Barwell Louise J, Isaac Nick J B, Kunin William E
Institute of Integrative and Comparative Biology, University of Leeds, LC Miall Building, Leeds, LS2 9JT, UK.
Biological Records Centre, Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK.
J Anim Ecol. 2015 Jul;84(4):1112-22. doi: 10.1111/1365-2656.12362. Epub 2015 Mar 21.
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.
2003年,对24种存在 - 缺失β多样性指标进行了综述,并确定了一些权衡和冗余情况。我们对基于丰度的β多样性指标的性能进行了一项平行调查。β多样性是一个多方面的概念,是空间生态学的核心。有多种指标可用于量化它:指标的选择是一个重要的决定。我们测试了β多样性指标的16个概念属性和两个抽样属性:指标应1)独立于α多样性,2)沿物种周转率梯度累积。当群落独立且同分布时,相似性应3)具有概率性。指标应4)最小值为零,并随5)物种周转率、6)物种排名解耦和7)均匀度差异程度单调增加。然而,完全的物种周转率应始终产生比极端的8)排名变化或9)均匀度差异更大的β值。指标应10)有固定的上限,11)对称(βA,B = βB,A),12)对双缺失和双存在具有双零不对称性,以及13)在一系列嵌套群落中不降低。此外,指标应独立于14)物种复制、15)丰度单位和16)抽样单位之间的总丰度差异。当使用样本推断β多样性时,指标应1)独立于样本大小,2)独立于不等样本大小。我们测试了29个指标的这些属性和五个“个性”属性。13个指标在所有概念和抽样属性方面表现较差或相当。对物种丰度的敏感性差异导致样本大小偏差与检测稀有物种间周转率能力之间的性能权衡。总体而言,尽管存在 - 缺失指标βsim总体表现良好,但基于丰度的指标在面对抽样不足时偏差要小得多。只有βBaselga R turn、βBaselga B - C turn和βsim纯粹测量物种周转率且独立于嵌套性。在其他指标中,对嵌套性的敏感性变化超过4倍。我们的结果表明现有β多样性指标之间存在大量冗余,同时缺乏对未观察到的共享和非共享物种的估计,这在新的基于丰度的指标设计中应予以解决。