Interpolating, extrapolating, and comparing incidence-based species accumulation curves
Interpolating, extrapolating, and comparing incidence-based species accumulation curves
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DOI：
10.1890/03-0557
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发表时间：
2004-10-01
期刊：
影响因子：
4.8
通讯作者：
Chang, J
中科院分区：
文献类型：
--
作者：
Colwell, RK;Mao, CX;Chang, J
基于样方样本或其他采样单元中物种的存在与不存在（发生率），提出了物种累积函数的通用二项式混合模型。该模型涵盖零和观察到的样本数之间的插值，以及超出观察到的样本集的外推。对于插值（基于样本的稀疏），开发了预期丰富度及其置信极限的易于计算的封闭式表达式（使用矩方法），完全消除了对重采样方法的需要，并允许对样本集之间的丰富度进行直接统计比较。开发了基于发生率的科尔曼（随机放置）模型，并将其与基于矩的插值方法进行了比较。对于超出经验样本集的外推（同时，作为插值的替代方法），描述了具有引导置信区间的基于似然的估计器，该估计器依赖于顺序的、AIC 引导的算法来拟合混合模型参数。基于矩和基于似然的估计器都用温带鸟类和热带种子、蚂蚁和树木的数据集进行了说明。基于矩的估计器被自信地推荐用于插值（基于样本的稀疏）。对于外推法，基于似然的估计器在将经验样本数量加倍或三倍时表现良好，但对于估计丰富度渐近线并不可靠。讨论了基于个体和基于样本的稀疏对空间（或时间）斑块的敏感性。
A general binomial mixture model is proposed for the species accumulation function based on presence-absence (incidence) of species in a sample of quadrats or other sampling units. The model covers interpolation between zero and the observed number of samples, as well as extrapolation beyond the observed sample set. For interpolation (sample-based rarefaction), easily calculated, closed-form expressions for both expected richness and its confidence limits are developed (using the method of moments) that completely eliminate the need for resampling methods and permit direct statistical comparison of richness between sample sets. An incidence-based form of the Coleman (random-placement) model is developed and compared with the moment-based interpolation method. For extrapolation beyond the empirical sample set (and simultaneously, as an alternative method of interpolation), a likelihood-based estimator with a bootstrap confidence interval is described that relies on a sequential, AIC-guided algorithm to fit the mixture model parameters. Both the moment-based and likelihood-based estimators are illustrated with data sets for temperate birds and tropical seeds, ants, and trees. The moment-based estimator is confidently recommended for interpolation (sample-based rarefaction). For extrapolation, the likelihood-based estimator performs well for doubling or tripling the number of empirical samples, but it is not reliable for estimating the richness asymptote. The sensitivity of individual-based and sample-based rarefaction to spatial (or temporal) patchiness is discussed.