Bacterial Dormancy Is More Prevalent in Freshwater than Hypersaline Lakes

Abstract

Bacteria employ a diverse array of strategies to survive under extreme environmental conditions but maintaining these adaptations comes at an energetic cost. If energy reserves drop too low, extremophiles may enter a dormant state to persist. We estimated bacterial dormancy and identified the environmental variables influencing our activity proxy in 10 hypersaline and freshwater lakes across the Western United States. Using ribosomal RNA:DNA ratios as an indicator for bacterial activity, we found that the proportion of the community exhibiting dormancy was 16% lower in hypersaline than freshwater lakes. Based on our indicator variable multiple regression results, saltier conditions in both freshwater and hypersaline lakes increased activity, suggesting that salinity was a robust environmental filter structuring bacterial activity in lake ecosystems. To a lesser degree, higher total phosphorus concentrations reduced dormancy in all lakes. Thus, even under extreme conditions, the competition for resources exerted pressure on activity. Within the compositionally distinct and less diverse hypersaline communities, abundant taxa were disproportionately active and localized in families Microbacteriaceae (Actinobacteria), Nitriliruptoraceae (Actinobacteria), and Rhodobacteraceae (Alphaproteobacteria). Our results are consistent with the view that hypersaline communities are able to capitalize on a seemingly more extreme, yet highly selective, set of conditions and finds that extremophiles may need dormancy less often to thrive and survive.

Keywords: Great Salt Lake; extremophiles; phosphorus; salinity; seed banks.

FIGURE 1

Extreme hypersaline lakes influenced the composition of active and total bacterial communities. The multivariate ordination was generated using principle coordinate analysis (PCoA) on a sample × OTU matrix of rDNA and rRNA (indicated by dashed lines) community libraries (97% similarity cutoff). Lake abbreviations are as follows: hypersaline lakes—Great Salt Lake, North Arm (NGSL); Great Salt Lake, South Arm (SGSL); Salton Sea (SS); Abert Lake (LA); Mono Lake (ML); and freshwater lakes—Mormon Lake (MRL); Riffe Lake (RL); Arivaca Lake (AL); Lily Lake (LL); and Silverwood Lake (SWL).

FIGURE 2

Heat map showing the distribution of six phyla and three Proteobacteria subclasses that contributed ≥1% of the relative recovery to rDNA and rRNA lake communities. Values are based on means (n = 5) with hierarchal clustering of ecosystem (bottom) and phylum (left).

FIGURE 3

Bacterial dormancy decreased linearly as the cutoffs estimating dormancy increased or became more stringent and was more prevalent in freshwater lakes. Indicator linear regression analysis (R² = 0.82, F_86,8 = 133, P < 0.001, n = 10) was based on the relative recovery of dormant OTUs across a range of cutoffs (0.1–0.9) calculated as 1 - (rRNA recovery/rDNA recovery) for each OTU from rDNA and rRNA community libraries. Dormancy was 16% lower in hypersaline than freshwater lakes measured as the percent decrease between the significantly different y-intercepts (P < 0.001) from the equations for each lake.

FIGURE 4

Bacterial dormancy decreases as lake salinity increases. The indicator regression analysis (R² = 0.96, F_8,1 = 50.0, P < 0.001, n = 10) was based on the relative recovery of dormant OTUs at the cutoff of 0.5 from the equation 1 - (rRNA recovery/rDNA recovery). Dormancy was calculated for each OTU from rDNA and rRNA community libraries.

FIGURE 5

Abundant bacteria were more likely to be active than dormant in hypersaline lakes. OTUs with a relative recovery ≤ 0.1% were considered rare, while OTUs with a relative recovery > 0.1 were considered abundant based on rDNA community libraries (97% similarity cutoff). Values are means ± SEM (n = 5) with different letters indicating significant differences (P < 0.05) based on a two-way ANOVA and a Tukey’s HSD test.

FIGURE 6

Heat map showing the distribution of abundant active (A) and dormant (B) lake taxa in 16–19 bacterial families. Values are based on means (n = 5) with hierarchal clustering of lakes (bottom) and families (left) that contributed ≥ 1% of the relative recovery to any rDNA lake community.