Patters and processes in the metacommunity structuring of
benthic diatoms
The morphological analysis recovered 40 genera, in contrast to the 90
genera recovered with metabarcoding. We found 98 species based on
morphological identification and 219 species based on metabarcoding. The
comparison between morphological and molecular inventories through Venn
diagrams showed considerable differences between both methods at genus
and species level. We found 10 genera detected only by light microscopy
(of which 8 of them had reference sequences in the database), 60 genera
detected only by metabarcoding and 30 genera (30%) detected by both
methods (Figure 3 a). At species level, we found 55 species
detected only by light microscopy (of which 27 of them had reference
sequences in the database), 176 species detected only by metabarcoding
and 43 species (15.7%) detected by both methods (Figure 3 b).
Stress values in nMDS ordinations for morphological (species–level nMDS
=0.22, genus–level nMDS =0.23) and molecular (species–level nMDS
=0.24, genus–level nMDS =0.22) data suggested a reasonable fit (Clarke,
1993). The Procrustes rotation analysis using nMDS scores(Figure 4) showed that most of the study sites displayed a
relatively low degree of similarity, indicating a poor correspondence in
the compositional variation between morphology–and molecular–based
metacommunities (here, PROTEST for species–level resolution data,\(m_{1-2}\)=0.29 and p=0.33; and PROTEST for genus–level resolution
data, \(m_{1-2}\) =0.30 and a p=0.28). The relatively high procrustean
residuals (red arrows in Figure 4 ) re–emphasised this mismatch
between the ordinal results of the test datasets at different taxonomic
resolutions. Importantly, the direction of the movement and the length
of the arrows in the procrustean plots were associated with the
distribution of both morphology– and molecular–based assemblages. In
this vein, the low correspondence found for both species– and
genus–level data was partially caused by e.g. Navicula
soehrensis , Cyclostephanos invisitatus , Cymbella
subhelvetica , Navicula notha , Tabellaria fenestrata ,Luticola goeppertiana and Amphora indistincta , which were
present exclusively in the morphology–based samples. Moreover,
molecular–based assemblages included a number of taxa that were absent
in morphological identifications such as, Nitzschia fruticosa ,Eunotia arcus , Attheya septentrionalis , Haslea
pseudostrearia , Lucanicum sp ., Leptocylindrus sp . andPseudictyota sp.
Species–level data based on morphological identifications showed the
strongest environment relationship (p≤0.01) of all different study
approaches, and the BIOENV routine selected conductivity, fluorides,
total phosphorus (TP) and total suspended solids (TSS) for the best
environmental distance matrix. Ammonium and TSS related to
species–level data based on DNA metabarcoding of the entire assemblage,
whereas fluorides and TSS structured the genus–level data based on
morphological and molecular approaches, respectively (Table 3) .
According to the non–ranked Mantel tests and partial non–ranked Mantel
tests, correlations between dissimilarity and distance matrices showed
that only environmental distances were significantly correlated with
biological –Jaccard– dissimilarities, whereas geographical distances
were never significantly correlated with compositional variation in
diatom metacommunities (Table 3) . Distance–based redundancy
analysis (db–RDA) showed that half of the environmental variables
selected by BIOENV were significantly related to variation in community
composition, but that only a rather small amount of compositional
variation (in terms of adjusted \(R^{2}\)values; here,\(R^{2}\)<0.3) could be explained by these environmental
variables (Table 4) . Of the four dissimilarity matrices
subjected to db–RDAs, species–level data based on morphological
identifications were best explained by the environmental variables and
the genus–level data based on DNA metabarcoding the worst.
We used Mantel correlograms to examine if there was significant spatial
autocorrelation at any distance class. In this regard, we detected only
very weak, but no significant spatial autocorrelation for morphological
and molecular data at different taxonomic resolutions (Figure
5) . Perhaps more importantly, re–running the analyses with the spatial
turnover component of the Jaccard index did not alter our main results