Disturbance
and recovery: a synthesis of microbial community reassembly following
disturbance across realms
Authors : Stephanie D. Jurburg*1,2, Shane A.
Blowes1,3, Ashley Shade4, Nico
Eisenhauer1,2, Jonathan M. Chase 1,3
1 German Centre for Integrative Biodiversity Research
(iDiv) Halle-Jena-Leipzig
2 Institute of Biology, Leipzig University
3 Institute of Computer Science, Martin-Luther
University Halle–Wittenberg, Halle (Saale)
4Department 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;German Centre for Integrative Biodiversity Research (iDiv)
Halle-Jena-Leipzig, Puschstraße 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)
Keywords : community assembly, microbiome, bacteria
Running title : Community reassembly in microbiomes
Article type : Letter
Abstract: 138 words
Main text: 3,804 words
Figures : 6
References : 85
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.
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 athttps://github.com/drcarrot/DisturbanceSynthesis,
and code used for statistical analyses is publicly available athttps://github.com/sablowes/microbiome-disturbance.
Both repositories will be combined and archived at Zenodo upon
acceptance.
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
(Clarket 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
(Philippotet 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
(Shadeet 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
(Hoet al. 2017). Over longer time scales, richness may either fail
to fully recover (at least within the period observed; e.g.,
(Pillaet al. 2020), recover fully
(Hillebrand
& Kunze 2020), or even be higher following disturbance
(Hartmannet al. 2015).
Second, we can measure compositional variation among local communities
following disturbance. Compositional dispersion can be taken as
an indicator of beta-diversity
(Andersonet 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
(Debrayet al. 2021). In microbial ecology, this observation of change
in dispersion following disturbance has been well documented (e.g.,
(Zaneveldet 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 viaturnover . Given enough time, we might expect the same taxa that
dominated prior to a disturbance to recover to their original abundances
(Philippotet al. 2021) . However, under some circumstances (e.g., strong
or long disturbances, invasion
(Ratajczaket al. 2017; Amor et al. 2020), it is also possible that
the disturbance could permanently alter dominance patterns in the
community (e.g.,
(Seekatzet al. 2015; Khan 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
(Chaseet 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
(Zaneveldet al. 2017), but experimental research has found that
disturbance can either reduce
(Lavrinienkoet al. 2020) or increase dispersion in host-associated
microbiomes
(Neelyet al. 2021), and that this pattern is dependent on the time
since disturbance
(Ferrenberget al. 2013). Across environments, microbiomes have been shown
to recover towards (negative turnover, e.g.,
(Shadeet al. 2012b; Jurburg et al. 2017), or to drift away from
(positive turnover, e.g.,
(Shawet 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
(Thompsonet 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
(Philippotet al. 2021). Unique characteristics of microbes, including
rapid evolution
(Niehuset al. 2015), extreme taxonomic richness
(Shoemakeret al. 2017), functional redundancy
(Curtis
& Sloan 2004), and dormancy
(Loceyet 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
(Konopkaet 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
(Kenneyet al. 2020), we focused on studies that repeatedly sampled the
microbiomes within 50 days post disturbance.
MATERIALS AND METHODS