Journal article
A two-stage probabilistic approach to multiple-community similarity indices
Biometrics, Vol.64(4), pp.1178-1186
2008
Abstract
A traditional approach for assessing similarity among N (N > 2) communities is to use multiple pairwise comparisons. However, pairwise similarity indices do not completely characterize multiple-community similarity because the information shared by at least three communities is ignored. We propose a new and intuitive two-stage probabilistic approach, which leads to a general framework to simultaneously compare multiple communities based on abundance data. The approach is specifically used to extend the commonly used Morisita index and NESS (normalized expected species shared) index to the case of N communities. For comparing N communities, a profile of N - 1 indices is proposed to characterize similarity of species composition across communities. Based on sample abundance data, nearly unbiased estimators of the proposed indices and their variances are obtained. These generalized NESS and Morisita indices are applied to comparison of three size classes of plant data (seedling, saplings, and trees) within old-growth and secondary rain forest plots in Costa Rica. © 2008, The International Biometric Society.
Details
- Title
- A two-stage probabilistic approach to multiple-community similarity indices
- Authors
- A Chao (Author) - National Tsing Hua University, TaiwanL Jost (Author) - Via a Runtun, EcuadorS C Chiang (Author) - National Tsing Hua University, TaiwanY H Jiang (Author) - National Tsing Hua University, TaiwanRobin L Chazdon (Author) - University of Connecticut, United States
- Publication details
- Biometrics, Vol.64(4), pp.1178-1186
- Publisher
- Wiley-Blackwell Publishing Ltd.
- Date published
- 2008
- DOI
- 10.1111/j.1541-0420.2008.01010.x
- ISSN
- 0006-341X
- Organisation Unit
- Tropical Forests and People Research Centre; University of the Sunshine Coast, Queensland; Forest Research Institute
- Language
- English
- Record Identifier
- 99451278902621
- Output Type
- Journal article
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