Context-dependent dynamics lead to the assembly of functionally distinct microbial communities

Abstract

Niche construction through interspecific interactions can condition future community states on past ones. However, the extent to which such history dependency can steer communities towards functionally different states remains a subject of active debate. Using bacterial communities collected from wild pitchers of the carnivorous pitcher plant, Sarracenia purpurea, we test the effects of history on composition and function across communities assembled in synthetic pitcher plant microcosms. We find that the diversity of assembled communities is determined by the diversity of the system at early, pre-assembly stages. Species composition is also contingent on early community states, not only because of differences in the species pool, but also because the same species have different dynamics in different community contexts. Importantly, compositional differences are proportional to differences in function, as profiles of resource use are strongly correlated with composition, despite convergence in respiration rates. Early differences in community structure can thus propagate to mature communities, conditioning their functional repertoire.

Fig. 1. Microcosm communities approach distinct equilibria.

a Relative abundances of the top 20 Amplicon Sequence Variants (ASVs) across the ten microcosms during the course of the serial transfer experiment. ASVs are listed one time each on the bar plot, with taxonomic classification in the legend. The DNA concentrations for each timepoint are graphed above as points, with a Loess fit as a solid line. b Communities change quickly and then stabilize in the two-dimensional Non-metric Multidimensional Scaling (NMDS) plot of Bray–Curtis dissimilarities of the microcosm community compositions. The microcosm name is listed in black next to the Day 0 point for each microcosm, and the lines connect the timepoints. Colored numbers indicate the mean effective number of species for each community post Day 21. c The Bray–Curtis dissimilarity of ASV relative abundances between adjacent days decreases over the course of the experiment. The thick black line shows a Loess fit to all data points, and the thin line marks Day 21. d At the end of the experiment, most ASVs were present in only one microcosm (top), but ASVs present in more microcosms tended to have higher mean relative abundances (bottom), shown as black circles with ±1 standard deviation error bars.

Fig. 2. Early richness predicts final richness and communities equilibrate at a common rate.

a Richness over time for each microcosm community. b Richness on Day 3 (1st timepoint) is strongly correlated with richness on Day 63 (21st timepoint). Linear model: R² = 0.9008, p = 1.714 × 10⁻⁵. c The probability of going extinct at transfer t. Colored lines are probability densities for individual microcosms. Black points are averages across microcosms, the black line is the maximum likelihood distribution with a common parameter across microcosms (see main text and Methods) given by the inverse mean extinction time. d Richness over time for each microcosm community, normalized by the richness on Day 3. The black line shows the exponential decay curve parametrized by the mean proportion of surviving ASVs (from b) and the common ASV extinction rate (from c).

Fig. 3. Species dynamics are context dependent.

a The y-axis lists all ASVs shared by at least two microcosms. Colored points mark which day the ASV was lost from that particular microcosm. Points at the far right of the graph are ASVs that persisted to the end of the serial transfer experiment. Red stars mark the ASVs shown in b. b Three examples of ASV dynamics in the different microcosms over time, where ASVs were lost early in some microcosms but persisted until the end in others. c Unweighted UniFrac distances in filtered and unfiltered communities (repetitions) are strongly correlated, highlighting that community dynamics are reproducible given the same inoculum. Mantel test, r = 0.85, p = 1 × 10⁻⁴; Linear model, R² = 0.78, p = 2.2 × 10⁻¹⁶. Samples from Days 0–21 are included here, to capture dynamics up until equilibration. Hexplot color indicates the density of values in each hexagon. d Probability density of correlation coefficients between the same ASVs in repetitions started from the same inocula, between the same ASVs in distinct microcosms, and between randomly chosen ASVs. Correlation coefficients are large when comparing replicate communities started from the same pitcher plant inocula. Between different microcosms, there is a moderate increase in the number of positive correlation coefficients relative to random pairs. e Mean Bray–Curtis dissimilarities between final community compositions is correlated with mean cosine distances between trajectories of the same ASV.

Fig. 4. Community composition strongly correlates with functional activity.

a Variance in percent CO₂ among the ten microcosms over the course of the serial transfer experiment. b Two-dimensional NMDS plot of the Bray–Curtis dissimilarities of functional activity (substrate use) as measured by EcoPlates. The microcosm name is listed in black next to the Day 0 point, and the lines connect the timepoints. c Procrustes rotation of the composition NMDS plot with the function NMDS plot for Day 63 (Procrustes correlation = 0.8991, p = 0.001). d Plot of Bray–Curtis dissimilarities in composition between all measured timepoints across all microcosms compared with the dissimilarities in functional profile for the corresponding samples (Mantel test r = 0.6403, p = 1 × 10⁻⁴), squared to better illustrate the spread of points. e Endochitinase activity over time for the five microcosms that strains were cultured from (top row) and frequency of the ASVs over time that map to strains with measurable endochitinase activity (bottom row). Endochitinase activity is shown in units/mL for the strains/ASVs in the bottom row by a gradient from gray (low activity) to red (high activity).

Fig. 5. Functional convergence depends on the type of function.

Microcosms with the same environmental conditions assemble communities with distinct equilibria due to historical contingencies (i). Core functions such as respiration converge in a common environment, in agreement with the common notion that function and taxonomy are decoupled (ii). However, our results show key functional differences in ‘auxiliary’ metabolic functions, such as chitinase activity or carbon source preference, are strongly coupled to community composition (iii).