Application of Self-Organizing Feature Map clustering and ordination to the analysis of subalpine meadows in North China

Artificial neural network theory is a newer mathematic branch discipline. The SOFM clustering and ordination were just introduced to plant ecology recently. In this article, these two methods were applied to study subalpine meadows in the Wutai Mountains, North China. The results showed that SOFM clustering classified 78 quadrats into 8 community types, basically representing the associations of the high and cold meadows in the Wutai Mountains. This classification was meaningful in ecology. The SOFM ordination reflected ecological gradients obviously, indicating that altitude was the most important factor in affecting the growth and distribution of the meadow vegetation, and slope and aspect also had certain roles. SOFM clustering and ordination methods performed well in this application, and this study showed that the combination of these two methods was better in ecological analysis. The conservation of meadows in the Wutai Mountains needs further to strengthened.