The role of predictive model data in designing mangrove forest carbon programs

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.