Richness and composition
The numbers of ESVs/OTUs/morphospecies per sample for metabarcoding and
microscopy data are summarized in Table 1. The numbers of ESVs/OTUs per
sample between HTS 10 and HTS 0.5 treatments demonstrated a strong
positive correlation (Spearman R > 0.863; Fig. 2a-c; Fig.
S1), and no significant differences in the (ESV/OTU) richness (Fig. 2d).
Accordingly, the intra-pipeline comparisons of HTS 10 and HTS 0.5 data
(20 comparable samples; Table 1) demonstrated high proportions of shared
ESVs/OTUs (63.9-87%; Fig. 3). For the genus level comparisons across
metabarcoding data sets, ESVs and OTUs were annotated to genus level
only when the similarity and coverage of the representative read of
ESV/OTU was ≥ 95% against a reference sequence. Similarly, a large
proportion of genera were shared between HTS treatments (87.7-92.2%;
Fig. 3). Interestingly, inter-pipeline comparisons revealed that higher
proportion of genera was shared between HTS 0.5 treatments compared with
HTS 10 (87.7% vs . 76.4%; Fig. S2). Compared to data generated
with the OTUs pipelines, the data from the ESVs pipeline harbored a
higher number of different genera (67 vs . 62 for HTS 10 and 69vs . 65 for HTS 0.5; Fig. S2). For the HTS 10 data, the unique
genera (8 genera; i.e. genera that were identified only in the
corresponding data set) of the ESVs data set represented a total of only
2.67% of sequences (range of <0.001% to 1.44%; Table S3).
For the HTS 0.5 data, the unique genera (4 genera) of the ESVs data set
represented total of less than 0.1% of sequences (range of <
0.001% to 0.016%; Table S3). The data set of 97% OTUs did not contain
any unique genera, and there was only one unique genus for the 95% OTUs
data (Sternimirus , sequence abundance < 1%; Fig. S2;
Table S3).
Morphological examination of the sediment samples recovered a total of
189 diatom taxa from 11 surface sediment samples (Table 1), which
included 59 genera (Fig. 4; Table S3). Unlike the per sample richness
correlations observed between the metabarcoding treatments (Fig. 2a-c),
correlations were not obvious between richness values from the
microscopy and metabarcoding data (P > 0.398 for all cases;
Fig. S3). Across treatments, detected species richness by microscopy
differed significantly only from the ESVs data (Fig. 2d). The detected
composition of genera by microscopy were compared with metabarcoding
data, which harbored 54 different genera for the ESVs data, 49 and 50
genera for the 97% and 95% OTU data, respectively. The genus level
comparisons (among 11 corresponding samples) revealed that 50.7-54.3%
of genera were shared between microscopy and metabarcoding treatments
(Fig. 4). Compared with the metabarcoding inventories, the microscopy
data set harbored larger proportion of unique genera (Fig. 4). From
these, the majority were represented in low abundances in the microscopy
data set (< 9 counted valves per sample). However, counts of
the valves assigned to Pseudostaurosira , one of the most abundant
genera that were completely missing from metabarcoding data, was 519
(11.77%) across the microscopy data set.
Comparing the relative abundance of valves and sequences of the matching
genera between microscopy and metabarcoding data, revealed overall
significant positive correlations (Spearman R > 0.317 and P
< 0.023; except for 97% OTUs HTS 10 vs . microscopy
data, where P = 0.067; Fig. S4). The outstanding exceptions werePantocsekiella and Achnanthidium , which had high relative
abundance in microscopy, but low abundance in metabarcoding data (Fig.
S4). Vice versa , Staurosira and Aulacoseira were
found to have high relative abundance in metabarcoding data, but low in
microscopy data (Fig. S4).