English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54367/62174 (87%)
Visitors : 10064924      Online Users : 121
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    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


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/54114


    Title: Sufficient sampling for asymptotic minimum species richness estimators
    Authors: Chao, Anne;Colwell, Robert K.;Lin, Chih-Wei;Gotelli, Nicholas J.
    教師: 趙蓮菊
    Date: 2009
    Publisher: Ecological Society of America
    Relation: ECOLOGY,Volume: 90,Issue: 4,Pages: 1125-1133,Published: APR 2009
    Keywords: TROPICAL RAIN-FOREST
    CANONICAL DISTRIBUTION
    ANT FAUNA
    BIODIVERSITY
    Abstract: 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
    Appears in Collections:[統計學研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML583View/Open


    在NTHUR中所有的資料項目都受到原著作權保護,僅提供學術研究及教育使用,敬請尊重著作權人之權益。若須利用於商業或營利,請先取得著作權人授權。
    若發現本網站收錄之內容有侵害著作權人權益之情事,請權利人通知本網站管理者(smluo@lib.nthu.edu.tw),管理者將立即採取移除該內容等補救措施。

    SFX Query

    與系統管理員聯絡

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback