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The relative importance of habitat complexity and surface area in assessing biodiversity: Fractal application on rocky shores

Author:
Vladimir E. Kostylev   Johan Erlandsson   Mak Yiu Ming   Gray A. Williams  


Journal:
Ecological Complexity


Issue Date:
2005


Abstract(summary):

Theoretical work predicts that complex habitats allow more species to co-exist in a given area. However, more field studies are still needed to clarify this relationship, especially in intertidal habitats. Furthermore, the potential separate effects of surface complexity and area on species richness and abundance have rarely been addressed. We tested the hypotheses that a more complex substratum or larger surface area will support a greater number of individuals and species of mobile macrofauna on three rocky shores in Hong Kong. Surface complexity, assessed by using fractals, was an important factor in species-area relationships. The number of species increased proportionally to habitat complexity and this relationship was homogeneous among different shores. Total abundance of animals, however, was more dependent on the available surface area. The slope of the size-frequency distribution of animals in samples taken on surfaces with different fractal dimensions (D) was significantly steeper with an increase in fractal dimension, showing that the relative abundance of small animals increased with surface complexity. Thus, surface complexity and area may be important in determining different aspects of the macrofaunal community structure on rocky shores. The resulting increase in surface area on more rough surfaces may introduce bias in density and species number assessments when two-dimensional sampling units (i.e., quadrats) are employed. It is necessary, therefore, to account for the surface complexity in the design and interpretation of the results of benthic studies. Using D as an index of surface complexity is very useful, but also involves some practical problems, e.g., surfaces may be anisotropic and different methods may give different estimates of D. Therefore, these different methods need to be calibrated before comparisons of D values between them are meaningful. (C) 2005 Elsevier B.V. All rights reserved.


Page:
272-286


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