生物多样性 ›› 2021, Vol. 29 ›› Issue (6): 790-797. DOI: 10.17520/biods.2021011
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:
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。
邹怡 (2021) 样本量不一致时的β多样性计算. 生物多样性, 29, 790-797. DOI: 10.17520/biods.2021011.
Yi Zou (2021) The calculation of β-diversity for different sample sizes. Biodiversity Science, 29, 790-797. DOI: 10.17520/biods.2021011.
图1 从样方 × 物种的多度数据转换成二维排序图的流程图。原始的多度数据通过计算相异指数可以得到一个两两样方配对的距离矩阵; 该距离矩阵可以通过合适的排序方法得到二维的降序图; 二维图中的一个点代表一个样方, 点与点之间的距离代表样方之间的物种组成是相异(距离比较远)还是相似(距离比较近)。
Fig. 1 Flow chart for converting plot × species multi-dimensional data into a two-dimensional ordination graph. The raw data can be calculated into a pair-wised distance matrix. The distance matrix can be descended into a two-dimensional ordination graph. One point in the ordination graph represents a sampling plot, while the distance between the points represents that the species composition between plot is dissimilar (distance is far) or similar (distance is relatively close).
图2 不同取样参数大小(m = 1, 8和140)下的基于弦标准化的预期共享物种数(CNESS)指数以及Chao-Jaccard指数的主坐标分析(PCoA)二维排序图
Fig. 2 Two-dimensional principal coordinates analysis (PCoA) plots based on chord-normalized expected species shared (CNESS) index with different sampling size parameter (m= 1, 8, 140) and based on the Chao-Jaccard index
图3 基于弦标准化的预期共享物种数(CNESS)指数的主坐标分析(PCoA)排序产生的两个轴(Axis-1与Axis-2)与基于Chao-Jaccard的PCoA排序产生的两个轴分别进行线性回归后的回归系数(R2)在不同m值下的变化
Fig. 3 Linear regression value (R2) between respective axes (Axis-1 and Axis-2) that generated by chord-normalized expected species shared (CNESS) index based and Chao-Jaccard based principal coordinates analysis (PCoA) plots at different m values
图4 弦标准化的预期共享物种数(CNESS) (m = 1、8和140)和Chao-Jaccard相异性的平均值的变化率与随机移除的个体数之间的关系
Fig. 4 Relationship between the changes of mean dissimilarity distance with the number of individuals randomly removed from the dataset for chord-normalized expected species shared (CNESS) index with different m value (m = 1, 8 and 140) and the Chao-Jaccard index
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