Improved allometric models to estimate the aboveground biomass of younger secondary tropical forests

• Published in: Global ecology and conservation 2023-01, Vol.41, p.e02359, Article e02359
• Main Authors: Liu, Bin, Bu, Wensheng, Zang, Runguo
• Format: Article
• Language: English
• Subjects: Aboveground components; Biomass estimation; Functional groups; Hainan Island; Simultaneous fitting; Wood density
• ISSN: 2351-9894; 2351-9894
• DOI: 10.1016/j.gecco.2022.e02359

Frequent human activities have resulted in a large number of tropical old-growth forests being replaced by secondary forests at different succession stages. The secondary forests have great potential for carbon accumulation, especially the younger secondary forests (≤ 20 years). Accurately quantifying the biomass of younger secondary forests could help in the implementation of global warming mitigation projects. Biomass estimation of species in different successional stages (i.e., specific functional groups) is helpful to understand the recovery process of younger secondary forests. However, there are few local biomass models developed for younger secondary tropical forests (YSTF), so it is necessary to verify the applicability of biomass models outside the region. In this study, we destructively sampled trees to generate biomass models of mixed species and specific functional groups in YSTF in Hainan Island, China. Improved allometric equations with DBH, tree height and wood density were more accurate in estimating biomass of YSTF (adjusted R2 ≥ 0.960). Models that included branch wood density had the same ability to estimate aboveground biomass as those that included stem wood density (P = 0.885). The existing allometric equations of mature forest or secondary forest have large deviations in estimating the aboveground biomass of YSTF in Hainan Island. Optimal biomass models for specific functional groups include different predictors. We propose that branch wood density can be used as an important predictor for estimating biomass in YSTF, thus reducing the time to disruptive sampling and tree damage in the field. In addition, different predictors play an important role in estimating biomass of functional groups, and hierarchical modeling based on functional groups can improve the accuracy of estimating biomass of younger secondary forests. •Improved allometric model can estimate the biomass of younger secondary tropical forests more accurately.•Branch wood density can as an important predictor of biomass estimation in younger secondary tropical forests.•Hierarchical modeling based on ecological characteristics can improve biomass estimation of younger secondary forests.