Measuring β-diversity with species abundance data.
Measuring β-diversity with species abundance data.
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DOI：
10.1111/1365-2656.12362
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发表时间：
2015-07
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通讯作者：
Kunin WE
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作者：
Barwell LJ;Isaac NJ;Kunin WE
2003年，对24个存在-不存在β多样性指标进行了审查，并确定了一些权衡和冗余。我们对基于丰度的β多样性度量的性能进行了平行调查。β多样性是一个多方面的概念，是空间生态学的核心。有多种度量可用于量化它：度量的选择是一个重要的决定。我们测试了β多样性度量的16个概念性质和两个抽样性质：度量应该1）独立于α多样性，2）沿着物种更替的梯度沿着累积。当集合是独立同分布时，相似性应该是概率性的。最小值为0，并随物种更替程度、物种等级解耦程度和均匀度差异单调增加。然而，完全物种更替产生的β值应该总是大于极端的等级变动或均匀度差异。10）对称性（βA，B = βB，A）; 12）双零不对称性（双缺失和双存在）; 13）在一系列嵌套集合中不减少。此外，度量应该独立于14）物种复制15）丰度单位和16）采样单位之间总丰度的差异。当使用样本来推断β多样性时，度量应该1）独立于样本大小，2）独立于不等样本大小。我们测试了这些属性的29个指标和5个“个性”属性。在所有概念和抽样属性中，有13个指标优于或等于。对物种丰度的敏感性差异导致样本量偏倚和检测稀有物种更替的能力之间的性能权衡。一般来说，基于丰度的度量在欠采样的情况下偏差要小得多，尽管存在-不存在度量βsim总体表现良好。只有βBaselga R turn、βBaselga B-C turn和βsim测量纯粹的物种更替，并且与嵌套无关。在其他指标中，对嵌套的敏感性变化>4倍。我们的研究结果表明，现有的β多样性指标之间存在大量冗余，同时缺乏对未知共享和非共享物种的估计，应该在设计新的基于丰度的指标时加以解决。
In 2003, 24 presence–absence β‐diversity metrics were reviewed and a number of trade‐offs and redundancies identified. We present a parallel investigation into the performance of abundance‐based metrics of β‐diversity. β‐diversity is a multi‐faceted concept, central to spatial ecology. There are multiple metrics available to quantify it: the choice of metric is an important decision. We test 16 conceptual properties and two sampling properties of a β‐diversity metric: metrics should be 1) independent of α‐diversity and 2) cumulative along a gradient of species turnover. Similarity should be 3) probabilistic when assemblages are independently and identically distributed. Metrics should have 4) a minimum of zero and increase monotonically with the degree of 5) species turnover, 6) decoupling of species ranks and 7) evenness differences. However, complete species turnover should always generate greater values of β than extreme 8) rank shifts or 9) evenness differences. Metrics should 10) have a fixed upper limit, 11) symmetry (βA,B = βB,A), 12) double‐zero asymmetry for double absences and double presences and 13) not decrease in a series of nested assemblages. Additionally, metrics should be independent of 14) species replication 15) the units of abundance and 16) differences in total abundance between sampling units. When samples are used to infer β‐diversity, metrics should be 1) independent of sample sizes and 2) independent of unequal sample sizes. We test 29 metrics for these properties and five ‘personality’ properties. Thirteen metrics were outperformed or equalled across all conceptual and sampling properties. Differences in sensitivity to species’ abundance lead to a performance trade‐off between sample size bias and the ability to detect turnover among rare species. In general, abundance‐based metrics are substantially less biased in the face of undersampling, although the presence–absence metric, βsim, performed well overall. Only βBaselga R turn, βBaselga B‐C turn and βsim measured purely species turnover and were independent of nestedness. Among the other metrics, sensitivity to nestedness varied >4‐fold. Our results indicate large amounts of redundancy among existing β‐diversity metrics, whilst the estimation of unseen shared and unshared species is lacking and should be addressed in the design of new abundance‐based metrics.