Tree size distributions in an old-growth temperate forest

Authors

  • Xugao Wang,

  • Zhanqing Hao,

  • Jian Zhang,

  • Juyu Lian,

  • Buhang Li,

  • Ji Ye,

  • Xiaolin Yao


X. Wang, Z. Hao (hzq@iae.ac.cn), J. Zhang, B. Li, X. Yao, and J. Ye, Inst. of Applied Ecology, Chinese Academy of Science, PO Box 417, CN–110016, Shenyang, PR China. – J. Lian, South China Botanical Garden, Chinese Academy of Science, CN–510650 Guangzhou, PR China.

Abstract

Despite the wide variation in the structural characteristics in natural forests, tree size distribution show fundamental similarities that suggest general underlying principles. The metabolic ecology theory predicts the number of individual scales as the −2 power of tree diameter. The demographic equilibrium theory predicts tree size distribution starting from the relationship of size distributions with growth and mortality at demographic equilibrium. Several analytic predictions for tree size distributions are derived from the demographic equilibrium theory, based on different growth and mortality functions. In addition, some purely phenomenological functions, such as polynomial function, have been used to describe the tree size distributions. In this paper, we use the metabolic ecology theory, the demographic equilibrium theory and the polynomial function to predict the tree size distribution for both the whole community and each species in an old-growth temperate forest in northeastern China. The results show that metabolic ecology theory predictions for the scaling of tree abundance with diameter were unequivocally rejected in the studied forest. Although these predictions of demographic theory are the best models for most of the species in the temperate forest, the best models for some species (Tilia amurensis, Quercus mongolica and Fraxinus mandshurica) are compound curves (i.e. rotated sigmoid curves), best predicted by the polynomial function. Hence, the size distributions of natural forests were unlikely to be invariant and the predictive ability of general models was limited. As a result, developing a more sophisticated theory to predict tree size distributions remains a complex, yet tantalizing, challenge.

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