生物多样性 ›› 2011, Vol. 19 ›› Issue (5): 581-588.DOI: 10.3724/SP.J.1003.2011.08015

• 研究报告 • 上一篇    下一篇

运用稀疏法分析物种丰富度的海拔梯度分布格局: 以样方实测乔木种数据为例

邢开雄, 康慕谊*(), 王强, 段锦, 戴诚   

  1. 北京师范大学地表过程与资源生态国家重点实验室, 北京师范大学资源学院中国生态资产评估研究中心, 北京 100875
  • 收稿日期:2011-01-20 接受日期:2011-06-13 出版日期:2011-09-20 发布日期:2011-10-08
  • 通讯作者: 康慕谊
  • 作者简介:*E-mail: kangmy@bnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(40671065)

Rarefaction approach to analyzing distribution patterns of species richness along altitudinal gradients: a case study with arborous species data

Xing Kaixiong, Kang Muyi*(), Wang Qiang, Duan Jin, Dai Cheng   

  1. State Key Laboratory of Earth Surface Processes and Resources Ecology, College of Resources Science & Technology, China Ecological Capital Assessment Research Center, Beijing Normal University, Beijing 100875
  • Received:2011-01-20 Accepted:2011-06-13 Online:2011-09-20 Published:2011-10-08
  • Contact: Kang Muyi

摘要:

植物物种丰富度随山地海拔梯度的变化格局是生物多样性研究的热点之一。基于种-面积关系的任何模型对群落物种数目所作估计, 其精度都依赖于样本的代表性、抽样尺度以及所涉及的分类群。作者以秦岭南坡森林群落样方实测的乔木种数据为例, 借鉴群落最小面积(minimum area, MA)的概念及其确定方式, 利用稀疏法(rarefaction)确定了能够反映研究区物种丰富度的最小表现样方数(minimum plot number), 利用3种分组方式将样方总体数据按海拔带分为不同的亚组计算各亚组的物种丰富度, 分析物种丰富度随海拔梯度的分布格局。结果表明: (1)在样方总体内计算任意数目的样方亚组的物种数时, 稀疏法可以整合整个研究区的物种组成特点, 避免单个样方数据对物种数估算的误差影响; 以最小表现样方数为基础来确定物种丰富度, 体现了物种数与样方数(所占面积)的非线性关系, 从而保证了计算结果的物种丰富度有充分的代表性。(2)秦岭南坡森林群落乔木物种的丰富度在中海拔范围(1,400-1,900 m)达到最大(≥80种), 而乔木物种密度分布的最大值出现在海拔1,890 m处(=9.5种/km2), 与以往研究结论基本一致。(3)等样方数高度带滑动分组方法结合物种密度计算分析, 不仅样方分组较详尽, 而且减少了各样方组间的微小差异, 是运用稀疏法考察区域物种丰富度时相对理想的样方分组方法。

关键词: 稀疏法, 最小表现样方数, 物种丰富度, 物种密度, 秦岭南坡

Abstract

Species richness variation along altitudinal gradients has been among the hot topics in ecology. Models that estimate richness based on species-area relationships are dependent on representativeness, sampling scale and the classification of samples. In this paper, we have derived a new method to estimate minimum plot number (MPN), which is the minimum number of plots required to accurately estimate the species richness of a concerned region. Our approach combines the rarefaction approach that has been widely used to estimate regional species richness with the concept of the minimum area needed to survey a plant community. We evaluated our approach in a case study using survey data on the arborous species of natural forests on the southern slope of the Qinling Mountain Range. We divided one hundred and thirty five 20 m×20 m plots into a number of plot subgroups along an altitudinal gradient in three different ways and calculated the richness of each subgroup using the rarefaction approach in order to assess the distributional pattern of species richness along the altitudinal gradient. The preliminary results are as follows: (1) Rarefaction approach can help to calculate the species richness in each subgroup with full consideration in its different plot number, and at the same time in the species composition features in the study area, avoiding fluctuates influenced by the individual plot. Our approach of estimating species richness using rarefaction and considering minimum plot number also has the advantage of being able to recognize non-linear relationships between plot area and species number within a plot. (2) The elevational zone with the highest arborous species richness (≥80 sp.) emerged as 1,400-1,900 m a.s.l., and the peak species density (=9.5 sp./km2) occurred at an altitude of 1,890 m a.s.l. (3) Compared with the other two methods of plot sub-grouping, the moving average method under the criteria of equal-plot number intervals based on MPN can not only get more detailed plot subgroups, but also avoid influence resulting from unequal area of plot subgroups. If aided with species density data from the same region, we believe this is an ideal sub-grouping approach to calculating species richness because the curves it generates provide conclusions that are consistent with previous studies, implying its applicability to other regional species richness studies.

Key words: rarefaction approach, minimum plot number, species richness, species density, the southern slope of Qinling Mountain Range