植物生态学报 ›› 2013, Vol. 37 ›› Issue (1): 26-36.DOI: 10.3724/SP.J.1258.2013.00003

• 研究论文 • 上一篇    下一篇

海南尖峰岭不同热带雨林类型与物种多样性变化关联的环境因子

许涵1,2, 李意德2,*(), 骆土寿2, 陈德祥2, 林明献2   

  1. 1中国林业科学研究院林木遗传育种国家重点实验室, 北京 100191
    2中国林业科学研究院热带林业研究所, 广州 510520
  • 收稿日期:2012-05-14 接受日期:2012-12-14 出版日期:2013-05-14 发布日期:2013-01-15
  • 通讯作者: 李意德
  • 作者简介:*(E-mail:liyide@126.com)
  • 基金资助:
    中央级公益性科研院所基本科研业务费专项“基于大样地系统的热带森林动态监测研究”(CAFYBB2011004);“尖峰岭大样地系统的数据集成与野外手册编制”(RITFYWZX201204);“海南岛热带雨林动态监测大样地建立及监测”(RITF-YWZX200902);林业公益性行业科研专项“热带雨林生物多样性维持与自然恢复策略研究”(201104057)

Environmental factors correlated with species diversity in different tropical rain forest types in Jianfengling, Hainan Island, China

XU Han1,2, LI Yi-De2,*(), LUO Tu-Shou2, CHEN De-Xiang2, LIN Ming-Xian2   

  1. 1State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing 100191, China
    2Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, China
  • Received:2012-05-14 Accepted:2012-12-14 Online:2013-05-14 Published:2013-01-15
  • Contact: LI Yi-De

摘要:

环境因子是影响物种分布并导致物种多样性形成的重要因素, 采伐后恢复的热带森林次生林和原始林的环境因子是否一致是一个很重要的问题。对于该问题的回答对长期监测热带森林次生林的变化具有重要意义。该文基于在海南尖峰岭地区设置的164个625 m2植被公里网格样地数据, 记录了每个样地的采伐历史并测定了其他的17个环境变量指标, 分析了17个环境因子之间的相关关系; 将164个样地划分成3种不同采伐历史的森林, 通过典范对应分析(CCA)探讨3种森林类型中影响物种分布的环境因子组成; 比较两种多元回归模型的优劣, 来揭示3种森林类型中影响物种丰富度形成的环境因子组成的差异。结果表明: 驱动海南尖峰岭地区物种分布并导致物种多样性差异的环境因子在森林采伐前后并不是一成不变的, 而是与森林采伐历史有关联的。除了人为森林采伐干扰外, 海拔梯度是形成海南尖峰岭热带天然林物种多样性的最重要因素。CCA分析显示: 原始林中, 物种分布与海拔、土壤交换性钙和交换性镁含量3个环境因子有较密切的关系, 也与4个土壤物理性质环境因子(土壤密度、土壤最大持水能力、毛细管持水量和毛管孔隙度)关系密切; 森林采伐后的恢复森林中, 土壤全磷和速效磷含量对物种分布的影响增强, 但皆伐后土壤交换性钙和交换性镁含量对物种分布的影响减弱。多元回归分析显示: 原始林的物种丰富度与海拔和土壤交换性钙含量显著相关, 径级择伐后恢复热带天然林的物种丰富度和海拔、土壤全磷含量和速效钾含量显著相关, 皆伐后恢复热带天然林的物种丰富度仅和海拔显著相关。研究结果还显示, 如果数据中存在空间自相关, 建立多元回归模型时应该考虑数据中的空间自相关属性, 虽然它并不总是存在的。

关键词: 环境因子, 森林采伐, 尖峰岭, 空间自相关, 物种分布, 物种多样性

Abstract:

Aims Environmental factors are key factors impacting species distribution and determining species richness. An important question is whether the major environmental factor are the same in old-growth forests and secondary forests produced by logging. This question is very important for monitoring long-term changes in secondary tropical rain forests.
Methods A unique, robust data set consisting of 164 625 m2 quadrats in a 160 km2 tropical rainforest was set up from August 2007 to June 2009 in the middle of the Jianfengling Natural Reserve, Hainan Island, China. Forest logging history was determined and 17 environmental factors were measured for each quadrat. First, we analyzed the relationship among these 17 factors. Then, these quadrats were classified into three forest types with different logging history: old-growth forests, diameter-limit logged forests and clear-cut forests. Canonical correspondence analysis (CCA) was used to analyze the environmental factors impacting species distributions. Step-forward multiple regression was used with and without considering spatial autocorrelation in the data set to disclose which environmental factors determined species richness.
Important findings The environmental factors impacting species distribution and determining species richness changed because of differences of forests logging history. Elevation is the second most important factor influencing patterns of species diversity. CCA showed that species distribution in old-growth forests is closely related to elevation, soil exchangeable calcium content, soil exchangeable magnesium content and four soil physical factors (soil density, maximum water holding content, capillary water-holding content and capillary porosity). Importance of soil total phosphorous content and available phosphorous content was greater in logged forests, but the importance of soil exchangeable calcium content and exchangeable magnesium content was lower in clear-cut forests. Multiple regression analysis also showed that species richness was significantly correlated with elevation and soil exchangeable calcium in the old-growth forests. While species richness was correlated with elevation, soil total phosphorous content and soil available potassium content in the diameter-limit logged forests and was only correlated with elevation in the clear-cut forests. Furthermore, it is suggested it is better to compare the spatial autocorrelation models with other models to describe the relationship between environmental factors and species richness, even it does not always exist in the ecological data set with spatial characteristics.

Key words: environmental factor, forest logging, Jianfengling, spatial autocorrelation, species distribution, species diversity