Discussion
Though the concept of a core community is prevalent in ecology,
specifically microbial ecology, our findings draw attention to the
inconsistency of examined methods and the potential analyses based on a
core set of taxa to be misleading. Application and comparison of four
commonly used core assignment methods to both simulated and empirical
datasets yielded conflicting results, with no clear threshold in
abundance or commonness defining core membership inclusion. Our results
show that in some situations core assignment methods agree, but in many
others, the methods do not reach consensus. We also reveal that this
variation in core assignments can produce different statistical results
and lead to different ecological interpretations. Furthermore, core
co-assignment (i.e. assignment by multiple methods) was limited by the
most conservative assignment method, which varied between datasets.
Rather than consistent assignments of taxa to a core, assignments were
not robust and instead differed among methods and their criteria. This
was also highlighted in our comparison of assigned core taxa to
statistically significant nodes from our cooccurrence network analyses,
with many taxa possessing high degree centrality being absent from core
assignments, and in the Arabidopsis dataset, many taxa assigned
as core possessing zero significant edges. Our finding highlights the
statistical nature of core assignments as opposed to an underlying
biological phenomenon and demonstrate the importance of the underlying
data structure for assigning a core. Researchers could evaluate the
statistical support for a distinction between core and non-core taxa and
thereby justify focusing on a subset of taxa for convenience. However,
use of a subset of the taxa will in all cases lead to some loss of
information about the communities. Instead, researchers could use all of
the available abundance data including rare taxa in statistical
procedures and rely on model-based methods to recognize groups of taxa
that differ in abundance (e.g. Harrison, Calder, Shastry, & Buerkle,
2020; Martin, Witten, & Willis, 2020). Beyond the statistical
considerations, the contribution of these less common individuals is
becoming increasingly recognized (Amor, Ratzke, & Gore, 2020; Jousset
et al., 2017).
Community ecology has a long-standing interest in distinguishing between
taxa that drive ecological functions and patterns, and taxa that are
less obviously important in ecological systems. Dubos et al. (1965)
ascribed importance to ‘indigenous flora’, defined as those
microorganisms present during the development of an animal that are so
ubiquitous they establish in all its members. These autochthonous
microbiota are similar to the concept of a climax community within a
habitat, where nonindigenous or transient taxa were compared to flowing
streams just passing through, not contributing to the functionality of
the system (Savage, 1977). Furthermore, it has been hypothesized that
core taxa drive and are disproportionately responsible for the functions
of ecological systems (Grime, 1998). These viewpoints of classic ecology
have been invoked as rationale to consider only the ‘indigenous flora’
or core taxa, in an attempt to reduce the noise arising when considering
mixtures of taxa with different levels of association with their host
environments (Astudillo-García et al., 2017). Thus, it is common,
current practice in studies of microbiomes to consider only taxa that
are common enough to meet criteria for membership in the core community
(e.g. Turnbaugh & Gordon, 2009; Turnbaugh et al., 2009; Wirth et al.,
2018). However, our results indicate this practice may lack statistical
support in some natural systems and may even go as far as to exclude
taxa that are important for microbial network structure.
Given that analyses of microbial communities are frequently based on
taxon counts for thousands of taxa in a single sample (high
dimensionality), the appeal of focusing our attention on fewer
dimensions and taxa is understandable. Dimension reduction can reduce
noise or variation among samples and resolve the strongest patterns in
data sets (Nguyen & Holmes, 2019). Because many statistical methods
lack power when applied to highly dimensional data (Nguyen & Holmes,
2019), scientists rarely analyze an entire ecological dataset and
instead focus on a subset of the most common individuals (Hawinkel,
Kerckhof, Bijnens, & Thas, 2019). From a conceptual standpoint, the
practice of focusing on a core set of taxa, and discarding variation in
the remaining taxa, comes at little cost if the ecological functions of
interest are associated with variation in the core set of taxa. There
are certainly examples (Winfree, Fox, Williams, Reilly, & Cariveau,
2015) and even theory (mass-ratio hypothesis; (Grime, 1998)) of
ecological processes being tied to variation in the abundance of a small
number of relatively common taxa. Yet, variation in ecological and
especially microbially-driven processes is sometimes associated with
rare taxa (e.g. sulfur reduction, nitrification, or methanogenesis) and
thus demonstrates the risk of discarding uncommon members from the
community of interest (Harrison et al., 2021; Jousset et al., 2017;
Mikkelson, Bokman, & Sharp, 2016; Shade et al., 2014). Our results
support this latter viewpoint and show that trends observed in the
common taxa (i.e. core assignments) do not always accurately represent
the entire taxon assemblage.
Similarly, in initial genomic studies of human trait variation that
considered millions of variable nucleotides (analogous to the high
number of taxa in microbiomes), researchers focused on those nucleotides
that had a particular minor allele frequency (commonly at least 0.05).
The conceptual rationale for the focus on a subset of genomic sites was
the hypothesis that common conditions (e.g., disease) should be
associated with common nucleotide variants (Lohmueller, Pearce, Pike,
Lander, & Hirschhorn, 2003; Pritchard & Cox, 2002). The statistical
rationale included the difficulty of estimating the effect of rarely
observed variants, as is true for rare taxa in a microbiome. The
hypothesis of common disease-common variant received poor support
for some traits of interest (Cirulli & Goldstein, 2010). Instead there
was a growing recognition of the potential contribution of rare alleles
and the potential exchangeability of neighboring rare variants that, in
aggregate, could explain trait variation (Zhou & Stephens, 2012).
Likewise, variation in ecological processes could be associated with
variation of the common taxa in communities, or through variation among
any of their members. As for genomics, this is a hypothesis to be tested
in ecology and for some systems discarding rare taxa will preclude
understanding ecological processes.
If one accepts the necessity of considering only common and prevalent
taxa, our comparison of core assignment methods should raise concern
about their subjectivity and inconsistency. In our simulations, we
considered core taxa to be those that were 2-25 times more common than
non-core taxa from the same samples, representing a range of plausible
taxon abundances. Analyses of simulated and two empirical datasets
highlight the inconsistency among the four common methods considered for
defining a core community and call into question the validity of
dichotomizing taxon abundances into core and non-core assignments. The
lack of a clear threshold in taxon abundance and coefficient of
variation across real datasets suggest that this divide may not be
supported in some cases. Furthermore, core taxa were more or less
associated with study variables than the entire taxon assemblage and
could lead to different ecological interpretation. The categorization of
taxa into core and non-core groups is contingent on both the criteria
used for identification and the underlying structure of the taxon table.
Our results demonstrate the statistical nature of core assignment
criteria and the forced dichotomization as opposed to an underlying
biological difference between core and non-core taxa. Our comparisons
along with other empirical studies (Caporaso et al., 2011; Clooney et
al., 2016; Pollock, Glendinning, Wisedchanwet, & Watson, 2018; Shafer
et al., 2017) indicate that the size and membership of the core
community are not robust to differences in bioinformatic methods and
core classification criteria.
The lack of consistency across core assignment methods should concern
researchers as inferences drawn from core assignment can change
drastically even when using the same dataset. Our simulations covered a
range of plausible community structures, providing core assignment
methods opportunity to potentially accurately assign core taxa. The
individual samples in our simulated datasets contained nearly identical
taxon distributions and offer core assignment methods a best case
scenario. The lack of agreement and consistency in core assignment even
under best case scenarios calls into question whether using these
methods in ecological studies is fruitful or misleading. For example,
researchers have related energy acquisition to variance in core gut
microbiota, and the role this may play in obesity (Ley, 2010; Turnbaugh
& Gordon, 2009; Turnbaugh et al., 2009). The observed relationship
between the energy acquisition and microbial community composition is
likely to depend on which criterion was used for identifying core taxa,
thus potentially leading to different interpretations.
While a focus on a core set of taxa has been common, research suggests
it may not be entirely warranted (Engel & Moran, 2013; Hammer, Sanders,
& Fierer, 2019; Martinson, Moy, & Moran, 2012) and that all taxa could
instead be used for analysis. More attention is now being paid to the
“rare biosphere” and the contribution these less abundant taxa make to
ecosystem processes (reviewed in Jousset et al., 2017), community
structure (Mikkelson et al., 2016; Shade et al., 2014), and as a
reservoir of metabolic diversity (Mikkelson et al., 2016). In ignoring
these rare taxa and focusing solely on common ones, researchers may
wrongfully attribute the functions of rare taxa to common ones. This is
especially concerning in human microbiome and agricultural studies where
environments are scanned for beneficial microbes, e.g. if the functions
of rare taxa are attributed to common ones, researchers may be chasing
the wrong taxa for biotechnological applications. In addition, this
dichotomous assignment of core and non-core ignores situations in which
taxa, both common and rare, form networks and function through
interactions. Our network analysis highlights many taxa that are
characterized by high degree centrality but were excluded from any core
assignment method, again demonstrating the danger of focusing solely on
taxa assigned to a core.
To proceed with the analysis of a core set of taxa, researchers can
either investigate the effects of focal taxa that were chosen based on
other information (analogous to candidate gene analysis), or closely
examine evidence for categorical differences in the abundance of taxa.
Statistical evidence for a distinction between core and non-core taxa
could come from consistent categorization by different core assignment
methods. Our analyses demonstrate that while a common core can be
assigned by multiple methods, co-assignments are limited by the most
conservative method, which can change based upon the underlying
structure of the taxon table.
Alternatively, researchers can rely on established statistical
approaches that incorporate variation in the abundance of all taxa.
These include standard methods for multivariate analysis, including
dimension reduction, including those implemented in statistical packages
such vegan (Oksanen et al., 2018) and phyloseq (McMurdie
& Holmes, 2013). Additionally, specialized methods for differential
abundance analysis exist, including Dirichlet multinomial models
(Grantham, Guan, Reich, Borer, & Gross, 2019; Harrison et al., 2020; La
Rosa et al., 2012; Shafiei et al., 2015) and related methods that model
the relative abundance of all taxa (Fernandes et al., 2014; Love, Huber,
& Anders, 2014; Mandal et al., 2015; Robinson, McCarthy, & Smyth,
2009; Wang et al., 2015).
In summary, our application of core assignment methods to simulated and
published data sets demonstrated the inconsistent classifications that
resulted from commonly applied criteria for determining membership in
the set of core taxa. Changes in the set of taxa assigned to the core
could lead to drastically different conclusions regarding statistical
associations and ecological consequences. These findings suggest that
analyses that rely on the identification of core taxa should be
disfavored in many cases and instead researchers can rely on
multivariate analyses that make use of all of the abundance data.
Data accessibility: Taxon tables, simulated data and all code used for
analysis are available online in the CoreMicro R package found at
https://github.com/MayaGans/CoreMicro.git and zenodo repository
(10.5281/zenodo.4909346)
.
Acknowledgments: This research was supported by the Microbial Ecology
Collaborative with funding from NSF award #EPS-1655726.
Author contributions:
M.G. and G.C. conceived the ideas presented. M.G., G.C., and C.A.B.
wrote code for testing core hypothesis and simulations. M.G., G.C.,
L.v.D., and C.A.B. developed and edited manuscript. CoreMicro R package
was developed by M.G. and G.C.
Table List:
List of core methods from Web of Science literature review
Summary of published data sets and core inclusion by method
Figure List:
- Heat map of true positive rate, false positive rate, and net
assignment value for assigning core taxa by method for simulated data
- Bivariate plot of core inclusion by method and data set
- Venn diagram of core co-assignments for each of the datasets used.
- Top panels (A & B): Venn diagrams of all core assignments compared to
significant nodes identified from cooccurrence network analysis.
Bottom panels (C & D): Venn diagrams of core assignments by
individual methods shared with significant nodes from cooccurrence
networks.
Supplementary tables – available at 10.5281/zenodo.4909346.
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Table 1. Five commonly employed core methods along with descriptions and
number of publications found using these methods within Web of Science,
accessed April 2018. The Venn Diagram method was not utilized in our
analysis due to its similarity to the proportion of replicates method.