Abundance determines the functional role of bacterial phylotypes in complex communities

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Bacterial communities are essential for the functioning of the Earth’s ecosystems1. A key challenge is to quantify the functional roles of bacterial taxa in nature to understand how the properties of ecosystems change over time or under different environmental conditions2. Such knowledge could be used, for example, to understand how bacteria modulate biogeochemical cycles3, and to engineer bacterial communities to optimize desirable functional processes4. Communities of bacteria are, however, extraordinarily complex with hundreds of interacting taxa in every gram of soil and every millilitre of pond water5. Little is known about how the tangled interactions within natural bacterial communities mediate ecosystem functioning, but high levels of bacterial diversity have led to the assumption that many taxa are functionally redundant6. Here, we pinpoint the bacterial taxa associated with keystone functional roles, and show that rare and common bacteria are implicated in fundamentally different types of ecosystem functioning. By growing hundreds of bacterial communities collected from a natural aquatic environment (rainwater-filled tree holes) under the same environmental conditions, we show that negative statistical interactions among abundant phylotypes drive variation in broad functional measures (respiration, metabolic potential, cell yield), whereas positive interactions between rare phylotypes influence narrow functional measures (the capacity of the communities to degrade specific substrates). The results alter our understanding of bacterial ecology by demonstrating that unique components of complex communities are associated with different types of ecosystem functioning.

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Fig. 1: Illustration of the workflow for microbiome association studies in common gardens.
Fig. 2: Associations between bacterial phylotypes and the functional measurements.
Fig. 3: Validation of the functional interactions using community mixture experiments.


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The research was funded by a European Research Council starting grant (311399-Redundancy) awarded to T.B. T.B. was also funded by a Royal Society University Research Fellowship. We are grateful for comments from T. Barraclough and A. Pascual Garcia.

Author information

The research was conceived by T.B. Experimental procedures were undertaken by D.W.R. Analysis and writing was done by T.B. and D.W.R.

Correspondence to Thomas Bell.

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Supplementary information

Supplementary Information

Supplementary Figures 1–7.

Reporting Summary

Supplementary Table 1

P values of the associations between the functional measurements and the phylotypes. The table lists the raw P values for each of the regressions between the 522 phylotypes and the 7 functional measurements across n = 753 communities.

Supplementary Table 2

P values of the functional interactions between the functional measurements and each of the pairs of phylotypes. The table lists the raw P values associated with the interaction term (b3) of a linear regression: y = b0 + b1s1 + b2s2 + b3(s1 × s2), where y is the functional measurement, b0 is the intercept, b1 and b2 are the slopes (coefficients) associated with phylotype 1 (s1) and phylotype 2 (s2), and b3 is the coefficient associated with the interaction between phylotype 1 and phylotype 2 (s1 × s2). The phylotypes analysed in each pairwise interaction are indicated in the rows and columns of the table. The functional measurement is listed in the first column of the table.

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