National Tsing Hua University Institutional Repository:Sufficient sampling for asymptotic minimum species richness estimators
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    National Tsing Hua University Institutional Repository > 理學院 > 統計學研究所 > 期刊論文 >  Sufficient sampling for asymptotic minimum species richness estimators


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    题名: Sufficient sampling for asymptotic minimum species richness estimators
    作者: Chao, Anne;Colwell, Robert K.;Lin, Chih-Wei;Gotelli, Nicholas J.
    教師: 趙蓮菊
    日期: 2009
    出版者: Ecological Society of America
    關聯: ECOLOGY,Volume: 90,Issue: 4,Pages: 1125-1133,Published: APR 2009
    关键词: TROPICAL RAIN-FOREST
    CANONICAL DISTRIBUTION
    ANT FAUNA
    BIODIVERSITY
    摘要: 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.
    URI: http://www.esajournals.org/doi/abs/10.1890/07-2147.1?journalCode=ecol
    http://www.esa.org/
    http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/54114
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