# Evaluating the core microbiota in complex communities:
A systematic investigation

Carmen Astudillo-García, James Bell, Nicole Webster, Bettina Glasl,
Jamaluddin Jompa, José Montoya, Michael Taylor

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► To cite this version:

Carmen Astudillo-García, James Bell, Nicole Webster, Bettina Glasl, Jamaluddin Jompa, et al. Evaluating the core microbiota in complex communities: A systematic investigation. Environmental Microbiology, 2017, 19 (4), pp.1450-1462. 10.1111/1462-2920.13647. hal-02979195

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Evaluating the core microbiota in complex communities: a systematic investigation

Carmen Astudillo-García<fnref n="1" />,<fnref n="2" />, James J. Bell<fnref n="3" />, Nicole S. Webster<fnref n="4" />, Bettina Glasl<fnref n="5" />, Jamaluddin Jompa<fnref n="6" />, Jose

M. Montoya<fnref n="7" /> & Michael W. Taylor<fnref n="1" />,<fnref n="8" />*

Associate Professor Mike Taylor

School of Biological Sciences, University of Auckland

Private Bag 92019, Auckland 1142, New Zealand

Tel. +64 9 3737599 x82280, Fax +64 9 3737416

Email mw.taylor@auckland.ac.nz

Running title: Core microbiota in complex communities

This is the author manuscript accepted for publication and has undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process, which may lead to differences
between this version and the Version record. Please cite this article as doi:10.1111/1462-2920.13647.

<footnote n="1">¹School of Biological Sciences, University of Auckland, Auckland, New Zealand.</footnote>

<footnote n="2">²Institute of Marine Science, University of Auckland, Auckland, New Zealand.</footnote>

<footnote n="3">³School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.</footnote>

<footnote n="4">⁴Australian Institute of Marine Science, Townsville, Australia.</footnote>

<footnote n="5">⁵AIMS@JCU, Australian Institute of Marine Science, College of Science and Engineering, James Cook University, Townsville, Australia</footnote>

<footnote n="6">⁶Research and Development Centre on Marine, Coastal and Small Islands, Hasanuddin University, Makassar, Indonesia</footnote>

<footnote n="7">⁷Ecological Networks and Global Change Group, Experimental and Theoretical Ecology Station, CNRS-University Paul Sabatier, Moulis, France.</footnote>

<footnote n="8">⁸Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.</footnote>

<footnote>*Corresponding author:</footnote>
Accepted Article

Key words: core microbiota, symbionts, ecological networks, sponges.
ORIGINALITY-SIGNIFICANCE STATEMENT

Defining the 'core microbiota' is an increasingly popular mechanism to simplify analyses and identify key microorganisms within a community. How a core is defined varies widely across studies, yet the potential influence of different definitions on results obtained has received little attention. We use a 16S rRNA gene dataset derived from the marine sponge microbiota to systematically evaluate the effect of 'core' definition when interrogating highly diverse microbial communities. We demonstrate that, while the parameters defining the core microbiota can influence the conclusions drawn and therefore caution should be exercised, our overall findings were relatively insensitive to changing core definitions.

Accepted Article
## SUMMARY

The study of complex microbial communities poses unique conceptual and analytical challenges, with microbial species potentially numbering in the thousands. With transient or allochthonous microorganisms often adding to this complexity, a 'core' microbiota approach, focusing only on the stable and permanent members of the community, is becoming increasingly popular. Given the various ways of defining a core microbiota, it is prudent to examine whether the definition of the core impacts upon the results obtained. Here we used complex marine sponge microbiotas and undertook a systematic evaluation of the degree to which different factors used to define the core influenced the conclusions. Significant differences in alpha- and beta-diversity were detected using some but not all core definitions. However, findings related to host specificity and environmental quality were largely insensitive to major changes in the core microbiota definition. Furthermore, none of the applied definitions altered our perception of the ecological networks summarising interactions among bacteria within the sponges. These results suggest that, while care should still be taken in interpretation, the core microbiota approach is surprisingly robust, at least for comparing microbiotas of closely related samples.

Accepted
INTRODUCTION

Microbial communities in nature are often highly complex, comprising hundreds or even thousands of species across a diverse array of taxa. Exemplary in this regard are the microbiotas of soil (Ramirez et al., 2014), the human gut (Bäckhed et al., 2012; Huse et al., 2012) and marine invertebrates such as sponges (Thomas et al., 2016). Each of these ecosystems harbours members of the Bacteria, Archaea and Eukarya domains, as well as innumerable viruses (Fierer et al., 2007; Taylor et al., 2007; Arumugam et al., 2011; Laffy et al., 2016). Such complexity poses formidable conceptual and analytical challenges, with each ecosystem member potentially capable of interacting with all others. These challenges are further amplified when one considers transient or allochthonous community members (Savage, 1977), which may contribute little to the functionality of the system but are still detected by today's sensitive molecular approaches. The concept of a 'core' microbiota, which only considers persistent (and sometimes abundant) members of a microbial community, has thus garnered increasing research attention (Shade and Handelsman, 2012).

Consideration of the core microbiota may be particularly beneficial in the study of host-microbe associations. Some holobiont associations involve highly specific partnerships between a single host and a single microbe, such as the well-known mutualism between the squid *Euprymna scolopes* and the bioluminescent bacterium *Vibrio fischeri* (Nyholm and McFall-Ngai, 2004). These relatively 'simple' symbiotic systems have provided many unique insights into the mechanisms of interaction; however, more complex systems involving a greater number of participants are ubiquitous in nature. In this context, host-microbe interaction networks offer fascinating insights into the ecology and evolution of symbiosis due to the key role these taxonomically diverse microbial communities play within their hosts. Among the most commonly studied host-microbe interaction systems are those comprised of vertebrates and their gut microbes which play an important role in host nutrition and pathogen defence. Such interactions have been described for
fishes (Sullam et al., 2012), birds (Waite and Taylor, 2014), reptiles (Martin et al., 2010) and mammals (Ley et al., 2008), as well as being a primary focus of research in humans (Turnbaugh et al., 2007; Hamady and Knight, 2009; Shafquat et al., 2014). The Human Microbiome Project utilised a core species approach to identify stable and consistent associations, facilitating the identification of correlations between changes in the microbiota and human health (Turnbaugh et al., 2007; Hamady and Knight, 2009; Huse et al., 2012).

Invertebrates have also been the focus of research into host-microbe interactions, both in terrestrial (e.g. insects (Dillon and Dillon, 2004)) and marine (e.g. corals (Bourne et al. 2016)) ecosystems. In marine systems, the hosts are in direct contact with seawater and the myriad of microorganisms within it. Hence, evaluating the host microbiota demands consideration of potential transient organisms, including those serving as food for prodigious filter feeders such as ascidians and marine sponges. Many sponges harbour highly diverse and abundant microbial communities (Taylor et al., 2007; Hentschel et al., 2012; Thomas et al., 2016), which can comprise up to 35% of sponge biomass and whose collective genomes ultimately define the sponge's phenotype (Webster and Thomas, 2016). The nature of the relationship between sponges and microbes ranges from pathogens/parasites (Webster et al., 2002; Webster, 2007) to mutualistic symbionts (Freeman et al., 2013). However, the great complexity of the marine sponge microbiota poses challenges when one considers the entire microbial community, as microbial species may number in the thousands (Webster et al., 2010; Thomas et al., 2016). One way to approach this challenge is to identify and analyse core species; that is, persistent and abundant species (Magurran and Henderson, 2003) either through time or among different environments, hosts or geographic locations.

We hypothesise that detection of changes in highly diverse systems, such as the marine sponge microbiota, may be affected by noise arising from this very diversity, only revealing true patterns when permanent and stable units, i.e. the core microbiota, are considered. Here we test this hypothesis by applying different core microbiota definitions to study the influence of environmental quality and sponge species identity on the sponge microbiota. Although some
definitions of the core microbiota did lead to significant differences among groups of samples, our
overall findings were remarkably resistant to the particular core microbiota definition which was
applied.

RESULTS AND DISCUSSION

Given the increasingly widespread use of the core microbiota concept in microbial systems including
the human gut (Hamady and Knight, 2009; Huse et al., 2012; Zhang et al., 2014; Cheng et al., 2016),
plant roots (Lundberg et al., 2012; Yeoh et al., 2016) and marine invertebrates (Schmitt et al., 2012;
Ainsworth et al., 2015; Thomas et al., 2016), it is prudent to examine whether the definition of the
core microbiota may impact the results obtained (and therefore the conclusions drawn) from a
study. To achieve this, we analysed the high-diversity microbial communities of three closely related
species of the marine sponge genus Xestospongia, which hosts microbes spanning 14 bacterial phyla
and 8 candidate phyla (Figure 1). These analyses were conducted within the broader – and highly
topical – ecological framework of determining how microbiotas respond to variation in
environmental quality (Cárdenas et al., 2014; Zeglin, 2015).

Robustness of the core microbiota approach

Overall, we found that even very substantial changes to the core microbiota definition did not
overtly influence findings of beta-diversity within the *Xestospongia* spp. microbiota (Table 1,
Supporting Information Tables S1 and S2). Indeed, for many of the applied core definitions, even
varying the percentage occurrence from 12% to 100% had negligible effect on the results obtained.
This was particularly true for the ‘overall core’ communities, although there were some beta-
diversity differences among the more stringent (90 and 100% core definitions) ‘specific core’
communities. Such stringent, species-specific criteria inherently select for OTUs exclusively associated with each host species, with the risk of artificially inflating differences between the respective microbiotas. In contrast to the robustness of core definition, choice of diversity metric had a larger effect, at least when examining the influence of environmental quality. With use of the Bray-Curtis dissimilarity metric, a significant effect of environment was obtained irrespective of core definition, whereas for Weighted UniFrac only one out of the 33 community types yielded a significant result (Table 1, Supporting Information Tables S1 and S2. For pairwise comparisons see Tables S3-S5). However, the two metrics were more similar (though not identical) when examining the effect of sponge species. nMDS ordination did not reveal specific groupings corresponding to host species identity or environmental quality for any of the applied core microbiota definitions (Fig. 2, Supporting Information Figure S1). Moreover, no significant differences in sample dispersion were observed within any of the datasets (PERMDISP, p>0.05).

The way in which the core microbiota was defined had a more pronounced effect on alpha-diversity. As might be expected when applying an increasingly stringent core definition, unweighted alpha-diversity metrics, such as number of observed species and Chao1, varied substantially according to which definition was used (Table 2). However, weighted metrics that also take taxon abundance into consideration (i.e. Shannon and Simpson diversity indices) did not show such variation across datasets. Of the 436 total OTUs identified, 370 were considered for the whole community analyses after rarefaction. This number decreased to 81 OTUs in the most stringent core microbiota definition, namely the overall core with 0.1% relative sequence abundance and 100% occurrence (for number of OTUs per community see Supporting Information Table S8). For each dataset, we also evaluated significant changes in alpha-diversity metrics among different sponge species and across habitats with different environmental quality. When all associated bacteria (i.e. whole community) were considered, alpha-diversity did not significantly change with sponge species identity or across sites with differing environmental quality (Table 2, Supporting Information Table S6). In contrast, marked differences were evident in the specific core community, with sponge
species, environmental quality and their interaction exhibiting significant differences using certain diversity metrics (Table 2, Supporting Information Table S6). For the overall core community, only the observed number of ‘species’ (97% OTUs) at sites of different environmental quality differed significantly.

There is little consensus within the literature about how to define a core microbiota. In a thought-provoking review article, Shade and Handelsman (2012) highlighted a number of key points to consider, such as whether to account only for OTU membership (presence/absence) or also abundance, as well as persistence through time. One strict core definition that is frequently used is that a given taxon must be present in *all* samples, as applied in a recent study of two species of *Anopheles* mosquitoes (Segata et al., 2016). Segata and colleagues identified a core microbiota of 12 OTUs shared by all tissues, which increased to 54 OTUs when the criterion was relaxed to consider those organisms present in at least 90% of samples. The same threshold was applied by Givens et al. (2015) when assessing the gut microbiota of 12 bony fish and three shark species. In the latter study, the core microbiota was defined as the group of OTUs present in all samples for a given species, thus creating up to 15 different core microbiotas. Overall, a wide range of core definitions have been applied across a broad array of environmental samples (e.g. Huse et al., 2012; Otani et al., 2014; Benjamino and Graf, 2016; Wang et al., 2016).

In our own previous research on the marine sponge microbiota, we defined a core community as comprising OTUs that were present in at least 70% of the 32 analysed sponge species (Schmitt et al., 2012). At a 97% OTU level, only three OTUs met this core definition (vs 8 with a less stringent 95% OTU definition). In a very recent study encompassing more than 800 samples from 81 sponge species, core OTUs were identified for the five sponge species with sufficient samples (at least 47 samples each) to conduct bacteria-bacteria interaction network analyses (Thomas et al., 2016). For a given host species, a core OTU was present in at least 85% of replicates from that species, yielding cores of 7 to 20 OTUs. By contrast, the core microbiotas of two coral species comprised phylotypes present in at least 30% of samples for each species (Ainsworth et al., 2015).
The most logical occurrence threshold to use will typically depend on the aims of a particular study. Here we investigated bacterial communities associated with three closely-related host species, allowing us to set a broad range of percentage occurrence values; in other studies that consider numerous distantly related host species (Schmitt et al. 2012; Thomas et al. 2016) this may be less appropriate, as the number of shared OTUs may be much lower.

Here we included a range of core microbiota definitions for our comparison, some of which take into account the relative abundance of individual OTUs and/or the percentage occurrence, while others do not. It is important to acknowledge the arbitrary nature of any core microbiota definition. Whilst it is not our intention here to advocate for a particular core definition, we draw attention to the potential for disparate conclusions when different definitions are applied, and encourage adopters of the core microbiota approach to explore the sensitivity of their data to the various definitions of 'core'.

## Networking symbionts: elucidating interactions within the marine sponge holobiont

Despite the ubiquity of interactions between prokaryotes and other species present in a given environment, ecological links between eukaryotes and prokaryotes have been poorly studied (Ings et al., 2009). This knowledge gap certainly extends to the interactions between marine sponges and their associated microbial communities. Other ecosystems rivalling in complexity to host-bacteria multispecies interactions tend to be dominated by specialist species, with a small number of generalists (Montoya et al., 2006; Bascompte, 2009; Björk et al., 2013). However, our results showed a low degree of specialism at the network level (Table 1; Supporting Information Table S7), much lower than what has been reported for free-living mutualistic networks, including pollination and seed dispersal networks, and ant-myrmecophyte and ant-nectar plant interaction systems (Blüthgen et al., 2007). Our network results are consistent with those of Thomas and colleagues (Thomas et al., 2016), in that their core sponge microbiotas were stable and characterised by
generalist symbionts exhibiting amensal and/or commensal interactions. The low degree of
specialism found in our study indicates that OTUs present in these communities are not particularly
host selective, and that host-microbe networks allow for more generalism to occur than in other
ecological networks. In addition, while multivariate analyses identified significant differences
(depending on core community definition and diversity metric considered) between bacterial
communities from different host species and different environmental conditions, deeper ecological
network analyses revealed that these differences were not mapped into the existence of network
modules or compartments (Table 1; Supporting Information Table S7). Network visualisation of core
communities showed that the OTUs comprising these cores were distributed across all sponge
species, across sites of different environmental quality and among each sponge species within each
quality site (see representative examples in Supporting Information Fig S2). The use of different core
community definitions had negligible effects on the outcomes of these ecological network analyses
(Table 1, Supporting Information Table S7). Other ecological networks, in particular food webs (Ings
et al., 2009; Thébault and Fontaine, 2010) and host-phage networks (Flores et al., 2011), are highly
modular, and functional group diversity tends to increase with modularity in food webs (Montoya et
al., 2015). The failure to detect any such modules in our sponge-associated bacterial community
network suggests that, in addition to the low degree of specialism, the system may not have a well-
defined network structure. Our study, however, focused on phylogenetically closely related host
species, so both generalism and lack of modularity is likely to occur. Closely related sponge species
commonly harbour similar microbial communities. For instance, analysis of full-length 16S rRNA
gene sequences derived from two geographically distant *Xestospongia* species (*X. muta* and *X.
testudinaria*) revealed similar bacterial community structures (Montalvo and Hill, 2011), and the
microbiotas of two *Ircinia* species (*l. fasciculata* and *l. variabilis*) were also found to be highly similar
(Erwin *et al.*, 2012). This relationship between bacterial community similarity and sponge
phylogenetic relatedness has also been observed at higher taxonomic levels, for example, within the
family *Geodiidae* (Schöttner *et al.*, 2013). However, factors specific to each host (e.g. nutrient levels,
depth, morphology, internal structure of the sponge) may also influence the associated microbial
community. Thomas *et al.* (2016) showed that most sponge species maintained low variability
within communities, thus indicating a selective habitat at the host species level. Greater selection of
microbes by hosts leading to high specialisation is observed among unrelated host species (e.g. Björk
*et al.* 2013), and thus we can hypothesise that host relatedness modulates specificity in host-
microbe systems.

Host species, environmental quality and the *Xestospongia* spp. microbiota

The dominant bacterial phyla found within *Xestospongia* spp. in this study reflected those commonly reported for marine sponges, namely *Proteobacteria* (*Gamma*-, *Delta*- and *Alphaproteobacteria*), *Chloroflexi*, *Acidobacteria*, *Actinobacteria*, *Cyanobacteria* and the candidate phylum PAUC34f (Figure 1). Many of these phyla feature so-called sponge-specific (SC) and sponge-and-coral-specific (SCC) clusters (Hentschel *et al.*, 2002; Taylor *et al.*, 2007; Simister *et al.*, 2012a). While not found exclusively within sponges (Webster *et al.*, 2010; Taylor *et al.*, 2013), the microorganisms represented by such clusters do tend to be heavily enriched in sponges (and corals). Within both the entire community and the core microbiota, we observed similar patterns for these clusters to those described above for analyses of alpha- and beta-diversity metrics. The percentage of OTUs assigned to SC and, to a lesser extent, SCC was higher when considering the core microbiota (Supporting Information Table S8). This is consistent with other studies that described these clusters as a stable and persistent group of microorganisms, which were much more highly represented in sponges than in other, non-sponge habitats (Hentschel *et al.*, 2002; Simister *et al.*, 2012a; Björk *et al.*, 2013).

In order to explore the *Xestospongia* spp. microbiota in more detail, we also conducted an
indicator value analysis (IndVal) to identify key OTUs that significantly discriminate between
different sample types (Supporting Information, Table S9, Figure S3). A total of seven OTUs,
affiliated with *Acidobacteria*, *Bacteroidetes*, "*Poribacteria*" and *Alpha*- and *Gammaproteobacteria*,
differentiated the different sponge species, although there was only one strict indicator per host species. A total of 29 OTUs affiliated with *Acidobacteria*, *Actinobacteria*, *Bacteroidetes*, *Chloroflexi*, *Cyanobacteria*, *Gemmatimonadetes*, "*Poribacteria*" and *Alpha-*, *Delta-* and *Gammaproteobacteria*, were additionally identified as indicators of environmental quality. Of these, three and 13 strict indicators were associated with high and low quality sites, respectively. No indicator OTU was found exclusively for moderate quality (Supporting Information, Table S9, Figure S3). While we did not employ the IndVal approach for defining a core microbiota, in certain contexts this may also be useful and warrants further investigation.

### Concluding remarks

A focus on the core microbiota can facilitate discrimination of the stable and permanent members of a microbial community. Here we evaluated the impact of different core microbiota definitions when interrogating highly diverse microbial systems. While due caution must be exercised when defining core communities, overall results are relatively insensitive to changing core definitions. Analysis of the core microbiota of closely related *Xestospongia* spp. found that OTUs present in these communities exhibit a high level of generalism and vary with the environmental quality of their habitat.

### EXPERIMENTAL PROCEDURES

In order to test our hypothesis about the core microbiota, we generated a sample dataset comprising three closely related species of marine sponge that occur across a gradient of environmental quality.
Sample collection and processing

Samples from three putatively different sponge species (species A, B and C) within the genus *Xestospongia* were collected at the Wakatobi Marine National Park, in southeast Sulawesi, Indonesia, in June 2012. These three *Xestospongia* species are considered to be cryptic species based on their assignment to three genetic groups using microsatellite data (see Bell *et al.* 2014). A similar pattern of cryptic speciation has also been shown for other local *Xestospongia* species with the use of mitochondrial markers (Setiawan *et al.*, 2016). Samples of the three species in our study were collected from sites experiencing different environmental conditions based on observations of live coral presence, water turbidity, fish abundance and distance to human populations, as previously described (McMellor and Smith, 2010; Bell *et al.*, 2014; Powell *et al.*, 2014). Seven sites were used based on their environmental conditions, ranging from high (Kerang Gurita and Tomea 2) to low (Wanci Harbour and Sampela) quality areas, together with sites of intermediate quality (Kaladupa Double Spur, the Ridge, and Tomea 1) (Supporting information, Table S10). Each sponge species was collected from each site (n=8 individuals per putative sponge species at each environmental condition area), except for species C that was not found at the lowest quality sites. Samples were preserved in 100% ethanol and stored at 4°C until processing. Previous research has shown that storage of sponge samples in absolute ethanol is appropriate for subsequent assessment of microbial communities (Reveillaud *et al.*, 2014).

DNA extraction and PCR amplification of 16S rRNA genes

Bacterial DNA extraction from approximately 30 mg of sponge tissue was carried out using the PowerSoil®-htp 96 Well Soil DNA Isolation Kit, following the manufacturer's protocol (MoBio Inc., Carlsbad, CA, USA). DNA yield and quality were evaluated using Nanodrop 2000 (Thermo Scientific) and extracts were normalised to a concentration of 5 ng/µL prior to PCR amplification. The
hypervariable V4-V5 region of the 16S rRNA gene was targeted using primers (533F and 907R) and thermal cycling conditions optimised for the sponge microbiota (Simister et al. 2012c; Cárdenas et al. 2014). Specific Illumina adaptor nucleotide sequences were also added to the primers. Correct-sized PCR products were purified using AMPure XP beads (Agencourt). DNA was quantified using Qubit, with DNA quality and quantity measured for a random selection of samples using the Agilent Bioanalyzer 2100. A second PCR to attach dual indices and Illumina sequencing adaptors, as well as the subsequent amplicon sequencing, was performed by the Centre for Genomics, Proteomics and Metabolomics through NZ Genomics Ltd at the University of Auckland, using the MiSeq Illumina sequencing platform. Sequence data were deposited in the NCBI Sequence Read Archive under accession number SRP064500.

## Sequence data processing

A total of 4,162,130 16S rRNA gene sequences were processed using mothur (Schloss et al., 2009) to join demultiplexed paired-end reads and to conduct subsequent quality trimming and data dereplication. After initial processing, 1,334,891 unique sequences were then considered for downstream analyses. The UPARSE pipeline (Edgar, 2013) was used for *de novo* OTU-picking, using USEARCH_64 (Edgar, 2010) *cluster_otu* command to construct a set of OTU representative sequences from the amplicon reads, with a 97% similarity threshold. Detection and removal of putative chimeras was done with *uchime_ref*, using SILVA as the reference database, identifying three chimeras among the 439 OTUs previously defined. The original (fungi) file of joined sequences was then mapped against the representative OTU sequences to include abundance data into the dataset, using the *usearch_global* command. The file created was converted to a biom table using biom software (McDonald et al., 2012), with *--convert* function, to run further analyses within QIIME 1.9 (Caporaso et al., 2010). Taxonomic assignment for each sequence was performed using the RDP classifier in QIIME 1.9, with Greengenes (version 13.5) (DeSantis et al., 2006) as the reference
database. In addition, to evaluate the specificity of the sponge-associated microbes, all OTUs were assessed for their affiliation with previously defined sponge- or sponge-coral -specific clusters (Simister et al. 2012a). Assignment to such clusters was carried out as described previously (Taylor et al., 2013).

## Defining the core microbiota

A central aim of this study was to determine whether applying different definitions of the “core” microbiota had an influence on the results obtained. To do so we evaluated the effect on the core communities of three factors for OTU selection: (1) percentage of occurrence, i.e. the percentage of samples in which an OTU has to be present in order to be considered for further analysis; (2) a minimum relative sequence abundance; and (3) the sample set used to calculate (1). To resolve which factor had the largest effect, we defined different core communities so that each of these three factors could be isolated and examined independently:

(1) To evaluate the impact of percentage of occurrence, minimum abundance threshold and sample set must be held constant. To do so, we applied five different occurrence percentages to two different core definitions ('specific core' and 'overall core', see below).

(2) To evaluate the impact of minimum abundance threshold, the percentage of occurrence and sample set must be held constant. To do so, we applied two different abundance thresholds to the whole community dataset, and to the two types of core definition.

(3) To evaluate the impact of the sample set selection, one must apply the same conditions, i.e. the same percentage of occurrence and relative abundance threshold, to all datasets. This is already covered with the analyses mentioned above.

To address the above, we generated and analysed 33 different communities, namely:

(1) Whole community, considering different relative abundance thresholds
Accepted Article

a. Whole community A – all detected bacterial OTUs were included in the analysis.

b. Whole community B – only OTUs with ≥0.01% relative sequence abundance across the entire dataset.

c. Whole community C – only OTUs with ≥0.1% relative sequence abundance across the entire dataset.

(2) Specific core community – we constructed 15 different core microbiotas considering:

a. only those OTUs that were present in ≥12% (1 sample), 25% (2), 50% (4), 90% (7) and 100% (8), of samples for a given sponge species / environmental quality combination (i.e. we effectively created 8 different core microbiotas = one for each combination and then combined all of them for the subsequent analyses).

b. only those OTUs with ≥0.01% relative sequence abundance across the entire dataset AND that were present in ≥12%, 25%, 50%, 90% and 100% samples for a given sponge species / environmental quality combination.

c. only those OTUs with ≥0.1% relative sequence abundance across the entire dataset AND that were present in ≥12%, 25%, 50%, 90% and 100% samples for a given sponge species / environmental quality combination.

This definition of core microbiota is specific in the sense that each treatment combination (of sponge species and environmental quality category) has its own core microbiota, based on occurrence - i.e., presence/absence – and in definitions b. and c. also on abundance.

(3) Overall core community – we constructed 15 core microbiotas considering:

a. only those OTUs that were present in ≥5% (3 samples), 25% (16), 50% (32), 90% (58) and 100% (64) of all samples.

b. only those OTUs with ≥0.01% relative sequence abundance across the entire dataset PLUS each OTU had to be present in ≥5%, 25%, 50%, 90% and 100% of samples.

c. only those OTUs with ≥0.1% relative sequence abundance across the entire dataset PLUS each OTU had to be present in ≥5%, 25%, 50%, 90% and 100% of samples.
In this case, the definitions were based on both occurrence and abundance across the
overall community (64 samples), thus defining different overall cores.

Two relative sequence abundance thresholds, of 0.01% and 0.1%, were chosen with the aim of retaining many rare taxa but discarding transient bacteria and accounting for sequencing errors.

Data representing each of these community types were selected from the original dataset, and all statistical analyses and comparisons described below (with the exception of Indicator Value Analyses) were conducted with the whole community and all of the different core microbiota datasets.

Statistical analyses of sequence data

Bacterial community analyses and statistical tests were conducted in QIIME 1.9. Alpha and beta diversity analyses were conducted after sample normalisation. To normalise sequencing depth, different rarefaction thresholds were applied to the different datasets (Supporting Information, Table S9) to reflect the lowest sequencing depth for each dataset.

For alpha diversity, unweighted species-based measures such as Chao1 (Chao, 1984) and number of observed species, and weighted species-based measures such as Shannon and Simpson diversity indices, were compared among sites of contrasting environmental quality and across sponge species. Beta diversity analyses were based on both Bray-Curtis (Bray and Curtis, 1957) and weighted UniFrac distance (Lozupone and Knight, 2005). The latter takes into account both the lineages contained in each group and the relative abundance of each type of organism, thus revealing community differences that are due to changes in relative taxon abundance. Both weighted UniFrac and Bray-Curtis distance matrices were used to draw non-metric multidimensional scaling (nMDS) ordinations and to conduct permutational analyses of variance (PERMANOVA) (Anderson, 2001). PERMANOVA analyses were conducted in PRIMER 6/PERMANOVA+ (Clarke and
Gorley, 2006) to test differences in microbial community abundance and composition among the three sponge species and amongst sites of differing environmental quality. Two fixed factors were considered (Species, *Sp*, and Quality, *Q*) as well as their interaction (*Sp* x *Q*). R (R Core Team, 2014) was used to construct MDS plots representing the similarity between samples, based on species abundance and composition.

To further explore the structure of the whole community, and with the aim of identifying relevant OTUs significantly associated with either different sponge species or different environmental qualities, we conducted Indicator Value Analysis (IndVal, De Cáceres and Legendre, 2009) following a previously described approach (Glasl *et al.*, 2016).

### Ecological network analyses

Ecological network analyses are becoming increasingly widely used within microbial ecology (Berry and Widder, 2014; Coyte *et al.*, 2015). We therefore wanted to explore the influence of the core microbiota definition on the properties of the ecological network. This was undertaken using the Bipartite package (Dormann *et al.*, 2008) in R (R Core Team, 2014).

Nodes in the network correspond to hosts and microbes, with links indicating the presence of a microbe in a host. Modularity and the presence of modules were calculated using the Quanbimo algorithm (Dormann and Strauss, 2014) for binary bipartite and weighted networks. Clusters or modules correspond to a group of species more connected with each other than with the rest of the system, and their presence can be interpreted as evidence of non-random assembly processes. Modularity *Q* was calculated as follows:

$$Q = \frac{1}{2m} \sum_{ij} (A_{ij} - K_{ij}) \delta(c_i, c_j)$$

where *m* is half the total number of observed links in the network, $A_{ij}$ is the weighted edge matrix, with values of 1 if a link between *i* and *j* exists, and 0 otherwise; and $K_{ij}$ the matrix of expected
weights, based on the null model. *c*ᵢ and *c*ⱼ represent the module to which a species *i* or *j* is assigned. Additionally, we calculated the degree of specialism of the network (*H*₂'). This was calculated by taking into account interaction frequencies, i.e. the relative abundance of each microbe on each host. We used an information theory metric developed by Blüthgen *et al.* (2006) that, in contrast to other quantitative metrics, is scale independent. The degree of specialisation in each web is defined as the deviation from an expected probability distribution of interactions (see Supporting Information for its calculation). The degree of specialisation ranges from 0 (high generalism degree) to 1.0 (high specialism degree).

## ACKNOWLEDGEMENTS

CAG was supported by an Encouraging and Supporting Innovation Doctoral Scholarship in Marine Science awarded by the University of Auckland. NSW was funded through an Australian Research Council Future Fellowship FT120100480. JMM acknowledges support by the Laboratory of Excellence "TULIP" (ANR-10-LABX-41; ANR-11-IDEX-002-02) and the Midi-Pyrenees Region. The Victoria University of Wellington University Research Fund provided funding for the collection of the specimens. Luke Thomas helped with the sample collections.

Accepted for publication
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Evaluating the core microbiota in complex communities: a systematic investigation

Carmen Astudillo-Garcia<fnref n="1" />,<fnref n="2" />, James J. Bell<fnref n="3" />, Nicole S. Webster<fnref n="4" />, Bettina Glasl<fnref n="5" />, Jamaluddin Jompa<fnref n="6" />, Jose

M. Montoya<fnref n="7" /> & Michael W. Taylor<fnref n="1" />,<fnref n="8" />*

Formatted: Superscript

*Corresponding author:

Associate Professor Mike Taylor

School of Biological Sciences, University of Auckland

Private Bag 92019, Auckland 1142, New Zealand

Tel. +64 9 3737599 x82280, Fax +64 9 3737416

Email mw.taylor@auckland.ac.nz

Running title: Core microbiota in complex communities

<footnote n="1">¹School of Biological Sciences, University of Auckland, Auckland, New Zealand.</footnote>

<footnote n="2">²Institute of Marine Science, University of Auckland, Auckland, New Zealand.</footnote>

<footnote n="3">³School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.</footnote>

<footnote n="4">⁴Australian Institute of Marine Science, Townsville, Australia.</footnote>

<footnote n="5">⁵AIMS@JCU, Australian Institute of Marine Science, College of Science and Engineering, James Cook University, Townsville, Australia</footnote>

<footnote n="6">⁶Research and Development Centre on Marine, Coastal and Small Islands, Hasanuddin University, Makassar, Indonesia</footnote>

<footnote n="7">⁷Ecological Networks and Global Change Group, Experimental and Theoretical Ecology Station, CNRS-University Paul Sabatier, Moulis, France.</footnote>

<footnote n="8">⁸Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand.</footnote>
Accepted Article

Key words: core microbiota, symbionts, ecological networks, sponges.
ORIGINALITY-SIGNIFICANCE STATEMENT

Defining the ‘core microbiota’ is an increasingly popular mechanism to simplify analyses and identify key microorganisms within a community. How a core is defined varies widely across studies, yet the potential influence of different definitions on results obtained has received little attention. We use a 16S rRNA gene dataset derived from the marine sponge microbiota to systematically evaluate the effect of ‘core’ definition when interrogating highly diverse microbial communities. We demonstrate that, while the parameters defining the core microbiota can influence the conclusions drawn and therefore caution should be exercised, our overall findings were relatively insensitive to changing core definitions.
## SUMMARY

The study of complex microbial communities poses unique conceptual and analytical challenges, with microbial species potentially numbering in the thousands. With transient or allochthonous microorganisms often adding to this complexity, a 'core' microbiota approach, focusing only on the stable and permanent members of the community, is becoming increasingly popular. Given the various ways of defining a core microbiota, it is prudent to examine whether the definition of the core impacts upon the results obtained. Here we used complex marine sponge microbiotas and undertook a systematic evaluation of the degree to which different factors used to define the core influenced the conclusions. Significant differences in alpha- and beta-diversity were detected using some but not all core definitions. However, findings related to host specificity and environmental quality were largely insensitive to major changes in the core microbiota definition. Furthermore, none of the applied definitions altered our perception of the ecological networks summarising interactions among bacteria within the sponges. These results suggest that, while care should still be taken in interpretation, the core microbiota approach is surprisingly robust, at least for comparing microbiotas of closely related samples.

Accepted for publication
INTRODUCTION

Microbial communities in nature are often highly complex, comprising hundreds or even thousands of species across a diverse array of taxa. Exemplary in this regard are the microbiotas of soil (Ramirez et al., 2014), the human gut (Bäckhed et al., 2012; Huse et al., 2012) and marine invertebrates such as sponges (Thomas et al., 2016). Each of these ecosystems harbours members of the Bacteria, Archaea and Eukarya domains, as well as innumerable viruses (Fierer et al., 2007; Taylor et al., 2007; Arumugam et al., 2011; Laffy et al., 2016). Such complexity poses formidable conceptual and analytical challenges, with each ecosystem member potentially capable of interacting with all others. These challenges are further amplified when one considers transient or allochthonous community members (Savage, 1977), which may contribute little to the functionality of the system but are still detected by today's sensitive molecular approaches. The concept of a 'core' microbiota, which only considers persistent (and sometimes abundant) members of a microbial community, has thus garnered increasing research attention (Shade and Handelsman, 2012).

Consideration of the core microbiota may be particularly beneficial in the study of host-
microbe associations. Some holobiont associations involve highly specific partnerships between a
single host and a single microbe, such as the well-known mutualism between the squid *Euprymna
scolopes* and the bioluminescent bacterium *Vibrio fischeri* (Nyholm and McFall-Ngai, 2004). These
relatively 'simple' symbiotic systems have provided many unique insights into the mechanisms of
interaction; however, more complex systems involving a greater number of participants are
ubiquitous in nature. In this context, host-microbe interaction networks offer fascinating insights
into the ecology and evolution of symbiosis due to the key role these taxonomically diverse
microbial communities play within their hosts. Among the most commonly studied host-microbe
interaction systems are those comprised of vertebrates and their gut microbes which play an
important role in host nutrition and pathogen defence. Such interactions have been described for
fishes (Sullam *et al.*, 2012), birds (Waite and Taylor, 2014), reptiles (Martin *et al.*, 2010) and mammals (Ley *et al.*, 2008), as well as being a primary focus of research in humans (Turnbaugh *et al.*, 2007; Hamady and Knight, 2009; Shafquat *et al.*, 2014). The Human Microbiome Project utilised a core species approach to identify stable and consistent associations, facilitating the identification of correlations between changes in the microbiota and human health (Turnbaugh *et al.*, 2007; Hamady and Knight, 2009; Huse *et al.*, 2012).

Invertebrates have also been the focus of research into host-microbe interactions, both in terrestrial (e.g. insects (Dillon and Dillon, 2004)) and marine (e.g. corals (Bourne et al. 2016)) ecosystems. In marine systems, the hosts are in direct contact with seawater and the myriad of microorganisms within it. Hence, evaluating the host microbiota demands consideration of potential transient organisms, including those serving as food for prodigious filter feeders such as ascidians and marine sponges. Many sponges harbour highly diverse and abundant microbial communities (Taylor *et al.*, 2007; Hentschel *et al.*, 2012; Thomas *et al.*, 2016), which can comprise up to 35% of sponge biomass and whose collective genomes ultimately define the sponge's phenotype (Webster and Thomas, 2016). The nature of the relationship between sponges and microbes ranges from pathogens/parasites (Webster *et al.*, 2002; Webster, 2007) to mutualistic symbionts (Freeman *et al.*, 2013). However, the great complexity of the marine sponge microbiota poses challenges when one considers the entire microbial community, as microbial species may number in the thousands (Webster *et al.*, 2010; Thomas *et al.*, 2016). One way to approach this challenge is to identify and analyse core species; that is, persistent and abundant species (Magurran and Henderson, 2003) either through time or among different environments, hosts or geographic locations.

We hypothesise that detection of changes in highly diverse systems, such as the marine sponge microbiota, may be affected by noise arising from this very diversity, only revealing true patterns when permanent and stable units, i.e. the core microbiota, are considered. Here we test this hypothesis by applying different core microbiota definitions to study the influence of environmental quality and sponge species identity on the sponge microbiota. Although some
definitions of the core microbiota did lead to significant differences among groups of samples, our overall findings were remarkably resistant to the particular core microbiota definition which was applied.

## RESULTS AND DISCUSSION

Given the increasingly widespread use of the core microbiota concept in microbial systems including the human gut (Hamady and Knight, 2009; Huse et al., 2012; Zhang et al., 2014; Cheng et al., 2016), plant roots (Lundberg et al., 2012; Yeoh et al., 2016) and marine invertebrates (Schmitt et al., 2012; Ainsworth et al., 2015; Thomas et al., 2016), it is prudent to examine whether the definition of the core microbiota may impact the results obtained (and therefore the conclusions drawn) from a study. To achieve this, we analysed the high-diversity microbial communities of three closely related species of the marine sponge genus *Xestospongia*, which hosts microbes spanning 14 bacterial phyla and 8 candidate phyla (Figure 1). These analyses were conducted within the broader – and highly topical - ecological framework of determining how microbiotas respond to variation in environmental quality (Cárdenas et al., 2014; Zeglin, 2015).

### Robustness of the core microbiota approach

Overall, we found that even very substantial changes to the core microbiota definition did not overtly influence findings of beta-diversity within the *Xestospongia* spp. microbiota (Table 1, Supporting Information Tables S1 and S2). Indeed, for many of the applied core definitions, even varying the percentage occurrence from 12% to 100% had negligible effect on the results obtained. This was particularly true for the ‘overall core’ communities, although there were some beta-diversity differences among the more stringent (90 and 100% core definitions) ‘specific core’
communities. Such stringent, species-specific criteria inherently select for OTUs exclusively associated with each host species, with the risk of artificially inflating differences between the respective microbiotas. In contrast to the robustness of core definition, choice of diversity metric had a larger effect, at least when examining the influence of environmental quality. With use of the Bray-Curtis dissimilarity metric, a significant effect of environment was obtained irrespective of core definition, whereas for Weighted UniFrac only one out of the 33 community types yielded a significant result (Table 1, Supporting Information Tables S1 and S2. For pairwise comparisons see Tables S3-S5). However, the two metrics were more similar (though not identical) when examining the effect of sponge species. nMDS ordination did not reveal specific groupings corresponding to host species identity or environmental quality for any of the applied core microbiota definitions (Fig. 2, Supporting Information Figure S1). Moreover, no significant differences in sample dispersion were observed within any of the datasets (PERMDISP, p>0.05).

The way in which the core microbiota was defined had a more pronounced effect on alpha-diversity. As might be expected when applying an increasingly stringent core definition, unweighted alpha-diversity metrics, such as number of observed species and Chao1, varied substantially according to which definition was used (Table 2). However, weighted metrics that also take taxon abundance into consideration (i.e. Shannon and Simpson diversity indices) did not show such variation across datasets. Of the 436 total OTUs identified, 370 were considered for the whole community analyses after rarefaction. This number decreased to 81 OTUs in the most stringent core microbiota definition, namely the overall core with 0.1% relative sequence abundance and 100% occurrence (for number of OTUs per community see Supporting Information Table S8). For each dataset, we also evaluated significant changes in alpha-diversity metrics among different sponge species and across habitats with different environmental quality. When all associated bacteria (i.e. whole community) were considered, alpha-diversity did not significantly change with sponge species identity or across sites with differing environmental quality (Table 2, Supporting Information Table S6). In contrast, marked differences were evident in the specific core community, with sponge
species, environmental quality and their interaction exhibiting significant differences using certain diversity metrics (Table 2, Supporting Information Table S6). For the overall core community, only the observed number of ‘species’ (97% OTUs) at sites of different environmental quality differed significantly.

There is little consensus within the literature about how to define a core microbiota. In a thought-provoking review article in 2012, Shade and Handelsman (2012) highlighted a number of key points to consider, such as whether to account only for OTU membership (presence/absence) or also abundance, as well as persistence through time. One strict core definition that is frequently used is that a given taxon must be present in all samples, as applied in a recent study of two species of *Anopheles* mosquitoes (Segata et al., 2016). Segata and colleagues identified a core microbiota of 12 OTUs shared by all tissues, which increased to 54 OTUs when the criterion was relaxed to consider those organisms present in at least 90% of samples. The same threshold was applied by Givens et al. (2015) when assessing the gut microbiota of 12 bony fish and three shark species. In the latter study, the core microbiota was defined as the group of OTUs present in all samples for a given species, thus creating up to 15 different core microbiotas. Overall, a wide range of core definitions have been applied across a broad array of environmental samples (e.g. Huse et al., 2012; Otani et al., 2014; Benjamino and Graf, 2016; Wang et al., 2016).

In our own previous research on the marine sponge microbiota, we defined a core community as comprising OTUs that were present in at least 70% of the 32 analysed sponge species (Schmitt et al., 2012). At a 97% OTU level, only three OTUs met this core definition (vs 8 with a less stringent 95% OTU definition). In a very recent study encompassing more than 800 samples from 81 sponge species, core OTUs were identified for the five sponge species with sufficient samples (at least 47 samples each) to conduct bacteria-bacteria interaction network analyses (Thomas et al., 2016). For a given host species, a core OTU was present in at least 85% of replicates from that species, yielding cores of 7 to 20 OTUs. By contrast, the core microbiotas of two coral species comprised phylotypes present in at least 30% of samples for each species (Ainsworth et al., 2015).
The most logical occurrence threshold to use will typically depend on the aims of a particular study. Here we investigated bacterial communities associated with three closely-related host species, allowing us to set a broad range of percentage occurrence values; in other studies that consider numerous distantly related host species (Schmitt et al. 2012; Thomas et al. 2016) this may be less appropriate, as the number of shared OTUs may be much lower.

Here we included a range of core microbiota definitions for our comparison, some of which take into account the relative abundance of individual OTUs and/or the percentage occurrence, while others do not. It is important to acknowledge the arbitrary nature of any core microbiota definition. Whilst it is not our intention here to advocate for a particular core definition, we draw attention to the potential for disparate conclusions when different definitions are applied, and encourage adopters of the core microbiota approach to explore the sensitivity of their data to the various definitions of 'core'.

### Networking symbionts: elucidating interactions within the marine sponge holobiont

Despite the ubiquity of interactions between prokaryotes and other species present in a given environment, ecological links between eukaryotes and prokaryotes have been poorly studied (Ings et al., 2009). This knowledge gap certainly extends to the interactions between marine sponges and their associated microbial communities. Other ecosystems rivalling in complexity to host-bacteria multispecies interactions tend to be dominated by specialist species, with a small number of generalists (Montoya et al., 2006; Bascompte, 2009; Björk et al., 2013). However, our results showed a low degree of specialism at the network level (Table 1; Supporting Information Table S7), much lower than what has been reported for free-living mutualistic networks, including pollination and seed dispersal networks, and ant-myrmecophyte and ant-nectar plant interaction systems (Blüthgen et al., 2007). Our network results are consistent with those of Thomas and colleagues (Thomas et al., 2016), in that their core sponge microbiotas were stable and characterised by
generalist symbionts exhibiting amensal and/or commensal interactions. The low degree of
specialism found in our study indicates that OTUs present in these communities are not particularly
host selective, and that host-microbe networks allow for more generalism to occur than in other
ecological networks. In addition, while multivariate analyses identified significant differences
(depending on core community definition and diversity metric considered) between bacterial
communities from different host species and different environmental conditions, deeper ecological
network analyses revealed that these differences were not mapped into the existence of network
modules or compartments (Table 1; Supporting Information Table S7). Network visualisation of core
communities showed that the OTUs comprising these cores were distributed across all sponge
species, across sites of different environmental quality and among each sponge species within each
quality site (see representative examples in Supporting Information Fig S2). The use of different core
community definitions had negligible effects on the outcomes of these ecological network analyses
(Table 1, Supporting Information Table S7). Other ecological networks, in particular food webs (Ings
et al., 2009; Thébault and Fontaine, 2010) and host-phage networks (Flores et al., 2011), are highly
modular, and functional group diversity tends to increase with modularity in food webs (Montoya et
al., 2015). The failure to detect any such modules in our sponge-associated bacterial community
network suggests that, in addition to the low degree of specialism, the system may not have a well-
defined network structure. Our study, however, focused on phylogenetically closely related host
species, so both generalism and lack of modularity is likely to occur. Closely related sponge species
commonly harbour similar microbial communities. For instance, analysis of full-length 16S rRNA
gene sequences derived from two geographically distant *Xestospongia* species (*X. muta* and *X.
testudinaria*) revealed similar bacterial community structures (Montalvo and Hill, 2011), and the
microbiotas of two *Ircinia* species (*l. fasciculata* and *l. variabilis*) were also found to be highly similar
(Erwin et al., 2012). This relationship between bacterial community similarity and sponge
phylogenetic relatedness has also been observed at higher taxonomic levels, for example, within the
family *Geodiidae* (Schöttner et al., 2013). However, factors specific to each host (e.g. nutrient levels,
depth, morphology, internal structure of the sponge) may also influence the associated microbial
community. Thomas et al. (2016) showed that most sponge species maintained low variability
within communities, thus indicating a selective habitat at the host species level. Greater selection of
microbes by hosts leading to high specialisation is observed among unrelated host species (e.g. Björk
et al. 2013), and thus we can hypothesise that host relatedness modulates specificity in host-
microbe systems.

## Host species, environmental quality and the *Xestospongia* spp. microbiota

The dominant bacterial phyla found within *Xestospongia* spp. in this study reflected those commonly reported for marine sponges, namely *Proteobacteria* (*Gamma*-, *Delta*- and *Alphaproteobacteria*), *Chloroflexi*, *Acidobacteria*, *Actinobacteria*, *Cyanobacteria* and the candidate phylum PAUC34f (Figure 1). Many of these phyla feature so-called sponge-specific (SC) and sponge-and-coral-specific (SCC) clusters (Hentschel *et al.*, 2002; Taylor *et al.*, 2007; Simister *et al.*, 2012a). While not found exclusively within sponges (Webster *et al.*, 2010; Taylor *et al.*, 2013), the microorganisms represented by such clusters do tend to be heavily enriched in sponges (and corals). Within both the entire community and the core microbiota, we observed similar patterns for these clusters to those described above for analyses of alpha- and beta-diversity metrics. The percentage of OTUs assigned to SC and, to a lesser extent, SCC was higher when considering the core microbiota (Supporting Information Table S8). This is consistent with other studies that described these clusters as a stable and persistent group of microorganisms, which were much more highly represented in sponges than in other, non-sponge habitats (Hentschel *et al.*, 2002; Simister *et al.*, 2012a; Björk *et al.*, 2013).

In order to explore the *Xestospongia* spp. microbiota in more detail, we also conducted an
indicator value analysis (IndVal) to identify key OTUs that significantly discriminate between
different sample types (Supporting Information, Table S9, Figure S3). A total of seven OTUs,
affiliated with *Acidobacteria*, *Bacteroidetes*, “*Poribacteria*” and *Alpha*- and *Gammaproteobacteria*,
differentiated the different sponge species, although there was only one strict indicator per host species. A total of 29 OTUs affiliated with *Acidobacteria*, *Actinobacteria*, *Bacteroidetes*, *Chloroflexi*, *Cyanobacteria*, *Gemmatimonadetes*, "*Poribacteria*" and *Alpha-*, *Delta-* and *Gammaproteobacteria*, were additionally identified as indicators of environmental quality. Of these, three and 13 strict indicators were associated with high and low quality sites, respectively. No indicator OTU was found exclusively for moderate quality (Supporting Information, Table S9, Figure S3). While we did not employ the IndVal approach for defining a core microbiota, in certain contexts this may also be useful and warrants further investigation.

## Concluding remarks

A focus on the core microbiota can facilitate discrimination of the stable and permanent members of a microbial community. Here we evaluated the impact of different core microbiota definitions when interrogating highly diverse microbial systems. While due caution must be exercised when defining core communities, overall results are relatively insensitive to changing core definitions. Analysis of the core microbiota of closely related *Xestospongia* spp. found that OTUs present in these communities exhibit a high level of generalism and vary with the environmental quality of their habitat.

## EXPERIMENTAL PROCEDURES

In order to test our hypothesis about the core microbiota, we generated a sample dataset comprising three closely related species of marine sponge that occur across a gradient of environmental quality.
Sample collection and processing

Samples from three putatively different sponge species (species A, B and C) within the genus *Xestospongia* were collected at the Wakatobi Marine National Park, in southeast Sulawesi, Indonesia, in June 2012. These three *Xestospongia* species are considered to be cryptic species based on their assignment to three genetic groups using microsatellite data (see Bell *et al.* 2014). A similar pattern of cryptic speciation has also been shown for other local *Xestospongia* species with the use of mitochondrial markers (Setiawan *et al.*, 2016). Samples of the three species in our study were collected from sites experiencing different environmental conditions based on observations of live coral presence, water turbidity, fish abundance and distance to human populations, as previously described (McMellor and Smith, 2010; Bell *et al.*, 2014; Powell *et al.*, 2014). Seven sites were used based on their environmental conditions, ranging from high (Kerang Gurita and Tomea 2) to low (Wanci Harbour and Sampela) quality areas, together with sites of intermediate quality (Kaladupa Double Spur, the Ridge, and Tomea 1) (Supporting information, Table S10). Each sponge species was collected from each site (n=8 individuals per putative sponge species at each environmental condition area), except for species C that was not found at the lowest quality sites.

Samples were preserved in 100% ethanol and stored at 4°C until processing. Previous research has shown that storage of sponge samples in absolute ethanol is appropriate for subsequent assessment of microbial communities (Reveillaud *et al.*, 2014).

DNA extraction and PCR amplification of 16S rRNA genes

Bacterial DNA extraction from approximately 30 mg of sponge tissue was carried out using the PowerSoil®-htp 96 Well Soil DNA Isolation Kit, following the manufacturer's protocol (MoBio Inc., Carlsbad, CA, USA). DNA yield and quality were evaluated using Nanodrop 2000 (Thermo Scientific) and extracts were normalised to a concentration of 5 ng/µL prior to PCR amplification. The
hypervariable V4-V5 region of the 16S rRNA gene was targeted using primers (533F and 907R) and thermal cycling conditions optimised for the sponge microbiota (Simister et al. 2012c; Cárdenas et al. 2014). Specific Illumina adaptor nucleotide sequences were also added to the primers. Correct-sized PCR products were purified using AMPure XP beads (Agencourt). DNA was quantified using Qubit, with DNA quality and quantity measured for a random selection of samples using the Agilent Bioanalyzer 2100. A second PCR to attach dual indices and Illumina sequencing adaptors, as well as the subsequent amplicon sequencing, was performed by the Centre for Genomics, Proteomics and Metabolomics through NZ Genomics Ltd at the University of Auckland, using the MiSeq Illumina sequencing platform. Sequence data were deposited in the NCBI Sequence Read Archive under accession number SRP064500.

## Sequence data processing

A total of 4,162,130 16S rRNA gene sequences were processed using mothur (Schloss et al., 2009) to join demultiplexed paired-end reads and to conduct subsequent quality trimming and data dereplication. After initial processing, 1,334,891 unique sequences were then considered for downstream analyses. The UPARSE pipeline (Edgar, 2013) was used for *de novo* OTU-picking, using USEARCH_64 (Edgar, 2010) *cluster_otu* command to construct a set of OTU representative sequences from the amplicon reads, with a 97% similarity threshold. Detection and removal of putative chimeras was done with *uchime_ref*, using SILVA as the reference database, identifying three chimeras among the 439 OTUs previously defined. The original (bast) file of joined sequences was then mapped against the representative OTU sequences to include abundance data into the dataset, using the *usearch_global* command. The file created was converted to a biom table using biom software (McDonald et al., 2012), with *-convert* function, to run further analyses within QIIME 1.9 (Caporaso et al., 2010). Taxonomic assignment for each sequence was performed using the RDP classifier in QIIME 1.9, with Greengenes (version 13.5) (DeSantis et al., 2006) as the reference
database. In addition, to evaluate the specificity of the sponge-associated microbes, all OTUs were assessed for their affiliation with previously defined sponge- or sponge-coral -specific clusters (Simister et al. 2012a). Assignment to such clusters was carried out as described previously (Taylor et al., 2013).

Defining the core microbiota

A central aim of this study was to determine whether applying different definitions of the “core” microbiota had an influence on the results obtained. To do so we evaluated the effect on the core communities of three factors for OTU selection: (1) percentage of occurrence, i.e. the percentage of samples in which an OTU has to be present in order to be considered for further analysis; (2) a minimum relative sequence abundance; and (3) the sample set used to calculate (1). To resolve which factor had the largest effect, we defined different core communities so that each of these three factors could be isolated and examined independently:

(1) To evaluate the impact of percentage of occurrence, minimum abundance threshold and sample set must be held constant. To do so, we applied five different occurrence percentages to two different core definitions ('specific core' and 'overall core', see below).

(2) To evaluate the impact of minimum abundance threshold, the percentage of occurrence and sample set must be held constant. To do so, we applied two different abundance thresholds to the whole community dataset, and to the two types of core definition.

(3) To evaluate the impact of the sample set selection, one must apply the same conditions, i.e. the same percentage of occurrence and relative abundance threshold, to all datasets. This is already covered with the analyses mentioned above.

To address the above, we generated and analysed 33 different communities, namely:

(1) Whole community, considering different relative abundance thresholds
a. Whole community A – all detected bacterial OTUs were included in the analysis.

b. Whole community B – only OTUs with ≥0.01% relative sequence abundance across the entire dataset.

c. Whole community C – only OTUs with ≥0.1% relative sequence abundance across the entire dataset.

(2) Specific core community – we constructed 15 different core microbiotas considering:

a. only those OTUs that were present in ≥12% (1 sample), 25% (2), 50% (4), 90% (7) and 100% (8), of samples for a given sponge species / environmental quality combination (i.e. we effectively created 8 different core microbiotas = one for each combination and then combined all of them for the subsequent analyses).

b. only those OTUs with ≥0.01% relative sequence abundance across the entire dataset AND that were present in ≥12%, 25%, 50%, 90% and 100% samples for a given sponge species / environmental quality combination.

c. only those OTUs with ≥0.1% relative sequence abundance across the entire dataset AND that were present in ≥12%, 25%, 50%, 90% and 100% samples for a given sponge species / environmental quality combination.

This definition of core microbiota is specific in the sense that each treatment combination (of sponge species and environmental quality category) has its own core microbiota, based on occurrence - i.e., presence/absence – and in definitions b. and c. also on abundance.

(3) Overall core community – we constructed 15 core microbiotas considering:

a. only those OTUs that were present in ≥5% (3 samples), 25% (16), 50% (32), 90% (58) and 100% (64) of all samples.

b. only those OTUs with ≥0.01% relative sequence abundance across the entire dataset PLUS each OTU had to be present in ≥5%, 25%, 50%, 90% and 100% of samples.

c. only those OTUs with ≥0.1% relative sequence abundance across the entire dataset PLUS each OTU had to be present in ≥5%, 25%, 50%, 90% and 100% of samples.
In this case, the definitions were based on both occurrence and abundance across the
overall community (64 samples), thus defining different overall cores.

Two relative sequence abundance thresholds, of 0.01% and 0.1%, were chosen with the aim of retaining many rare taxa but discarding transient bacteria and accounting for sequencing errors.

Data representing each of these community types were selected from the original dataset,
and all statistical analyses and comparisons described below (with the exception of Indicator Value
Analyses) were conducted with the whole community and all of the different core microbiota
datasets.

Statistical analyses of sequence data

Bacterial community analyses and statistical tests were conducted in QIIME 1.9. Alpha and beta diversity analyses were conducted after sample normalisation. To normalise sequencing depth, different rarefaction thresholds were applied to the different datasets (Supporting Information, Table S9) to reflect the lowest sequencing depth for each dataset.

For alpha diversity, unweighted species-based measures such as Chao1 (Chao, 1984) and
number of observed species, and weighted species-based measures such as Shannon and Simpson
diversity indices, were compared among sites of contrasting environmental quality and across
sponge species. Beta diversity analyses were based on both Bray-Curtis (Bray and Curtis, 1957) and
weighted UniFrac distance (Lozupone and Knight, 2005). The latter takes into account both the
lineages contained in each group and the relative abundance of each type of organism, thus
revealing community differences that are due to changes in relative taxon abundance. Both
weighted UniFrac and Bray-Curtis distance matrices were used to draw non-metric multidimensional
scaling (nMDS) ordinations and to conduct permutational analyses of variance (PERMANOVA)
(Anderson, 2001). PERMANOVA analyses were conducted in PRIMER 6/PERMANOVA+ (Clarke and
Gorley, 2006) to test differences in microbial community abundance and composition among the three sponge species and amongst sites of differing environmental quality. Two fixed factors were considered (Species, Sp, and Quality, Q) as well as their interaction (Sp x Q). R (R Core Team, 2014) was used to construct MDS plots representing the similarity between samples, based on species abundance and composition.

To further explore the structure of the whole community, and with the aim of identifying relevant OTUs significantly associated with either different sponge species or different environmental qualities, we conducted Indicator Value Analysis (IndVal, De Cáceres and Legendre, 2009) following a previously described approach (Glasl et al., 2016).

### Ecological network analyses

Ecological network analyses are becoming increasingly widely used within microbial ecology (Berry and Widder, 2014; Coyte et al., 2015). We therefore wanted to explore the influence of the core microbiota definition on the properties of the ecological network. This was undertaken using the Bipartite package (Dormann et al., 2008) in R (R Core Team, 2014).

Nodes in the network correspond to hosts and microbes, with links indicating the presence of a microbe in a host. Modularity and the presence of modules were calculated using the Quanbimo algorithm (Dormann and Strauss, 2014) for binary bipartite and weighted networks. Clusters or modules correspond to a group of species more connected with each other than with the rest of the system, and their presence can be interpreted as evidence of non-random assembly processes. Modularity Q was calculated as follows:

$$Q = \frac{1}{2m} \sum_{ij} (A_{ij} - K_{ij}) \delta(c_i, c_j)$$

where m is half the total number of observed links in the network, Aij is the weighted edge matrix, with values of 1 if a link between i and j exists, and 0 otherwise; and Kij the matrix of expected
weights, based on the null model. *c*<sub>*i*</sub> and *c*<sub>*j*</sub> represent the module to which a species *i* or *j* is assigned. Additionally, we calculated the degree of specialism of the network (*H*<sub>2</sub>'). This was calculated by taking into account interaction frequencies, i.e. the relative abundance of each microbe on each host. We used an information theory metric developed by Blüthgen *et al*. (2006) that, in contrast to other quantitative metrics, is scale independent. The degree of specialisation in each web is defined as the deviation from an expected probability distribution of interactions (see Supporting Information for its calculation). The degree of specialisation ranges from 0 (high generalism degree) to 1.0 (high specialism degree).

ACKNOWLEDGEMENTS

CAG was supported by an Encouraging and Supporting Innovation Doctoral Scholarship in Marine
Science awarded by the University of Auckland. NSW was funded through an Australian Research
Council Future Fellowship FT120100480. JMM acknowledges support by the Laboratory of
Excellence "TULIP" (ANR-10-LABX-41; ANR-11-IDEX-002-02) and the Midi-Pyrenees Region. The
Victoria University of Wellington University Research Fund provided funding for the collection of the
specimens. Luke Thomas helped with the sample collections.
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<figure><img src="image_3.png" /><figcaption>Figure 1. Relative 16S rRNA gene sequence abundance of bacterial phyla present in each dataset considered: whole community A, B and C; specific core 12%-100% and overall core 5%-100% both with no minimum abundance threshold, 0.01% and 0.1% relative abundance threshold. The definition of each core community is outlined in Experimental Procedures. Percentages indicated in each community refer to percentage occurrence.</figcaption></figure>

210x297mm (300 x 300 DPI)
<figure><img src="image_4.png" /><figcaption>Figure 2. Non-metric multidimensional scaling (nMDS) plot based on weighted UniFrac distance matrices for the whole bacterial community (a), specific core 100% occurrence (no minimum abundance) (b), specific core 100% occurrence AND 0.01% relative abundance threshold (c), specific core 100% occurrence AND 0.1% relative abundance threshold (d), and overall core 100% occurrence (no minimum abundance) (e) overall core 100% occurrence AND 0.01% relative abundance (f) overall and core 100% occurrence AND 0.1% minimum abundance threshold (g). In the legend, A, B, C refers to host species A, B and C, respectively; H, M, L refers to High, Moderate and Low quality habitats respectively.</figcaption></figure>

210x297mm (300 x 300 DPI)

Wiley-Blackwell and Society for Applied Microbiology

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Accepted Article

Table 1. Influence of core microbiota definition on statistical evaluation of differences relating to environmental quality and sponge species, as well as the key ecological network properties modularity (Q) and specialisation (H₂). Core community definitions are as outlined in Experimental Procedures. A, B, and C refer to minimum relative abundance thresholds applied to each community (0, 0.01 and 0.1% respectively). Percentages indicated in each community refer to percentage occurrence (12-100% for 'Specific core' and 5-100% for 'Overall core'). Sig. = denotes a statistically significant difference (p<0.05). Further details on the statistical analyses are provided in Supplementary Information Table S1 and S2.
<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Weighted UniFrac</th><th colspan="4">Bray-Curtis</th></tr><tr><th>Community</th><th>Environ. quality</th><th>Sponge species</th><th>spxq</th><th>Environ. quality</th><th>Sponge species</th><th>spxq</th><th>Q</th><th>H<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="3">Whole community</td><td></td><td>0.1% abund.</td><td>whole A</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td></td><td>0.01% abund.</td><td>whole B</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td></td><td>0.1% abund.</td><td>whole C</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td rowspan="6">Specific core community</td><td rowspan="6">No min. abund.</td><td>Core 12%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 50%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 90%</td><td>-</td><td>sig.</td><td>-</td><td>sig.*</td><td>sig.*</td><td>sig.</td><td>Low</td><td>Low</td></tr><tr><td>Core 100%</td><td>-</td><td>sig.</td><td>-</td><td>sig.*</td><td>sig.*</td><td>sig.</td><td>Low</td><td>Low</td></tr><tr><td>Core 12%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td rowspan="6">Overall core community</td><td rowspan="6">0.1% abund.</td><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 50%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 90%</td><td>sig.</td><td>sig.</td><td>-</td><td>sig.*</td><td>sig.*</td><td>sig.</td><td>Low</td><td>Low</td></tr><tr><td>Core 100%</td><td>-</td><td>sig.</td><td>-</td><td>sig.*</td><td>sig.*</td><td>sig.</td><td>Low</td><td>Low</td></tr><tr><td>Core 12%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td rowspan="6">0.01% abund.</td><td rowspan="6">No min. abund.</td><td>Core 5%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 50%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 90%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 100%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 5%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td rowspan="6">0.1% abund.</td><td rowspan="6">0.01% abund.</td><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 50%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 90%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 100%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 5%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr><tr><td>Core 25%</td><td>-</td><td>-</td><td>-</td><td>sig.</td><td>-</td><td>-</td><td>Low</td><td>Low</td></tr></tbody></table>

<footnote>* Species and Environmental quality factors interaction also significant (p<0.05)</footnote>
<table><caption>Table 2. Core communities for which some alpha diversity mean values differ at p<0.05. Only those metrics for which significant differences were found are shown here; others, as well as significant p-values, are included in Table S6. Percentages indicated in each community refer to percentage occurrence. In *Sponge species*, A, B, C refers to host species A, B and C, respectively; in *Environmental quality*, H, M, L refers to High, Moderate and Low quality habitats, respectively.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Sponge species</th><th colspan="2">Environ. quality</th></tr><tr><th>% occurrence</th><th>Observed sp</th><th>Chao 1</th><th>Observed sp</th><th>Chao 1</th></tr></thead><tbody><tr><td rowspan="6">Specific core community</td><td rowspan="2">no min. abund.</td><td>90%</td><td>B-C//A-C</td><td>B-C//A-C</td><td>H-M//M-L</td><td>H-M//M-L</td><td></td></tr><tr><td>100%</td><td>B-C//A-C</td><td>B-C//A-C</td><td>H-M//M-L</td><td>H-M//M-L</td><td></td></tr><tr><td rowspan="3">0.01% abund.</td><td>12%</td><td>-</td><td>-</td><td>H-M</td><td>-</td><td></td></tr><tr><td>90%</td><td>B-C//A-C</td><td>B-C//A-C</td><td>H-M//M-L</td><td>H-M//M-L</td><td></td></tr><tr><td>100%</td><td>B-C//A-C</td><td>B-C//A-C</td><td>H-M//M-L</td><td>H-M//M-L</td><td></td></tr><tr><td rowspan="2">0.1% abund.</td><td>90%</td><td>A-B//B-C//A-C</td><td>B-C//A-C</td><td>H-M//H-L</td><td>H-M</td><td></td></tr><tr><td>100%</td><td>A-B//B-C//A-C</td><td>B-C//A-C</td><td>H-M//H-L</td><td>H-M</td><td></td></tr><tr><td rowspan="6">Overall core community</td><td rowspan="2">no min. abund.</td><td>90%</td><td>-</td><td>-</td><td>H-L</td><td>-</td><td></td></tr><tr><td>5%</td><td>-</td><td>-</td><td>H-M</td><td>-</td><td></td></tr><tr><td rowspan="3">0.01% abund.</td><td>50%</td><td>-</td><td>-</td><td>H-L</td><td>-</td><td></td></tr><tr><td>5%</td><td>-</td><td>-</td><td>H-L</td><td>-</td><td></td></tr><tr><td>90%</td><td>-</td><td>-</td><td>H-L</td><td>-</td><td></td></tr><tr><td rowspan="2">0.1% abund.</td><td>100%</td><td>-</td><td>-</td><td>H-L</td><td>-</td><td></td></tr><tr><td>90%</td><td>-</td><td>-</td><td>H-M</td><td>-</td><td></td></tr></tbody></table>