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\begin{document}

\title{Disturbance and recovery: a synthesis of microbial community reassembly
following disturbance across realms}



\author[1]{Stephanie Jurburg}%
\author[1]{Shane Blowes}%
\author[2]{Ashley Shade}%
\author[3]{Nico Eisenhauer}%
\author[1]{Jonathan Chase}%
\affil[1]{German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig}%
\affil[2]{Michigan State University}%
\affil[3]{University of Leipzig}%


\vspace{-1em}



  
  \date{May 01, 2024}


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\selectlanguage{english}
\begin{abstract}
Disturbances alter the diversity and composition of microbial
communities, but whether microbiomes from different environments exhibit
similar degrees of resistance or rates of recovery has not been
evaluated. Here, we synthesized 86 time series of disturbed mammalian,
aquatic, and soil microbiomes to examine how the recovery of microbial
richness and community composition differed after disturbance. We found
no general patterns in compositional variance (i.e., dispersion) in any
microbiomes over time. Only mammalian microbiomes consistently exhibited
decreases in richness following disturbance. Importantly, they tended to
recover this richness, but not their composition, over time. In
contrast, aquatic microbiomes tended to diverge from their
pre-disturbance composition following disturbance. By synthesizing
microbiome responses across environments, our study aids in the
reconciliation of disparate microbial community assembly frameworks, and
highlights the role of the environment in microbial community reassembly
following disturbance.%
\end{abstract}%



\sloppy


\textbf{Disturbance and recovery: a synthesis of microbial community
reassembly following disturbance across realms}

\textbf{Authors} : Stephanie D. Jurburg*\textsuperscript{1,2}, Shane A.
Blowes\textsuperscript{1,3}, Ashley Shade\textsuperscript{4}, Nico
Eisenhauer\textsuperscript{1,2}, Jonathan M. Chase \textsuperscript{1,3}

\textsuperscript{1} German Centre for Integrative Biodiversity Research
(iDiv) Halle-Jena-Leipzig

\textsuperscript{2} Institute of Biology, Leipzig University

\textsuperscript{3} Institute of Computer Science, Martin-Luther
University Halle--Wittenberg, Halle (Saale)

\textsuperscript{4}Department of Microbiology and Molecular Genetics,
Department of Plant Soil and Microbial Sciences, and Program in Ecology,
Evolution and Behavior, Michigan State University, East Lansing MI 48824

*Correspondence: Stephanie D. Jurburg,
s.d.jurburg@gmail.com\emph{;}German Centre for Integrative Biodiversity
Research (iDiv) Halle-Jena-Leipzig, Puschstra\selectlanguage{ngerman}ße 4, 04103, Leipzig,
Germany

Email addresses: S.A.B. (shane.blowes@idiv.de); A.S. (shadeash@msu.edu)
N.E. (nico.eisenhauer@idiv.de); J.M.C. (jonathan.chase@idiv.de)

\textbf{Keywords} : community assembly, microbiome, bacteria

\textbf{Running title} : Community reassembly in microbiomes

\textbf{Article type} : Letter

\textbf{Abstract:} 138 words

\textbf{Main text:} 3,804 words

\textbf{Figures} : 6

\textbf{References} : 85

\textbf{Author statement} : S.D.J., A.S., N.E., and J.M.C conceived of
the idea; S.D.J. obtained the data, performed bioinformatics and null
models, and wrote the first draft; S.A.B. performed statistical
analyses; all authors contributed to revisions.

\textbf{Data statement:} Accession numbers for the datasets included in
this study are included in the supplementary data of this article, and
are publicly available. Code used for sequence processing and null
modelling is publicly available
at\emph{https://github.com/drcarrot/DisturbanceSynthesis}, and code used
for statistical analyses is publicly available
at\emph{https://github.com/sablowes/microbiome-disturbance}. Both
repositories will be combined and archived at Zenodo upon acceptance.

\textbf{Novelty statement:} We compare the recovery of disturbed
microbial communities across 86 aquatic, mammalian, and soil time series
by measuring richness, compositional dispersion, and compositional
turnover over time. We find that patterns of community reassembly in
microbiomes are environment-specific, reconciling existing,
environment-specific hypotheses of microbiome responses to disturbance.
This is the first study to explicitly compare microbial community
reassembly across environments within time scales that are relevant to
bacterial life histories.

ABSTRACT

Disturbances alter the diversity and composition of microbial
communities, but whether microbiomes from different environments exhibit
similar degrees of resistance or rates of recovery has not been
evaluated. Here, we synthesized 86 time series of disturbed mammalian,
aquatic, and soil microbiomes to examine how the recovery of microbial
richness and community composition differed after disturbance. We found
no general patterns in compositional variance (i.e., dispersion) in any
microbiomes over time. Only mammalian microbiomes consistently exhibited
decreases in richness following disturbance. Importantly, they tended to
recover this richness, but not their composition, over time. In
contrast, aquatic microbiomes tended to diverge from their
pre-disturbance composition following disturbance. By synthesizing
microbiome responses across environments, our study aids in the
reconciliation of disparate microbial community assembly frameworks, and
highlights the role of the environment in microbial community reassembly
following disturbance.

INTRODUCTION

Disturbances to ecological communities (i.e., externally imposed forces
that cause mortality or stress to at least some of the members in the
community) represent a critical process that influences the diversity
and composition of taxa in those communities (Rykiel 1985). The
resistance, resilience and invariance of communities following
disturbances over time (Clark\emph{et al.} 2021) provide insight into
how ecological communities reassemble in the face of natural and
anthropogenic disturbances, which are growing in frequency and magnitude
(Newman 2019). The study of metacommunity assembly seeks to understand
how taxa co-occur in local communities and the regions in which they are
embedded, and how this varies through time, such as in the face of
external perturbations (e.g., (Leibold \& Chase 2017).

There are a multitude of ways to quantify how metacommunities respond
to, or recover from disturbance, each of which gives insights into
different parts of the community assembly process (overviewed in Figure
1, see also (Philippot\emph{et al.} 2021). First, we can measure summary
variables such as taxa richness in a given locality immediately
following the disturbance, which gives us a picture of the resistance of
the community to disturbance. Over longer periods of time following the
disturbance, measuring richness can lend insight into the recovery of
the community. Across both aquatic and terrestrial ecosystems, richness
is found to decline at local scales after disturbance (Murphy \& Romanuk
2014), and similar patterns have been observed in microbial communities
(Shade\emph{et al.} 2012a). However, richness can also increase if
competitively dominant taxa are more strongly depressed by the
disturbance (Kondoh 2001), or in microbes, if the disturbance involves
the addition of novel taxa (e.g., with sewage sludge amendments to soil
(Ho\emph{et al.} 2017). Over longer time scales, richness may either
fail to fully recover (at least within the period observed; e.g.,
(Pilla\emph{et al.} 2020), recover fully (Hillebrand \& Kunze 2020), or
even be higher following disturbance (Hartmann\emph{et al.} 2015).

Second, we can measure compositional variation among local communities
following disturbance. Compositional \textbf{dispersion} can be taken as
an indicator of beta-diversity (Anderson\emph{et al.} 2006). Following
disturbance, dispersion can decrease, for example, if only a subset of
taxa can persist and/or priority effects are reduced (Chase 2003, 2007).
Alternatively, dispersion can increase, for example, if different taxa
are favored due to stochastic differences in which taxa persist
following disturbances (Debray\emph{et al.} 2021). In microbial ecology,
this observation of change in dispersion following disturbance has been
well documented (e.g., (Zaneveld\emph{et al.} 2017).

Third, we can measure to what extent community composition recovers
following disturbance. The tendency of the community to return to its
pre-disturbance conformation, or to tend away from this original
conformation over time (i.e., distance from undisturbed controls) can be
quantified via\textbf{turnover} . Given enough time, we might expect the
same taxa that dominated prior to a disturbance to recover to their
original abundances (Philippot\emph{et al.} 2021) . However, under some
circumstances (e.g., strong or long disturbances, invasion
(Ratajczak\emph{et al.} 2017; Amor \emph{et al.} 2020), it is also
possible that the disturbance could permanently alter dominance patterns
in the community (e.g., (Seekatz\emph{et al.} 2015; Khan \emph{et al.}
2019). Using a null modelling approach, it is possible to further
partition observed changes in dispersion and turnover into compositional
and richness changes (Chase\emph{et al.} 2011) and shed light on the
processes driving microbial community recovery.

Evidence for the effect of disturbance on the dispersion and turnover of
microbial communities is mixed and environment-dependent. For example,
the recovery of disturbed animal-associated microbiomes has been studied
in relation to host health. It has been suggested that the microbiomes
of sick hosts generally exhibit greater dispersion than healthy or
undisturbed microbiomes (Zaneveld\emph{et al.} 2017), but experimental
research has found that disturbance can either reduce
(Lavrinienko\emph{et al.} 2020) or increase dispersion in
host-associated microbiomes (Neely\emph{et al.} 2021), and that this
pattern is dependent on the time since disturbance (Ferrenberg\emph{et
al.} 2013). Across environments, microbiomes have been shown to recover
towards (negative turnover, e.g., (Shade\emph{et al.} 2012b; Jurburg
\emph{et al.} 2017), or to drift away from (positive turnover, e.g.,
(Shaw\emph{et al.} 2019), their pre-disturbance compositions. Among
other factors, different microbial habitats have varying degrees of
spatial and temporal heterogeneity, microbial species pool sizes, and
resource availability, which may affect community reassembly processes
and result in different patterns among environments. For example, animal
gut microbiomes have relatively low diversity (Thompson\emph{et al.}
2017) and are dispersal-limited due to the host physiology, likely
affecting the recovery of diversity. In contrast, soil microbiomes are
extremely diverse, but poorly connected (Vos et al. 2013), likely
affecting local dispersal and recolonization following disturbance.

Studying the responses of microbial communities through a metacommunity
assembly lens (Leibold \& Chase 2017) can yield a generalized
understanding of how microbial communities assemble following
disturbance across environments, which is a pressing question in
microbial ecology (Philippot\emph{et al.} 2021). Unique characteristics
of microbes, including rapid evolution (Niehus\emph{et al.} 2015),
extreme taxonomic richness (Shoemaker\emph{et al.} 2017), functional
redundancy (Curtis \& Sloan 2004), and dormancy (Locey\emph{et al.}
2020) may result in deviations from the patterns observed in eukaryotic
systems.

A better understanding of how microbiomes assemble following disturbance
could also aid in the development of treatments of diseases in plants
and animals, allow us to anticipate the effect of environmental change
on the soil biota, and contribute towards developing novel practices for
microbiome management (Konopka\emph{et al.} 2015). To assess
commonalities and differences in microbiome disturbance responses across
environments, we performed a synthetic analysis of each of the responses
discussed above (richness, dispersion and turnover) in the short and
long term response to disturbance in aquatic, mammal-associated, and
soil microbiomes. Given the rapid rates of compositional turnover in
many microbiomes (Kenney\emph{et al.} 2020), we focused on studies that
repeatedly sampled the microbiomes within 50 days post disturbance.

MATERIALS AND METHODS

\textbf{Dataset selection}

Using Google Scholar and Web of Science (a list of keywords is available
as Supplementary Materials), we collected studies from each system where
an experimental disturbance was imposed and 16S rRNA gene amplicon
sequencing datasets were available. Specifically, we chose studies,
which : 1) were sequenced in Illumina or IonTorrent platforms; 2)
sequenced the v3-v4 regions of the 16S rRNA gene; 3) were published
after 2014; 4) repeatedly sampled microbial communities following a
discrete disturbance or environmental change; 5) included samples from
before the disturbance (i.e., controls), at least one sampling within a
week after disturbance, and at least one sampling within a month after
disturbance; and 6) included experimental triplicates (i.e., three
samples per time point). Criteria 1-3 ensured that the sequencing
techniques were comparable between studies, and avoided the biases
associated with sampling different regions of the 16S rRNA gene
(Yang\emph{et al.} 2016). Whether shifts in community composition occur
at similar rates across environments is seldom compared using empirical
data. Criteria 4-6 ensured that the time scales were comparable between
time series, that the effects of environmental change could be
compared\emph{within} time series between different time points, and
that the variability of the microbiomes at each time point could be
measured. We defined a pulse disturbance as a ``discrete, short-term
event'' (Shade\emph{et al.} 2012a). We excluded datasets for which raw
sequencing data was not publicly available, and stopped data collection
in (Ho\emph{et al.} 2017) October 2020. In all, 29 datasets matched our
criteria (David\emph{et al.} 2015; Seekatz \emph{et al.} 2015; Datta
\emph{et al.}2016; D\selectlanguage{polish}ž\selectlanguage{english}unkov\selectlanguage{ngerman}á \emph{et al.} 2016; Fuentes \emph{et al.}
2016; Vaquer-Sunyer \emph{et al.} 2016; Venkataraman \emph{et al.} 2016;
Wu\emph{et al.} 2016; Dong \emph{et al.} 2017; Jurburg \emph{et al.}
2017, 2018, 2019; Qian \emph{et al.} 2017; de Vries \emph{et al.} 2018;
Flancman \emph{et al.} 2018; Frenk \emph{et al.} 2018; Kennedy \emph{et
al.} 2018; Li \emph{et al.} 2018, 2019; Mateos \emph{et al.} 2018; van
Kruistum \emph{et al.} 2018; Lavelle \emph{et al.} 2019; Lu \emph{et
al.} 2019; Santi \emph{et al.} 2019; Ward \emph{et al.} 2019;
Yan\emph{et al.} 2020) Table S1). We grouped these time series into
three environmental categories: aquatic, mammal-associated, and soil
microbiomes (including rhizosphere microbiomes).

\textbf{Sequence reprocessing}

We obtained raw 16S rRNA gene amplicon data and metadata from the NCBI
Sequence Read Archive with the exception of two datasets, one of which
came from another database, and the other was obtained directly from the
authors (see Table S1 for accession numbers). We processed sequences in
R 3.4.3 (Team 2017) using the \emph{dada2} package (Callahan\emph{et
al.} 2016a). Prior to processing, we visually inspected two sequences
per study with the \emph{plotQualityProfile} to determine whether the
reads had been merged prior to archiving and to confirm that primers
were not present. We only used forward reads because reverse reads were
not available for all studies. Following inspection, we trimmed and
truncated sequences on a study-by-study basis (see Table S1 for trimming
and truncation lengths) to preserve a 90 bp segment, the minimum
recommended in the Earth Microbiome Project protocols (Thompson\emph{et
al.} 2017). We filtered, dereplicated, and chimera\selectlanguage{english}-checked each read
using standard workflow parameters (Callahan\emph{et al.} 2016b), and
assigned reads to exact sequence variants (ASVs, 100\% sequence identity
in the 90 bp segment) with the SILVA v.132 training set (Quast\emph{et
al.} 2013). We removed reads not assigned to the domain Bacteria.
Details about the percentage of reads lost at each step of sequence
processing, per study, are included in Figure S1. Due to the wide range
of sequencing depths across samples, we standardized samples to 1,500
reads per sample. To ensure that our findings were not affected by
observation depth, we ran all analyses in parallel using the deepest
possible observation depth (with a lower bound of 1,500 reads per
sample) for each study (Table S1). As our findings were consistent
regardless of standardization (Figure S2), and we present only the
results from the global standardization. To examine the completeness of
each sample relative to the total richness in a community, we calculated
coverage (Chao and Jost 2012) using the BetaC package (Engel\emph{et
al.} 2020). On average, our samples represented 0.96+-0.05 (mean+-sd) of
the community. We removed any time points that had less than three
experimental replicates for each time series. We coded time series so
that time (days) \selectlanguage{english}[?] 0 occurred after disturbance, and time \textless{} 0
denoted the pre-disturbance community. In line with our conservative
approach to sequence processing, our analyses focused on the dominant
portion of the community.

\textbf{Calculation of richness and turnover metrics}

We calculated richness and turnover metrics using the
\emph{phyloseq}package's data structure (McMurdie \& Holmes
2013)\emph{.} We calculated species richness as the number of unique
ASVs per sample.

We used Bray-Curtis distances to quantify two aspects of compositional
variation. First, for each time series and for each time point, we
calculated dispersion as the pairwise Bray-Curtis distances between all
combinations of experimental replicates. For studies that resampled the
same experimental unit (e.g., host organism or microcosm) over time, we
excluded pairwise comparisons between samples from the same experimental
units. Second, to quantify turnover, we calculated pairwise distances
between all control samples (i.e., pre-disturbance) and all subsequent
replicate samples at each time point following disturbance. Communities
that largely recover to their pre-disturbance conformation will have a
negative slope through time, while communities that become increasingly
different from the pre-disturbance community over time will have a
positive slope estimate (Figure 1).

To disentangle compositional changes from changes in richness, we
randomly permuted abundance values within each sample 1000 times,
preserving the number of taxa and observations for each sample, and
recalculated turnover and dispersion metrics for each matrix to derive a
null expectation for each. For both metrics, Z-scores were calculated
as\(\frac{observed-\mu^{\text{expected}}}{\sigma^{\text{expected}}}\). We present analyses of the Z-scores in the main
text, and analyses of the raw compositional metrics (i.e., Bray-Curtis)
in the Supplementary Methods. All code for bioinformatics processing and
null models is available at
https://github.com/drcarrot/DisturbanceSynthesis.

\textbf{Statistical analyses}

We fit statistical models to assess how 1) richness, 2) dispersion, and
3) turnover change in response to the disturbance relative to
undisturbed control samples immediately after disturbance (i.e.,
before-after analyses), and through time. Unless otherwise specified, we
performed all analyses at the ASV level. Before-after analyses compared
data from prior to the disturbance to samples taken \textless{} 4 days
post disturbance, and models included before-after and environment (i.e,
aquatic, mammal, soil) as categorical covariates that were allowed to
interact. A list of the studies excluded from before-after analyses due
to lack of samples is included in Table S1. We assessed diversity change
through time with models fit to data from the first 50 days
post-disturbance only (i.e., pre-disturbance samples were not included,
with the exception of the turnover analysis that quantified the
compositional change between pre-disturbance and post-disturbance
assemblages). We fit time series models with time (in days) as a
continuous covariate; time was centered by subtracting the mean duration
from all observations prior to modelling and allowed to interact with
the environment. We fit all models with the same, hierarchical grouping
(or random-effects) structure. To account for methodological variation
between studies, we included varying intercepts in all models; and,
because many studies included more than one disturbance type (e.g.,
(D\selectlanguage{polish}ž\selectlanguage{english}unkov\selectlanguage{ngerman}á\emph{et al.} 2016), we included varying slopes and intercepts
for time series within studies (i.e., one time series per disturbance
type).

We assumed a negative-binomial error distribution and a log-link
function for models fit to species richness calculations (i.e., the
before-after and time series models). In addition to the parameters and
the grouping structure described above, the shape parameter of the
negative-binomial distribution (that estimates aggregation) was also
allowed to vary among studies. Models fit to the z-transformed
compositional change metrics (i.e., Bray-Curtis) assumed Gaussian error
distributions and identity-link functions. In addition to the parameters
and grouping structure described above, we also modelled residual
variation as a function of environment to account for
heteroscedasticity, with additional random variation among studies also
modelled.

For Bayesian inference and estimates of uncertainty, we fit models using
the Hamiltonian Monte Carlo (HMC) sampler Stan (Carpenter\emph{et al.}
2017), which were coded using the `brms' package (Bürkner 2017). We used
weakly regularising priors, and visual inspection of the HMC chains
showed excellent convergence. All code for statistical analyses is
available at https://github.com/sablowes/microbiome-disturbance.

RESULTS

Our dataset included 2588 samples in 86 time series from 29 studies
(Table S1) belonging to soil micro- and mesocosms (n=49), seawater
mesocosms (n=16), and mammalian microbiomes (n=21) that were sampled
multiple times within 50 days after disturbance (Figure 2a). Across all
samples, we detected 56,480 ASVs. Of the 824 families in our dataset,
85.6\% were detected in seawater, mammalian, and soil microbiomes
(Figure S3). Coverage was highest in mammalian microbiomes (0.98±0.02;
mean ± sd), lowest and most variable in soil microbiomes (0.93±0.06),
and was significantly different between environments (ANOVA,
\emph{F}=475.1, p\textless{}0.001, Figure 2b).

\textbf{Richness in disturbed and recovering microbiomes}

Prior to disturbance, mean richness was highest in soil microbiomes with
327 ASVs {[}95\% CI: 196 - 506{]} , followed by aquatic 184 {[}111-
281{]}, and mammalian 86 {[}51 - 133{]} microbiomes (Figure 3a). While
all environments exhibited slight decreases in microbiome richness
following disturbance, only the decrease in the mammalian microbiomes
differed from zero. On average, the post-disturbance richness in
mammalian microbiomes was approximately 43\% of that found
pre-disturbance (Figure 3a), and over time, richness increased
approximately 2\% (1-3\%) per day (Figure 3b). In general, the mammalian
microbiomes that lost the most richness after disturbance also recovered
this richness most rapidly over the following 50 days (Figure S4).

\textbf{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 (Shade\emph{et al.} 2018; Kenney \emph{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,
(Jurburg\emph{et 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 (Seekatz\emph{et al.} 2015; D\selectlanguage{polish}ž\selectlanguage{english}unkov\selectlanguage{ngerman}á \emph{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, (Santi\emph{et al.} 2019); or humic acid amendments in
soil, (Li\emph{et 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 (Yao\emph{et al.} 2016),
and the intensity of disturbance (Van de Guchte \emph{et al.} 2020), and
can exacerbate the effect of disturbance on the microbiome (e.g.,
through inflammation (Van de Guchte \emph{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
(Gupta\emph{et al.} 2017) and socializing (Raulo\emph{et 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 (Rillig\emph{et al.} 2015). Furthermore,
the duration of disturbances varied, especially relative to bacterial
life histories and ecologies (Kenney\emph{et 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
(Ferrenberg\emph{et al.} 2013; Lavrinienko \emph{et al.} 2020; Neely
\emph{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 (Chase\emph{et
al.} 2011; Stegen \emph{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 (Nemergut\emph{et
al.} 2014) and resilience (Paine\emph{et 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 (Mestre\emph{et 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 (Rillig\emph{et 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 (Thompson\emph{et al.}
2017), where our sample completeness estimates (i.e., coverage) were
lowest. Rare taxa are important sources of variation in soil microbiomes
(Jousset\emph{et al.} 2017; Jiao \emph{et al.} 2019), and are key
drivers of community assembly (Shade\emph{et 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 \selectlanguage{english}(NSF
1749544).

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FIGURES\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Measuring microbial recovery after disturbance.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 2. Samples used in this synthesis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 3. The effect of disturbance on microbiome richness, immediately
(\textless{}4 days) after disturbance (a), and over 50 days of recovery
(b).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 4. Consistent effect of disturbance on microbiome dispersion are
not detected, immediately (\textless{}4 days) after disturbance (a), or
over 50 days of recovery (b).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure
5. The effect of disturbance on community recovery is
environment-dependent.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 6. The immediate effect of a disturbance is not related to the
microbiome's long-term compositional recovery.

LEGENDS

Figure 1. Measuring microbial recovery after disturbance. Community
richness can be measured before disturbance (yellow), immediately after
disturbance (green), and over longer time periods (blue, a).
Compositional dynamics can be assessed using compositional dissimilarity
metrics (i.e., Bray-Curtis, b). Dispersion can be measured as the
pairwise dissimilarity between experimental replicates, and turnover can
be measured as the pairwise dissimilarity between pre-disturbance and
disturbed communities (red). Following disturbance, communities
dispersion can increase or decrease over time if communities become more
or less dissimilar, respectively (c). They may also return to their
pre-disturbance composition (negative turnover trend) or become
increasingly dissimilar from the pre-disturbance community (positive
turnover trend) Insets are the relationship between time (x-axis) and
turnover (dashed line, y-axis) or dispersion (solid line, y-axis).

Figure 2. Samples used in this synthesis. We selected time series that
had control samples and multiple samples after disturbance (a). A
vertical black line denotes a disturbance event in all cases; samples
taken on the day of disturbance (before or after) are shown along this
line. A vertical grey line indicates the fourth day after disturbance.
Studies which had not sampled the recovering microbiome within
\textless{}4 days after disturbance were excluded from assessments of
the immediate impacts of disturbance on richness and dispersion. All
samples were standardized to 1,500 observations per sample, and had an
average coverage \textgreater{}0.9 (b).

Figure 3. The effect of disturbance on microbiome richness, immediately
(\textless{}4 days) after disturbance (a), and over 50 days of recovery
(b). Richness was calculated as the number of observed taxa in each
sample, and is presented with a log\textsubscript{2}-transformed y-axis.
Circles represent samples, and are colored by study. In \emph{a,} solid
black circles indicate the mean across time series per environment with
a 95\% CI indicated by error bars. In \emph{b} , regression lines for
each time series are colored by study, and the solid black line shows
the mean response across time series per environment. The 95\% CI is
displayed as a grey shaded area, and environments for which overall
trends deviate from zero are indicated with an asterisk (*) on the
bottom right corner.

Figure 4. Consistent effect of disturbance on microbiome dispersion are
not detected, immediately (\textless{}4 days) after disturbance (a), or
over 50 days of recovery (b). Dispersion was calculated as the pairwise
Bray-Curtis distance between replicates for each time point within each
time series, and each circle is a pairwise comparison, colored by study.
In \emph{a,} solid black circles indicate the mean across time series
per environment with a 95\% CI. In \emph{b} , regression lines for each
time series are colored by study, and a solid black line indicates the
mean response across time series per environment. The 95\% CI of the
overall response in each environment is displayed as a grey shaded area.
y-axis scales differ per environment, and the overall trends from each
environment did not deviate from zero.

Figure 5. The effect of disturbance on community recovery is
environment-dependent. For each time series, recovery was calculated as
the pairwise distance between post-disturbance samples and
pre-disturbance controls. Each point is a pairwise comparison, colored
by study. recover to increasingly different conformations Regression
lines for each time series are colored by study, and a solid black line
indicates the mean response across time series per environment. The 95\%
CI is displayed as a grey shaded area, and environments for which
overall trends deviate from zero are indicated with an asterisk (*) on
the bottom right corner.

Figure 6. The immediate effect of a disturbance is not related to the
microbiome's long-term compositional recovery. Each point is a time
series, colored by its environment. Immediate richness responses were
calculated as the effect of disturbance on community richness (Figure
3a), and log-transformed. Turnover rates were calculated as the slope
estimate for turnover over time. Error bars show the 95\% CI for both
metrics. Large circles indicate the mean response per environment.

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