中国科学院大学宋翔等人报道了，树木和灌木木材密度对多种气候和土壤因子梯度适应的参数化。相关论文于2024年9月26日发表于《大气科学进展》。

据悉，木材密度(WD)是木材的一项重要品质和功能特性。然而，尽管WD与非生物因子之间的关系对于模拟或预测功能性状的空间分布，以及植被对气候变化的响应非常重要，但在当前的地球系统模型或动态全球植被模型(ESMs/DGVMs)中，WD往往被过度简化，被定义为所有植物功能类型(PFT)或每个个体PFT的全球统一常数。

这种过度简化可能会导致木本PFTs形态、生态系统过渡和植被-大气相互作用的模拟偏差。此外，从野外观测中得出的，关于WD与非生物因子之间关系的现有结论仍然是混合的，这使得模型参数化的改进变得困难。

本研究系统地研究了气候和土壤因子对不同PFTs的WD的影响。研究人员利用其整理的138,604个观测值的全球数据库，构建了预测每个PFT内WD的最佳拟合模型。对于树木PFTs的WDs，气候是比土壤特征更重要的影响因素，而对于灌木PFTs，气候和土壤的影响是同等重要的。

在所有六个PFTs中，拟合模型预测与观测WD之间的相关系数范围为0.49至0.93。预测值与观测值在整个气候空间上表现出良好的一致性。研究人员预计将该结果纳入DGVMs，将改进树高和森林分异覆盖率的模拟，特别是在中央林区和森林过渡区。

附：英文原文

Title: Parameterization of Tree and Shrub Stem Wood Density Adaptions to Multiple Climate and Soil Factor Gradients

Author: Xiang SONG, Jinxu LI, Xiaodong ZENG

Issue&Volume: 2024-09-26

Abstract: Wood density (WD) is an important quality and functional trait of wood. However, despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits, as well as responses of vegetation to climate changes, in current Earth system models or dynamic global vegetation models (ESMs/DGVMs), WD is often oversimplified, being defined as a globally uniform constant either for all plant functional types (PFTs) or for each individual PFT. Such oversimplifications may lead to simulation biases in the morphology of woody PFTs, as well as ecosystem transition and vegetation–atmosphere interactions. Moreover, existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed, making model parameterization improvements difficult. This study systematically investigated the influences of climate and soil factors on WD across various PFTs. Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138 604 observations. For WDs of tree PFTs, climate emerges as a more influential factor than soil characteristics, whereas for shrub PFTs the effects of climate and soil are of equivalent significance. Across all six PFTs, correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93. The predicted and observed WD exhibit good agreement across climate space. It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage, particularly in the central forest areas and forest transition zones.

DOI: 10.1007/s00376-024-4034-9

Source: http://www.iapjournals.ac.cn/aas/article/doi/10.1007/s00376-024-4034-9