Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, 37996, USA.
Ecology. 2017 Apr;98(4):933-939. doi: 10.1002/ecy.1753.
Beta diversity is an important metric in ecology quantifying differentiation or disparity in composition among communities, ecosystems, or phenotypes. To compare systems with different sizes (N, number of units within a system), beta diversity is often converted to related indices such as turnover or local/regional differentiation. Here we use simulations to demonstrate that these naive measures of dissimilarity depend on sample size and design. We show that when N is the number of sampled units (e.g., quadrats) rather than the "true" number of communities in the system (if such exists), these differentiation measures are biased estimators. We propose using average pairwise dissimilarity as an intuitive solution. That is, instead of attempting to estimate an N-community measure, we advocate estimating the expected dissimilarity between any random pair of communities (or sampling units)-especially when the "true" N is unknown or undefined. Fortunately, measures of pairwise dissimilarity or overlap have been used in ecology for decades, and their properties are well known. Using the same simulations, we show that average pairwise metrics give consistent and unbiased estimates regardless of the number of survey units sampled. We advocate pairwise dissimilarity as a general standardization to ensure commensurability of different study systems.
β多样性是生态学中的一个重要指标，用于量化群落、生态系统或表型之间组成的差异或离散度。为了比较具有不同大小（N，系统内单位数量）的系统，β多样性通常转换为相关指数，如周转率或局部/区域分化。在这里，我们使用模拟来演示这些简单的不相似性度量依赖于样本量和设计。我们表明，当 N 是采样单元（例如，样方）的数量而不是系统中“真实”社区的数量（如果存在的话）时，这些分化度量是有偏估计量。我们建议使用平均成对不相似性作为直观的解决方案。也就是说，我们不是试图估计 N 个社区的度量，而是主张估计任何两个随机社区（或采样单元）之间的预期不相似性-特别是当“真实”N 未知或未定义时。幸运的是，成对不相似性或重叠的度量在生态学中已经使用了几十年，它们的性质是众所周知的。使用相同的模拟，我们表明，平均成对度量无论采样的调查单元数量如何，都能给出一致和无偏的估计。我们主张使用成对不相似性作为一般标准化方法，以确保不同研究系统的可衡量性。
