CoRRE Trait Data: A collection of 17 categorical and continuous traits for nearly 6000 grassland species worldwide

In our changing world, it is critical to understand and predict plant community responses to global change drivers. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands, however their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is incredibly sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Database, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 5,917 vascular plant species across 196 plant families present in 551 grassland experiments from around the world. The database contains complete categorical trait records for all 4,079 plant species, obtained from a comprehensive literature search. Additionally, the database contains nearly complete coverage (99.97%) of species mean values for continuous traits for a subset of 3,891 plant species across 168 plant families, predicted from observed trait data drawn from TRY and a variety of other plant trait databases using Bayesian Probabilistic Matrix Factorization (BHPMF) and multivariate imputation using chained equations (MICE). These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.

在不断变化的当今世界，理解并预测植物群落对全球变化驱动因子的响应至关重要。植物功能性状有望成为包括草原在内的众多生态系统的关键预测工具，但其应用需依托完整的植物群落与功能性状数据集。然而，全球数据库中此类数据的存储量极度匮乏，尤其是在少数常用性状与常见物种之外的领域。在此我们发布CoRRE性状数据库（CoRRE Trait Database），该库涵盖17项性状（9项分类性状、8项连续性状），可用于预测全球551项草原实验中，隶属于196个植物科的5917种维管植物对全球变化的响应。该数据库包含全部4079种植物的完整分类性状记录，数据均通过全面的文献检索获取。此外，针对隶属于168个植物科的3891种植物，该数据库覆盖了其连续性状物种均值的99.97%；此类数据通过从TRY及其他多种植物性状数据库中提取的观测性状数据，采用贝叶斯概率矩阵分解（Bayesian Probabilistic Matrix Factorization, BHPMF）与链式方程多重插补（Multivariate Imputation by Chained Equations, MICE）方法预测得到。本数据集将助力阐明全球草原生态系统中种群、群落与生态系统对全球变化响应的内在机制。