Microbial Functional Diversity Correlates with Species Diversity along a Temperature Gradient

Abstract

Microbial community diversity is often correlated with physical environmental stresses like acidity, salinity, and temperature. For example, species diversity usually declines with increasing temperature above 20°C. However, few studies have examined whether the genetic functional diversity of community metagenomes varies in a similar way as species diversity along stress gradients. Here, we investigated bacterial communities in thermal spring sediments ranging from 21 to 88°C, representing communities of 330 to 3,800 bacterial and archaeal species based on 16S rRNA gene amplicon analysis. Metagenomes were sequenced, and Pfam abundances were used as a proxy for metagenomic functional diversity. Significant decreases in both species diversity and Pfam diversity were observed with increasing temperatures. The relationship between Pfam diversity and species diversity followed a power function with the steepest slopes in the high-temperature, low-diversity region of the gradient. Species additions to simple thermophilic communities added many new Pfams, while species additions to complex mesophilic communities added relatively fewer new Pfams, indicating that species diversity does not approach saturation as rapidly as Pfam diversity does. Many Pfams appeared to have distinct temperature ceilings of 60 to 80°C. This study suggests that temperature stress limits both taxonomic and functional diversity of microbial communities, but in a quantitatively different manner. Lower functional diversity at higher temperatures is probably due to two factors, including (i) the absence of many enzymes not adapted to thermophilic conditions, and (ii) the fact that high-temperature communities are comprised of fewer species with smaller average genomes and, therefore, contain fewer rare functions. IMPORTANCE Only recently have microbial ecologists begun to assess quantitatively how microbial species diversity correlates with environmental factors like pH, temperature, and salinity. However, still, very few studies have examined how the number of distinct biochemical functions of microbial communities, termed functional diversity, varies with the same environmental factors. Our study examined 18 microbial communities sampled across a wide temperature gradient and found that increasing temperature reduced both species and functional diversity, but in different ways. Initially, functional diversity increased sharply with increasing species diversity but eventually plateaued, following a power function. This pattern has been previously predicted in theoretical models, but our study validates this predicted power function with field metagenomic data. This study also presents a unique overview of the distribution of metabolic functions along a temperature gradient, demonstrating that many functions have temperature "ceilings" above which they are no longer found.

Keywords: functional diversity; geothermal spring; hot spring; metabolic diversity; metagenomic diversity; species diversity; taxonomic diversity; temperature.

FIG 1

Alpha diversity of Bacteria based on 16S rRNA gene amplicon OTUs (a to f) or amplicon sequence variants (ASVs) (g to i) versus temperature in sediments collected from the Dewar Creek hot spring (open triangles) and all 9 springs sampled in this study (black circles). Panels show observed OTUs (a, d, and g), Chao1_OTU (b, e, and h), and Shannon_OTU (c, f, and i) versus sample temperature. Diversity indices were calculated using 13,460 (a to f) or 8,000 (g to i) 16S rRNA gene sequence reads per sample, amplified with a Bacteria-specific primer set, and clustered at 97% (a to c) or 99% (d to i) identity. The best-fit Gaussian least-squares regressions to the Dewar Creek data set (dashed lines, light text) and the 9 spring data set (solid lines, bold text) are shown along with regression fit parameters.

FIG 2

Alpha diversity indices calculated on Pfams detected in metagenomes of geothermal springs. Panels show observed Pfams (a), Chao1_Pfam (b), and Shannon_Pfam (c) versus sample temperature. Diversity indices were calculated using 600,000 Pfam-assigned reads per sample. The solid lines represent the best-fit nonlinear Gaussian least-squares regressions to the data.

FIG 3

Functional diversity of Bacteria based on Pfam analysis versus taxonomic diversity based on 16S rRNA gene amplicon OTU (a to c) or ASV (d to f) analysis in geothermal spring sediments. Panels show observed occurrences (a and d), Chao1 (b and e), and Shannon (c and f). The solid lines represent the best-fit power (FD = cSD^a) regressions to the data where FD is functional diversity (y axis) and SD is species diversity (x axis).; c and a indicate the best-fit values to the model. Colors of data points correspond to temperature in degrees Celsius.

FIG 4

(a) Venn diagram comparing the number of Pfams detected in three pools of samples, grouped by temperature. Each pool included 6 samples each rarefied to 600,000 Pfam-assigned reads. Exact sample temperatures are listed in Table 3. (b) Principal-coordinate analysis (PCoA) plot showing samples separating by temperature, in degrees Celsius, along PC1.