Prioritizing forestation in China through incorporating biogeochemical and local biogeophysical effects

A database of plot-scale forest type and forest biomass was constructed by integrating four existing databases: the Forest Carbon Database (ForC) (Anderson-Teixeira et al., 2018), the Forest Observation System (FOS) (Schepaschenko D et al., 2019), a fieldwork database established by Zhu et al. (2017), and datasets collected by Ye et al. (2021). These four existing databases provide information on geographic location (latitude and longitude), dominant species or forest types, stand age, and aboveground or belowground biomass or biomass carbon stock. It should be noted that sites in the ForC dataset may consist of multiple different plots. In this case, (1) if the plots shared both the same forest type and the same stand age, the reported observations were averaged; (2) otherwise, if they shared the same forest type but had different stand ages, then they were treated as different entries; (3) if their forest types were different, then, again, they were treated as different entries. Following this procedure, to ensure the consistency of the constructed database in this study, forest types were classified into coniferous, broad-leaved, and mixed forests based on the information on dominant species and forest types contained in these four databases. In addition, biomass density (in Mg ha-1) was converted to biomass carbon density (in Mg C ha-1) using the conversion factor of 0.5 (Petersson et al., 2012). The above processing generated a database containing information on geographic location, forest types, stand age, and aboveground or belowground biomass carbon density, consisting of 3411 observations. Reference Anderson-Teixeira K, Wang M, McGarvey J, et al. ForC: A global database of forest carbon stocks and fluxes. Ecology 2018;99:1507. Schepaschenko D, Chave J, Phillips O, et al. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Sci. Data 2019;6: Zhu J, Hu H, Tao S, et al. Carbon stocks and changes of dead organic matter in China's forests. Nat Commun 2017;8:151. Ye J, Yue C, Hu Y, et al. Spatial patterns of global-scale forest root-shoot ratio and their controlling factors. Sci Total Environ 2021;800:149251. Petersson H, Holm S, Ståhl G, et al. Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass – A comparative study. For. Ecol. Manage. 2012; 270: 78-84.本研究整合四类现有数据库，构建了样地尺度的森林类型与森林生物量数据库：森林碳数据库（Forest Carbon Database, ForC）（Anderson-Teixeira等，2018）、森林观测系统（Forest Observation System, FOS）（Schepaschenko D等，2019）、Zhu等（2017）建立的野外调查数据库，以及Ye等（2021）采集的数据集。 上述四类数据库可提供地理位置（经纬度）、优势物种或森林类型、林分年龄，以及地上/地下生物量或生物量碳储量相关信息。 需要说明的是，ForC数据库中的单个站点可能包含多个不同样地。针对此类情况，处理规则如下： (1) 若样地的森林类型与林分年龄均一致，则对报告的观测值取平均值； (2) 若样地森林类型一致但林分年龄存在差异，则将其视为独立条目； (3) 若样地森林类型不同，则同样将其视为独立条目。 为保障本研究构建数据库的一致性，本研究依据四类数据库中包含的优势物种与森林类型信息，将森林类型划分为针叶林、阔叶林与混交林三类。 此外，本研究采用0.5的转换系数（Petersson等，2012），将生物量密度（单位：Mg·ha⁻¹）转换为生物量碳密度（单位：Mg C·ha⁻¹）。 经上述处理流程后，本研究最终得到包含地理位置、森林类型、林分年龄以及地上/地下生物量碳密度信息的数据库，共计3411条观测记录。 参考文献 Anderson-Teixeira K, Wang M, McGarvey J, 等. ForC：全球森林碳储量与通量数据库. Ecology 2018;99:1507. Schepaschenko D, Chave J, Phillips O, 等. 森林观测系统：构建用于森林生物质遥感的全球参考数据集. Sci. Data 2019;6: Zhu J, Hu H, Tao S, 等. 中国森林枯落物与死亡有机质的碳储量及其变化. Nat Commun 2017;8:151. Ye J, Yue C, Hu Y, 等. 全球尺度森林根冠比的空间格局及其控制因子. Sci Total Environ 2021;800:149251. Petersson H, Holm S, Ståhl G, 等. 用于评估活体生物量碳储量变化的单木生物量方程或生物量扩展因子：一项比较研究. For. Ecol. Manage. 2012; 270: 78-84.