Sufficient sampling for asymptotic minimum species richness estimators
Sufficient sampling for asymptotic minimum species richness estimators
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DOI：
10.1890/07-2147.1
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发表时间：
2009-04-01
期刊：
影响因子：
4.8
通讯作者：
Gotelli, Nicholas J.
中科院分区：
文献类型：
--
作者：
Chao, Anne;Colwell, Robert K.;Gotelli, Nicholas J.
生物多样性采样是劳动密集型的，生物群的很大一部分通常由非常低丰度的物种代表，这些物种通常没有被生物多样性调查发现。统计方法被广泛用于估计现存物种的渐近数量，包括尚未检测到的物种。需要额外的抽样来检测和识别这些物种，但丰富度估计器并不表明需要多少抽样努力(额外的个体或样本)才能达到物种积累曲线的渐近线。在这里，我们开发了第一个统计严格的非参数方法，用于估计检测任意比例(包括100%)的估计的渐近物种丰富度所需的额外个体、样本或样本面积的最小数量。该方法使用基于稀有物种在原始样本数据中的频率的渐近丰富度的Chao1和Chao2非参数估计。为了评估提出的方法的性能，我们从两个大型生物多样性清单(英国的鳞翅目灯光陷阱捕获和巴拿马巴罗科罗拉多岛的木本植物普查[BCI])中随机抽样个体或样方。仿真结果表明，该方法性能较好，但在小样本情况下略显保守。对BCI结果的分析表明，该方法对物种发生的小尺度空间聚集所产生的非独立性是稳健的。当将该方法应用于7个已公布的生物多样性数据集时，捕获所有估计物种所需的额外抽样工作量为原始样本的1.05至10.67倍(中位数约为2.23)。检测90%的物种所需的工作量大大减少(0.33-1.10倍于原始工作量；中位数接近0.80)。提供了一种Excel电子表格工具，用于计算大量数据或重复事件数据的必要抽样工作量。
Biodiversity sampling is labor intensive, and a substantial fraction of a biota is often represented by species of very low abundance, which typically remain undetected by biodiversity surveys. Statistical methods are widely used to estimate the asymptotic number of species present, including species not yet detected. Additional sampling is required to detect and identify these species, but richness estimators do not indicate how much sampling effort (additional individuals or samples) would be necessary to reach the asymptote of the species accumulation curve. Here we develop the first statistically rigorous nonparametric method for estimating the minimum number of additional individuals, samples, or sampling area required to detect any arbitrary proportion (including 100%) of the estimated asymptotic species richness. The method uses the Chao1 and Chao2 nonparametric estimators of asymptotic richness, which are based on the frequencies of rare species in the original sampling data. To evaluate the performance of the proposed method, we randomly subsampled individuals or quadrats from two large biodiversity inventories (light trap captures of Lepidoptera in Great Britain and censuses of woody plants on Barro Colorado Island [BCI], Panama). The simulation results suggest that the method performs well but is slightly conservative for small sample sizes. Analyses of the BCI results suggest that the method is robust to nonindependence arising from small-scale spatial aggregation of species occurrences. When the method was applied to seven published biodiversity data sets, the additional sampling effort necessary to capture all the estimated species ranged from 1.05 to 10.67 times the original sample (median approximate to 2.23). Substantially less effort is needed to detect 90% of the species (0.33-1.10 times the original effort; median approximate to 0.80). An Excel spreadsheet tool is provided for calculating necessary sampling effort for either abundance data or replicated incidence data.