FIGURE 6.
Beta diversity analyses of species level taxonomic data and level 4 KEGG functional data. Hierarchical clustering of Bray–Curtis distance matrices suggest that samples cluster according to mat type based on taxonomic (A) and functional content (C). Principal coordinate analysis (PCoA) plotting Bray–Curtis distance matrices calculated from taxonomic (B) and functional content (D) also suggest clustering of samples according to mat type. PERMANOVA (adonis) indicates that grouping of samples according to mat type accounts for 50% (B, p = 0.001) of the variation in Bray–Curtis distances based on species level taxonomic data and 67% (C, p = 0.001) of variation based on level 4 KEGG functions.
