Complexity of forest communities:a case study of three different forest types in Heishiding Nature Reserve, Guangdong

Abstract

The complexity of a forest community is defined as having the average amount of its information by eliminating the uncertainties of species and layers of a tree individual randomly selected from all trees in the forest community. The joint entropy H(X, Y) is proposed to measure the complexity of a forest community H(X, Y) = H(X) + H(Y|X), in which H(X) = - and H(Y|X) = - , where S stands for the number of tree species (X), N for the total number of individuals in the forest community, n_i for the number of the ith tree species, and n_(ij) for the number of the ith tree species in the jth layer. H(X) is defined as the compositional complexity of tree species and H(Y|X) as the structural complexity of tree species. The higher the H(X, Y) value, the greater the complexity in the forest community. A case study is presented based on the survey data from three types of forest communi- ties in Heishiding Nature Reserve, Guangdong Province. Three sampling plots were established, each with a size of 60 m×60 m, representing coniferous forest, mixed coniferous broad-leaved forest and ever- green broad-leaved forest. Each plot was divided into 36 quadrats with a size of 10 m×10 m. The data for all trees with DBH≥1 cm were gathered, including their coordinates in the sampling plots. Tree sizes were divided into four categories based on their DBH: DBH≥1, 5, 10, and 30 cm. Using computer sim- ulation, 13 types of quadrat sizes (12 m×12 m, 16 m×16 m,…, 60 m×60 m) within a plot were ob- jectively selected based on the method of nested quadrat sampling. The results show that the order of H(X, Y) of three typical forest types is as follows: evergreen broad-leaved forestmixed coniferous broad-leaved forestconiferous forest. At the same time, the fractal relationships between H(X, Y) and sampling size among the three forest types reveal that H(X, Y) has a statistical self-similarity feature.