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\title{Molecular ecology and microbiomes in the wild: methodological advances,
common pitfalls and future directions}



\author[1]{Nick Fountain-Jones}%
\author[2]{Tatiana Giraud}%
\author[3]{Lucie Zinger}%
\author[4]{Holly Bik}%
\author[5]{Simon Creer}%
\author[6]{Elin Videvall}%
\affil[1]{University of Tasmania}%
\affil[2]{Université Paris-Sud}%
\affil[3]{Institut de Biologie de l’ENS (IBENS), Département de Biologie, École normale supérieure, CNRS, INSERM, Université PSL, Paris, France}%
\affil[4]{University of Georgia}%
\affil[5]{Bangor University}%
\affil[6]{Brown University}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\selectlanguage{english}
\begin{abstract}
The study of microbiomes across organisms and environments has become a
prominent focus in molecular ecology. This perspective article explores
methodological advancements, common challenges and future directions in
the field. Key research areas include understanding the drivers of
microbiome community assembly, linking microbiome composition to host
genetics, exploring microbial functions, transience, and spatial
partitioning, and disentangling non-bacterial components of the
microbiome. Methodological advancements, such as quantifying absolute
abundances, sequencing complete genomes, and utilizing novel statistical
approaches, are also useful tools for understanding complex microbial
diversity patterns. Our aims are to encourage robust practices in
microbiome studies and inspire researchers to explore the next frontier
of this rapidly changing field.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The study of microbial communities across host species and environments
(hereafter the `microbiome') is a major focus of research in the field
of molecular ecology. As such, \emph{Molecular Ecology}
and\emph{Molecular Ecology Resources} regularly publish papers and
special issues in the field. \emph{Molecular Ecology} is at the
forefront of examining the importance of microbiomes across ecosystems,
from increasing our understanding of host-pathogen (e.g., Bergner et
al., 2020; Wille et al., 2018) and host-symbiont interactions (e.g.,
Rubin et al., 2019) to investigating the impact of climate and other
environmental factors on microbial populations (e.g., Santos-J\selectlanguage{ngerman}únior et
al., 2022; Wu et al., 2022). Complementing these efforts,\emph{Molecular
Ecology Resource} s publishes significant methodological advances that
continue to shape the field (e.g., Harrison et al., 2020; Schnell et
al., 2015; Stothart et al., 2021). Based on our collective experience as
subject editors at \emph{Molecular Ecology} and\emph{Molecular Ecology
Resources} , here we discuss some of the best practices and advances for
analysing microbiomes, from study design to data analysis, and highlight
anticipated future directions in the field. With this article we hope to
inspire and encourage researchers to obtain more robust insights from
microbiome data, which will enable the field to advance and tackle the
new horizons enabled by recent advances in technology.

\textbf{Study design}

As in any study, the sampling or experimental design of microbiome
studies should include sufficient independent replicates, avoiding
confounding effects as much as possible, with the samples representing
appropriate ecological scales given the processes investigated.
Microbiome sampling design must also be well-planned and appropriate to
the specific hypothesis that is being tested. When testing hypotheses
pertaining to the impact of outlier external drivers (e.g., fire,
pollution events, natural disasters), studies would ideally feature
samples that were collected both before and after the event, that are
not confounded by habitat type, geography or physicochemistry. Before
embarking on microbiome studies in the wild, particularly those of which
are opportunistic (i.e., with samples originally collected for other
purposes), researchers should carefully consider if autocorrelation of
factors beyond their control could impede the interpretation of results.
In other words, researchers must be realistic about what can be
accomplished with limited sample sets, since rigorous hypothesis testing
requires equally rigorous sampling protocols and study design.

In addition, the sampling of microbial communities should take into
account their high heterogeneity at small spatial scales due to
micro/mesoscale heterogeneity of their environment (Vos et al., 2013;
Zhang et al., 2014) or neutral assembly dynamics (Woodcock et al.,
2007)). For example, composite samples (i.e. pooled individual samples)
can be combined prior to homogenisation and sub-sampling, in order to
reduce the local, micro-scale heterogeneity if it is irrelevant to the
questions being studied (George et al., 2019). Here, knowledge of how,
and at what scale, the target community responds to external drivers
will inform adequate sampling design. For example, a composite 0.2mg
sediment sample is likely to be representative of the bacterial,
archaeal, and microbial eukaryotic biospheres, but will not sample
microscopic invertebrate taxa effectively, due to issues of scale
(Nascimento et al., 2018). Smaller samples will contain some microscopic
taxa and trace environmental DNA but they are inadequate at representing
the underlying meio- and macro-faunal communities. As the target
organisms grow in size, the sample volume and spatial extent of the
studied area should be correspondingly expanded.

\textbf{Sample preparation and bioinformatics}

In addition to the above considerations, the study design needs to
account for the sensitivity and error-prone nature of many
molecular-based approaches. Both shotgun metagenomics and DNA
metabarcoding (i.e., amplicon sequencing of marker genes). include
numerous opportunities for introducing false negatives and positives
during the data generation process, starting from sample collection to
the laboratory, sequencing, bioinformatics, and data analyses. Details
of these issues are already largely covered in another \emph{Molecular
Ecology} editorial (Zinger et al., 2019), but to summarize briefly, some
possible pitfalls include sample contamination stemming from the field
or lab environment (de Goffau et al., 2018; Salter et al., 2014),
extraction/PCR amplification biases, and errors generated during PCR and
sequencing. Technical considerations, such as sample volume and choice
of lab reagents, are, in many cases, the result of a compromise between
the research question, logistical feasibility, time and available funds
(Taberlet et al., 2018). However, any compromise of the protocol should
still allow one to appropriately address the research question. In
addition, we want to re-emphasize the importance of adequately
describing the whole data production workflow in the methods section of
manuscripts (e.g., primer sequences, polymerase, molecular labelling
strategy). Environmental or lab contamination is a particularly large
problem for samples with low microbial biomass (Eisenhofer et al.,
2019), and the collection of such low biomass samples cannot be avoided
in many study designs (e.g. host-associated microbiomes of small
organisms, or depauperate environmental habitats). The sequencing of
negative controls (and potentially also positive controls and technical
replicates) alongside experimental samples is important for quantifying
errors and artefacts (e.g., Davis et al., 2018), and can improve data
curation procedures through tuned, experiment-specific criteria,
including for samples with low microbial biomass. While there is more
than one way to implement such efforts, a thorough description of the
controls, a rationale for including them, and the ways they are
integrated into data analysis, are essential practices of good
microbiome science (Hakimzadeh et al., 2023).

One overlooked problem in microbiome studies is cross-contamination
between samples during library preparation procedures (Kim et al., 2017;
Zinger et al., 2019), which can result in an artificial reduction in
beta diversity (i.e., compositional differences between samples) and an
increase in alpha diversity. Such cross-contamination can occur during
the PCR plate preparation process through pipetting errors or aerosol
production. Random positioning of samples in the PCR plates provides a
relatively simple approach to reduce this problem (Minich et al., 2019;
Taberlet et al., 2018). More often, - and insidiously -
cross-contamination can occur during the PCR cycles, a bias referred to
as tag-switches (Carlsen et al., 2012; Esling et al., 2015), tag-jumps
(Schnell et al., 2015), or, more recently in the microbiome literature,
cross-talks (Edgar, 2018; Minich et al., 2019). During this laboratory
step, amplicon molecules from different samples can recombine within
conserved primer sequences, resulting in the production of new molecules
containing the genuine DNA sequence, but the wrong sample/barcode label.
As a consequence, the most abundant taxa will be detected in many
samples, including the negative controls (Esling et al., 2015; Minich et
al., 2019; Taberlet et al., 2018), preventing simple removal of all taxa
occurring in controls as a fix for field and lab contamination.
Alleviating the problem of tag-switches can be achieved with modified
library preparation protocols (e.g., Carøe \& Bohmann, 2020),
appropriate sample labelling strategies and \emph{a posteriori} using
the information contained in both samples and negative controls (Bohmann
et al., 2022; Hakimzadeh et al., 2023).

One limitation in microbiome studies, using either DNA metabarcoding or
metagenomics, is the compositional nature of the sequence data (i.e.,
described as proportions or probabilities, not absolute number of
molecules). Like other count-based sequencing approaches, this
limitation arises because the concentration of PCR products is
standardised prior to sequencing and the number of molecules read is
limited by the sequencing platform (Gloor et al., 2017). As a result, we
are unable to obtain data on absolute abundances and biomass for the
different microbial members of the community. However, exciting new
developments are emerging to overcome these limitations, relying on
known reference values of DNA molecule abundances, allowing simple
conversion of relative abundance into absolute values. Two broad classes
of methods based on this approach have emerged: (i) quantification of
target markers using q/ddPCR prior to metabarcoding (Barlow et al.,
2020; Callahan et al., 2019; Ji et al., 2019) and (ii) introduction of
exogenous DNA molecules spike-ins (i.e. DNA molecules of known sequence
and quantity to calibrate measurements). Related to the latter, a
host-associated microbiome PCR approach (HamPCR, Lundberg et al., 2021)
represents a promising method to assess the ratio of the microbial
population size relative to the amount of host tissue (i.e. microbial
load).

Despite obtaining a better estimate of the absolute number of molecules
in a sample, it is still challenging to convert this number into the
actual number of microbial cells. The calculation is often difficult
because most gold-standard barcoding genes used for bacteria, fungi, and
protists have multiple copies in the genome, with precise numbers
varying across taxa and in unpredictable ways (Louca et al., 2018).
Another problem is that some of the retrieved molecules can be derived
from extracellular DNA or DNA adsorbed on cell debris or particles, i.e.
correspond to non-living organisms (Torti et al., 2015).The proportion
of extracellular DNA in the environment is often not known but can be
estimated with different approaches (reviewed by Nagler et al., 2022).
Further, because extracellular DNA is often degraded, approaches
including long-read sequencing targeting larger genomic regions will
likely overcome this issue. Approaches that are able to quantify or
eliminate extracellular DNA can prove useful when having correct
snapshots of the microbial community is crucial (e.g., when studying
short-term processes with repeated observations capturing microbiome
variation within host individuals) but are likely less relevant when
studying processes operating at larger temporal scales (e.g., microbiome
response to climate change).

Lastly, incorporating site-occupancy modelling in microbiome studies
presents an exciting avenue to quantify measurement uncertainty and to
account for imperfect detection (e.g., Ficetola et al., 2015;
McClenaghan et al., 2020; Willoughby et al., 2016). Site-occupancy
models use data collected over multiple visits to sites (or across
multiple technical/biological replicates) to quantify how likely it is
to detect a taxon when it is present. For microbiome studies, including
both biological and technical PCR replicates can enable rigorous
statistical predictions regarding the true or false positive detection
of microbial species within the community. Further, these predictions
can be utilised to improve study design (Fukaya et al., 2022;
McClenaghan et al., 2020). How many replicates of each type is required
is an open question, although biological replicates may improve
detection probabilities (Willoughby et al., 2016).

\textbf{Beyond estimating diversity: Exciting advances in statistics}

Modelling advances in community ecology offer exciting opportunities to
understand the complex patterns in microbial diversity and complement
robust sampling designs (e.g., Grantham et al., 2020; Trego et al.,
2022). In addition, novel methods for analyzing amplicon sequencing data
are continuously emerging, primarily focused on the human gut microbiome
but adaptable to other microbial ecology fields with suitable study
designs and datasets (e.g., Trego et al., 2022). Broadly, these tools
can be categorized into quantifying community assembly processes,
mapping occurrence networks, capturing spatial/temporal dynamics,
integrating multi-omics, identifying differentially abundant taxa,
finding species-environment associations, and predicting functional
patterns (Trego et al., 2022). However, despite the frequent use of
high-throughput sequencing, there has been a slow uptake of these new
analytical techniques, and many studies do not go much beyond basic
comparisons of alpha and beta diversity estimates across samples. While
important inferences can be made by examining overall patterns of
composition and diversity (e.g., Grosser et al., 2019; Motta et al.,
2018), they offer only a starting point toward having a more mechanistic
understanding of the ecological drivers of microbiome variation (Shade,
2017).

Common analytical approaches to quantify differences in beta diversity
across microbiome samples, such as the permutational multivariate
analysis of variance (PERMANOVA), are algorithmic (i.e., not based on a
statistical model) and do not explicitly account for uncertainty in
ecological data (Björk et al., 2018; Warton et al., 2012, 2015).
Importantly, making inferences about microbiome variation is often
difficult using algorithmic distance-based approaches (Björk et al.,
2018; Warton et al., 2012). Model-based approaches such as joint species
distribution models (JSDMs) or stacked models (Powell-Romero et al.,
2023) are multi-response extensions of generalized linear mixed models
(GLMMs) that can overcome some of the limitations of the algorithmic
methods to elucidate patterns of microbiome variation (e.g., Björk et
al., 2018; Grantham et al., 2020). Often using a Bayesian framework,
JSDMs simultaneously analyze multiple species and environmental
variables, allowing for the assessment of community-level responses to
environmental change and host effects (Björk et al., 2018; Ovaskainen et
al., 2017; Pollock et al., 2014). JSDMs can i) incorporate information
on species traits and phylogenetic relatedness, improving estimation
accuracy and power when there is a phylogenetic signal (Ovaskainen et
al., 2017), and ii) analyse patterns of taxon covariance to infer
microbial co-occurrence networks (Björk et al., 2018; Fountain-Jones et
al., 2020, 2023). Microbial co-occurrence networks are valuable tools in
microbiome science, as they offer insights (but see \emph{Current gaps
and future directions} below) into the associations among microbial
taxa, enhancing our understanding of microbial community dynamics and
functioning. JSDM-based co-occurrence networks have an added advantage
of interpretation as the major environmental and host effects shaping
microbial presences are controlled for (i.e., an inferred association
between microbes is then not likely a mere product of a shared
environmental response). However, GLMM-based JSDM co-occurrence networks
cannot untangle the relative roles of taxa associations, and
environmental or host effects (Clark et al., 2018; Fountain-Jones et
al., 2020) and tend to not scale well with large datasets (Pichler \&
Hartig, 2021). Approaches such as conditional random fields (CRF, Clark
et al., 2018), multi-response interpretable machine learning (mrIML,
Fountain-Jones et al., 2021), MIMIX (Microbiome MIXed Model, Grantham et
al., 2020) and scalable JSDMs (sjSDM, Pichler \& Hartig, 2021) can
overcome these limitations. Importantly, approaches such as MrIML and
MiMiX allow for predictions and treatment effects to be extracted for
individual taxa, which can be useful if researchers have a set of focal
taxa. We note that these methods are not appropriate in all situations.
For particularly large datasets (thousands of samples), new
distance-based methods such as D-MANOVA (Chen \& Zhang, 2021) or
multivariate distance matrix regression (MDMR, Zapala \& Schork, 2012)
may be better options. Boshuizen \& te Beest (2023) have provided a
complete guide of the pitfalls in analysing amplicon data. While the
tools mentioned here represent only a tiny fraction of the potential
methods available, we encourage readers to go beyond diversity metrics
and differentially abundant taxa to gain more mechanistic insights into
microbiome data from wild species.

Incorporating some of the methodological advances in bioinformatics and
statistics, coupled with robust study design, and rigorous laboratory
techniques, will improve current research efforts in the field (see Fig.
1 for a summary). Moreover, taking into consideration both the
limitations and opportunities of these various approaches allow us to
open up new exciting avenues in the field of microbiome ecology
research.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1:} Overview of some of the considerations and techniques
that can be employed across a general microbiome workflow. JSDMs: Joint
species distribution models. q/dd PCR quantitative/ double digest PCR.

\textbf{Current gaps and future directions in microbiome ecology
research}

Given the above considerations, how do we move the field forward? Here
we outline some applicable research directions that will generate
significant impact, by helping to close some of the most pressing
knowledge gaps in the near future.

1. \textbf{Obtaining a better understanding of the ecological,
evolutionary and mechanistic drivers of microbiome community assembly}.
A key research gap in microbiome ecology is the need for a comprehensive
understanding of the drivers of community assembly. While significant
progress has been made regarding microbiomes associated with humans and
model organisms (e.g., Drosophila or Arabidopsis), further study on
non-model animal and plant systems is required. Included within this
goal is the investigation of phylosymbiosis -- the topological
congruence between host phylogenetic distance and the compositional
similarity patterns of their associated microbiota (Brown et al., 2023);
a pattern that can arise from both ecological and evolutionary
processes. Mechanistic studies elucidating the specific processes that
govern microbial transmission, colonization, competition and succession
will help explain the presence or absence of phylosymbiosis signals
across host species or populations, and are more broadly essential for a
deeper understanding of microbiome assembly (Coyte et al., 2021). 2.
\textbf{Linking microbiome composition and host genetics.} To gain
insights into the ability of hosts to select specific microbes that may
benefit their health or reproduction, future research should aim to link
host-associated microbiome composition and diversity with genes and
genomic regions of hosts. This hologenomic approach will enable the
identification of host genetic factors, such as immune genes, that are
key players in shaping the host microbiome and ultimately the resulting
host phenotypes. By integrating both host genome and microbiome data,
researchers can make progress at uncovering host-microbiome interactions
(Sutherland et al., 2022; West et al., 2023). Incorporation of long-read
sequencing and hybrid assembly approaches which utilize both short and
long reads now offer exciting opportunities for advancing this research
area. 3. \textbf{Linking microbiomes to host traits and phenotypes}.
Understanding the connection between host-associated microbiomes and
host phenotypes is a related and important research avenue.
Investigating the influence of the microbiome on host development,
behavior, metabolism, and life-history traits can therefore provide
valuable insights (e.g., Bestion et al., 2017; Wood et al., 2022). For
example, some nematode species are known to demonstrate extreme
phenotypic plasticity in response to environmental cues (e.g. chemical
or bacterial stimuli (Hauquier et al., 2017; Sommer et al., 2017), and
one open question is whether microbioal taxa play an integral role in
initiating such host developmental switch genes (which are themselves
under epigenetic control in the case of \emph{Pristionchus} spp. fig
nematodes). By integrating microbiota data with detailed trait
measurements of diverse hosts, researchers will be able to identify host
phenotypes associated with certain microbiome compositions and start to
unravel the underlying mechanisms of how certain microbes can influence
the phenotypes of hosts, and vice versa. 4. \textbf{Exploring microbial
functions within host-associated microbiomes}. While microbial community
composition and diversity have been extensively studied in microbiome
ecology, there is a significant need to explore the functional
attributes of whole communities, localized populations, and individual
microorganisms (genes and pangenomes) within a microbiome. Investigating
microbial functions, such as metabolic pathways and molecular
interactions between members of the microbiome and with the host, can
provide important insights into the contributions of specific microbial
taxa/consortia and their functional roles in host and ecosystem health
(B\selectlanguage{ngerman}échade et al., 2023; e.g., Hicks et al., 2018; Karmacharya et al.,
2019). Furthermore, isolation and culturing of microbial strains can
provide complementary information not otherwise accessible through
community -omics alone (e.g., physiological profiling of microbial
growth rates and chemical/antibiotic sensitivity), while also paving the
way for future experimental work using such host-associated microbial
isolates. 5. \textbf{Disentangle the role of the non-bacterial
components of the microbiome}. Although bacteria have been the primary
focus of microbiome ecology research due to their overwhelming
abundance, other components such as viruses, fungi, or protists play
crucial roles in host-microbe interactions and ecosystem functioning
(Jervis et al., 2021; Raghwani et al., 2023). Future investigations that
include these non-bacterial components will allow us to more
comprehensively understand the dynamics of the microbiome as a whole
community, its interaction with hosts, and its role in the ecosystem. 6.
\textbf{Elucidate the role of host-associated microbiomes in wildlife
disease and conservation ecology}. Understanding the role of microbiomes
in biological conservation, such as wildlife disease susceptibility and
resistance, is an emerging and timely field of research within
microbiome ecology. Investigating the interactions between host
genetics, environmental factors and microbial communities can shed light
on disease dynamics and the mechanisms through which microbiomes
modulate host immune responses in wildlife populations (e.g., Bozzi et
al., 2021; Gao et al., 2021; Jervis et al., 2021). Likewise, studies
linking environmental microbiomes with land use, habitat fragmentation,
and climate change can provide important information on how to address
ecosystem challenges in a changing world. 7. \textbf{Disentangling
diet-microbiome associations}. Diet is a well-known driver of microbiome
composition in hosts, but the mechanisms by which components of the diet
promote certain taxa and ultimately influences host health remain
unclear. Therefore, the complexity of diet-microbiome associations
requires further investigation (Kartzinel et al., 2019). Future research
should aim to unravel the specific dietary components that shape
microbial communities, how diet diversity is related to microbial
diversity, and the mechanisms through which these interactions
ultimately influence host health. Longitudinal studies and controlled
dietary interventions can provide valuable insights into the specifics
of diet-microbiome relationships (Couch et al., 2021). 8.
\textbf{Unravelling microbial interactions within the
microbiome.}Elucidating the nature of microbial interactions is crucial
to understand the dynamics of microbial ecosystems. Patterns of
co-occurrence are often used to evaluate microbial interactions (e.g.,
competition), yet doing so is problematic (see Blanchet et al. (2020)).
Interactions are highly scale-dependent, which poses unique challenges
for microbial communities with fine spatial structuring (Goberna \&
Verdú, 2022; Peng et al., 2023). Future experimental and observational
studies at relevant scales, with large numbers of samples across time
and including robust measures of abundance, will be able to better
quantify microbial interactions (Blanchet et al., 2020). Statistical
advances utilising generalized Lotka--Volterra models across time-series
(Stein et al., 2013), or employing conditional probabilities to more
directly capture how taxa relate to each other will also help infer
interactions (Blanchet et al., 2020). 9. \textbf{Determine microbial
strain diversity and evolution within hosts}. Microbial communities are
often highly heterogeneous and different strains of a single microbial
species can exhibit significant genetic and functional variability
(Anderson \& Bisanz, 2023; Goyal et al., 2022). Investigating microbial
genetic diversity and the role of horizontal gene transfer are therefore
crucial to better understand the adaptive processes and functional
implications within a microbiome (Barreto \& Gordo, 2023). For example,
a shift in \emph{Escherichia coli} clones was documented in the gut
microbiome of ageing mice, and these were characterized by an increase
in bacterial mutations targeting stress-response genes (Barreto et al.,
2020). Integrating high-resolution genomic techniques with longitudinal
and repeated sampling schemes can capture key patterns in temporal
variation among microbial communities and significantly improve our
understanding of how microbes evolve within individual hosts or specific
environments.

\textbf{Conclusions}

We are currently at a major turning point on how we can derive valuable
insights on the ecological processes shaping non-model and environmental
microbiomes in the wild. The adoption of robust laboratory and
bioinformatic techniques, together with sophisticated statistical
approaches, enhance our ability to gain deeper insights into the factors
influencing microbiome variation and the intricate relationships among
microbial taxa. However, all of these techniques rely on robust study
designs and appropriate sampling scales to address specific research
questions. Ultimately, this perspective piece serves as a broad outline
of some of the considerations that ought to be considered in the field
of microbiome ecology. Hopefully, with robust practices, we can, in
turn, start to untangle the complex processes acting on these incredibly
important but understudied communities.

\textbf{Acknowledgments}

We would like to thank Jacob A. Russell, Eric Coissac and Kayla King for
their useful discussions and edits to the text.

\textbf{Author Contributions}

NFJ and EV conceived the project. All authors contributed to writing the
first draft of the manuscript and subsequent editing.

\textbf{Conflict of interest}

None.

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