Analysis of Similarity (ANOSIM)

ANOSIM is a distribution-free method of multivariate data analysis widely used by community ecologists. It is primarily employed to compare the variation in species abundance and composition among sampling units (= Beta diversity) in terms of some grouping factor or experimental treatment levels. ANOSIM has gained widespread use among marine ecologists because of the highly popular, easy to use PRIMER for Windows software package. ANOSIM is simply a modified version of the Mantel Test based on a standardized rank correlation between two distance matrices. It uses a model matrix coding for group membership (or treatment levels) as the explanatory variable in an ANOVA-like analysis. Recent work (see references below) has shown distance-based methods (e.g., ANOSIM, Mantel Test, BIOENV, BEST) are inappropriate for analyzing Beta diversity because they do not correctly partition the variation in the data and do not provide the correct Type-I error rates. ANOSIM and Mantel tests should therefore be restricted to analyzing the variation of Beta diversity, not Beta diversity itself. Raw data based approaches offer a more appropriate and more powerful alternative for analysis of Beta diversity. These include NP-MANOVA (perMANOVA), redundancy analysis (RDA), distance-based RDA (db-RDA, DISTLM), and canonical analysis of principal coordinates (CAP). Software implementing these methods include: (1) Fathom Toolbox for Matlab, (2) vegan package for R, and (3) PERMANOVA+ for PRIMER.




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Anderson, M. J., T. O. Crist, J. M. Chase, M. Vellend, B. D. Inouye, A. L. Freestone, N. J. Sanders, H. V. Cornell, L. S. Comita, K. F. Davies, S. P. Harrison, J. B. Kraft, J. C. Stegen, and N. G. Swenson. 2011. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecology Letters 14: 19–28.

Laliberté, É. 2008. Analyzing or explaining beta diversity: Comment. Ecology 89: 3232-3237.

Legendre, P. 2008. Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. Journal of Plant Ecology 1(1): 3-8. doi:10.1093/jpe/rtm001

Legendre, P., D. Borcard, and P. R. Peres-Neto. 2005. Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecological Monographs 75(4): 435-450.

Legendre, P., D. Borcard, and P. R. Peres-Neto. 2008. Analyzing or explaining beta diversity: Comment. Ecology 89: 3238-3244.

Legendre, P., and L. Legendre. 2012. Numerical ecology, 3rd English edition. Developments in Environmental Modelling, Vol. 24. Elsevier Science BV, Amsterdam. xiv + 990 pp.

McArdle, B. H. and M. J. Anderson. 2001. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 290-297.

Pélissier, R., P. Couteron, and S. Dray. 2008. Analyzing or explaining beta diversity: Comment. Ecology 89: 3227-3232.

Tuomisto, H., and K. Ruokolainen. 2008. Analyzing or explaining beta diversity: Reply. Ecology 89: 3244-3256.