Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change.

Accurate estimations of the aboveground biomass (AGB) of rare and endangered species are particularly important for protecting forest ecosystems and endangered species and for providing useful information to analyze the influence of past and future climate change on forest AGB. We investigated the feasibility of using three developed and two widely used models, including a generalized regression neural network (GRNN), a group method of data handling (GMDH), an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a support vector machine (SVM), to estimate the AGB of Dacrydium pierrei (D. pierrei) in natural forests of China. The results showed that these models could explain the changes in the AGB of the D. pierrei using a limited amount of meteorological data. The GRNN and ANN models are superior to the other models for estimating the AGB of D. pierrei. The GMDH model consistently produced comparatively poor estimates of the AGB. Three climate scenarios, including the representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were compared with the climate situation of 2013-2017. Under these scenarios, the AGB of D. pierrei females with the same diameter at breast height (DBH) would increase by 13.0 ± 31.4% (mean ± standard deviation), 16.6 ± 30.7%, and 18.5 ± 30.9% during 2041-2060 and 15.6 ± 32.1%, 21.2 ± 33.2%, and 24.8 ± 32.7% during 2061-2080; the AGB of males would increase by 16.3 ± 32.3%, 21.7 ± 32.5%, and 22.9 ± 32.6% during 2041-2060 and 22.3 ± 30.8%, 27.2 ± 31.8%, and 30.1 ± 34.4% during 2061-2080. The R2 values of all models range from 0.82 to 0.95. In conclusion, this study suggests that these advanced models are recommended to estimate the AGB of forests, and the AGB of forests would increase in 2041-2080 under future climate scenarios.