Journal of Applied Mathematics
Volume 2013 (2013), Article ID 320905, 6 pages
Research Article

An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory

1School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
2College of Life Sciences, Beijing Normal University, Beijing 100875, China

Received 26 February 2013; Revised 8 May 2013; Accepted 14 May 2013

Academic Editor: Hector Pomares

Copyright © 2013 Naiqi Song and Jin-Tun Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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