Accessing the rare biosphere of acidobacterial communities

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The first analysis addressed the problem of "real richness" to rule out the possibility of detection artificial events caused by imbalanced sampling completeness. Here, Chao1 estimation was done on observed, non-rarefied richness S, as Chao1 relies on the abundance of singleton species per sample.

It turned out that – in accordance with the observed rarefaction curves – seasonal progression of observed (Sobs) and estimated species richness (SChao1) mirrored each other (Figure 47). This is strong evidence for uniform singleton abundance and indicates very high sample completeness. In a second step, species accumulation curves were calculated to test if the six subsets (by sampling season) yielded species saturation as well. While this test partially overlaps with assessing single measures of α-diversity, it does add a β-diversity component based on raw species richness. It was evident, that each of the six subsets offered high richness coverage, indicated by the asymptotic progression of the species accumulation curves (Figure 48).

Overlapping confidence intervals demonstrated that 20 samples were needed to distinguish June from the other communities, i.e. the detection of blooming events affecting species richness. For pairwise comparisons excluding June, 40 samples are required to escape overlaps of confidence intervals, with November, August and May communities never being distinguishable in this type of analysis. The high sample completeness, as supported by both rarefaction approaches and Chao1 estimation, lead to the decision to use unrarefied, relative data for OTU modelling, as rarefying the OTU table would have resulted in a data loss which would have most likely affected rare biosphere (Appendix 8.12), while for the extrapolated samples data would have been made up.

Having established the high sample completeness/coverage, the distribution between generalist and specialist species was further explored. Surprisingly, no gradient in site occupancy existed; instead accumulation between the two extremes was found (Figure 49). The distribution histogram including data of the entire sampling season was not different from those depicted (not shown). 303 OTUs were observed in 90% of all sites, of which 252 OTUs appeared in at least 354 sites. On the contrary, 593 OTUs were detected in less than 10% of all sites. Combined, these two fractions represented 74.1% of the observed OTUs. This finding raised two questions: i) Are subgroups evenly represented between specialists and generalist OTUs? ii) Do season-specific rare communities exist?

The first question was empirically addressed by visualizing distribution densities, additionally taking into account read abundances (Figure 50). A clear log-linear relation between total read abundance and OTU presence was found for most subgroups with a balanced distribution of generalist and rare OTUs, but comparably less species in-between, as already demonstrated in Figure 49. Few subgroups featured species with higher abundances than expected from their overall presence. These OTUs are drivers of β-diversity turnover, as they likely readily respond to local fluctuations in their environment.

Examples are OTUs of subgroup 3 and 7. Most dominant generalists are found in the abundant subgroups (6, 4, 3, and 17), but also appear in the lesser present subgroups e.g. 11 or 18. In these

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cases, they represent more than 99% of all reads affiliated with their subgroup. The relevance of the hot spot OTUs in June is highlighted by the fact that subgroups 1, 2, 12, and 13 were nearly extinct from the dataset after removing those sites. These subgroups did encompass ubiquitous species, but those were 100-fold less abundant than the dominating OTUs and did not exceed the total abundance of the most frequent hot spot OTUs.

I then addressed the second question whether or not distinct rare communities existed per sampling date, which would point at changing micro-niches which are occupied by specific subcommunities or a general succession of rare species. VENN-partitioning, however, revealed that this was not the case (Figure 51). 729 OTUs were present in all six months, and only June – due to the hotspot communities – featured a high number of season-specific OTUs. Fractions of shared OTUs were generally low for all comparison levels, except between June and November, explained by the presence of spurious fractions of some of the hot spot OTUs in two sites in November (Figure 18). In regards to the ecological role of rare OTUs, a diverse set of environmental niches was demonstrated by adjusting boosted GAMs for zero inflated abundance distribution (see 3.2). It was also evident that rare species were exhibiting much larger effect ranges than dominant species. Here, I emphasize on the contribution of rare biosphere to overall trends in high dimensional datasets.

For this, the OTU dataset was arbitrarily split in three subsets (coined "Dominant", "Rare", and "Very rare", respectively) and RDA was performed on all three sets with forward selected environmental variables (Figure 52). The RDA with the dominant species yielded the highest amount of significantly explaining environmental variables. As this dataset features very few hot spot OTUs, the ordination space is relaxed and the blooming phenomenon is not visually detectable. For the rare dataset, graminoid biomass became a main driver of the community switches in June, and the Nmic/pH gradient was present as well, to which the bulk community OTUs again aligned. However, the amount of explained variance (6.9%) was low compared to the dominant-biased RDA (22.4%), and the ordination space is smaller. Simultaneous regression of both sets would likely mask the strong effect of graminoid biomass. The dataset containing the least abundant OTUs lead to a significant (p<0.05) RDA, but the low amount of explained variance (1.2 %), the presence of just two significant axes – both linear combinations of the same variables (pH and graminoid biomass) - and the highly reduced ordination space made this analysis difficult to assess. In general, the excess of zero observations does not cooperate well with RDA (as it is based on linear regression and works with Euclidian distances).

These results are in general accordance to the hypothesis that the "hot moments" in June are induced by graminoid plants and nicely demonstrate the importance of sophisticated data mining.

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Figure 45. Rarefaction curves based on Hill numbers, separated by sampling date. Each panel corresponds to one of three Hill numbers: ⁰D (top panel: unweighted species richness), ¹D (center panel: linearized Shannon Entropy), and ²D (bottom panel: linearized Simpson diversity). Each individual curve represents a sample, with points indicating sample completeness. The end of each x-coordinate marks the endpoint of rarefaction (with twice the smallest sample size as target reference). Dashed lines indicate individual samples, which were extrapolated to the reference sample (here displayed by month; the procedure was repeated for the smallest sample size in the whole dataset). Calculations were made according to Chao, et al. (2013) and graphics were generated with the iNEXT package. Raw data, along with rarefaction plots for each individual sample, is available in the electronic appendix ("Additional results - Rarefaction results").

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Figure 46. Size- and coverage-based rarefaction. The right plot shows the conventional size-based rarefaction curves for each sample. Dotted lines represent small samples which were extrapolated to the reference sample size (36,660 reads).

The left panel present coverage-based saturation curves. The small panel depicts sample completeness as a function of coverage versus reads, which represents a bridge between both approaches (see Chao & Jost, 2012). Curve inflexion points occur between 200-300 species in each case. Size-based rarefaction does not reach full, but approximate sample completeness, whereas coverage-based rarefaction represents full coverage for each sample.

Figure 47. Seasonal progression of observed (Sobs), estimated (SChao1), and rarefied species richness (⁰D), along with estimates for the linearized Shannon entropy (¹D), and Simpson diversity (²D). ⁰D and ²D were discussed in chapter 2.6.2.

The Shannon entropy is shown for completeness, as it resembles the Simpson diversity, i.e. no differences between

"typical" and "dominant" species were detected. Red asterisks indicate significant transitions between samples dates at p<0.05.

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Figure 48. Species accumulation curves for six subassemblages representing the six sampling dates. The curves show the amount of new species which are found when adding new samples to the overall community with 999 permutations.

Shaded areas indicated 95% confidence intervals.

Figure 49. Histograms of species distribution per month. X-coordinates show the site occupancy, with bars representing the amount of species per bin of 2 samples.

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Figure 50. Species distribution of Acidobacteria in the ScaleMic plot. Each dot represents a OTU by plotting its accumulated read abundance (all sites) over the site occupancy. The y-coordinate is log-transformed. Each panel represents one clade/subgroup in Acidobacteria.

Figure 51. VENN-partitioning for the six subcommunities representing one sampling dates. Each month is represented by a differently colored object. Numbers in brackets indicate total OTUs found in all samples per month (see also Figure 48).

Numbers in each field of the VENN-diagram give the amount of shared OTUs between given months.

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Figure 52. RDA performed on three subsets of the acidobacterial OTU dataset, sorted and split by mean abundances. Left panel: 340 most dominant, cosmopolitan OTUs, center panel: 340 rare OTUs, right panel: 548 very rare OTUs. CI abbreviates constrained inertia, i.e. the amount of explained variance.

In regards to seasonality, bacterial species may seasonally switch between rare and abundant in aquatic systems, with higher relative activity (i.e. rRNA/rDNA ratios) during the stages of lesser abundances (Campbell, et al., 2011). However, in the acidobacterial dataset of the ScaleMic study, we could not find this feature, as except for the hot spot OTUs, rare taxa stayed rare across the sampling season, in contrast to spurious Nitrospira OTUs (Stempfhuber, et al., 2015), which did exhibit changing abundance ratios over the season.

Fuhrman (2009) defines the boundary between common and rare microbial species as between 0.1%

and 1% of the total community. In this context, all but seven acidobacterial species would qualify as rare biosphere. With higher resolving sequencing techniques, community compositions appear to be less even, and efforts have been made to define rare species reflecting recent technological advancements (Shade, et al., 2014).

185 3.5.2.1 References

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