Fig 1.
Effect of sequence processing methodology on the rank abundance of operational units.
The relative abundance of ASVs (obtained from DADA2) and OTUs defined by 97% or 99% identity (obtained from MOTHUR). Units were sorted according to their rank of abundance and represented separately for each community type (a: mussel gut microbiome, b: sediment, c: seston). Inserts focus on the 500 most abundant operational units. The evenness of the relative abundance of the operational units computed Bulla’s O are displayed on each plot.
Fig 2.
Effect of sequence processing methodology on alpha diversity metrics for microbiome analyses.
Measure of taxonomic richness (expressed as the number of OTUs or ASVs and Shannon alpha diversity depending on the methodology used, namely ASV, 97%-OTU, and 99%-OTU, at three different levels of rarefaction (1,000; 2,000 and 3,000 sequences per sample) within each sample type studied (a-c). To ease the visualization of differences across methods and rarefaction levels, the y-axis of plots has been log transformed. The effect of methodological choices on other alpha diversity metrics is available in S3 Fig in S1 File.
Table 1.
Correlation between alpha diversity metrics between OTU- and ASV-based datasets across every rarefaction level tested.
Fig 3.
Effect of sequence processing methodology on ecological patterns of microbial alpha diversity.
Distribution of alpha diversity values on each river is represented by a boxplot. Kruskal-Wallis tests assessed significant differences between rivers with p-values indicated on each panel. Types of communities are represented using horizontal panels. Patterns within ASVs, 97%-OTUs, and 99%-OTUs are compared using the three vertical panels on each plot. The richness index (left plot) and Shannon alpha diversity (right plot) are represented for each community type and sequence processing methodology.
Table 2.
Correlation between beta-diversity metrics between OTU- and ASV-based datasets across every rarefaction level tested.
Fig 4.
Effect of sequence processing on the ecological signal based on beta-diversity estimates in microbial communities.
The intensity of the effect of (a) community type, (b) sampling river, and (c) collection site on community structure was assessed using separated PERMANOVAs for each factor, and each combination of sequence processing method x rarefaction level x index of dissimilarity. All rarefaction levels are aggregated on this figure. Differences across rarefaction levels are reported in S7 Fig in S1 File.
Fig 5.
Composition of mussel gut microbiome, sediment, and seston communities found using OTU-based and ASV-based methodologies.
(a) The relative abundance of the 11 most common bacterial classes in datasets rarefied to 2,000 sequences/sample. (b) Heatmap representing agreement of the 20 most detected bacterial genera across methodologies and rarefaction levels. Numbers represent the percentage of those 20 genera in common across the compared treatments (‘shared’); color intensity represents overall Spearman’s correlation coefficient of the shared genera between treatments.