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).
Bibliography
Amor, D.R.,
Ratzke, C. & Gore, J. (2020). Transient invaders can induce shifts
between alternative stable states of microbial communities. Sci.
Adv., 6, eaay8676.
Anderson, M.J.,
Ellingsen, K.E. & McArdle, B.H. (2006). Multivariate dispersion as a
measure of beta diversity. Ecol. Lett., 9, 683–693.
Buma, B. (2021).
Disturbance ecology and the problem of n = 1: A proposed
framework for unifying disturbance ecology studies to address theory
across multiple ecological systems. Methods Ecol. Evol.
Bürkner, P.-C.
(2017). brms: an R package for bayesian multilevel models
using stan. J. Stat. Softw., 80.
Callahan, B.J.,
McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A. & Holmes, S.P.
(2016a). DADA2: High-resolution sample inference from Illumina amplicon
data. Nat. Methods, 13, 581–583.
Callahan, B.J.,
Sankaran, K., Fukuyama, J.A., McMurdie, P.J. & Holmes, S.P. (2016b).
Bioconductor Workflow for Microbiome Data Analysis: from raw reads to
community analyses. [version 2; peer review: 3 approved].F1000Res., 5, 1492.
Carpenter, B.,
Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M.,et al. (2017). Stan: a probabilistic programming language.Grantee Submission, 76, 1–32.
Chase, J.M., Kraft,
N.J.B., Smith, K.G., Vellend, M. & Inouye, B.D. (2011). Using null
models to disentangle variation in community dissimilarity from
variation in α-diversity. Ecosphere, 2, art24.
Chase, J.M. (2003).
Community assembly: when should history matter? Oecologia, 136,
489–498.
Chase, J.M.
(2007). Drought mediates the importance of stochastic community
assembly. Proc Natl Acad Sci USA, 104, 17430–17434.
Clark, A.T.,
Arnoldi, J.-F., Zelnik, Y.R., Barabas, G., Hodapp, D., Karakoç, C.,et al. (2021). General statistical scaling laws for stability in
ecological systems. Ecol. Lett., 24, 1474–1486.
Curtis, T.P. &
Sloan, W.T. (2004). Prokaryotic diversity and its limits: microbial
community structure in nature and implications for microbial ecology.Curr. Opin. Microbiol., 7, 221–226.
Datta, M.S.,
Sliwerska, E., Gore, J., Polz, M.F. & Cordero, O.X. (2016). Microbial
interactions lead to rapid micro-scale successions on model marine
particles. Nat. Commun., 7, 11965.
David, L.A., Weil,
A., Ryan, E.T., Calderwood, S.B., Harris, J.B., Chowdhury, F., et
al. (2015). Gut microbial succession follows acute secretory diarrhea
in humans. MBio, 6, e00381-15.
Debray, R.,
Herbert, R.A., Jaffe, A.L., Crits-Christoph, A., Power, M.E. &
Koskella, B. (2021). Priority effects in microbiome assembly. Nat.
Rev. Microbiol.
Dong, Z., Wang,
K., Chen, X., Zhu, J., Hu, C. & Zhang, D. (2017). Temporal dynamics of
bacterioplankton communities in response to excessive nitrate loading in
oligotrophic coastal water. Mar. Pollut. Bull., 114, 656–663.
Džunková, M.,
D’Auria, G., Xu, H., Huang, J., Duan, Y., Moya, A., et al.(2016). The Monoclonal Antitoxin Antibodies (Actoxumab-Bezlotoxumab)
Treatment Facilitates Normalization of the Gut Microbiota of Mice with
Clostridium difficile Infection. Front. Cell. Infect. Microbiol.,
6, 119.
Engel, T.,
Blowes, S.A., McGlinn, D.J., May, F., Gotelli, N.J., McGill, B.J.,et al. (2020). Using coverage-based rarefaction to infer
non-random species distributions. BioRxiv.
Ferrenberg, S.,
O’Neill, S.P., Knelman, J.E., Todd, B., Duggan, S., Bradley, D.,et al. (2013). Changes in assembly processes in soil bacterial
communities following a wildfire disturbance. ISME J., 7,
1102–1111.
Flancman, R.,
Singh, A. & Weese, J.S. (2018). Evaluation of the impact of dental
prophylaxis on the oral microbiota of dogs. PLoS ONE, 13,
e0199676.
Frenk, S., Hadar,
Y. & Minz, D. (2018). Quality of irrigation water affects soil
functionality and bacterial community stability in response to heat
disturbance. Appl. Environ. Microbiol., 84.
Fuentes, S.,
Barra, B., Caporaso, J.G. & Seeger, M. (2016). From Rare to Dominant: a
Fine-Tuned Soil Bacterial Bloom during Petroleum Hydrocarbon
Bioremediation. Appl. Environ. Microbiol., 82, 888–896.
Gupta, V.K., Paul,
S. & Dutta, C. (2017). Geography, Ethnicity or Subsistence-Specific
Variations in Human Microbiome Composition and Diversity. Front.
Microbiol., 8, 1162.
Hartmann, M.,
Frey, B., Mayer, J., Mäder, P. & Widmer, F. (2015). Distinct soil
microbial diversity under long-term organic and conventional farming.ISME J., 9, 1177–1194.
Hillebrand, H. &
Kunze, C. (2020). Meta-analysis on pulse disturbances reveals
differences in functional and compositional recovery across ecosystems.Ecol. Lett., 23, 575–585.
Ho, A., Ijaz,
U.Z., Janssens, T.K.S., Ruijs, R., Kim, S.Y., de Boer, W., et al.(2017). Effects of bio-based residue amendments on greenhouse gas
emission from agricultural soil are stronger than effects of soil type
with different microbial community composition. Glob. Change Biol.
Bioenergy.
Jiao, S., Wang,
J., Wei, G., Chen, W. & Lu, Y. (2019). Dominant role of abundant rather
than rare bacterial taxa in maintaining agro-soil microbiomes under
environmental disturbances. Chemosphere, 235, 248–259.
Jousset, A.,
Bienhold, C., Chatzinotas, A., Gallien, L., Gobet, A., Kurm, V.,et al. (2017). Where less may be more: how the rare biosphere
pulls ecosystems strings. ISME J., 11, 853–862.
Jurburg, S.D.,
Cornelissen, J.J.B.W.J., de Boer, P., Smits, M.A. & Rebel, J.M.J.
(2019). Successional Dynamics in the Gut Microbiome Determine the
Success of Clostridium difficile Infection in Adult Pig Models.Front. Cell. Infect. Microbiol., 9, 271.
Jurburg, S.D.,
Konzack, M., Eisenhauer, N. & Heintz-Buschart, A. (2020). The archives
are half-empty: an assessment of the availability of microbial community
sequencing data. Commun. Biol., 3, 474.
Jurburg, S.D.,
Natal-da-Luz, T., Raimundo, J., Morais, P.V., Sousa, J.P., van Elsas,
J.D., et al. (2018). Bacterial communities in soil become
sensitive to drought under intensive grazing. Sci. Total
Environ., 618, 1638–1646.
Jurburg, S.D.,
Nunes, I., Stegen, J.C., Le Roux, X., Priemé, A., Sørensen, S.J.,et al. (2017). Autogenic succession and deterministic recovery
following disturbance in soil bacterial communities. Sci. Rep.,
7, 45691.
Kennedy, R.C.,
Fling, R.R., Robeson, M.S., Saxton, A.M., Schneider, L.G., Darcy, J.L.,et al. (2018). Temporal dynamics of gut microbiota in
triclocarban-exposed weaned rats. Environ. Sci. Pollut. Res.
Int., 25, 14743–14751.
Kenney, T., Gao,
J. & Gu, H. (2020). Application of OU processes to modelling temporal
dynamics of the human microbiome, and calculating optimal sampling
schemes. BMC Bioinformatics, 21, 450.
Khan, M.J.,
Jurburg, S.D., He, J., Brodie, G. & Gupta, D. (2019). Impact of
microwave disinfestation treatments on the bacterial communities of
no‐till agricultural soils. European Journal of Soil Science.
Kondoh, M. (2001).
Unifying the relationships of species richness to productivity and
disturbance. Proc. Biol. Sci., 268, 269–271.
Konopka, A.,
Lindemann, S. & Fredrickson, J. (2015). Dynamics in microbial
communities: unraveling mechanisms to identify principles. ISME
J., 9, 1488–1495.
van Kruistum, H.,
Bodelier, P.L.E., Ho, A., Meima-Franke, M. & Veraart, A.J. (2018).
Resistance and Recovery of Methane-Oxidizing Communities Depends on
Stress Regime and History; A Microcosm Study. Front. Microbiol.,
9, 1714.
Lavelle, A.,
Hoffmann, T.W., Pham, H.-P., Langella, P., Guédon, E. & Sokol, H.
(2019). Baseline microbiota composition modulates antibiotic-mediated
effects on the gut microbiota and host. Microbiome, 7, 111.
Lavrinienko, A.,
Tukalenko, E., Kesäniemi, J., Kivisaari, K., Masiuk, S., Boratyński, Z.,et al. (2020). Applying the Anna Karenina principle for wild
animal gut microbiota: Temporal stability of the bank vole gut
microbiota in a disturbed environment. J. Anim. Ecol., 89,
2617–2630.
Leibold, M.A. &
Chase, J.M. (2017). Metacommunity Ecology, Volume 59 (Monographs
in Population Biology, 59). Princeton University Press, Princeton, NJ.
Li, L., Wang, S.,
Li, X., Li, T., He, X. & Tao, Y. (2018). Effects of Pseudomonas
chenduensis and biochar on cadmium availability and microbial community
in the paddy soil. Sci. Total Environ., 640–641, 1034–1043.
Li, P., Liu, J.,
Jiang, C., Wu, M., Liu, M. & Li, Z. (2019). Distinct Successions of
Common and Rare Bacteria in Soil Under Humic Acid Amendment - A
Microcosm Study. Front. Microbiol., 10, 2271.
Locey, K.J.,
Muscarella, M.E., Larsen, M.L., Bray, S.R., Jones, S.E. & Lennon, J.T.
(2020). Dormancy dampens the microbial distance-decay relationship.Philos. Trans. R. Soc. Lond. B Biol. Sci., 375, 20190243.
Lu, T., Zhou, Z.,
Zhang, Q., Zhang, Z. & Qian, H. (2019). Ecotoxicological effects of
fungicides azoxystrobin and pyraclostrobin on freshwater aquatic
bacterial communities. Bull. Environ. Contam. Toxicol., 103,
683–688.
Mateos, I.,
Combes, S., Pascal, G., Cauquil, L., Barilly, C., Cossalter, A.-M.,et al. (2018). Fumonisin-Exposure Impairs Age-Related Ecological
Succession of Bacterial Species in Weaned Pig Gut Microbiota.Toxins (Basel), 10.
McMurdie, P.J. &
Holmes, S. (2013). phyloseq: an R package for reproducible interactive
analysis and graphics of microbiome census data. PLoS ONE, 8,
e61217.
Mestre, M.,
Ruiz-González, C., Logares, R., Duarte, C.M., Gasol, J.M. & Sala, M.M.
(2018). Sinking particles promote vertical connectivity in the ocean
microbiome. Proc Natl Acad Sci USA, 115, E6799–E6807.
Murphy, G.E.P. &
Romanuk, T.N. (2012). A meta-analysis of community response
predictability to anthropogenic disturbances. Am. Nat., 180,
316–327.
Murphy, G.E.P. &
Romanuk, T.N. (2014). A meta-analysis of declines in local species
richness from human disturbances. Ecol. Evol., 4, 91–103.
Neely, W.J.,
Greenspan, S.E., Stahl, L.M., Heraghty, S.D., Marshall, V.M., Atkinson,
C.L., et al. (2021). Habitat Disturbance Linked with Host
Microbiome Dispersion and Bd Dynamics in Temperate Amphibians.Microb. Ecol.
Nemergut, D.R.,
Shade, A. & Violle, C. (2014). When, where and how does microbial
community composition matter? Front. Microbiol., 5, 497.
Newman, E.A.
(2019). Disturbance ecology in the anthropocene. Front. Ecol.
Evol., 7.
Niehus, R., Mitri,
S., Fletcher, A.G. & Foster, K.R. (2015). Migration and horizontal gene
transfer divide microbial genomes into multiple niches. Nat.
Commun., 6, 8924.
Paine, R.T.,
Tegner, M.J. & Johnson, E.A. (1998). Compounded Perturbations Yield
Ecological Surprises. Ecosystems, 1, 535–545.
Philippot, L.,
Griffiths, B.S. & Langenheder, S. (2021). Microbial Community
Resilience across Ecosystems and Multiple Disturbances. Microbiol.
Mol. Biol. Rev., 85.
Pilla, R.,
Gaschen, F.P., Barr, J.W., Olson, E., Honneffer, J., Guard, B.C.,et al. (2020). Effects of metronidazole on the fecal microbiome
and metabolome in healthy dogs. J. Vet. Intern. Med., 34,
1853–1866.
Qian, J., Ding,
Q., Guo, A., Zhang, D. & Wang, K. (2017). Alteration in successional
trajectories of bacterioplankton communities in response to co-exposure
of cadmium and phenanthrene in coastal water microcosms. Environ.
Pollut., 221, 480–490.
Quast, C., Pruesse,
E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al.(2013). The SILVA ribosomal RNA gene database project: improved data
processing and web-based tools. Nucleic Acids Res., 41, D590-6.
Ratajczak, Z.,
D’Odorico, P., Collins, S.L., Bestelmeyer, B.T., Isbell, F.I. &
Nippert, J.B. (2017). The interactive effects of press/pulse intensity
and duration on regime shifts at multiple scales. Ecol. Monogr.,
87, 198–218.
Raulo, A., Allen,
B.E., Troitsky, T., Husby, A., Firth, J.A., Coulson, T., et al.(2021). Social networks strongly predict the gut microbiota of wild
mice. ISME J.
Rillig, M.C.,
Antonovics, J., Caruso, T., Lehmann, A., Powell, J.R., Veresoglou, S.D.,et al. (2015). Interchange of entire communities: microbial
community coalescence. Trends Ecol. Evol., 30, 470–476.
Rillig, M.C.,
Muller, L.A. & Lehmann, A. (2017). Soil aggregates as massively
concurrent evolutionary incubators. ISME J., 11, 1943–1948.
Rykiel, E.J.
(1985). Towards a definition of ecological disturbance. Australian
Journal of Ecology, 10, 361–365.
Santi, I.,
Tsiola, A., Dimitriou, P.D., Fodelianakis, S., Kasapidis, P.,
Papageorgiou, N., et al. (2019). Prokaryotic and eukaryotic
microbial community responses to N and P nutrient addition in
oligotrophic Mediterranean coastal waters: Novel insights from DNA
metabarcoding and network analysis. Mar. Environ. Res., 150,
104752.
Seekatz, A.M.,
Theriot, C.M., Molloy, C.T., Wozniak, K.L., Bergin, I.L. & Young, V.B.
(2015). Fecal Microbiota Transplantation Eliminates Clostridium
difficile in a Murine Model of Relapsing Disease. Infect. Immun.,
83, 3838–3846.
Shade, A., Dunn,
R.R., Blowes, S.A., Keil, P., Bohannan, B.J.M., Herrmann, M., et
al. (2018). Macroecology to unite all life, large and small.Trends Ecol. Evol., 33, 731–744.
Shade, A., Jones,
S.E., Caporaso, J.G., Handelsman, J., Knight, R., Fierer, N., et
al. (2014). Conditionally rare taxa disproportionately contribute to
temporal changes in microbial diversity. MBio, 5, e01371-14.
Shade, A., Peter,
H., Allison, S.D., Baho, D.L., Berga, M., Bürgmann, H., et al.(2012a). Fundamentals of microbial community resistance and resilience.Front. Microbiol., 3, 417.
Shade, A., Read,
J.S., Youngblut, N.D., Fierer, N., Knight, R., Kratz, T.K., et
al. (2012b). Lake microbial communities are resilient after a
whole-ecosystem disturbance. ISME J., 6, 2153–2167.
Shaw, L.P.,
Bassam, H., Barnes, C.P., Walker, A.S., Klein, N. & Balloux, F. (2019).
Modelling microbiome recovery after antibiotics using a stability
landscape framework. ISME J., 13, 1845–1856.
Shoemaker, W.R.,
Locey, K.J. & Lennon, J.T. (2017). A macroecological theory of
microbial biodiversity. Nat. Ecol. Evol., 1, 107.
Stegen, J.C., Lin,
X., Fredrickson, J.K., Chen, X., Kennedy, D.W., Murray, C.J., et
al. (2013). Quantifying community assembly processes and identifying
features that impose them. ISME J., 7, 2069–2079.
Team, R.C.
(2017). R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. 2016.
Thompson, L.R.,
Sanders, J.G., McDonald, D., Amir, A., Ladau, J., Locey, K.J., et
al. (2017). A communal catalogue reveals Earth’s multiscale microbial
diversity. Nature, 551, 457–463.
Van de Guchte, M.,
Burz, S.D., Cadiou, J., Wu, J., Mondot, S., Blottière, H.M., et
al. (2020). Alternative stable states in the intestinal ecosystem:
proof of concept in a rat model and a perspective of therapeutic
implications. Microbiome, 8, 153.
Vaquer-Sunyer,
R., Reader, H.E., Muthusamy, S., Lindh, M.V., Pinhassi, J., Conley,
D.J., et al. (2016). Effects of wastewater treatment plant
effluent inputs on planktonic metabolic rates and microbial community
composition in the Baltic Sea. Biogeosciences, 13, 4751–4765.
Venkataraman, A.,
Sieber, J.R., Schmidt, A.W., Waldron, C., Theis, K.R. & Schmidt, T.M.
(2016). Variable responses of human microbiomes to dietary
supplementation with resistant starch. Microbiome, 4, 33.
de Vries, F.T.,
Griffiths, R.I., Bailey, M., Craig, H., Girlanda, M., Gweon, H.S.,et al. (2018). Soil bacterial networks are less stable under
drought than fungal networks. Nat. Commun., 9, 3033.
Ward, C.S., Pan,
J.-F., Colman, B.P., Wang, Z., Gwin, C.A., Williams, T.C., et al.(2019). Conserved microbial toxicity responses for acute and chronic
silver nanoparticle treatments in wetland mesocosms. Environ. Sci.
Technol., 53, 3268–3276.
Wu, B., Wang, X.,
Yang, L., Yang, H., Zeng, H., Qiu, Y., et al. (2016). Effects of
Bacillus amyloliquefaciens ZM9 on bacterial wilt and rhizosphere
microbial communities of tobacco. Applied Soil Ecology, 103,
1–12.
Yang, B., Wang, Y.
& Qian, P.Y. (2016). Sensitivity and correlation of hypervariable
regions in 16S rRNA genes in phylogenetic analysis. BMC
Bioinformatics, 17, 135.
Yan, L., Hui, N.,
Simpanen, S., Tudeer, L. & Romantschuk, M. (2020). Simulation of
microbial response to accidental diesel spills in basins containing
brackish sea water and sediment. Front. Microbiol., 11, 593232.
Yao, J., Carter,
R.A., Vuagniaux, G., Barbier, M., Rosch, J.W. & Rock, C.O. (2016). A
Pathogen-Selective Antibiotic Minimizes Disturbance to the Microbiome.Antimicrob. Agents Chemother., 60, 4264–4273.
Zaneveld, J.R.,
McMinds, R. & Vega Thurber, R. (2017). Stress and stability: applying
the Anna Karenina principle to animal microbiomes. Nat.
Microbiol., 2, 17121.
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