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Fig. S1 NeighborNet split network based on MCA or 97% universal threshold using the Site dataset.

Fig. S2 NeighborNet split network based on MCA or 97% universal threshold using the Local dataset.

Fig. S3 NeighborNet split network based on MCA or 97% universal threshold using the Regional dataset.

Fig. S4 Procrustes analysis of the Site and Regional datasets.

Fig. S5 Supervised learning results of OTU numbers and error rates for the Local dataset.

Table S1 Mantel-r and P-values using the 97% universal threshold and the monophyletic clade approach (MCA) on the three datasets

Table S2 ANOSIM and BEST analyses for the three datasets using the monophyletic clade approach (MCA) and the 97% universal threshold to delineate OTUs

Table S3 Correlations beta-diversity distance matrices and Procrustes analyses between the monophyletic clade approach (MCA) and the 97% approach

Methods S1 This outlines the sequence processing and analysis associated with the MCA and 97% OTU delineation methods.