Evaluating the performance of species richness estimators: sensitivity to sample grain size
Evaluating the performance of species richness estimators: sensitivity to sample grain size
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DOI：
10.1111/j.1365-2656.2006.01048.x
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发表时间：
2006-01-01
影响因子：
4.8
通讯作者：
Gaspar, C
中科院分区：
文献类型：
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作者：
Hortal, J;Borges, PAV;Gaspar, C
1.比较了15个物种丰富度估计器(3个基于物种积累曲线的渐近估计器，11个非参数估计器和1个基于物种-面积关系的估计器)，考察了它们在估计固氮罗里席尔瓦森林表层节肢动物物种丰富度方面的表现。通过对五个岛屿的天然林残留物中的78个样带进行标准化抽样获得的数据被聚集在七种不同的颗粒中(即定义单一样本的方式)：岛屿、自然区域、样带、陷阱对、陷阱、数据库记录和个人，以评估使用不同抽样单位对物种丰富度估计的影响。估计的物种丰富度分数既取决于所考虑的估计者，也取决于用于汇总数据的粒度。然而，有几个估计器(ACE、Chao1、Jackknife1和2以及Bootstrap)是精确的，尽管存在籽粒差异。威布尔和最近的几个估计量[由Rosenzweig等人提出。(保护生物学，2003，17,864-874)，和Ugland等人。(《动物生态学杂志》，2003，72,888-897)]表现很差。使用较小粒度(陷阱对、陷阱对、记录和个体)开发的估计在一些估计值(上述加上ICE、Chao2、Michaelis-Menten、负指数和Cranch)中表现出类似的分数。这四个样本量的估计值也高度相关。与其他研究相反，我们得出结论，大多数物种丰富度估计器在生物多样性研究中可能是有用的。由于它们固有的公式，几个非参数和渐近估计对样本聚集方式的差异不敏感。因此，它们可以用来比较不同抽样策略获得的物种丰富度得分。我们的结果还指出，来自小颗粒尺寸的物种丰富度估计可以直接进行比较，而其他估计器在这些情况下可以给出更准确的结果。我们提出了一个基于我们的结果和文献的决策框架，以评估应该使用哪个估计器来比较不同地点的物种丰富度分数，这取决于原始数据的粒度和可用的数据类型(物种出现或丰度数据)。
1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations.2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao1, Jackknife1 and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly.3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated.4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).