Chin J Plant Ecol ›› 2006, Vol. 30 ›› Issue (6): 1030-1039.DOI: 10.17521/cjpe.2006.0132

• Research Articles • Previous Articles     Next Articles

A REVIEW ON METHODS FOR MEASURING COMMUNITY STRUCTURAL COMPLEXITY

JIN Sen()   

  1. College of Forest Science, Northeast Forestry University, Harbin 150040, China
  • Received:2006-03-27 Accepted:2006-06-09 Online:2006-11-30 Published:2006-11-30

Abstract:

Ecological complexity has received increasing attention in recent years. Structural complexity is one of the most important parts of ecological complexity. This paper reviews the literature on the development of concepts and measures of community structure. Findings indicate that currently used methods can be classified into three groups: measures based on biodiversity, algorithmic complexity and geometrical properties. The many measures based on biodiversity are commonly used. The algorithmic method is new and has not been widely used. It represents the complexity of community structure by the difference of mean length of Huffman code of community attributes and 12th order of Rényi entropy. The methods based on geometrical properties are also commonly used, employing fractal dimension as the most important index. A common problem for all these methods is the difficulty of comparing community complexity with different complexity measures. Also, these methods weakly discriminate among different structures. For the methods based on biodiversity, it is difficult to objectively determine weights of the attributes used. For the algorithmic complexity based methods, the ecological meanings of the measures are still uncertain. Some of them are closely correlated with biodiversity indices, and the others need field testing. Future studies should focus on the following: 1) Determine algorithmic measures with high discriminant ability that differ from biodiversity indices both in concept and values. Because many algorithmic measures have been widely used in fields other than ecology, interesting results could be generated when applying these measures to description of complexity of community structure. 2) Objectively determine weights of attributes with ecological meaning used in complexity measures, a problem that exists in biodiversity and algorithmic measures that use multiple attributes. 3) Connect measures of structural complexity with functions and processes, a critical goal in the study of complexity of community structure.

Key words: Complexity, Structure, Algorithmic complexity, Measure, Kolmogorov complexity, Huffman code, Ecological complexity