stock and flux assessments taking place over the past couple of decades, concrete knowledge of carbon source/sink patterns is largely lacking, particularly in the biodiversity-rich Asia-Pacific (AP) region with its 68 493 km ^2 of mangrove area. Thus, to understand the gaps in mangrove blue carbon research in the AP region, we summarize a recent decade-long inventory of carbon stock pools (aboveground, belowground and soil) and biogeochemical flux components (burial, export/import, soil-air and water-air CO _2 flux) across 25 AP countries to understand the current knowledge and gaps. While carbon stock assessments of individual components are available for all 25 countries, whole ecosystem carbon stocks—including live and standing dead aboveground and belowground, downed woody debris and soil carbon stocks—are often lacking, even in highly researched countries like Indonesia. There is restricted knowledge around biogeochemical carbon fluxes in 55% of the countries, suggesting poor carbon flux research across the region. Focusing on flux components, reports on sediment-to-sea carbon exports are extremely limited (coming from just nine countries in the AP region). There is notable scarcity of data on carbon export fluxes in Indonesian mangroves. Given the key role AP mangroves play in climate change mitigation worldwide, more detailed and methodologically comparable investigation of biogeochemical source/sink processes is required to better understand the role of this large carbon source in global carbon stocks and fluxes, and hence, global climate.

Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change’s (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC’s Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions.