A two-stage probabilistic approach to multiple-community similarity indices

Area of Science:

• Ecology
• Community Ecology
• Quantitative Ecology

Background:

• Traditional ecological studies often rely on pairwise comparisons to assess community similarity.
• Pairwise comparisons overlook shared information among three or more communities, limiting comprehensive analysis.
• Existing similarity indices may not fully capture complex relationships in multi-community systems.

Purpose of the Study:

• To develop a general probabilistic framework for simultaneous comparison of multiple ecological communities (N > 2).
• To extend the Morisita and normalized expected species shared (NESS) indices to accommodate N-community comparisons.
• To provide a robust method for characterizing species composition similarity across multiple communities.

Main Methods:

• A two-stage probabilistic approach is proposed for multi-community similarity assessment.
• The framework generalizes the Morisita and NESS indices for N communities.
• Development of nearly unbiased estimators for proposed indices and their variances using sample abundance data.

Main Results:

• A profile of N-1 indices is introduced to characterize species composition similarity across multiple communities.
• The generalized NESS and Morisita indices provide a more complete measure of community similarity.
• The method was successfully applied to compare different plant size classes in Costa Rican rainforest plots.

Conclusions:

• The proposed probabilistic framework offers a significant advancement over traditional pairwise comparisons for multi-community analysis.
• Generalized NESS and Morisita indices provide a more accurate and comprehensive understanding of ecological community structure.
• This approach enhances ecological research by enabling more robust comparisons of complex community datasets.