A Two-Stage Probabilistic Approach to Multiple-Community Similarity Indices
Version of Record online: 19 MAR 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1178–1186, December 2008
How to Cite
Chao, A., Jost, L., Chiang, S. C., Jiang, Y.-H. and Chazdon, R. L. (2008), A Two-Stage Probabilistic Approach to Multiple-Community Similarity Indices. Biometrics, 64: 1178–1186. doi: 10.1111/j.1541-0420.2008.01010.x
- Issue online: 24 NOV 2008
- Version of Record online: 19 MAR 2008
- Received May 2007. Revised December 2007. Accepted December 2007.
- Abundance data;
- Beta diversity;
- Morisita index;
- NESS index;
- Shared species;
- Species overlap
Summary 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.