Disturbance Regimes Predictably Alter Diversity in an Ecologically Complex Bacterial System

Abstract

Diversity is often associated with the functional stability of ecological communities from microbes to macroorganisms. Understanding how diversity responds to environmental perturbations and the consequences of this relationship for ecosystem function are thus central challenges in microbial ecology. Unimodal diversity-disturbance relationships, in which maximum diversity occurs at intermediate levels of disturbance, have been predicted for ecosystems where life history tradeoffs separate organisms along a disturbance gradient. However, empirical support for such peaked relationships in macrosystems is mixed, and few studies have explored these relationships in microbial systems. Here we use complex microbial microcosm communities to systematically determine diversity-disturbance relationships over a range of disturbance regimes. We observed a reproducible switch between community states, which gave rise to transient diversity maxima when community states were forced to mix. Communities showed reduced compositional stability when diversity was highest. To further explore these dynamics, we formulated a simple model that reveals specific regimes under which diversity maxima are stable. Together, our results show how both unimodal and non-unimodal diversity-disturbance relationships can be observed as a system switches between two distinct microbial community states; this process likely occurs across a wide range of spatially and temporally heterogeneous microbial ecosystems.

Importance: The diversity of microbial communities is linked to the functioning and stability of ecosystems. As humanity continues to impact ecosystems worldwide, and as diet and disease perturb our own commensal microbial communities, the ability to predict how microbial diversity will respond to disturbance is of critical importance. Using microbial microcosm experiments, we find that community diversity responds to different disturbance regimes in a reproducible and predictable way. Maximum diversity occurs when two communities, each suited to different environmental conditions, are mixed due to disturbance. This maximum diversity is transient except under specific regimes. Using a simple mathematical model, we show that transient unimodality is likely a common feature of microbial diversity-disturbance relationships in fluctuating environments.

FIG 1

Behavior of the undisturbed control communities over the 32-day experiment. (A) Proportional composition of heterotroph genera across sampling time points. (Only the top 10 most abundant genera are displayed; all other genera are grouped together as “Other.”) Taxonomic annotations represent the most resolved identification based on the Greengenes database. The het1 and het2 OTUs comprise >99% of the sequence reads in their respective genera. (B) The phosphate concentration remained above 15 μM through day 16 and then fell below the detection limit (<1 μM) by day 32 (blue line); the total cell density measured by flow cytometry more than doubled over the same period (green line). (Error bars show standard deviation [SD]; lack of an error bar means the error is smaller than the marker diameter.) (C) Phylum-level community composition of the starting enrichment culture (grown in flask) and undisturbed controls (grown in 96-well plates) through time (green, Cyanobacteria; blue, Proteobacteria; yellow, Bacteroidetes; red, other). Error bars show SD of relative abundances.

FIG 2

Response of microbial community structure to disturbance rate. (A) Principal coordinate plot (PCoA) showing community structure similarity among biomass removal treatments on day 16, colored by disturbance rate. The gray dashed arrow indicates approximate trajectory of samples in ordination space along the disturbance gradient. Asterisks in panel A indicate disturbance treatments with significantly greater variability among replicates than the control (PERMDISP, P < 0.05). (B) Relative abundances of heterotroph genera by biomass removal disturbance rate on day 16. (Only the 10 most abundant genera are shown.) The het1, het2, and het3 OTUs each comprise >99% of the sequence reads that mapped to their respective genera. (C) PCoA showing community structure similarity among UV treatments on day 16 (second experiment), as in panel A. (D) Relative abundances of heterotroph genera across UV disturbance rates on day 16, as in panel B.

FIG 3

Relationship between microbial community alpha diversity (black, Shannon entropy; blue, OTU richness) and disturbance rate. (A) Diversity versus disturbance rate for biomass removal treatments on day 16. (B) Diversity versus disturbance rate for UV treatments on day 16. Error bars show SD.

FIG 4

A simplified two-state competitive Lotka-Volterra model for exploring disturbance-diversity relationships. (A) Dynamics of het1 (red) and het2 (orange) for the undisturbed condition. (B) Dynamics of the resource subject to competition in the undisturbed condition. (C) Dynamics of the species ratio α in the undisturbed condition. (D to F) Dynamics of the species ratio α with increasing frequency of disturbance per panel. Increasing color saturation indicates increasing intensity of disturbance. The dashed horizontal line shows the diversity maximum, where the species ratio alpha is equal to 1. (G) DDR for the model at several time points. These time points can be seen as the vertical lines of matching saturation in panels D, E, and F. Increasing saturation of the gray lines indicates later time points. The jagged structure within each DDR reflects coupling between the oscillations of the Lotka-Volterra model and the frequency of disturbance; we expect these effects are exaggerated by the simple structure of the model.

FIG 5

Effect of disturbance frequency and intensity on Shannon diversity for both the model and experiments. (A) Shannon entropy heat map over disturbance intensity and frequency axes for the Lotka-Volterra model. White lines show hyperbolic disturbance rate isoclines. (B) Shannon entropy in disturbance experiments. Shown are cases where multiple combinations of disturbance frequency and intensity gave equivalent disturbance rates. The x-axis labels designate the disturbance type (biomass removal or UV), the disturbance intensity (5 or 10 [representing the percentage of volume or minutes of exposure, respectively]), and the frequency (1, 0.5, or 0.25 day⁻¹). Error bars show standard deviation. ns, not significant.