Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; Department of Biological Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC, Canada.
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.
J Environ Manage. 2019 Mar 15;234:167-179. doi: 10.1016/j.jenvman.2018.12.090. Epub 2019 Jan 5.
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 R 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.
准确估算珍稀濒危物种的地上生物量（AGB）对于保护森林生态系统和濒危物种非常重要，并且为分析过去和未来气候变化对森林 AGB 的影响提供了有用的信息。我们研究了使用三种已开发和两种广泛使用的模型（广义回归神经网络（GRNN）、数据处理组方法（GMDH）、自适应神经模糊推理系统（ANFIS）、人工神经网络（ANN）和支持向量机（SVM））估算中国天然林白杄（Dacrydium pierrei）AGB 的可行性。结果表明，这些模型可以使用有限的气象数据来解释 D. pierrei AGB 的变化。GRNN 和 ANN 模型在估算 D. pierrei AGB 方面优于其他模型。GMDH 模型始终对 AGB 产生相对较差的估计。与 2013-2017 年的气候情况相比，比较了三种气候情景，包括代表性浓度途径（RCP）2.6、RCP 4.5 和 RCP 8.5。在这些情景下，具有相同胸径（DBH）的 D. pierrei 雌性的 AGB 将分别增加 13.0±31.4%（平均值±标准偏差）、16.6±30.7%和 18.5±30.9%，在 2041-2060 年和 15.6±32.1%、21.2±33.2%和 24.8±32.7%，在 2061-2080 年；雄性的 AGB 将增加 16.3±32.3%、21.7±32.5%和 22.9±32.6%，在 2041-2060 年和 22.3±30.8%、27.2±31.8%和 30.1±34.4%，在 2061-2080 年。所有模型的 R 值范围在 0.82 到 0.95 之间。总之，本研究建议使用这些先进的模型来估算森林的 AGB，并且在未来的气候情景下，2041-2080 年森林的 AGB 将增加。
