Dispersion and turnover
All microbial communities were underdispersed relative to the null expectation, and 97% of Z-scores were negative. On average, dispersion did not change in response to disturbance for any environment, either immediately or over time (Figure 4, Table S2). We found a decrease in the Bray-Curtis dispersion values for mammalian microbiomes (Figure S5), however this pattern was not present in the Z-scores, indicating that this decrease was due to losses in richness rather than compositional change .
Following disturbance, aquatic microbiomes exhibited positive turnover, tending away from their pre-disturbance controls over time (slope estimate: 0.06 [0.04-0.08]). No temporal patterns were observed in mammalian (slope estimate: -0.02 [-0.04- -0.01]; Figure 5, 6) or soil (slope estimate: 0 [-0.01- 0.02]) microbiomes. We found that mammalian microbiomes exhibited negative turnover when assessed with the raw Bray-Curtis values, tending to recover to their pre-disturbance composition. However, once again, our null model confirmed that this pattern was due to an increase in richness, not due to compositional recovery (Figure S6). Immediate richness loss was not directly related to turnover responses over time, however both parameters were environment-dependent (Figure 6).
DISCUSSION
By investigating patterns of microbial community reassembly at time scales that are relevant to microbiome turnover rates and bacterial life histories (Shadeet al. 2018; Kenney et al. 2020), our study takes advantage of relatively standardized and available microbiome data (i.e., the V3-V4 region of the 16S rRNA marker gene, (Jurburget al. 2020). We found that in general, microbiomes experienced modest richness losses and no change in dispersion following disturbance. We also found environment-specific responses: aquatic microbiomes tended away from their pre-disturbance composition following disturbance, while mammalian and soil microbiomes exhibited no clear patterns. These findings can help reconcile disparate observations previously noted in the literature.
Contrary to our expectation, we only found modest losses in richness following disturbance. On average, only mammalian microbiomes experienced significant richness loss. This loss likely underscores the efficacy of antibiotics and other targeted treatments, which were the disturbance most frequently used to induce disturbances in mammalian microbiomes (Seekatzet al. 2015; Džunková et al. 2016); disturbances in the other environments in our study tended to be untargeted (e.g., inorganic nitrogen and phosphorus inputs in aquatic microbiomes, (Santiet al. 2019); or humic acid amendments in soil, (Liet al. 2019). The host’s response may have also played a role in both the immediate response and the long-term recovery of richness following disturbance. Host responses depend on the degree of richness loss, the identity of the lost taxa (Yaoet al. 2016), and the intensity of disturbance (Van de Guchte et al. 2020), and can exacerbate the effect of disturbance on the microbiome (e.g., through inflammation (Van de Guchte et al. 2020). We found that more strongly disturbed mammalian microbiomes tended to recover their richness more rapidly, and here, too, host behaviors such as eating (Guptaet al. 2017) and socializing (Rauloet al. 2021) may have functioned as mechanisms of active dispersal. The mild decline in richness following disturbance in aquatic and soil microbiomes contrasts the stronger responses found in eukaryotic communities to disturbance (Murphy & Romanuk 2012, 2014). This may be because the disturbances applied in these communities only mildly affected them, or because rare taxa were disproportionately affected and we were unable to detect their decrease due to the conservative approaches selected for sequence processing.
Our work further highlights the need to reframe the consequences of disturbances in the microbiome (Buma 2021). We included a wide range of disturbances that are not usually considered in the eukaryotic literature. For example, when sterilized, organic amendments represent a novel source of resources, but when applied unsterilized, they also include a potentially invasive community, a scenario that deviates from the classic invasion literature (Rilliget al. 2015). Furthermore, the duration of disturbances varied, especially relative to bacterial life histories and ecologies (Kenneyet al. 2020). Pulse disturbances which last multiple days may encompass multiple life cycles for many microbial taxa. Similarly, disturbances which may be considered long-term changes for macroorganisms (i.e., oil pollution), may represent short-term resource pulses for oil-degrading bacteria. In a world in which microbiomes are exposed to increasing disturbance pressures, developing a set of descriptors for disturbances based on their effect on the microbiome’s niche space and competitive landscape is urgently needed.
We found no patterns in dispersion, either immediately after or over time following disturbance, in any environment. While changes in dispersion are often reported in the microbial literature (Ferrenberget al. 2013; Lavrinienko et al. 2020; Neely et al.2021), dispersion is generally measured as pairwise Bray-Curtis distances among experimental or field replicates, and thus confound changes in richness with compositional changes (Chaseet al. 2011; Stegen et al. 2013). Our null modelling approach allowed us to partition these two sources of change, revealing that in general. compositional dispersion does not consistently increase or decrease following disturbance, even when richness changes (i.e., in mammalian microbiomes). In particular, despite recovering their richness, mammalian microbiomes did not recover their composition, in line with findings across a wide range of ecosystems (Hillebrand & Kunze 2020). Whether these altered microbial community compositions are important for the maintenance of community functions (Nemergutet al. 2014) and resilience (Paineet al. 1998) is an open, but pressing issue.
Surprisingly, aquatic microbiomes tended to become more dissimilar from their pre-disturbance compositions over time. Due to the different temporal sampling schemes included in this synthesis, it is not possible to determine whether the communities were generally drifting towards a specific composition. The observed pattern may be due to the high connectivity of aquatic microbiomes (Mestreet al. 2018), where dispersal limitation is greatly reduced relative to other microbiomes and nutrients are constantly mixed, facilitating the random recolonization of taxa following disturbance. Indeed, in the highly heterogeneous soil environment, microbiomes exhibited little consistent responses to disturbance, likely due to the extreme diversity and heterogeneity found in this system (Rilliget al. 2017), or due to technical limitations of this study. The conservative approaches we employed for the selection, processing and analysis of the data aimed to facilitate cross-study comparisons, but limited the contribution of rare or lowly abundant taxa to our analysis. This effect was likely stronger for soil, which has the highest overall richness (Thompsonet al. 2017), where our sample completeness estimates (i.e., coverage) were lowest. Rare taxa are important sources of variation in soil microbiomes (Joussetet al. 2017; Jiao et al. 2019), and are key drivers of community assembly (Shadeet al. 2014).
Our synthesis is intended to explicitly compare microbial community reassembly patterns in response to disturbance across realms that are highly segmented by researcher identity. Our study reconciles several hypotheses that have been proposed for microbiomes in the different realms. First, we find support for the tendency to drift away from the pre-disturbance condition in aquatic systems. Second, we find that mammalian microbiomes often recover their richness, but not their composition following disturbance (e.g., they maintain the same number of taxa but have different memberships with different relative contributions). Third, we find little general evidence for changes in compositional dispersion (after accounting for changes in richness) following disturbance. Finally, our study reframes microbial community reassembly from a community ecology perspective. Future work should focus on distinguishing targeted from untargeted disturbances, to determine what constitutes a disturbance for a microbiome given its ecosystem context, and to perform research at temporal scales which match microbial life histories and turnover /growth in microbiomes. Overall, this work provides a new perspective on the dynamics and generalities of microbiome disturbance responses that is supported by directly comparable metrics, equivalent temporal scales among datasets, and a consistent modeling approach. It suggests that, after standardizing and focusing on macroecological patterns, the environment matters for microbiome assembly.
ACKNOWLEDGEMENTS
We would like to thank A. Heintz-Buschart for their help and S. Tem and A Clark for valuable discussions. We acknowledge support by the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118, 202548816) and the synthesis centre of iDiv (sDiv). The study has in part been performed using the High-Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research - UFZ and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. AS acknowledges support from National Science Foundation CAREER ​(NSF 1749544).
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