Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/86221
標題: Abundance-Based Similarity Indices and Their Estimation When There Are Unseen Species in Samples
作者: Chao, Anne
Chazdon, Robin L.
Colwell, Robert K.
Shen, Tsung-Jen
關鍵字: Models, Statistical;Species Specificity;Trees;Tropical Climate;Biodiversity;Biometry
Project: Biometrics, Volume 62, Issue 2, Page(s) 361-371.
摘要: 
A wide variety of similarity indices for comparing two assemblages based on species incidence (i.e., presence/absence) data have been proposed in the literature. These indices are generally based on three simple incidence counts: the number of species shared by two assemblages and the number of species unique to each of them. We provide a new probabilistic derivation for any incidence-based index that is symmetric (i.e., the index is not affected by the identity ordering of the two assemblages) and homogeneous (i.e., the index is unchanged if all counts are multiplied by a constant). The probabilistic approach is further extended to formulate abundance-based indices. Thus any symmetric and homogeneous incidence index can be easily modified to an abundance-type version. Applying the Laplace approximation formulas, we propose estimators that adjust for the effect of unseen shared species on our abundance-based indices. Simulation results show that the adjusted estimators significantly reduce the biases of the corresponding unadjusted ones when a substantial fraction of species is missing from samples. Data on successional vegetation in six tropical forests are used for illustration. Advantages and disadvantages of some commonly applied indices are briefly discussed.
URI: http://hdl.handle.net/11455/86221
ISSN: 0006341X
DOI: 10.1111/j.1541-0420.2005.00489.x
Appears in Collections:統計學研究所

Show full item record
 

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.