• 研究报告 •

### 凋落叶空间扩散模型在常绿阔叶林的适用性分析

1. (1华东师范大学生态与环境科学学院, 上海200062; 2浙江天童森林生态系统国家野外科学观测研究站, 浙江宁波315114; 3Université Catholique de Louvain, Earth and Life Institute, Croix du Sud 2/009, 1348 LouvainlaNeuve, Belgium; 4中国科学院上海辰山植物科学研究中心, 上海201602)
• 出版日期:2014-11-18 发布日期:2014-11-18

### Applicability analysis of spatially explicit model of leaf litter in evergreen broadleaved forests.

ZHAO Qing-qing1,2, LIU He-ming1,2, JONARD Mathieu3, WANG Zhang-hua4, WANG Xi-hua1,2

1. (1College of Ecological and Environmental Science, East China Normal University, Shanghai 200062, China; 2Tiantong National Station of Forest Ecosystem, Ningbo 315114, Zhejiang, China; 3Université Catholique de Louvain, Earth and Life Institute, Croix du Sud 2/009, 1348 LouvainlaNeuve, Belgium; 4Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai 201602, China)
• Online:2014-11-18 Published:2014-11-18

Abstract: The spatially explicit model of leaf litter can help to understand its dispersal process, which is very important to predict the distribution pattern of leaves on the surface of the earth. In this paper, the spatially explicit model of leaf litter was developed for 20 tree species using litter trap data from the mapped forest plot in an evergreen broadleaved forest in Tiantong, Zhejiang Province, eastern China. Applicability of the model was analyzed. The model assumed an allometric equation between diameter at breast height (DBH) and leaf litter amount, and the leaf litter declined exponentially with the distance. Model parameters were estimated by the maximum likelihood method. Results showed that the predicted and measured leaf litter amounts were significantly correlated, but the prediction accuracies varied widely for the different tree species, averaging at 49.3% and ranging from 16.0% and 74.0%. Model qualities of tree species significantly correlated with the standard deviations of the leaf litter amount per trap, DBH of the tree species and the average leaf dry mass of tree species. There were several ways to improve the forecast precision of the model, such as installing the litterfall traps according to the distribution of the tree to cover the different classes of the DBH and distance apart from the parent trees, determining the optimal dispersal function of each tree species, and optimizing the existing dispersal function.