Diversity, Productivity, and Stability of an Industrial Microbial Ecosystem

Abstract

Managing ecosystems to maintain biodiversity may be one approach to ensuring their dynamic stability, productivity, and delivery of vital services. The applicability of this approach to industrial ecosystems that harness the metabolic activities of microbes has been proposed but has never been tested at relevant scales. We used a tag-sequencing approach with bacterial small subunit rRNA (16S) genes and eukaryotic internal transcribed spacer 2 (ITS2) to measuring the taxonomic composition and diversity of bacteria and eukaryotes in an open pond managed for bioenergy production by microalgae over a year. Periods of high eukaryotic diversity were associated with high and more-stable biomass productivity. In addition, bacterial diversity and eukaryotic diversity were inversely correlated over time, possibly due to their opposite responses to temperature. The results indicate that maintaining diverse communities may be essential to engineering stable and productive bioenergy ecosystems using microorganisms.

FIG 1

Sample dissimilarities. Panel a shows the Bray-Curtis dissimilarities among the samples between the bacterial (16S) samples, and panel b shows the dissimilarities between the eukaryotic (ITS2) samples. Seasons are denoted with a color bar atop the x axis as fall (orange), winter (blue), spring (green), and summer (silver).

FIG 2

Area plots. The plots depict the relative abundances of various taxa and are organized with increasing level of rank in their corresponding taxonomy for 16S (left-hand side) and ITS2 (right-hand side) compositions. Plots a and b represent the relative abundances at the phylum and kingdom levels, whereas plots c and d and e and f further analyze the compositions at the class and genus levels, respectively. Taxa that had no information at their respective rank are shown in parentheses using the lowest available taxonomic rank. The black shading between days 152 and 190 represents the time interval that includes the 4 time points of fungicide application.

FIG 3

Diversity patterns. Panels a and b show the diversity patterns of bacterial (16S) and eukaryotic (ITS2) data, respectively, over time. Panels c (16S) and d (ITS2) show the distributions of the diversities at the two different time periods.

FIG 4

Correlation matrix of all phenotypic variables: Ecosystem variables forming a clique using the CAST algorithm are represented with a single color in the color bar, as also suggested by the orange correlation blocks in the matrix. Cell colors are based on the Pearson correlation coefficients according to the given color map.

FIG 5

Pond ecosystem and taxonomic composition correlations. Panel a shows the correlations between ecosystem clusters and diversities at the kingdom level, whereas panel b shows bacterial phylum relative abundance correlations.

FIG 6

Correlations of the productivity mean and standard deviation versus algal diversity. The scatter plot shows the correlations between algal diversity versus the mean and standard deviation of productivity measurements centered around genomic sampling days for 2 h = 8 weeks (see Materials and Methods) using the regression lines. Algal diversity is positively correlated with mean productivity (Pearson R = 0.33, P = 1.1 × 10⁻¹) and negatively correlated with standard deviation in productivity (Pearson R = −0.6, P = 1.9 × 10⁻³).