Mangrove root biomass and the uncertainty of belowground carbon estimations

Mangroves sequester large amounts of carbon (C) and they are increasingly recognized for their potential role in climate change mitigation programs. However, there is uncertainty in the C content of many mangrove forests because the amount of C stored in the roots is usually estimated from allometric equations and not from direct field measurements. There are only a handful of allometric equations in mangroves that are used worldwide to estimate root biomass, however, root biomass can vary from the allometric relationship if the environmental conditions are different from those where the equation was developed. In this study, we compiled recent information on how mangrove roots are affected by environmental conditions. Then, we explored the effect of sampling methodology on root biomass estimations. Finally, we compared published values of root biomass from field measurements against our estimations from allometric equations. The goal was to calculate the uncertainty associated with the estimation of root biomass and thus, the belowground C content of mangroves. The results showed that sampling methodology has a significant effect on root biomass estimations. The highest biomass estimations are reported where both live and dead roots are measured and when the roots are sampled by digging trenches. When comparing measured values against estimations from allometric equations, on average the general allometric equation provided root biomass values that were 40 +/- 12% larger than those obtained from field measurements with cores. The result suggests that either: (a) sampling with cores largely underestimates root biomass, or (b) allometric equations overestimate root biomass when used outside the region where they were developed. The uncertainty in root biomass estimates from allometric equations corresponds to 4-15% of the ecosystem C stock (trees + soil), with higher uncertainties in forests with low tree density and low interstitial salinity. We provide a statistical model that includes salinity, forest density and root biomass to correct for this systematic bias. The estimated uncertainty is important to consider when quantifying C budgets at large spatial scales and to validate methodological approaches to C stock estimations.