An index for measuring functional diversity in plant communities based on neural network theory (Q2375499)

Summary: The functional diversity in plant communities is a key driver of ecosystem processes. Effective methods for measuring functional diversity are important in ecological studies. A new method based on neural networks, the self-organizing feature map (SOFM), is put forward and described. A case application to the study of the functional diversity of Phellodendron amurense communities in Xiaolongmen Forest Park of Beijing is carried out in this paper. The results showed that the SOFM index is an effective method in the evaluation of functional diversity and its change in plant communities. Significant nonlinear correlations of the SOFM index with the commonly used methods, FAD, MFAD, FDp, FDc, FRic, and FDiv indices, also proved that the SOFM index is useful in the studies of functional diversity.