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

该项目旨在开发新的，客观的方法来定义和找到植物和无脊椎动物的最佳功能组（FG）的基础上，将从长期和实验，充分研究和发表的草地群落的实地数据产生的数值衍生的特征。这些FG将不会简单地表示功能相似物种的大量丰度的集合，而是形成表示FG的所有成员物种的丰度矩阵，并且因此保留每个单个物种可能有助于FG的性质的详细信息。研究人员将通过实验验证所得出的森林生态群对相关生态功能的实际影响。森林生态群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物，可以将生物复杂性降低到可管理的规模，从而可以推导出社区动态及其结构背后的一般原则或机制。由于其物种的独立性，FGs可以理想地用于系统的比较，在较长的时间段和沿着大的地理距离，这是一个必要的条件，研究，在社区水平上，全球变化对生物多样性的影响沿着气候梯度。然而，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.