A new statistical approach for assessing similarity of species composition with incidence and abundance data
A new statistical approach for assessing similarity of species composition with incidence and abundance data
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DOI：
10.1111/j.1461-0248.2004.00707.x
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发表时间：
2005-02-01
期刊：
影响因子：
8.8
通讯作者：
Shen, TJ
中科院分区：
文献类型：
--
作者：
Chao, A;Chazdon, RL;Shen, TJ
经典的Jaccard和Sorensen成分相似性指数（以及依赖于相同变量的其他指数）对样本量非常敏感，特别是对于具有许多稀有物种的组合。此外，由于这些指数完全基于存在-不存在数据，因此无法对它们进行准确的估计。我们为这些指数的经典的、基于发生率的形式提供了一个概率推导，并将这种方法扩展到基于物种丰度数据的新的jaccard型或sorensen型指数。然后，我们根据（重复的）发生率或丰度为基础的样本数据，提出了包括未见共享物种影响的这些指数的估计值。在抽样模拟中，当样本中缺少相当大比例的物种时，这些新的估计值被证明比经典指数的偏差要小得多。基于物种丰富的经验数据集，我们展示了如何结合未见的共享物种的影响不仅提高了准确性，而且可以改变结果的解释。
The classic Jaccard and Sorensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence-absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence-based forms of these indices and extend this approach to formulate new Jaccard-type or Sorensen-type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence- or abundance-based sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species-rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results.