Development and experimental validation of objective procedures to determine functional groups: A tool for biodiversity and global change research

该项目旨在开发新颖、客观的方法，根据长期、实验、充分研究和发表的草地群落野外数据产生的数字衍生特征，定义和找到植物和无脊椎动物的最佳功能群（FG）。这些FG不会简单地表示功能相似物种的大量丰度，而是形成丰度矩阵，代表FG的所有成员物种，因此保留每个单一物种的详细信息可能有助于FG的特性。将通过实验验证推导出来的生态系统对相关生态功能的实际影响。群落是具有重要生态特征的物种的集合单位，因此执行相同的生态系统过程。因此，它们是功能上有意义的替代品，可以将生物复杂性降低到可管理的规模，从而推导出群落动态及其结构背后的一般原理或机制。由于物种的独立性，FGs可以理想地用于长时间和大地理距离的系统比较，这是在群落水平上研究全球变化对气候梯度生物多样性影响的必要条件。然而，一个弱或不适当的FG分类也会导致弱或无意义的结果。我们的项目将产生一套标准化的程序，使其他人能够以一致的方式分析生物多样性和全球变化研究中典型的大量社区数据。

This project aims to develop novel, objective methods to define and find optimum functional groups (FG) for plants and invertebrates based on numerically derived traits that will be generated from long-term and experimental, well studied and published field data of grassland communities. These FGs will not simply represent a lumping together of bulk abundances of functionally similar species but instead form abundance matrices representing all member species of a FG and so retaining detailed information each single species may contribute to the properties of a FG. The derived FGs will be validated experimentally for their actual impact on relevant ecological functions.FGs are aggregated units of species sharing important ecological traits, and therefore perform the same ecosystem processes. As such, they are functionally meaningful surrogates that reduce biotic complexity to a manageable size, allowing to derive general principles or mechanisms behind community dynamics and their structure. Due to their independence of species, FGs can ideally be used for system comparisons across long time periods and along large geographic distances, which is a necessary condition to study, on community level, global change effects on biodiversity along climate gradients. A weak or inappropriate FG classification, however, will also lead to weak or meaningless results. Our project will result in a set of standardized procedures that allow others to analyze, in a consistent way, large amounts of community data typical for biodiversity and global change research.