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Abstract
Quercus liaotungensis natural secondary forest is an important vegetation formation and has a large distribution area in Lingkong Mountain Nature Reserve, Shanxi Province, China. The spatial patterns of trees at different life stages give important clues about the underlying processes driving regeneration and succession of the forest. In this paper, the trees of a population were mapped, characterized and the spatial distribution patterns and spatial associations of Q. liaotungensis among different life stages (juveniles – J, premature – P, mature – M, overmature – O) were analyzed using O-ring univariate O(r) and bivariate O(subscript 12)(r) statistics. We found that: (1) Q. liaotungensis was a discontinuously regenerating population. (2) The distribution patterns of Q. liaotungensis varied at different life stages. Q. liaotungensis (J) and Q. liaotungensis (M) showed significant aggregations at scale 0–19 m and 0–23 m, respectively. Q. liaotungensis (P) exhibited significant aggregations at the majority of scales, whereas Q. liaotungensis (O) showed a random distribution pattern at most scales. (3) Intraspecific spatial association varied with tree size and scales. Negative or independent association was a dominant pattern for Q. liaotungensis at different life stages, whereas positive associations were found at small scales for only three pairs: Q. liaotungensis (J)–Q. liaotungensis (P), Q. liaotungensis (J)–Q. liaotungensis (M), and Q. liaotungensis (P)–Q. liaotungensis (M).
Acknowledgments
This project is supported by the National Bureau of Forestry 948 project (No. 2010-4-15). The authors thank all those who provided helpful suggestions and critical comments on this manuscript and anonymous reviewers. The authors also thank Dr Osbert Jianxin Sun (MOE Key laboratory for Silviculture and Conservation and Institute of Forestry and Climate Change Research, Beijing Forestry University, Beijing, China) for providing helpful information for this paper. We also thank Dr T. Wiegand for use of the Programita software, and Jinsong Wang, Qi Zhao, and Yingying Qin for their fieldwork and data collection.
