文献类型：期刊文献

英文题名：Testing the significance of different tree spatial distribution patterns based on the uniform angle index

作者：Zhao, Zhonghua[1,2] Hui, Gangying[1,2] Hu, Yanbo[1,2] Wang, Hongxiang[1,2] Zhang, Gongqiao[1,2] von Gadow, Klaus[3]

第一作者：赵中华;Zhao, Zhonghua

通信作者：Hui, GY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China;[2]State Forestry Adm, Key Lab Tree Breeding & Cultivat, Beijing 100091, Peoples R China;[3]Univ Gottingen, D-37073 Gottingen, Germany

年份：2014

卷号：44

期号：11

起止页码：1419-1425

外文期刊名：CANADIAN JOURNAL OF FOREST RESEARCH

收录：;EI(收录号：20144600211696);Scopus(收录号：2-s2.0-84909952205);WOS:【SCI-EXPANDED(收录号:WOS:000346370000012)】；

基金：We are grateful to Tianxi Lin, Xianlong Zhang, Wenzhen Liu, and Xiaolong Shi for their invaluable help during this study. We also thank the National Natural Science Foundation of China (31370638) and all of the reviewers and Editors for reading the manuscript and providing very useful comments.

语种：英文

外文关键词：forest; spatial patterns of trees; uniform angle index; significance test; method

摘要：The uniform angle index (UAI) is used to characterize the spatial distribution of a forest community or of individual tree species within that community. This study presents an empirical assessment of the performance of a new statistical test used in conjunction with the UAI. In this study, the UAI is applied to observed and simulated communities. The effect of plot size and density are examined and the results are compared with the criteria of the aggregation index R of Clark and Evan (CE-R) and Ripley's L function (RL). The results suggest that the UAI performs well in determining a particular class of spatial pattern and that, in contrast to the CE-R index, it is unaffected by population density because the standard deviation can be estimated when the density is known using a new empirical relationship presented in this paper. The results of this study represent an improvement in the development of the UAI, verifying its performance and confirming its overall utility. Our comparative analysis positions the UAI among its historical competitors, the CE-R and RL criteria. The UAI index, which does not require expensive mapping of tree positions, can produce results that are equivalent to those obtained by RL and more robust and reliable than those obtained by CE-R.