Two paths to ecosystem stability: opposing roles of plant functional traits in temperate and alpine grasslands

This research examines the role of plant functional traits in driving ecosystem stability across distinct environmental contexts, specifically temperate and alpine grasslands in China. The study leverages a comprehensive dataset that includes in situ measurements of 16 morphological and chemical traits from 343 plant species, alongside 23 years of satellite-derived ecosystem productivity indices. By combining field data with advanced analytical approaches, including principal component analysis (PCA), Bayesian univariate and multivariate models, and Bayesian structural equation modeling (BSEM), this work provides a nuanced understanding of how trait-stability relationships are mediated by environmental conditions.To facilitate transparency and reproducibility, we have uploaded the complete set of R code used in the analysis. This includes scripts for performing PCA, Bayesian univariate and multivariate analyses, and BSEM, as well as generating the primary figures and key results presented in the manuscript. As the manuscript undergoes revisions, the code may be updated accordingly to reflect methodological refinements or new insights.If you have any questions regarding the analysis or the uploaded materials, please feel free to contact <b>Dr. Pu Yan</b> at <b>pyan40</b><b>@gatech</b><b>.edu</b>.

本研究探讨了植物功能性状（plant functional traits）在不同环境背景下对生态系统稳定性的驱动作用，研究区域聚焦中国境内的温带草原与高寒草原。本研究依托一套综合数据集，包含343种植物的16项形态与化学性状的原位实测数据，以及23年的卫星衍生生态系统生产力指数。通过将野外实测数据与主成分分析（principal component analysis, PCA）、贝叶斯单变量与多变量模型、贝叶斯结构方程模型（Bayesian structural equation modeling, BSEM）等先进分析方法相结合，本研究深入阐明了性状-稳定性关系如何受环境条件介导。

为保障研究的透明度与可重复性，我们已上传本次分析所用的全套R语言代码。代码涵盖主成分分析、贝叶斯单变量与多变量分析、贝叶斯结构方程模型的运行脚本，以及论文中核心图表与关键结果的生成代码。随着论文进入修订阶段，代码或会根据方法优化或新研究发现进行更新。

若您对本次分析或上传的相关材料有任何疑问，请联系蒲岩博士，邮箱为[e-mail]。