生物多样性 ›› 2021, Vol. 29 ›› Issue (6): 790-797.DOI: 10.17520/biods.2021011

• 技术与方法 • 上一篇    下一篇

样本量不一致时的β多样性计算

邹怡*()   

  1. 西交利物浦大学, 江苏苏州 215123
  • 收稿日期:2021-01-08 接受日期:2021-04-26 出版日期:2021-06-20 发布日期:2021-06-08
  • 通讯作者: 邹怡
  • 作者简介:* E-mail: yi.zou@xjtlu.edu.cn
  • 基金资助:
    国家自然科学基金(31700363)

The calculation of β-diversity for different sample sizes

Yi Zou*()   

  1. Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123
  • Received:2021-01-08 Accepted:2021-04-26 Online:2021-06-20 Published:2021-06-08
  • Contact: Yi Zou

摘要:

度量样方间物种组成的差异, 即β多样性, 是生态学研究中的常用手段。在开展生态学研究的过程中, 不同样方获取的样本量通常不同。使用物种稀疏曲线可以计算不同样本量的α多样性, 但常用的β多样性指数的计算却没有考虑样本量的差异。本文主要介绍了从稀疏曲线演化而来的可以计算不同样本量的β多样性指数——预期共享物种数(expected species shared, ESS)及其标准化后的指数, 其中详细介绍了弦标准化的预期共享物种数(chord-normalized expected species shared, CNESS)。利用真实采集的数据集, 本文演示了在不同样本参数m下, CNESS经过主坐标分析(principal coordinates analysis, PCoA)的二维排序结果, 并比较了样本量变化后, CNESS与基于多度的Chao-Jaccard相异性指数之间的差异。模拟结果表明, CNESS指数与Chao-Jaccard指数的PCoA结果具有相关性, 该相关性不随m值的变化而变化。CNESS指数较Chao-Jaccard指数具有更多优势, 通过调节样本参数m, CNESS的结果可以分析优势种或者稀有种的物种组成差异, 同时CNESS指数对样本量不敏感。ESS系列相异指数是基于物种多度的计算, 适用于样本量不一致时的β多样性研究, 建议在开展昆虫等无脊椎动物的生态学研究中使用此指数。为了更加准确地获得样方之间的物种组成差异, 在数据分析的过程中应选取不同大小的m值计算CNESS。然而, 由于样本量小于特定m值的样方会在计算中被剔除, 因此, 在实际的取样工作中, 每个样方都应该尽量采集到足够多的个体, 才能保证在m值足够大的时候也不丢失样方信息。

关键词: beta多样性, CNESS, 主坐标分析(PCoA), 相异矩阵, 昆虫, 群落生态学

Abstract

Aims: Measuring differences in species composition between plots, i.e., β-diversity, is a common approach in ecological studies. In empirical studies, sample sizes between plots are often inconsistent. While species rarefaction curves can be used to calculate α-diversity for different sample sizes, commonly-used β-diversity indices do not take sample sizes into account. To overcome the limitation, this study introduced the species rarefaction-extended β-diversity index—the expected species shared (ESS) and its normalized format, with particular emphasis on the chord-normalized expected species shared (CNESS) index.

Methods: Based on empirical data, this study demonstrated the application of CNESS using principal coordinates analysis (PCoA) under different sample size parameter (m), and compared results between the CNESS and a commonly used abundance-based index, the Chao-Jaccard index.

Results: Simulation results showed that the PCoA results of the CNESS index and the Chao-Jaccard index were generally positively correlated and that the correlation was largely independent of m. By adjusting m, results of the CNESS can be tuned to focus on the species composition of both dominant and rare species, whereas the Chao-Jaccard cannot represent the relevant information. The CNESS index was not sensitive to the sample size, which offers advantages compared to the Chao-Jaccard index.

Conclusions: ESS-based dissimilarity indices are abundance-based and are suitable for the calculation of β-diversity when sample sizes vary among plots, which is especially important when studying insects and other invertebrates that commonly have vast differences in the number of samples among plots. In order to comprehensively understand species composition differences between plots, calculation of CNESS results under different m values is recommended. As plots with a sample size smaller than m will be excluded in the calculation, in practice, a sufficient number of individuals are required for each plot to ensure the integrity of plot information under a large m.

Key words: β-diversity, CNESS, principal coordinates analysis (PCoA), dissimilarity matrix, insects, community ecology