Biodiversité Du Microbiome Cutané Des Organismes Marins : Variabilité, Déterminants Et Importance Dans L’Écosystème Marlène Chiarello

Biodiversité du microbiome cutané des organismes marins : variabilité, déterminants et importance dans l’écosystème Marlène Chiarello

To cite this version:

Marlène Chiarello. Biodiversité du microbiome cutané des organismes marins : variabilité, détermi- nants et importance dans l’écosystème. Microbiologie et Parasitologie. Université Montpellier, 2017. Français. NNT : 2017MONTT092. tel-01693132

HAL Id: tel-01693132 https://tel.archives-ouvertes.fr/tel-01693132 Submitted on 25 Jan 2018

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Délivré par l’Université de Montpellier

Préparée au sein de l’école doctorale GAIA Et de l’unité de recherche MARBEC (Centre pour la Biodiversité marine, l’Exploitation et la Conservation)

Spécialité : Écologie Fonctionnelle et Sciences Agronomiques

Présentée par Marlène Chiarello

Biodiversité du microbiome cutané des organismes marins : variabilité, déterminants et importance dans l’écosystème

Soutenue le 29 novembre 2017 devant le jury composé de

M. Thierry BOUVIER, DR, CNRS Directeur M. Frédéric DELSUC, DR, CNRS Examinateur M. Pierre GALAND, DR, CNRS Invité M. Fabrice NOT, DR, CNRS Invité M. Loïc PELLISIER, Asst. Prof., ETH Zürich Rapporteur M. Télesphore SIME-NGANDO, DR, CNRS Rapporteur Mme Eve TOULZA, MCF, Université de Perpignan Examinateur M. Sébastien VILLÉGER, CR, CNRS Co-encadrant

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q! 3.4 Manuscrit A r! ! s! Contrasted performance of taxonomic and phylogenetic diversity indices to detect t! assembly rules and dissimilarities between microbial communities u! v! Chiarello M. 1, Bouvier T. 1 and Villéger S. 1 w! 1Marine Biodiversity, Exploitation and Conservation (MARBEC), Université de Montpellier, x! CNRS, IRD, IFREMER, Place Eugène Bataillon, Case 093, 34 095 Montpellier Cedex 5, po! France pp! pq! En préparation pr! ps! Des informations supplémentaires complètent ce manuscrit, disponibles en section 7.3 pt! pu! pv! pw! px! qo! qp! qq! qr! qs! qt! qu! qv! qw! qx! ro! rp! rq! rr!

! vt! rs! Abstract rt! Many diversity indices have been proposed for measuring facets and components of ru! biodiversity. Most of these metrics were designed for and tested on macrobial communities, rv! but microbial communities have features that distinguish them from the macrobial ones, e.g. rw! very high species richness and a low evenness of species abundances, that could impact the rx! performance of diversity indices to reveal ecological signals. so! Here, we simulated microbial communities to test the performance of (1) six phylogenetic sp! diversity indices to efficiently detect the assembly rule that shaped communities, and (2) sq! twelve beta-diversity indices to detect differences between communities. Indices accounting sr! for abundance of species (e.g. Chao’s PDq indices with q=1 or 2) were the most efficient to ss! detect clustering assembly. The distinction between two communities was improved with st! indices accounting for phylogenetic relatedness between species (e.g. weighted Unifrac). su! Overall results highlighted that the choice of an index can strongly impact the conclusions of sv! a microbial ecological study. sw! sx! to! tp! tq! tr! ts! tt! tu! tv! tw! tx! uo! up! uq! ur! us! ut! uu! uv!

! vu! uw! INTRODUCTION ux! Biodiversity is a multivariate concept that gathers complementary facets accounting for vo! components measurable at different scales. The taxonomic facet of biodiversity considers vp! species as independent units, while the phylogenetic diversity includes their phylogenetic vq! distinctiveness Both facets can include two components: considering only the presence- vr! absence of species or phylogenetic lineages leads to the ‘compositional’ diversity (giving an vs! equal weight to all species in a community), while accounting for their abundance refers to vt! the ‘structural’ diversity. These facets and components measure the diversity within a vu! community (alpha-diversity) or the dissimilarity between communities (beta-diversity). These vv! alpha- and beta-diversities are the scales of biodiversity. vw! vx! The use of complementary indices is required to have a comprehensive assessment of wo! biodiversity as well as to understand its ecological determinants (e.g. (Lozupone et al. 2007; wp! Tucker et al. 2016)). Many biodiversity indices have been proposed for the last decades wq! towards these aims (see a non-exhaustive list Table 1) with several indices currently available wr! for measuring facets and components of alpha- and beta-diversity with even several indices ws! for a given facet × component (e.g. weighted-Unifrac and Chao’s phylogenetic beta-diversity wt! index βPD(q=1), Table 1). wu! wv! Numerous studies using simulations and/or case studies have classified indices to look for ww! complementarity or redundancy (Cadotte et al. 2010; Tucker et al. 2016; Miller, Farine, and wx! Trisos 2016), while other studies have evaluated their performance to reveal non-random xo! assembly processes such as phylogenetic clustering, i.e. when species present in a local xp! community are less phylogenetically diverse than expected given species richness and species xq! regional pool. (Kembel 2009; Mazel et al. 2015; Miller, Farine, and Trisos 2016) or to detect xr! structural or compositional differences between communities, i.e. when clustering habitats xs! (Koleff, Gaston, and Lennon 2003; Barwell, Isaac, and Kunin 2015; Baselga and Leprieur xt! 2015). However, these studies focused mainly on communities with small richness values ( i.e. xu! less than 100 species), which typically occur in case of temperate communities of macro- xv! organisms. In addition, the influence of a high unevenness of species abundances, and of the xw! subsequent high proportion of rare species, which is typical from microbial communities, has xx! not been assessed (but see e.g. (Hill et al. 2003; Haegeman et al. 2013)). poo!

! vv! Biodiversity facet Taxonomic diversity Phylogenetic diversity

Weight given to relative q=0 q=1 q=2 q=0 q=1 q=2 abundances TD(q=0) TD(q=1) TD(q=2) PD(q=0) PD(q=1) PD(q=2) ======TD (q=0) TD (q=1) TD (q=2) Faith’s PD Allen Rao Alpha diversity LC LC LC = = = Richness Shannon Simpson

PD LC (q=0) PD LC (q=1) PD LC (q=2)

βTD(q=0) βTD(q=1) βTD(q=2) βPD(q=0) βPD(q=1) βPD(q=2) = = Sorensen PhyloSor

Beta diversity Jaccard Bray-Curtis Unifrac W-Unifrac

g-Unifrac (d ∈[0,1])

g-Unifrac(d=1) = W-Unifrac pop! Table 1 : Diversity indices tested in this study. Chao’s and Leinster & Cobbold’s unified poq! frameworks were respectively indicated as D(q) (corresponding to the qD in (Chao, Chiu, and q Z por! Jost 2014)) and D LC (q) (corresponding to the D in (Leinster and Cobbold 2012)), with pos! prefixes T- or P- indicating respectively the taxonomic or phylogenetic version of both of pot! them. The parameter q indicates weight given to species abundance. Similarly, d parameter of pou! g-Unifrac indices permits indicates the importance given to relative abundances of OTUs. pov! Some indices of both frameworks are related to other alpha diversity metrics when the latter pow! are converted into Hill numbers; this equivalence was indicated by a ‘=’ sign. pox! ppo! Recent advances in genomics (e.g. Next Generation Sequencing, NGS) have revolutionized ppp! the assessment of microbial biodiversity, both in terms of accuracy and completeness (Fierer ppq! et al. 2007; Logares et al. 2014; Frade et al. 2016). Massive sampling through space and/or ppr! time (see e.g. > 200 samples in world oceans (Yutin et al. 2007; Sunagawa et al. 2015) has pps! revealed (i) the tremendous microbial richness in soil and water (e.g. more than 2 000 OTUs ppt! in 1g of soil (Roesch et al. 2007)), (ii) the highly skewed distribution of abundance among ppu! species, i.e. that a few species dominate in each community while many species are ppv! represented by a few cells (e.g. 99% of OTUs contributing to less than 0.1% of total ppw! abundance in soil (Baldrian et al. 2012)), and (iii) the presence of distant phylogenetic ppx! lineages within most microbial communities (e.g. Lactobacillus and Actinobacteria that pqo! frequently co-occur in human vagina diverged more than 3 billions years ago) (Hedges, pqp! Dudley, and Kumar 2006; Ma, Forney, and Ravel 2012). pqq!

! vw! pqr! Only a few studies have yet assessed indices behavior in the specific case of microbial pqs! communities and they all focused on comparing different components and/or facets of pqt! biodiversity (e.g. comparing the abundance-weigthed and unweighted versions of Unifrac or pqu! Leinster & Cobbold’s measures (Lozupone et al. 2007; Veresoglou et al. 2014)), without pqv! further intra-facet of intra-component comparisons. Therefore, the performance of diversity pqw! indices to detect non-random assembly rules in microbial communities or differences between pqx! them remains unknown, while there is an urgent need for validating the ability of diversity pro! indices to detect such patterns. prp! prq! Here, using simulated realistic communities with various characteristics ( i.e. varying levels of prr! richness and evenness), we compared the performance of the most used taxonomic and prs! phylogenetic diversity indices for (i) detecting the assembly rule that shaped a microbial prt! community, and (ii) revealing ecological differences between a set of communities. pru! We hypothesized that the richness of microbial OTUs as well as the unevenness of their prv! abundance affect the performance of diversity indices to detect assembly rules and prw! dissimilarities between communities. prx! pso! MATERIALS AND METHODS psp! We aimed to test intrinsic performance of diversity indices to detect patterns in microbial psq! communities, i.e. independently from potential biases estimates of OTUs abundances due to psr! sampling and/or sequencing (Lundin et al. 2012). We thus simulated microbial communities, pss! not NGS outputs. We simulated a stochastic pure birth phylogenetic tree of 6 000 OTUs and pst! used this tree and the corresponding species as the regional pool for all subsequent analyzes. psu! Local communities were simulated according to 16 combinations of species richness and psv! abundance distribution. We considered 4 levels of OTU richness ( S=40, 200, 1 000 and 2 000 psw! OTUs), which represent the range of values commonly observed in samples of natural psx! microbial communities. We simulated 4 levels of unevenness of OTUs abundance for each pto! level of species richness. More precisely, abundances were randomly sorted from a lognormal ptp! distribution of unit means, which standard deviations were of σ=0.25, 0.5, 1 or 2 respectively. ptq! The higher this parameter σ, the higher is the unevenness of abundances, i.e. the higher is the ptr! difference between most abundant and least abundant OTUs (see S2). pts! ptt! ptu!

! vx! ptv! 1. Performance of alpha-diversity indices for detecting phylogenetic clustering ptw! 1.1 Simulation of microbial communities according to contrasted assembly rules ptx! We first tested the performance of phylogenetic alpha-diversity indices to detect whether puo! phylogenetic clustering shaped a community. Phylogenetic clustering is detected when pup! species in a community are less distantly related to each other than expected from a random puq! sorting of species from the regional pool (Mouquet et al. 2012). Phylogenetic clustering is pur! often driven by environmental filtering of phylogenetically conserved biological features such pus! as tolerance to a stress (Horner-Devine and Bohannan 2006), sometimes in interaction with put! other mechanisms (Cadotte and Tucker 2017). We did not aim to simulate a drastic puu! phylogenetic clustering affecting phylogenetic composition but rather a more realistic puv! situation where marked abiotic or biotic constraints favor microbes that have the ecological puw! characteristics needed to dominate in a community while OTUs that do not have such pux! characteristics could occur (for instance through immigration from other source habitats) but pvo! remain at low abundance (Gibbons et al. 2013; Lynch and Neufeld 2015). pvp! pvq! We simulated 40 types of communities (see supplementary information S1) differing in their pvr! richness and unevenness of OTU abundances ( i.e. the level of dominance in the community), pvs! and their phylogenetic assembly rules (2 modalities, clustering and random assembly). pvt! Then, for each of these simulated distributions of OTUs abundances ( i.e. for each S×σ pvu! scenario) we simulated two microbial communities according to two assembly rules. First, a pvv! community was simulated under a random assembly process, by assigning simulated pvw! abundances to OTUs randomly picked along the phylogenetic tree. Then, a second pvx! community with the same structure of abundance (i.e. with the same S×σ), resulting from a pwo! hypothetical phylogenetic clustering, was simulated using a three-steps procedure: (i) we an pwp! OTU randomly picked of the phylogenetic tree was attributed the highest relative abundance, pwq! (ii) phylogenetic distances between this dominant OTU and all other OTUs of the tree were pwr! computed, and (iii) the remaining S-1 abundance values sorted in a decreasing order were pws! successively attributed to OTUs using a random sampling (no replacement) with odds pwt! proportional to the inverse of the distance with the dominant OTU. This way, OTUs closer to pwu! the dominant OTU on the phylogenetic tree had a higher probability to be sampled earlier pwv! during the process, and therefore to have higher abundances. This procedure ensured that pww! dominant OTUs were phylogenetically more similar than expected at random, while allowing pwx! the many rare OTUs to be distant from these dominant OTUs. Simulation of random and pxo! phylogenetically clustered communities was independently replicated 500 times for each level

! wo! pxp! of species richness and unevenness of abundances. Simulated communities thus do not pxq! account for potential non-random patterns in species frequency and regional total abundance pxr! observed in some metacommunities (e.g. mass-effect processes) or trait-evolution (Kraft et al. pxs! 2007; Miller, Farine, and Trisos 2016). pxt! Simulation process yielded a total of 16,000 microbial communities (4 levels of OTUs pxu! richness S × 4 levels of abundance unevenness σ × 2 assembly rules × 500 replicates) (S1). pxv! pxw! 1.2 Selection and computation of alpha-diversity indices pxx! Numerous indices are currently available in the literature (see e.g. Tucker et al (2016) for qoo! phylogenetic indices). The purpose of this paper was not to evaluate all of them, especially qop! because many of them are highly correlated (Tucker et al. 2016). We selected two sets of qoq! indices that are based on Hill numbers and allow to measure taxonomic and phylogenetic qor! diversity with the same unit ( i.e. equivalent number of species, or Hill numbers, (see (Jost qos! 2007; Chao, Chiu, and Jost 2014), Table 1). The first set of indices comes from Chao et al. qot! framework. This unified framework is based on a single formula that account for different qou! biological entities (species or length of phylogenetic branches), while permitting to choose the qov! relative weight given to species dominance (e.g. abundance, biomass) (parameter q) ( (Chao, qow! Chiu, and Jost 2014) ). When q= 0 the index is insensitive to dominance, and thus measures qox! compositional diversity. When q=1, the dominance of entities and their phylogenetic qpo! distinctiveness have the same contribution to the index value. When q=2, abundances qpp! dominance of entities have twice more weight than the phylogenetic distances. We chose qpq! Chao’s indices because they are related to well-known alpha-diversity metrics, when qpr! converted into Hill numbers (Jost 2007). For instance, phylogenetic Chao’s diversity indices qps! with q=0, 1, and 2 are related to the widely used Faith’s PD, Allen’s and Rao’s indices, qpt! respectively (Table 1) (Rao 1982; Faith 1992; Allen, Kon, and Bar -Yam 2009). The second qpu! set of indices tested comes from the unified framework of (Leinster and Cobbold 2012), qpv! which is also based on a single parameter modulating the importance given to dominant qpw! entities, but with a similarity-based approach instead of the dissimilarity-based approach used qpx! by Chao et al. (Chao, Chiu, and Jost 2010). Contrary to Chao’s indices, phylogenetic diversity qqo! indices proposed by Leinster & Cobbold (PD LC (q=0, 1 or 2)) do not have equivalents among qqp! classical phylogenetic diversity metrics. These 6 phylogenetic alpha-diversity indices were qqq! computed using the entropart R-package (Marcon and Hérault 2014). qqr!

! wp! qqs! 1.3 Assessing indices performance to detect phylogenetic clustering qqt! The ability of each phylogenetic index to detect phylogenetic clustering for each scenario of qqu! OTU richness and unevenness of OTU abundances was assessed by comparing the diversity qqv! of each assemblage simulated under the clustering assembly rule with the diversity of all the qqw! communities simulated under a random process. More precisely, we computed the P-value as qqx! the rank of the observed diversity value in the clustering assembled community among the qro! increasingly sorted diversity values of all r=500 replicates of randomly assembled qrp! communities corrected by ( r-1). We considered that a diversity index efficiently detected qrq! phylogenetic clustering when P<0.05 , meaning that the diversity value in the observed qrr! community was lower than the 5% lowest diversity values in the random communities. This qrs! null-model approach corresponds to the “richness-like” category in Gotelli (2000), i.e. qrt! simulated communities have the same species richness and same abundance distribution than qru! the observed community and regional pool is the same for all communities (e.g. (Mazel et al. qrv! 2015)). qrw! qrx! qso! 2. Performance of beta-diversity indices to detect differences between communities qsp! 2.1 Datasets simulation qsq! The objective was to use this set to assess the performance of beta-diversity indices to detect qsr! differences between community types, such as the ones expected when studies communities qss! from environments experiencing different abiotic and/or biotic conditions. We defined a set of qst! 5 hypothetical community types from A to E with type B being as type A (to test for false qsu! positive detection) and types C, D and E being increasingly different from A (see S3 for a qsv! comprehensive scheme of the method). For each community type, 6 communities were qsw! randomly simulated using the same clustering procedure as for alpha-diversity (see above). A qsx! total of 50 sets of such 30 communities were simulated. qto! qtp! Communities were simulated following successive steps. First, a ‘localizer OTU’ was qtq! randomly sampled on the phylogenetic tree, and was used to create all the communities, but qtr! was not included in any of them. Phylogenetic distances between this localizer and all other qts! OTUs on the tree were computed and ranked from the smallest to the largest distance value, qtt! and separated into 400 quantiles. Then, a ‘reference OTU’ was randomly sampled in the qtu! quantiles for each type, using the rule described below, and kept constant for all communities qtv! of the given type, but not included in any of them:

! wq! qtw! d For A and B community types, the same reference OTU was taken, and was sampled qtx! within the distances to the localizer OTU lower than the third quantile (0.5% closest quo! OTUs); these communities were used to quantify erroneous detection of a difference qup! between communities of a same type ( i.e. false positives). quq! d The reference OTU of community type C was randomly sampled among the OTUs qur! located between the 1.75 to 2.25% closest OTUs (i.e. among 7 th , 8th and 9 th quantiles); qus! d The reference OTUs from community types D and E were respectively randomly qut! sampled among the 4.75-5.25% and 9.75-10.25% closest OTUs (i.e. among. 19 th , 20 th quu! and 21 th quantiles, and 39 th , 40th and 41 th quantiles, respectively). quv! Then, phylogenetic distances between each reference OTU and all other OTUs on the tree quw! were computed, and to ensure that OTUs that would be sampled in the next steps would be qux! close enough to their reference OTU, only OTUs with a distance below the median of all qvo! distance values were retained for the next step ( i.e. among 3000 OTUs, which is higher than qvp! the maximum richness of communities we tested). The dominant OTU of a given community qvq! was then sampled on the tree with a probability proportional to the inverse of the distance to qvr! the reference OTU, and it received the highest simulated abundance . Finally the remaining S- qvs! 1 abundances values were attributed to the OTUs following the clustering procedure used qvt! before. This procedure ensured that communities within a type were more similar to each qvu! other than with communities from other types, while allowing for rare species being qvv! phylogenetically similar between community types. qvw! qvx! 2.2 Selection and computation of beta-diversity indices qwo! We computed a total of 12 beta-diversity indices from three frameworks. The first set of qwp! indices grouped classic Jaccard and Bray-Curtis indices. Jaccard is a presence/absence beta- qwq! diversity measure, correlated to the Sorensen (Sorensen= Jacccard / (2- Jaccard)). The Bray- qwr! Curtis index is the generalization of the Sorensen index, accounting for the relative qws! abundances of species (Bray and Curtis 1957). These two indices were computed using the qwt! betapart R-package (Baselga and Orme 2012). qwu! qwv! The second set of indices came from the Unifrac framework that extended the Jaccard index qww! to account for phylogenetic information (C. Lozupone and Knight 2005), and which is qwx! frequently used in microbial ecology (e.g. (Pontarp et al. 2012; Clemente et al. 2015)). qxo! Unweighted Unifrac (U-Unifrac), measuring the fraction of unshared branch length between

! wr! qxp! species from two communities on a common phylogenetic tree, is insensitive to OTUs qxq! abundances (C. Lozupone and Knight 2005). This index was then extended to include the qxr! relative abundance of phylogenetic lineages (Weighted-Unifrac, (C. A. Lozupone et al. qxs! 2007)). Chen et al. (Chen et al. 2012) proposed a set of generalized Unifrac distances (g- qxt! Unifrac), permitting to imbalance the importance given to abundances and the phylogenetic qxu! distances between OTUs using a single parameter d varying between 0 and 1: g-Unifrac(d=0) qxv! gives a low weight to OTUs relative abundances (while being distinct from U-Unifrac), while qxw! g-Unifrac(d=1) gives a stronger weight to OTU abundances and is identical to the Weighted- qxx! Unifrac (W-Unifrac). We computed g-Unifrac indices with d = 0, 0.5 and 1, as well as the U- roo! Unifrac, using the GUniFrac package (Chen and ORPHANED 2012). rop! roq! The third set of indices came from Chao et al. framework, based on a multiplicative ror! decomposition of diversity (Chao, Chiu, and Jost 2014). In other words, beta-diversity ros! between two communities is computed as the ratio between the gamma-diversity (diversity of rot! the pooled communities) and the average of the alpha-diversity of the two communities (Chao rou! et al. 2014). Taxonomic and phylogenetic beta-diversity indices were computed using the rov! formula accounting for species or phylogenetic lineages, respectively, giving weights of q=0, row! 1 and 2 to their relative abundances. We then scaled the beta-diversity values between 0 and 1 rox! as proposed by Villéger et al. (Villéger et al. 2012). These beta-diversity indices were rpo! calculated using personal functions based on the entropart R-package, provided online: rpp! https://github.com/marlenec/chao , and in S4. Note that some of Chao’s beta-diversity indices rpq! are closely related to other indices obtained by a multiplicative decomposition of diversity rpr! (Chao et al. 2014). For instance, Chao’s taxonomic beta-diversity index with q=0 ( βTD(q=0)) rps! is related to Sorensen index (Table 1). rpt! rpu! All these indices vary from 0, when communities share the same phylogenetic lineages (in rpv! case of U-Unifrac) or relative abundances of OTUs (in case of Bray-Curtis) to 1, when rpw! communities are composed of distinct phylogenetic lineages or are dominated by different rpx! OTUs. rqo! rqp! 2.3 PERMANOVAs rqq! For each of the 16 ( S×σ) scenarios, all the beta-diversity indices were computed for the 66 rqr! pairs of communities compared, that is 30 pairs within each community type (i.e. 15 rqs! combinations of 2 communities among the 6 from the same type) plus 36 pairs of

! ws! rqt! communities belonging to distinct community types (S3). Then, for each index and each of rqu! the 4 comparisons of community types (A vs. B, A vs. C, A vs. D and A vs. E) a rqv! PERMANOVA was performed on a matrix containing all 66 values of beta-diversity using rqw! the ‘adonis’ function in Vegan R-package (Dixon 2003). The explanatory variable used was rqx! the type of each community, and the distinction between the compared community types was rro! considered successful when the PERMANOVA raised a P-value<0.05. Note that as rrp! communities from type A and B were simulated using the same parameters, they are expected rrq! to be not significantly different and thus this simulation allows to measure false-positive rate. rrr! rrs! The entire simulation procedure was replicated 50 times, i.e. from the random sorting of the rrt! community types to the PERMANOVA. Finally we computed the percentage of successful rru! PERMANOVAs as a measure of index performance. rrv! rrw! 3. Statistical analyses rrx! To disentangle the relative influence of the simulation scenarios ( S×σ) and the chosen alpha rso! or beta-diversity index on the success of detection of clustering or environmental difference rsp! we ran Boosted Regression Trees (BRT) using P-values from the clustering experiment or the rsq! PERMANOVAs. For the clustering detection test, a single BRT model was constructed using rsr! the 48 000 P-values (4 S × 4 σ × 6 indices × 500 replicates) with the richness S, the rss! unevenness σ, and alpha-diversity index as explanatory variables. For the experiment rst! focusing on dissimilarity between communities, BRT models were constructed separately on rsu! each facet of biodiversity ( i.e. on P-values raised from PERMANOVAs performed rsv! respectively on taxonomic and phylogenetic beta-diversity indices) and on each pair of rsw! community types (A/C, A /D and A/E), making a total of 6 BRT models. Taxonomic and rsx! phylogenetic BRTs were constructed respectively on a total of 4 000 P-values (4 S × 4 σ × 5 rto! indices) and 5 600 P-values (4 S × 4 σ × 7 indices). Both taxonomic and phylogenetic BRTs rtp! were constructed using the richness, unevenness, and beta-diversity index as explanatory rtq! variables. BRT were fitted using the ‘gbm.step’ function provided in the dismo R-package rtr! (Hijmans, Phillips, and Elith 2016), with a bag fraction of 0.75, a learning rate of 0.001 and a rts! tree complexity of 3, following the recommendations of (Elith, Leathwick, and Hastie 2008). rtt! All BRT models were fitted using more than 1 000 trees. rtu! rtv! rtw!

! wt! rtx! RESULTS ruo! 1. Performance of phylogenetic alpha-diversity indices to detect clustering assembly rup! Phylogenetic Chao’s and Leinster & Cobbold’s alpha-diversity indices showed contrasted ruq! patterns with varying levels of q (Fig. 1 & S5). As expected, Chao’s PD(q=0) was rur! independent of abundance unevenness, while PD(q=1) and PD(q=2) decreased as unevenness rus! increased. Contrary to the Chao’s index, PD LC (q=0) varied with unevenness, and ranged from rut! 6.2 to 5.4 for a community composed of 1 000 OTUs. Similarly, PD LC (q=1) and PD LC (q=2) ruu! decreased as the unevenness increased, but with a lower slope than Chao’s indices (S5). ruv! Overall, clustering detection efficiency ranged between 2% to 100%. The used index had the ruw! strongest impact on the clustering detection (BRT model, relative influences of 75.1%, 22.7% rux! and 2.2%, respectively for the used alpha-diversity index, the species richness and abundance rvo! unevenness). Abundance-weighted indices (including PD LC (q=0), which give a small weight rvp! to abundances) performed up to 7 times better than indices taking only account of OTUs rvq! presence/absence (Fig. 1 and S6). For instance, PD(q=0), which is independent from OTUs rvr! abundances, detected clustering in only 8.4 to 17.2% of cases, while PD(q=2), which is rvs! weighted by OTUs abundances, reached a detection efficiency of 18 to 100%. The most rvt! efficient indices of Chao’s and Leinster & Cobbold’s frameworks ( i.e. PD(q=1 and 2) and rvu! PD LC (q=1 and 2)) did not significantly differ for all assemblage tested (Kruskal Wallis, rvv! P>0.05). rvw! rvx!

! wu! rwo! rwp! Figure 1: Efficiency of clustering detection by Chao’s indices . Values are expressed as rwq! percentage of significant P-values (<0.05) across the 500 replicates, for each level of OTUs rwr! richness ( S, titles of panels) and each level of unevenness of their abundances ( σ, abscisses). rws! rwt! 2. Efficiency to detect differences between communities rwu! As expected, the detection efficiency of the difference between community types was higher rwv! for very distinct types, averaging 61.3±33.4% and 74.7±33.0% (respectively for taxonomic rww! and phylogenetic diversity indices) when comparing communities from A and E types, and rwx! 44.6±32.7% and 55.2±35.8% when comparing A and C types (Fig. 2, Fig. 3 and S7 & S8). rxo! The detection of significant differences was however variable for a given pair of community rxp! types, depending mostly on the used index (Fig. 2 and Fig. 3). rxq! rxr! rxs! rxt!

! wv! rxu!

! ww! rxv! rxw! rxx!

! wx! soo! First, abundance-weighted indices detected a significant difference between community types sop! in 83.3±24.6% of cases, while composition-based indices detected a difference in only soq! 21.6±15.0% of cases, whatever the pair of community types and the facet of biodiversity sor! considered. Detection of difference also varied between biodiversity facets. sos! sot! For taxonomic diversity, both the identity of the beta-diversity index and intrinsic features of sou! the community (richness and, in a lesser extent, unevenness) influenced P-values raised from sov! PERMANOVAs (Table 2). For instance, the percentage of difference detected was higher in sow! communities composed of more than 40 OTUs, especially in case of relative abundance- sox! weighted indices (Fig. 2 and S7). By contrast, the identity of phylogenetic index explained 90 spo! to 93% of variability of PERMANOVA P-values (Table 2), with a higher performance of spp! abundance-weighted indices than composition-based indices (Fig. 3 and S8). spq!

Taxonomic beta- Phylogenetic beta-

diversity diversity Types Factor Relative influence (%) Relative influence (%) Beta-diversity Index 68.7 90.6 A vs C Richness ( S) 22.0 6.0 Uneveness ( σ) 9.3 3.4 Beta-diversity Index 59.0 92.6 A vs D Richness ( S) 33.4 5.9 Uneveness ( σ) 7.6 1.5 Beta-diversity Index 51.5 91.8 A vs E Richness ( S) 38.3 5.9 Uneveness ( σ) 10.2 2.3 spr! Table 2: Relative influence of the used index, OTUs richness and unevenness on the sps! efficiency of community types detection . The relative influence values of each tested factor spt! are based on the number of times the factor is selected for splitting while constructing each spu! tree, weighted by the squared improvement to the model resulting from each split and spv! averaged over all trees. BRT models were performed on P-values raised from spw! PERMANOVAs testing the difference between community types, separately for taxonomic spx! and phylogenetic beta-diversity indices. sqo! sqp! sqq! Moreover, considering only the efficiency of abundance-weighted indices, phylogenetic sqr! diversity indices were on average more efficient than the composition-based ones, the former sqs! averaging 83.34±17% to 97±8.3% of environmental detection (for A/C to A/E comparisons), sqt! and the latter averaging 62.96±30.3% to 77.6±30.4% of detection (see Fig. 2, Fig. 3, S7 and

! xo! squ! S8). Finally, the proportion of erroneous detection of a difference between identical sqv! communities (i.e. A vs. B types) was overall low (3.8±3.6%) (S9). However, in case of sqw! moderately skewed rich communities (S=1000, sigma=0.5), all taxonomic and phylogenetic sqx! indices raised erroneous detection of a difference in more than 6% of cases. False-detection sro! rate higher than 10% were found for the communities with only 40 species and high evenness srp! of abundance (S=40, sigma=0.25) for all phylogenetic diversity indices (14.5±4.4% across srq! tested indices). srr! srs! Indices measuring the same facet and component of biodiversity generally reached similar srt! efficiencies. For instance the Bray-Curtis index had intermediate behavior between βTD(q=1) sru! and βTD(q=2), and the Jaccard index reached similar efficiencies as βTD(q=0) (Fig. 2 & S7). srv! Similarly, W-Unifrac efficiencies were comparable to that of βPD(q=2) (Fig. 3 & S8). srw! srx! DISCUSSION sso! Chao’s and Leinster & Cobbold’s frameworks are both based on Hill numbers (Leinster and ssp! Cobbold 2012; Chao, Chiu, and Jost 2014). Their taxonomic diversity indices are numerically ssq! identical. However their phylogenetic diversity indices showed contrasted patterns, especially ssr! at q=0 (S5). While Chao’s PD(q=0) was independent of the unevenness of abundance in the sss! community, its equivalent from Leinster & Cobbold framework (PD LC (q=0)) decreased as the sst! unevenness increased. Such a sensitivity to OTUs abundances prevent from considering this ssu! index as a relevant compositional index of biodiversity (Leinster and Cobbold 2012). ssv! Consequently, Chao et al. framework is the only one taking account of the compositional ssw! component of phylogenetic diversity, i.e. the diversity of phylogenetic lineages present, ssx! independently of their respective dominance (Chao, Chiu, and Jost 2010). sto! stp! Considering Chao’s framework, phylogenetic clustering was hardly detected by its stq! composition-based index, as PD(q=0) detected it in less than 20% of simulated cases (Fig. 1). str! Abundance-weighted Chao’s indices, by contrast, were much more efficient, detecting sts! clustering in ca. 86.3±23.3% of cases. This low efficiency of composition-based indices is stt! related to their key property that is giving the same importance to all the OTUs present, stu! whatever their relative abundance in the community. Consequently, composition-based stv! indices are unlikely to distinguish between a clustered community and a randomly assembled stw! one, due to the many rare OTUs that blur the phylogenetic closeness between dominant stx! OTUs. Yet, detecting clustering in natural communities using composition-based indices is

! xp! suo! unlikely but not impossible when phylogenetic clustering is drastic and is thus affecting sup! composition. Horner and Bohannan (Horner-Devine and Bohannan 2006) identified clustering suq! in 55% of microbial assemblages studied from distinct environments (sediments, soil, sur! freshwater mesocosms). (Miller, Farine, and Trisos 2016) showed a high performance of sus! Faith’s PD, ( i.e. PD(q=0) in our study), to reveal habitat filtering using simulated sut! communities and richness-like null model. They also showed a slightly lower efficiency of suu! abundance-weighted indices to reveal this pattern. The differences between their results and suv! ours may be related to the difference in simulation of phylogenetically clustered communities suw! since Miller et al (2016) simulated species abundances independently from community sux! composition (that was simulated based on trait-evolution) while we simulated both processes svo! simultaneously. (Freilich and Connolly 2015) already argued that environmental filtering svp! should impact both species occurrence and abundance, as species that are the best adapted to a svq! given environment would also be the most abundant, and that other species that do not have svr! the physiological traits necessary to grow in a given environment would not be abundant but svs! could still be present. Accordingly, they showed a higher performance of abundance-weighted svt! indices to detect environmental filtering compared to indices based only on species svu! composition. Abundance-weighted indices are thus more likely to detect phylogenetic svv! clustering, and therefore should be used in priority. svw! svx! The efficiency of clustering detection was dependent on the intrinsic features of the swo! communities (richness, unevenness). Clustering was poorly detected in poor communities (40 swp! OTUs), as even the most efficient indices (PD(q=2) and PD LC (q=2)) never reached 80% swq! efficiency. This efficiency was particularly low when the unevenness was high, dropping the swr! detection to ~20%. In communities with very few OTUs and a few ones contributing mostly sws! to the cumulated OTU abundance ( i.e. 40% of OTUs totalizing more than 95% of total swt! abundance, S3), values of abundance-weighted indices were very low in both clustered and swu! randomly assembled communities (e.g. average PD(q=2) of 1.6±0.4 and 4.1±1.4, swv! respectively), making difficult to disentangle these two patterns. This suggests that for such sww! species-poor communities, it may be challenging to detect clustering. It also underlines that swx! low sampling and sequencing effort of OTUs-rich communities that results in low observed sxo! OTUs richness because of the absence of the rarest species (Hill et al. 2003; Bent and Forney sxp! 2008; McCoy and Matsen IV 2013; Haegeman et al. 2013) could prevent from unraveling sxq! ecological processes. In our simulation, we did not consider the sampling issues that could sxr! rise from NGS data, as insufficient sampling, PCR artifacts and sequencing errors. However,

! xq! sxs! considering that these biases mainly impact the rarest members of the community (Pinto and sxt! Raskin 2012). Overall, our results highlight the need of a first analysis of community features sxu! (richness, unevenness) before further analyses and that abundance-weighted indices should be sxv! preferred to the only use of composition-based indices to detect non-random assembly rules. sxw! Last, previous studies based on simulated species-poor communities ( i.e. less than 200 sxx! species) demonstrated that efficiency of clustering detection depends on tree shape (Kraft et too! al. 2007; Mazel et al. 2015). Additionally, Mazel et al. tested different indices on mammal gut top! microbial assemblages and showed contrasted sensitivity of indices to length of basal or toq! terminal branches of the tree. Further studies are therefore needed to test for effect of tor! phylogenetic tree shape, especially for presence of many long basal branches, typical of tos! microbial phyla that diverge more than 3 billions years ago. tot! tou! The weaker performance of composition-based indices was also outlined for detecting tov! differences between distinct community types, since composition-based beta-diversity indices tow! detected such differences in only ~20% of cases (e.g. Unifrac, Fig. 3). Contrastingly, tox! abundance-weighted indices reached 70% to more than 90% efficiency, respectively for tpo! taxonomic and phylogenetic diversity indices (Fig. 2, Fig. 3, S7 and S8). The weight given to tpp! the q and d values for Chao’s and g-Unifrac abundance-weighted indices did not impact the tpq! detection, in all but two cases. The first one was the g-Unifrac(d=0), which gives a very small tpr! weight to the abundances of lineages, and had an intermediate efficiency between abundance- tps! weighted and composition-based indices (Fig. 3). Its efficiency was particularly low in tpt! moderately distinct communities (from type A vs. types D and C). This may be due to the low tpu! sensitiveness of g-Unifrac(d=0) to differences in abundances of phylogenetic lineages tpv! between communities that have similar phylogenetic composition. tpw! The second case was for closer communities (A vs. C types), where W-Unifrac and βPD(q=2) tpx! were slightly more efficient than their equivalents that were less weighted by abundances ( i.e. tqo! respectively g-Unifrac(d=0 and 0.5) and βPD(q=1), Fig. 3). For instance, βPD(q=2) was more tqp! sensitive to difference in OTUs abundances compared to βPD(q=1) when communities hosed tqq! the same phylogenetic lineages. tqr! tqs! However when communities share the same abundant OTUs while having different rare tqt! species abundance-weighted indices would hardly detect differences. For instance, Chen et al. tqu! (Chen et al. 2012), showed a higher sensitivity of U-Unifrac and g-Unifrac(d=0.5) to tqv! variations of the rare and the moderately abundant lineages, respectively. This result

! xr! tqw! highlights the need of comparing indices accounting differently for OTUs abundance to tqx! disentangle differences between communities driven by dominant and rare species. tro! trp! Besides the higher performance of abundance-weighted indices, our simulations also trq! demonstrated that accounting for taxonomic or phylogenetic facet of biodiversity had a strong trr! impact on the detection of differences between communities. The performance of taxonomic trs! diversity indices to detect the difference between community types was highly dependent on trt! the intrinsic features of the communities (richness and unevenness), while these features had tru! much less impact on the performance of phylogenetic diversity indices (Table 2). trv! Accordingly, the efficiency of abundance-weighted phylogenetic diversity indices was less trw! variable than the abundance-weighted taxonomic diversity indices ( i.e. respectively trx! interquartile ranges of 78-100% and 39.5-100% efficiency), and suggests that phylogenetic tso! diversity indices would be more reliable. Abundance-weighted phylogenetic diversity indices tsp! had on average 20% higher detection rates than the abundance-weighted taxonomic ones. tsq! This higher efficiency of phylogenetic diversity indices was particularly striking for tsr! phylogenetically similar communities (A vs. C types), while for more distant communities (A tss! vs. E types), abundance-weighted taxonomic diversity indices reached nearly the same tst! efficiency as the abundance-weighted phylogenetic diversity indices (Fig. 2 & 3 and S7 & tsu! S8). This smaller gap between taxonomic and phylogenetic diversity indices in this latter case tsv! was driven by the very few OTUs in common between communities belonging to more tsw! distant community types (A vs. E) that enhanced the detection of the difference without the tsx! use of phylogenetic information. tto! ttp! Several studies highlighted the need of considering the phylogenetic relationships between ttq! organisms to assess the biodiversity of assemblages, which contain more biological ttr! information than OTU membership (C. Lozupone and Knight 2005; Chen et al. 2012). This tts! was confirmed by Doll et al. (2013), who used both real sets of communities and simulated ttt! assemblages to reveal the benefits of adding phylogenetic information into alpha-diversity ttu! metrics. However, to our knowledge, the present study is the first that demonstrated the ttv! higher efficiency of phylogenetic beta-diversity indices for detecting differences between ttw! communities. ttx! tuo! We also demonstrated that efficiency of detection of phylogenetic clustering and differences tup! was affected by the intrinsic features of the communities (richness, unevenness). Indeed,

! xs! tuq! detection of difference between communities was facilitated in communities with moderately tur! to highly skewed ( σ=1 or 2) distributions of OTUs abundances, especially in those containing tus! less than 1000 OTUs, as abundance-weighted phylogenetic diversity indices were ~20% more tut! efficient than in less skewed communities. In poorer communities, less information ( i.e. less tuu! phylogenetic lineages) is indeed available to discriminate communities. In these conditions, if tuv! the compared communities are dominated by a few OTUs, which likely differ, they should be tuw! more easily discriminated by abundance-weighted indices. In most cases diversity indices tux! provided false positive rate lower than 5% ( i.e. detected significant differences between tvo! communities belonging to the same type, S9). False positive rate exceeded 5% in only a few tvp! scenarios: all taxonomic and phylogenetic beta-diversity indices in species-rich communities tvq! with high evenness abundance ( i.e. S=1000, σ =0.5) and only phylogenetic beta-diversity tvr! indices for communities with only 40 species and high evenness abundance ( S=40, σ=0.25), tvs! where false positive rate raised 18%. However, in simulations close to microbial tvt! communities, i.e. > 1 000 OTUs having highly-skewed abundances ( σ ≥ 1), phylogenetic tvu! beta-diversity indices raised false positives in less than 6% of cases testifying for their tvv! relevance. tvw! tvx! To conclude, our simulations demonstrated that phylogenetic diversity indices accounting two! only for species presence/absence (i.e. Faith’s PD) should be used with caution when aiming twp! to detect non-random assembly processes while Rao’s phylogenetic diversity index (as twq! modified by Chao, i.e. PD(q=2)) performed well in most situations. Similarly, phylogenetic twr! beta-diversity indices weighted by OTUs relative abundance performed much better than tws! composition-based phylogenetic diversity indices and than to all taxonomic diversity indices. twt! W-Unifrac reached similar efficiencies to Chao’s βPD(q=2) but Chao’s diversity indices twu! allow computing facets and components of biodiversity within a unique framework, while g- twv! Unifrac indices (including W-Unifrac) are restricted to measuring phylogenetic beta-diversity. tww! Analyzing complementary indices describing the taxonomic and phylogenetic facets twx! including presence and abundance of OTUs as well as their phylogenetic relationships is txo! certainly the most pragmatic way to fully understand the processes that shaped the txp! multifaceted microbial biodiversity, i.e. there is no single perfect index. Finally, properties of txq! indices highlighted here with simulated microbial communities are valid when studying any txr! type of species-rich communities, including tropical assemblages of plants or vertebrates. txs!

! xt!

! xu!

! xv!

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! poq! FEMS Microbiology Ecology , 91, 2015, !v061

doi: 10.1093/femsec/ !v061 Advance Access Publication Date: 5 June 2015 Research Article

RESEARCH ARTICLE High diversity of skin-associated bacterial communities of marine !shes is promoted by their

high variability among body parts, individuals and Downloaded from species Marl ene` Chiarello∗, S ebastien´ Vill eger,´ Corinne Bouvier, Yvan Bettarel and Thierry Bouvier http://femsec.oxfordjournals.org/

UMR 9190 Marine Biodiversity, Exploitation and Conservation (MARBEC), Universit e´ de Montpellier, CNRS, IRD, IFREMER, Place Eugene` Bataillon, Case 093, 34 095 Montpellier Cedex 5, France

∗Corresponding author: UMR 9190 Marine Biodiversity, Exploitation and Conservation (MARBEC), Universit e´ de Montpellier, CNRS, IRD, IFREMER, Place Eug ene` Bataillon, Case 093, 34 095 Montpellier Cedex 5, France. Tel: +334-67-14-40-93; E-mail: [email protected] One sentence summary: Skin-associated bacterial communities of marine !shes are different from surrounding bacterioplankton with an overall high diversity ascribed to its variability among body parts, individuals and species. Editor: Julian Marchesi by guest on September 11, 2015

ABSTRACT

Animal-associated microbiotas form complex communities, which are suspected to play crucial functions for their host !tness. However, the biodiversity of these communities, including their differences between host species and individuals, has been scarcely studied, especially in case of skin-associated communities. In addition, the intraindividual variability (i.e. between body parts) has never been assessed to date. The objective of this study was to characterize skin bacterial communities of two teleostean !sh species, namely the European seabass ( Dicentrarchus labrax ) and gilthead seabream (Sparus aurata ), using a high-throughput DNA sequencing method. In order to focus on intrinsic factors of host-associated bacterial community variability, individuals of the two species were raised in controlled conditions. Bacterial diversity was assessed using a set of four complementary indices, describing the taxonomic and phylogenetic facets of biodiversity and their respective composition (based on presence/absence data) and structure (based on species relative abundances) components. Variability of bacterial diversity was quanti !ed at the interspeci !c, interindividual and intraindividual scales. We demonstrated that !sh surfaces host highly diverse bacterial communities, whose composition was very different from that of surrounding bacterioplankton. This high total biodiversity of skin-associated communities was supported by the important variability, between host species, individuals and the different body parts (dorsal, anal, pectoral and caudal !ns).

Keywords: Sparus aurata ; Dicentrarchus labrax ; skin microbiome; next generation sequencing; phylogenetic diversity

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INTRODUCTION experienced by the animals from their intrinsic characteristics (e.g. physiology, behavior) at the sampling time. Skin surface All animals host at their surface and in several internal organs and hence bacterial epibionts are indeed directly exposed to the consortia of microorganisms, namely bacteria, archaea, fungi external biotic and abiotic components from the surrounding and viruses, collectively called microbiotas. These microbiotas water column, while marine vertebrates are vertically and hor- form diversi !ed communities and play critical roles for their izontally very mobile, which induces spatio-temporal variabil- host, as they facilitate nutrient absorption, regulate metabolism ity (i.e. effects of seasonality and geographical location) in the and defend against pathogen invasion (Sekirov et al. 2010 ). composition of skin microbiota (Le Nguyen et al. 2008 ; Wilson, Skin habitat is a unique interface, in "uenced both by sur- Danilowicz and Meijer 2008 ). To quantify the variability of this rounding environment (air, water, soil) and host-associated fac- parameter among individuals and species independently from tors (health state, mobility, excretion of wastes and mucus, and environmental variability, it is therefore necessary to use ani- immune molecules secretion). These interacting factors lead to mals raised in the same environment. a patchy physical and chemical environment at the surface of Another current gap in the description of biodiversity of skin an individual and to contrasted environments between individ- microbial communities is the lack of simultaneous assessment uals (Shephard 1994 ; Grice and Segre 2011 ). Therefore, a vari- of both taxonomic (i.e. based on species or OTUs) and phy- ability of skin microbiome in terms of abundance and diversity logenetic (i.e. based on phylogenetic lineages) diversity facets is expected at both interindividual and intraindividual scales. (Escalas et al. 2013 ). Indeed, phylogenetic diversity has been pro- Human skin microbiota has been particularly well studied, es- posed to be a better predictor of community functioning than pecially since the launch of the Human Microbiome Project in taxonomic diversity because it accounts for complementarities 2007 (Turnbaugh et al. 2007 ). These studies highlighted the high Downloaded from among species (Zavarzin, Stackebrandt and Murray 1991 ; Fierer, diversity of human skin microbiota (Schommer and Gallo 2013 ). Bradford and Jackson 2007 ). For instance, using marine bacterial They also evidenced that human skin-associated bacterial com- species, Gravel et al. (2012 ) experimentally showed that the phy- munities were highly variable between body parts, and between logenetic diversity of planktonic bacterial communities strongly individuals (Fierer et al. 2010 ). These interindividual and intrain- explained the productivity of the community, suggesting func- dividual variations have been related to individual physiology

tional complementarity of different phylogenetic lineages (even http://femsec.oxfordjournals.org/ (e.g. age, sex, health state, immune system), personal habits (e.g. if functional conservatism along phylogenetic lineages is a de- hygiene, cosmetic use, clothing) and local-scale parameters (e.g. bated issue; see Achenbach and Coates 2000 , and Wellington, pH, temperature, humidity), even if the speci !c impact of each Berry and Krsek 2003 ). Moreover, communities composed of dis- of these drivers, and the underlying interactions at a microbial tantly related bacterial species stabilize community production scale were not systematically demonstrated (see Grice and Segre when they are exposed to perturbations (Awasthi et al. 2014 ). 2011 for a comprehensive review). Changes in the phylogenetic diversity of skin microbiome may In contrast to human, skin microbiotas of animals are yet therefore change its functions, and may thus disturb its home- still largely unknown. Among them, marine vertebrates, which ostatic relations with the host and !nally may favor disease. represent more than 10 000 species on Earth ( www.iobis.org ), The phylogenetic diversity should then be considered when as- were only occasionally investigated during the last two decades

sessing the level and variability of skin microbiome diversity. by guest on September 11, 2015 (Larsen et al. 2013 ). In addition, most of the recent studies on For example, two communities dominated by different OTUs, marine vertebrates focused on the gastrointestinal microbiome i.e. having a high taxonomic structural dissimilarity will have (Mouchet et al. 2012 ; Xing et al. 2013 ), and revealed tight interac- a low phylogenetic dissimilarity if abundant OTUs are phyloge- tions between the host and its gut microbial communities (P erez´ netically close. et al. 2010 ). Bacterial epibionts of marine vertebrates remain In addition, each diversity facet (taxonomic and phyloge- largely understudied, yet they are believed to play major roles in netic) should be assessed accounting not only for composition maintaining host health (Boutin et al. 2012 ). The few reports pub- (species presence/absence) but also for the structure of com- lished to date found that the bacterial community composition munity by considering species relative abundances. Indeed, two was different among six Atlantic teleostean !sh species, and communities can appear to be highly dissimilar in terms of phy- highly different from that of surrounding planktonic communi- logenetic composition (i.e. they host phylogenetically very dis- ties (Larsen et al. 2013 ). Similarly, a recent study, focusing on wild tant species) only because of their rare species, and thus be humpback whale skin-associated bacterial communities, evi- similar in terms of phylogenetic structure (i.e. when taking ac- denced that despite individuals share a core set of species, bac- count of species relative abundances) (Escalas et al. 2013 ). terial community composition was variable between individu- In this study, we assessed the interspeci !c, intraspeci !c and als because of differences in host physiology (Apprill et al. 2014 ). intraindividual variability of the taxonomic and phylogenetic di- Moreover, while the entire !sh body, including the head, trunk versity of skin bacterial communities of two marine !sh species, and also the !ns, is recovered by the same integument, body namely the European seabass ( Dicentrarchus labrax ) and gilthead parts of marine !shes may harbor contrasted local conditions seabream ( Sparus aurata ), bred in controlled environmental con- due to (i) disparate epidermal mucous composition throughout ditions. Our !rst objective was to determine whether bacterial body’s surface ( Angeles´ Esteban 2012 ), (ii) variable exposure to diversity differed between the skin-associated bacterial com- nutrient excretion "uxes through gills and vent, and (iii) variable munities and the surrounding bacterioplankton. Our second water "ow during swimming. These environmental variations aim was to test whether two !sh species host different skin bac- at !sh surface may drive variations of skin-associated bacterial terial communities. Finally, we assessed the variability of bacte- communities between body parts. Such differences in skin mi- rial diversity between individuals per !sh species, and, within crobial diversity between body parts have never been assessed individuals, between different parts of the body (i.e. anal, caudal, to date on marine animals. Additionally, the only studies that as- dorsal and pectoral !ns), and compared it with the interspeci !c sessed marine animals skin microbial diversity focused on wild difference. As mentioned above, an effect of host species has individuals or !sh kept in in situ cages, making dif !cult to disen- already been evidenced in other wild teleostean species. We tangle the effects of past and current environmental conditions expected that this important variability should persist in

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Chiarello et al. 3

controlled conditions between seabass and seabream- 519R (5 ′-GTNTTACNGCGGCKGCTG-3 ′) (Vergin et al. 1998 ; Ishak associated bacterial communities, because of intrinsic phys- et al. 2011 ) and the HotStarTaq Plus Master Mix (Qiagen, Venlo, iological differences between these species. Additionally, we Netherlands) as follows: initial denaturation at 94 ◦C for 3 min, expected a high level of intraindividual variability of !sh skin followed by 28 cycles of 94 ◦C for 30s, 53 ◦C for 40s and 72 ◦C for bacterial associates, due to differences in habitats between the 1 min, ending with a !nal extension at 72 ◦C for 5 min. After different body parts studied ( Angeles´ Esteban 2012 ). ampli !cation, equimolar amounts of DNA were mixed, puri !ed (Ampure beads kit, Agencourt Bioscience Corporation, USA) and sequenced using a Roche 454 FLX titanium pyrosequencer. We MATERIALS AND METHODS obtained a total of 104 548 ( >200 bp) reads from the sequencing Sampling of PCR amplicons from the 36 samples. The nucleotide sequence data reported are available in the NCBI SRA database under the Four European seabass ( D. labrax ) and four gilthead seabreams accession number SRP050454. (S. aurata ) were sampled at the Marine Station of University of Montpellier (S ete,` France). After larval stage, the two species Sequence processing and taxonomic classi !cation were raised in the same conditions in two monospeci !c tanks 3 (5 m ) for 2.5 and 7 years, respectively. The two tanks were con- Sequences were processed following the SOP analysis nected to the same water !ltration system (activated carbon !l- pipeline of Schloss, Gevers and Westcott ( 2011 ; http://www. ter, no sterilization) in a closed circulating water system, and mothur.org/wiki/454 SOP , 02/2014) using Mothur (Schloss et al. tanks were regularly !lled with subsurface water of the Thau la- 2009 ). Brie "y, anormal "ows (homopolymers >8bp, >1 mis- Downloaded from goon (renewal of 2% vol. per day). Physicochemical conditions match to the barcode, or >2 mismatches to the primer), and were almost identical in the two tanks at the time of sampling very short "ows ( <200 bp) were discarded. Then, sequences (see data S1, Supporting Information). Individuals of the two were determined using a maximum-likelihood approach using species were fed with the same commercial pellets and received PyroNoise (Quince et al. 2011 ). Chimera were detected and no anti- or probiotic treatment during their entire life. Individ- eliminated using UCHIME (Edgar et al. 2011 ). uals were hooked, suspended in air by the hook shaft, stunned Up to 71 744 unique sequences with an average length of http://femsec.oxfordjournals.org/ and killed by cervical dislocation by a certi !ed animal manipu- 244 bp were retained. Sequences presenting more than 97% lator (following the European directive 2010/63/UE on the pro- identity were clustered, and a representative sequence (i.e. the tection of animals used for scienti !c purposes). This protocol closest sequence of all other sequences) for each cluster was was chosen to avoid contacts between !sh surface and other selected. Using these sequences, clusters were classi !ed us- surfaces (tank wall, soil or hands of experimenters). Immedi- ing the Ribosomal Database Project II Classi !er (Wang et al. ately after death, dorsal, caudal, left pectoral and anal !ns were 2007 ). Non-prokaryotes and mitochondrial clusters were ex- collected with ethanol-rinsed scissors and surgical pliers and cluded. The number of sequences varied between samples (data placed into sterile cryotubes. Sex was determined by direct ob- S2, Supporting Information) and these differences may not re- servation of gonads. There were three male seabass and one fe- "ect true difference in richness and biomass of bacterial com- male, and three female seabream and one male. Two samples

munities but rather difference in sampling effort (e.g. mucus by guest on September 11, 2015 of 100 mL of tank water were collected in each tank and !ltered volume) and/or ef !ciency of ampli !cation and sequencing. To through a 47 mm 0.2 µm polycarbonate membrane (Whatman, correct for this uneven number of sequences, we calculated tax- Clifton, USA). The four !lters were then placed in sterile cry- onomic and phylogenetic diversities on bootstrapped samples ◦ otubes. All samples were snap-frozen at −196 C in liquid nitro- (Bryant et al. 2008 ). More precisely, we considered 1000 random- ◦ gen, transported to the lab and stored at −80 C for 1 week before ized subsamples of 113 sequences (the minimal number of se- being analyzed. quences among the 36 samples) for each community. We then only considered the mean of diversity indices among the 1000 DNA extraction, ampli !cation and sequencing bootstrapped samples because their variances were negligible. ! Bacterial DNA recovery from n surface was adapted from Amal- Phylogenetic analyses !tano and Fazi ( 2008 ) for complex matrices. Each !n was im- merged into 6 mL of a PBS solution containing 0.5% of tween 20 All representative sequences were aligned using MAFFT v7 (FFT- (vol/vol) and vortexed at maximum speed during 10 min (Vortex NS2) (Katoh et al. 2002 ) and a phylogenetic tree was recon- genie 2, Scienti !c Industries, Bohemia, USA). The solution was structed using FastTree 2 (Price, Dehal and Arkin 2010 ), imple- then !ltered through a 47 mm 0.2 µm polycarbonate membrane mented in QIIME software (Caporaso et al. 2010 ). The tree was (Whatman, Clifton, USA). Bacterial DNA was extracted by using rooted using a set of eight archaeal 16S sRNA gene sequences the DNeasy °R Blood & Tissue kit (Qiagen, Venlo, Netherlands), obtained from SILVA database (Quast et al. 2013 ). A chronogram following the modi !ed manufacturer’s protocol facilitating lysis was then adjusted on the phylogenetic tree using the ‘chronos’ of Gram-positive bacteria. DNA was eluted in 100 µL of buffer function ( discrete model, 20 evolution rates) provided in the R- AE and quanti !ed by "uorescence using the Qubit dsDNA BR As- package ape (Paradis, Claude and Strimmer 2004 ). This function say kit (Invitrogen, Carlsbad, USA) and the Qubit °R 3.0 Fluorom- provides a dated ultrametric tree using a maximum-likelihood eter. Concentrations averaged 78.8 ng µL−1 (±9.6, n = 36). DNA algorithm and calibration points, provided in data S3 (Support- quality was assessed by spectrophotometry (Nanodrop 1000, ing Information).

Wilmington, USA). Values of A 260nm /A 280nm and A 260nm /A 230nm av- ± ± eraged 2.3 ( 0.2) and 4.6 ( 0.5), respectively. All DNA samples Alpha diversity computation were then diluted to 10 ng µL−1. An external laboratory (Research and Testing Laboratory, Lubbock, USA) performed PCR Ampli !- Alpha diversity was described using a set of four complemen- cation of the V1–V3 region of the 16S rRNA gene using univer- tary indices, describing taxonomic and phylogenetic composi- sal bacterial primers 27F (5 ′-AGRGTTTGATCMTGGCTCAG-3 ′) and tional diversity (i.e. taxonomic and phylogenetic richness based,

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respectively, on presence/absence of OTUs and phylogenetic lin- Effects of species, individual, sex and type of !n on alpha di- eages), and taxonomic and phylogenetic structural diversity (i.e. versity values were tested using Kruskal–Wallis tests, and sub- taking account of relative abundances of OTUs and phylogenetic sequent pairwise comparisons were performed using post-hoc lineages, respectively). Man–Whitney tests ( pgirmess package, Giraudoux 2011 ). Taxonomic richness (S) was assessed as the number of dif- Three independent non-parametric analyses (PERMANOVA) ferent OTUs in each community. Phylogenetic richness (Faith’s of the effect of host species, individuals and body parts on the PD), based on the sum of branch lengths of the phylogenetic tree variability of the structure and composition of the microbiota grouping OTUs present in the sample, was calculated using the (i.e. on each four beta diversity matrices) were performed using Picante R-package (Kembel et al. 2010 ). Taxonomic structural di- the ‘adonis’ function of the Vegan package (Dixon 2003 ). versity was assessed using Shannon alpha diversity (Shannon To compare the community composition in each type of sam- 1948 ). Phylogenetic structural diversity was assessed using Allen ples, Venn diagrams were constructed using eulerAPE (Micallef alpha diversity (Allen, Kon and Bar-Yam 2009 ). Allen index of di- and Rodgers 2014 ) and the R package VennDiagram (Chen and versity is similar to the Shannon diversity, excepted that it is Boutros 2011 ). based on phylogenetic branch lengths instead of OTUs. Allen index was calculated using the ‘ChaoPD’ function of entropart RESULTS package (Marcon and H erault´ 2014 ). These two indices were expressed in equivalent number of species, as recommended by Alpha diversity Jost ( 2007 ). This transformation allows direct comparisons between diversity values (Chao, Chiu and Jost 2014 ). Alpha diversity in water and on !sh skin Alpha diversity patterns differed, depending on the facet (taxo- Downloaded from nomic or phylogenetic) and component (composition or struc- Beta diversity computation ture) considered. Indeed, the taxonomic richness (related to species composition) was signi !cantly higher in each water Alpha diversity indices describe diversity at a local scale. To fully replicate (averaging ca. 46 OTUs ± 2.3 after bootstrap subsam- assess bacterial diversity, it is also necessary to measure beta di- pling, n = 4 water replicates) than on !sh skin (ca. 22 OTUs ± 7.2 http://femsec.oxfordjournals.org/ versity, i.e. the dissimilarity between communities. Similarly to per bootstrapped sample, n = 32; but note there were 73 ± 5.4 alpha diversity computation, dissimilarity was assessed using a OTUs per individual) (Kruskal–Wallis test (KW), P < 0.05, Fig. 1a). set of four indices describing each facet (phylogenetic and taxo- On the contrary, taxonomic alpha diversity (accounting for rel- nomic) and component (compositional or structural) of diversity. ative abundances of OTUs) was not signi !cantly different be- Compositional (i.e. based on presence/absence matrices) tax- tween the two types of samples (Shannon alpha diversity, KW P onomic and phylogenetic beta diversities were assessed by the > 0.05, Fig. 1c). The high taxonomic richness of planktonic com- Sorensen (Sørensen 1948 ; Koleff, Gaston and Lennon 2003 ) and munities was indeed mainly due to rare OTUs (OTUs accounting phyloSor dissimilarity indices (Bryant et al. 2008 ; Leprieur et al. for <1% of total abundance), representing about 89.9 ± 0.8% of 2012 ), respectively, using the betapart R-package (Baselga and the OTUs present in water. On the contrary, OTU abundances Orme 2012 ). PhyloSor is similar to the Sorensen index, excepted were more evenly distributed in skin communities, with fewer by guest on September 11, 2015 that it is calculated on branch lengths. These two beta diversity rare OTUs (4.0 ± 7.3% of present OTUs). Among all identi !ed measurements are scaled between 0 (when communities share OTUs, only 7% were found in water replicates, while all of them the same OTUs or phylogenetic lineages) and 1 (when commu- were found in at least one !sh sample. nities have no OTU or phylogenetic lineages in common). Additionally, while phylogenetic richness (PD) did not sig- Structural (i.e. accounting for entities relative abundances) ni !cantly differ between water and !sh (KW, P > 0.05, Fig. 1b), taxonomic and phylogenetic beta diversities were calculated us- phylogenetic alpha diversity (Allen alpha diversity, based on the ing the multiplicative decomposition of Shannon and Allen in- relative abundances of phylogenetic lineages) was signi !cantly dices, respectively, following the general framework proposed lower in water than in !sh skin samples (KW, P < 0.05, Fig. 1d). by Chao, Chiu and Jost ( 2014 ). These two beta diversity mea- Besides, planktonic communities were signi !cantly phylogenet- sures were scaled between 0 (when, in case of taxonomic beta ically underdispersed, i.e. the OTUs forming the community diversity, communities share the same OTUs at the same abun- were clustered on the phylogenetic tree ( SES.PD = −2.95 ± 1.8, dances) and 1 (when communities have no OTU in common) as P = 0.02 ± 0.01), while !sh communities were neither underdis- suggested by Vill eger´ et al. (2012 ), and were therefore directly persed nor overdispersed ( SES.PD = −0.36 ± 0.8, P = 0.39 ± 0.2). comparable to Sorensen and PhyloSor indices (Chao, Chiu and Jost 2014 ). These four beta diversity indices were calculated at Alpha diversity patterns in !sh skin samples the intraindividual and interindividual, and interspecies scale. For all facets and components of the alpha diversity, there was no signi !cant difference between (i) the two !sh species (inter- ! Statistical analyses speci c), (ii) the individuals of each species (interindividual) and (iii) body parts (intraindividual) (KW, P > 0.05, Fig. 1). Phylogenetic and taxonomic richness tend to be correlated as the increasing the number of OTUs increase the probability of Beta diversity covering more phylogenetic lineages. Consequently, we computed the standardized effect size of the PD index (SES.PD) Beta diversity between planktonic replicates comparing the observed PD value and its expected value un- Taxonomic composition of planktonic communities presented der a null model maintaining sample species richness, using an important level of variability between water replicates, the SES.PD function of the Picante package (Kembel 2009 ). A as shown by Sorensen’s dissimilarity index averaging 0.65 positive/negative SES.PD value indicates a phylogenetic over- (±0.01, n = 4) (Fig. 2). When considering phylogenetic prox- /underdispersion, i.e. OTUs found in the sample are more/less imity between OTUs, dissimilarity dropped by 30% (phyloSor phylogenetically distant than expected. dissimilarity index, 0.46 ± 0.01). Values of dissimilarity taking

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Figure 1. Richness and alpha diversity values of bacterial communities, at all scales (interspeci !c, inter- and intraindividual) studied. Points and error bars indicate respectively mean and con !dence interval limits (5th and 95th centiles of values obtained from the 1000 bootstraped subsamples) of diversity indices. To facilitate graphs interpretation, phylogenetic richness ( PD , graph ( b)) was respectively scaled to the total branch lengths of the phylogenetic tree. ( c) Taxonomic structural alpha diversity for each bacterial community, calculated on relative abundance of OTUs using Shannon index. ( d) Phylogenetic structural alpha diversity, calculated on relative abundances of terminal branches of the chronogram. On each graph, an asterisk indicates a signi !cant difference of richness and alpha diversity values by guest on September 11, 2015 between water and !n samples (KW, P < 0.05)

account of relative abundances of OTUs (Shannon beta diver- 0.69 ± 0.11 (Shannon beta diversity, Fig. 2). However, when con- sity) or that of phylogenetic lineages (Allen beta diversity) de- sidering phylogenetic relationships between lineages, interspe- creased (0.27 ± 0.01 and 0.06 ± 0.003, respectively). This indi- ci !c compositional and structural dissimilarity values halved cated a strong homogeneity of water replicates when taking into (phyloSor, 0.43 ± 0.07, S4, and Allen beta diversity, 0.27 ± 0.07, account abundant OTUs and their phylogenetic relatedness. Fig. 2) compared respectively to Sorensen and to Shannon beta diversity. Skin-associated bacterial communities are thus phy- Beta diversity between water and !sh skin logenetically more similar than taxonomically. However, these ! All values of dissimilarity between water and !sh skin were relatively low interspeci c dissimilarities compared to the tax- high, for each facet and component of diversity considered. onomic ones were still higher than expected in a null expecta- Taxonomic compositional and structural beta diversity was tion model with hypothesis of no effects of host species effect P < almost maximal (Sorensen’s dissimilarity index 0.97 ± 0.03; (Table 1, PERMANOVA, 0.05). Shannon beta diversity 0.94 ± 0.07) (data S4, Supporting Infor- mation Fig. 2). Bacterial dissimilarity between those two habitats Beta diversity of skin-associated bacterial community within each decreased by 31 and 45% when we accounted phylogenetic dis- species tance alone or associated with OTUs relative abundances (re- Interindividual and intraindividual dissimilarity values were ± spectively, for phyloSor 0.66 0.05 and Allen beta diversity 0.49 particularly high, as taxonomic and phylogenetic compositional ± 0.1). Nevertheless, whatever the facets and components of di- and structural variability of skin communities for the two !sh ! versity considered, the planktonic and sh skin-associated bac- species was comparable to that observed at the interspeci !c ! terial communities differed signi cantly (Table 1, PERMANOVA, scale (S4, Supporting Information Fig. 2). However, despite these P < 0.05). high differences among individuals and body parts, they were not higher than expected in a null model with hypothesis of Beta diversity of skin-associated bacterial communities between !sh no effects of !n type or individual (PERMANOVA, P > 0.05, species Table 1). In the same manner, there was no effect of !sh sex on Interspeci!c dissimilarity was high in terms of taxonomic any facet and component of diversity (PERMANOVA, P > 0.05, composition and structure, averaging, respectively, 0.81 ± Table 1) for each !sh species. For each species, skin-associated 0.03 (Sorensen dissimilarity, S4, Supporting Information) and bacterial communities were therefore variable and were neither

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Figure 2. Dissimilarity (beta diversity) among bacterial communities, between individuals for each !n type (‘interindividual’) and between !n types for each individual (‘intra-individual’). Total beta diversity within species (‘intra-speci !c’), between species (‘interspeci !c’), between water replicates (‘inter-replicate’), and between water and !n samples (‘interhabitat’) are also computed. Taxonomic ( a) and phylogenetic ( b) structural beta diversities were computed using the multiplicative decomposition of the Shannon and the Allen indices, respectively. Squares represent mean beta diversity values at the considered scale, and error bars indicate standard deviation of the n diversity values (at the center of each square) used to calculate the mean. Different letters indicate signi !cant differences between beta diversity values (KW,

P < 0.05). http://femsec.oxfordjournals.org/

Table 1. Effect of four factors studied on microbial communities assessed using permutational ANOVAs (PERMANOVA, 999 permutations) on dissimilarity matrices. Bold values indicate a signi !cant effect of the tested factor ( P < 0.05). For each facet and component of biodiversity, the name of the dissimilarity index is provided in parenthesis.

Factor Habitat Species Individual Fin Facets and components of biodiversity P r 2 P r 2 P r 2 P r 2

Taxonomic composition (Sorensen) 0.001 0.48 0.080 0.05 0.353 0.24 0.091 0.12 Phylogenetic composition (PhyloSor) 0.001 0.52 0.114 0.05 0.666 0.22 0.502 0.09

Taxonomic structure (Shannon) 0.001 0.48 0.01 0.06 0.96 0.18 0.06 0.14 by guest on September 11, 2015 Phylogenetic structure (Allen) 0.001 0.54 0.01 0.11 0.66 0.26 0.20 0.10

predictable by individuals nor by body parts. However, in each dominant clades did not signi !cantly differ between the two individual, skin-associated bacterial communities differed be- species (KW, P < 0.05). tween !ns, as some OTUs were unique to certain !n samples (Fig. 3) DISCUSSION Seabass and seabream skin harbored more diverse Dominant phylogenetic groups bacterial communities than water

Planktonic communities were essentially dominated by the Skin-associated bacterial communities were particularly di- phyla Proteobacteria (38–54% of sequences obtained from water verse, since as many as ca. 73 ( ±5.4) OTUs were detected on samples) and Bacteroidetes (41–52% of sequences) (Fig. 4). Skin each individual, while only ca. 46 ( ±2.3) OTUs were detected communities were mainly composed of members of the phyla in 100 mL of water (Fig. 1). Additionally, OTU abundances were Proteobacteria (30–85% of sequences obtained from !n samples), particularly uneven in planktonic communities, with few dom- Actinobacteria (2–53%), Bacteroidetes (0.4–27%) and Firmicutes (0.4– inant and a great number of rare ones. Such an uneven dis- 12%) (Fig. 4). Actinobacteria and Firmicutes lineages were not de- tribution of OTU abundances in seawater has been previously tected in the planktonic communities. At !ner taxonomic lev- reported in the Mediterranean sea by a study using amplicon- els, there were also disparities between epibiotic and planktonic based sequencing (Pommier et al. 2010 ). As expected, planktonic communities. Planktonic Proteobacteria were mainly composed communities were composed of typical marine classes such as of Gammaproteobacteria and Alphaproteobacteria , whereas skin- the Alphaproteobacteria and Gammaproteobacteria and Flavobac- associated Proteobacteria amount to a large fraction of Betapro- teria (Barbern and Casamayor 2010 ) (Fig. 4). They likely origi- teobacteria . The same disparity was observed for the Bacteroidetes, nated from the marine water that was used to !ll up the ex- mostly comprised of Flavobacteria in planktonic communities, perimental tanks. In contrast, bacterial OTUs of skin samples and of Sphingobacteria and Flavobacteria in skin-associated com- had more even abundances distribution than the planktonic munities. The two !sh species were dominated by the same ma- communities, and when considering whole individuals, phylo- jor bacterial clades (Fig. 4), and the relative abundances of these genetic diversity of skin-associated bacterial communities was

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Figure 3. Venn diagrams of detected OTUs ( a) on skin surface of the two !sh species and in water samples, and ( b) on each !sh !n type, summed with all individuals of the two !sh species. Venn diagram (a) is scaled to actual numbers of OTUs and overlaps. Venn diagram (b) is not proportional to numbers of OTUs and overlaps.

50% more diverse than the one of bacterioplankton (Fig. 4). during their entire lives, which likely greatly minimizes such ar- !

Even distribution of OTU abundances and high diversity of phy- tifacts. This line of evidence from different sh species and life http://femsec.oxfordjournals.org/ logenetic lineages (Fig 1) of skin-associated bacterial commu- histories highlights that the speci !city of skin biotope promotes nities could be related to the particular nutritive conditions a speci !c signature of skin bacterial community compared to existing at the !sh surface. Most of teleostean !shes secrete mu- planktonic cells. cus, which is constituted by a high diversity of gel-forming gly- coproteins, glycosaminoglycans and proteins (Shephard 1994 ). Phylogenetic homogeneity of skin-associated bacterial Such components can serve as nutrient sources for epibiotic communities bacteria (Bordas et al. 1998 ), which thus provide a mix of different resource niches. Environmental complexity and resource The diversity of bacterial epibionts was highly variable across partitioning has been shown to favor rich bacterial diversity all scales (i.e. interspeci !c, interindividual and intraindividual, (Ramette and Tiedje 2007 ; Schauer et al. 2009 ). By contrast, in a

S4, Supporting Information Fig. 2). However, among the 15 phyla by guest on September 11, 2015 closed water-circulated system, particles are trapped by a !ltra- detected on !sh skin, more than 95% of OTUs belonged to only tion system, potentially inducing particularly homogeneous en- four clades, namely the phyla Actinobacteria and Firmicutes and vironmental conditions. In such situation, water may be consid- the classes Sphingobacteria and β-proteobacteria (Fig. 4). This dom- ered as a desert from a nutritional viewpoint as it provides less inance of a core set of phyla drove the low values of phyloge- nutrients and less nutrient types (Azam and Malfatti 2007 ). This netic structural and compositional dissimilarity (i.e. phyloSor may explain the signi !cant phylogenetic clustering observed in and Allen indices, S4, Supporting Information and Fig. 2) com- water samples. The phylogenetic diversity of skin-associated pared to taxonomic structural and compositional dissimilarity bacterial communities recorded in our study is greater than values. This detected skin bacterial clades are similar to those that had been evidenced by rDNA 16S cloning and sequenc- identi!ed in the gut microbiota of wild and reared S. aurata ing approach on six !sh species of the Atlantic ocean, where and D. labrax , but harbored different genera (Carda-Di eguez,´ a total of only !ve different phyla were identi !ed (while as Mira and Fouz 2014 ; Kormas et al. 2014 ), potentially depicting a many as 15 phyla were detected on !shes in our study) (Larsen speci!c character of the skin habitat. Interestingly, these skin- et al. 2013 ). Such difference could be of methodological order, associated clades have already been reported in skin micro- as clone libraries are known to underestimate bacterial rich- biota of other vertebrates as teleosts (Wang et al. 2010 ; Larsen ness (e.g. comparative study on ant microbiome (Kautz et al. et al. 2013 ), marine mammals (Apprill et al. 2014 ), amphibians 2013 ). (Walke et al. 2014 ) and human (Grice and Segre 2011 ). Moreover, Beyond their high diversity, skin bacterial communities ex- a recent review focusing on marine macroscopic algae and in- hibited a very different composition from that of their plank- vertebrates surfaces also revealed the same core of high-level tonic counterparts, as indicated by the low number of common bacterial clades (Wahl et al. 2012 ). To explore such apparent con- OTUs (21 OTUs i.e. 3% of all detected OTUs, Fig. 3) and high servatism, we compared the representative sequence of pre- dissimilarity values (S4, Supporting Information Fig. 2) between dominant OTUs belonging to each previously cited core phyla these two types of habitats, whatever the facets and compo- and classes with sequences available on Genbank database nents of diversity considered. This agrees with recent inves- using BLASTn (http://blast.ncbi.nlm.nih.gov/ ) (Fig. 5). We ob- tigations on wild or outdoor farmed teleostean !shes (Wang served joint OTUs with other marine vertebrates associated mi- et al. 2010 ; Larsen et al. 2013 ), although such studies did not crobiota, water and soil bacterial communities, but the most permit disentangling intrinsic drivers of microbiome diversity unforeseen result was that as high as 40% of these !sh skin- from confounding extrinsic factors (e.g. !sh that experienced speci !c sequences were highly similar (99–100% identity, 97– different water masses due to their mobility and/or change in 100% coverage) with sequences of OTUs previously isolated from water masses). Here !shes were raised in controlled conditions healthy human skin surface. These OTUs were identi !ed as

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Figure 4. Relative abundances of main bacterial phyla and classes in each sample. Bacterial sequences that couldn’t be classi !ed at the phylum level with a con !dence threshold of 80% were depicted as ‘Unidenti !ed’.

Propionibacterium sp. (representing 1.7–28.8% of sequences in !sh Bacterial skin-associated community structure is samples), followed by Corynebacterium sp. (0–9.7%), Ochrobac- dependent on !sh species trum sp. (0–9.3%), Geobacillus sp. (0–8.9%) and Staphylococcus sp. (0–7.0%). This similarity between human and !sh skin micro- OTU composition in skin-associated bacterial communities dif- biomes was unexpected, as employees of the breeding station fered strongly between the two !sh species, as around 70% of were systematically wearing latex gloves for the maintenance OTUs detected at the surface of each species were not detected of the aquaculture system. In addition, all OTUs belonging to in the other species (Fig. 3). The high taxonomic structural beta these core clades and recorded in human skin were not de- diversity (ca. 70%), which is more informative because it takes tected in water (either because they were absent or suf !ciently into account the relative abundances of OTUs, indicated that rare and below the detection threshold, Fig. 5). Shared bacterial among shared species, the bacterial associates of the two !sh species between !sh and human is surprising but supports the species were dominated by different OTUs. However, the drop hypothesis that some biotic surface characteristics, which do of beta diversity values when taking account for phylogenetic not exist in the aqueous surrounding environments, may drive af !liation of OTUs (phylogenetic structural beta diversity ca. skin-associated community structure and that these clades 30%, Fig 2) demonstrated that, while both species were domi- may present phylogenetically conserved traits permitting their nated by different OTUs, these OTUs belonged to phylogeneti- growth on living surface. Here, further studies are needed to de- cally close bacterial clades. In addition, this moderate level of termine whether such similarities at skin surface exist in other phylogenetic structural beta diversity was marked by a signif- animal clades, or if it is a characteristic of teleostean !shes or a icant interspeci !c difference (Table 1), suggesting that species result of our particular experimental design. Moreover, for an ex- host close but distinct phylogenetic lineages. Such lines of evi- haustive assessment of microbial diversity, such analysis should dence for host species speci !city were recently observed on six be extended to viruses, Archaea and microeukaryotes, as that teleostean species by Larsen et al. (2013 ). In this study, authors has been done with human skin microbiome (excepted in the suggested that several physiological species-speci !c factors (e.g. case of viruses) (Findley et al. 2013 ; Probst, Auerbach and Moissl- skin mucous composition, antimicrobial properties) could lead Eichinger 2013 ). to such pattern. However, as their observations were made from

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Chiarello et al. 9

Figure 5. Results of GenBank search upon the 50 most abundant OTUs of main phyla recovered in skin samples. For each OTU, the representative sequence was compared to Genbank sequences using BLASTn. For each sequence, the best match ( ≥97% coverage, ≥99% identity) was selected, and the origin of the samples was noted. OTUs with no good matches were grouped in the ‘Unisolated’ class. Downloaded from wild animals, it is not easy to partition these effects from others, intrinsic feature of the studied !sh skin bacterial community. related to species ecology (i.e. mobility, food, contact with biotic Consequently, the effect of geographical location reported above and abiotic surfaces), and environmental variations (i.e. food may indeed exceed the intrinsic variability. availability, physicochemical conditions of surrounding water masses). Here, hosts were raised under the same conditions (e.g.

same food, no antibiotic treatments, same physicochemical con- Within individuals http://femsec.oxfordjournals.org/ ditions). We therefore can assert that this host species speci !city The bacterial community composition and structure differed be- is primarily due to intrinsic physiologic factors. tween body parts, as shown by the high values of intraindividual Larsen et al. (2013 ) suggested that skin microbiota selection beta diversity (S4, Supporting Information and Fig 2), which was from host species could be considered (i) as an active selection as high as the one between the two !sh species. Indeed, 53–61% by the host, or/and (ii) as a passive selection of bacterial species of OTUs detected on each !n were not detected on other ones able to grow on !sh surface. Indeed, while general mechanisms (Fig. 3), suggesting niche specialization within individuals. How- of innate immune system are largely conserved among !shes ever, this intraindividual variability was not explained by !n type and other vertebrates, the species-related variability of skin im- when considering the four individuals in each species because mune components has been proven for !sh ( Angeles´ Esteban of the large interindividual variability (Table 1, Fig 2). In other

2012 ). For example, lysozyme activity, as well as the nature of words, there is always a difference in bacterial community com- by guest on September 11, 2015 antimicrobial peptides secreted in the mucus layer, was found position and structure between the four !ns in each individual, to be different between several freshwater !sh species ( Angeles´ but when comparing two individuals, a single !n type host dif- Esteban 2012 ; Nigam et al. 2012 ). ferent communities. This is in contradiction with studies on the In addition, the structure of bacterial communities could be human skin microbiome, which reported that bacterial commu- affected by their speci !c capacities to adhere and grow on skin nities were primarily shaped by skin parts rather than individu- mucus, as on a culture medium. Fish skin mucus remains poorly als or time (Costello et al. 2009 ; Grice et al. 2009 ). documented, yet some species-related variation in mucus char- Here the absence of a common community composition acteristics has been reported, such as mucin composition pattern between !n types or individuals, coupled with a huge and hydration (i.e. mucin concentration) (Roberts and Powell variability at these two scales, suggests that (i) the body parts 2005 ). Variations in mucus hydration induce changes in its vis- studied and individuals did not particularly differ in terms of coelasticity, which may in "uence bacterial attachment ( Angeles´ habitat quality, and therefore (ii) bacterial communities com- Esteban 2012 ). However, no study to date compared the skin mu- position is unpredictable, either because being dependent of cus chemical composition between gilthead seabream and Eu- stochastic events of bacterial colonization and extinction or de- ropean seabass. pendent on a large number of interacting factors (e.g. local release of nutrients or antimicrobial molecules). This therefore underlines the need to explore surface-associated communi- Skin bacterial communities are variable among ties variability across the entire body, and especially around oral and within individuals gape, gills and lateral line, which may harbor more contrasted microenvironmental conditions. Further studies are also needed Among individuals to determine if such pattern exists in other marine or terrestrial The high level of interindividual variability observed (Fig. 2) has vertebrates. already been reported between individuals of wild or in situ cap- Another possibility is that skin microbiome may temporally tive teleosts and cetaceans, and was partly explained by the vary independently in each individual and body parts. Indeed, a geographical location (Le Nguyen et al. 2008 ; Larsen et al. 2013 ; strong temporal variability of skin-associated bacterial commu- Apprill et al. 2014 ). In our study, however, and as discussed be- nities has been recently reported in human (Costello et al. 2009 ). fore, animals of each species were raised rigorously in the same In the case of marine teleosts, studies about temporal dynam- conditions from birth to sampling, which precludes any extrin- ics of skin microbiota are very scarce. While a few studies evi- sic environmental in "uence on bacterial !sh skin associates. denced changes in the composition of cutaneous bacterial com- This high level of variability between individuals is therefore an munities due to seasonality and diet changes (Larsen et al. 2013 ;

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10 FEMS Microbiology Ecology , 2015, Vol. 91, No. 7

Landeira-Dabarca, Sieiro and Alvarez´ 2013 ), an intrinsic tempo- Baselga A, Orme CDL. betapart: an R package for the study of ral dynamic has never been assessed. Assessing the intraindi- beta diversity. Methods Ecol Evol 2012; 3:808–12. vidual variability of the microbiome for additional !sh species Bordas MA, Balebona MC, Rodriguez-Maroto JM, et al. Chemo- and for other aquatic and terrestrial animals is therefore needed taxis of pathogenic vibrio strains towards mucus surfaces of to con !rm our !ndings and to identify the drivers of this intrain- Gilt-Head Sea Bream (Sparus aurataL.). Appl Environ Microb dividual variability. 1998; 64 :1573–5. To conclude, our study demonstrated that !sh surface is col- Boutin S, Bernatchez L, Audet C, et al. Antagonistic effect of in- onized by a skin-specialized community of bacteria, composed digenous skin bacteria of brook charr ( Salvelinus fontinalis ) by clades that are not detected in the overlaying water. Skin as- against Flavobacterium columnare and F. psychrophilum . Vet Mi- sociates were characterized by a high diversity, which seems crobiol 2012; 155 :355–61. to be promoted by their important variability between species, Bryant JA, Lamanna C, Morlon H, et al. Microbes on mountain- individuals and body parts. As diversity is generally positively sides: contrasting elevational patterns of bacterial and plant correlated to microbial communities functioning and stability diversity. P Natl Acad Sci USA 2008; 105 :11505–11. (Wittebolle et al. 2009 ; De Schryver and Vadstein 2014 ), and neg- Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows anal- atively correlated with susceptibility to invaders (De Roy et al. ysis of high-throughput community sequencing data. Nat 2013 ; De Schryver and Vadstein 2014 ), this unique biodiversity Methods 2010; 7:335–6. may favor !sh resistance to pathogen invasion through the skin Carda-Di eguez´ M, Mira A, Fouz B. Pyrosequencing survey of (Wang et al. 2010 ). Testing the relationship between microbial di- intestinal microbiota diversity in cultured sea bass ( Di- versity and infection by pathogens becomes therefore an urgent centrarchus labrax ) fed functional diets. FEMS Microbiol Ecol Downloaded from challenge. 2014; 87 :451–9. Chao A, Chiu C-H, Jost L. Unifying species diversity, phylogenetic diversity, functional diversity and related similar- SUPPLEMENTARY DATA ity/differentiation measures through hill numbers. Annu Rev Ecol Evol S 2014; 45 :297–324. Supplementary data are available at FEMSEC online .

Chen H, Boutros PC. VennDiagram: a package for the generation http://femsec.oxfordjournals.org/ of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 2011; 12 :35. ACKNOWLEDGEMENTS Costello EK, Lauber CL, Hamady M, et al. Bacterial community variation in human body habitats across space and time. Sci- We thank C. Amiel and G. Sposito from the SMEL station, S ete,` ence 2009; 326 :1694–7. for providing !shes and for their help during the sampling pro- De Roy K, Marzorati M, Negroni A, et al. Environmental condi- cess. We thank D. Kalenitchenko and P.E. Galand for their help tions and community evenness determine the outcome of with genetic data analysis, and L. Dejouy and F. Rieuvilleneuve biological invasion. Nat Commun 2013; 4:1383. for their help during the sampling process. We are grateful to De Schryver P, Vadstein O. Ecological theory as a founda- three anonymous reviewers and the editor for their comments ISME J

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! pps! p! p`o ,8JRP;OEQ!!! q! ! r! Captive bottlenose dolphins and killer whales harbor a species-specific skin microbiota s! that varies among individuals t! u! Chiarello M. 1*, Villéger S. 1, Bouvier C. 1, Auguet JC. 1, and Bouvier T. 1 v! w! 1 Marine Biodiversity, Exploitation and Conservation (MARBEC), Université de Montpellier, x! CNRS, IRD, IFREMER, Place Eugène Bataillon, Case 093, 34 095 Montpellier Cedex 5, po! France pp! pq! En révision chez Scientific Reports pr! ps! Des informations supplémentaires complètent ce manuscrit, disponibles en section 7.4 pt! pu! pv! pw! px! qo! qp! qq! qr! qs! qt! qu! qv! qw! qx! ro! rp! rq! rr!

! ppt! rs! ABSTRACT rt! Marine animals surfaces host diverse microbial communities, which play major roles for ru! host’s health. Most inventories of marine animal surface microbiota have focused on corals rv! and fishes, while cetaceans remain overlooked. The few studies focused on wild cetaceans, rw! making difficult to distinguish intrinsic inter- and/or intraspecific variability in skin rx! microbiota from environmental effects. We used high-throughput sequencing to assess the so! skin microbiota from 4 body zones of 8 bottlenose dolphins (Tursiops truncatus ) and killer sp! whales (Orcinus orca ), housed in captivity (Marineland park, France). Overall, cetacean skin sq! microbiota is more diverse than planktonic communities and is dominated by different sr! phylogenetic lineages and functions. In addition, the two cetacean species host different skin ss! microbiotas. Within each species, variability was higher between individuals than between st! body parts, suggesting a high individuality of cetacean skin microbiota. Overall, the skin su! microbiota of the assessed cetaceans related more to the humpback whale and fishes’ than to sv! microbiotas of terrestrial mammals. sw! sx! to! tp! tq! tr! ts! tt! tu! tv! tw! tx! uo! up! uq! ur! us! ut! uu! uv!

! ppu! uw! INTRODUCTION ux! Marine animals’ surfaces are associated with highly diverse microbial communities, which vo! play major roles for their health, including protection against macrofouling, and pathogens vp! (Wahl et al. 2012; Bourne, Morrow, and Webster 2016). These surface microbiota were vq! shown to be both distinct from surrounding planktonic samples (Wahl et al. 2012), and host- vr! species specific (Bourne, Morrow, and Webster 2016), suggesting that they could have vs! coevolved with their animal hosts (McFall-Ngai et al. 2013). In addition, marine animal vt! surface microbiota are dynamic assemblages (Glasl, Herndl, and Frade 2016), with vu! composition of microbial Operational Taxonomic Units (OTUs) as well as their relative vv! abundance varying between host life stages (Lema, Bourne, and Willis 2014), surrounding vw! environmental conditions (Zaneveld et al. 2016) and geographical location (Salerno, Bowen, vx! and Rappé 2016). However, most of these findings have been reported from marine wo! invertebrates, and especially corals. Whether these observations could be generalized to wp! marine vertebrates, which constitute the most important biomass fraction of macroorganisms wq! in the global ocean, is barely unknown (but see recent work on fishes (Larsen et al. 2013; wr! Chiarello et al. 2015) and whales (Apprill et al. 2014)). Among marine vertebrates, mammals ws! are represented by more than 100 species belonging to three clades (pinnipeds, cetaceans and wt! sirenians) which respective ancestors were terrestrial. Marine mammals hence have biological wu! features, including skin structure, similar to terrestrial mammals. Therefore, assessing the wv! composition of skin microbiota of marine mammals could shed light on the importance of ww! evolutionary legacies and adaptation to marine environment in shaping skin microbiota of wx! animals. xo! xp! The only marine mammal skin microbiota described to date is the one of the free-ranging xq! humpback whale from the North Pacific. Apprill et al. (Apprill et al. 2011, 2014) showed that xr! individuals share a core skin microbiota and that variability in taxonomic and phylogenetic xs! diversity of skin microbiota among individuals is driven by geographical location and the xt! health state of the whale. However, such studies on wild animals do not allow disentangling xu! individual-driven variation of skin microbiota from the effect of environmental conditions. xv! Animals housed in controlled environment offer the opportunity to measure the interspecific xw! and inter-individual variability of animals skin microbiota independently from environmental xx! variability, and to assess the intra-individual variability of their microbiota (Bik et al. 2016). poo!

! ppv! pop! Besides assessing the taxonomic and phylogenetic facets of skin microbiota, describing its poq! functional role is fundamental to understand the link between microbiota and host health. por! Indeed, skin is the first line of defense from pathogen infections in mammals with skin pos! microbiota closely interacting with its host cells from the epidermis to the deep dermis pot! (Nakatsuji et al. 2013), to modulate immunity (Wang et al. 2016; Belkaid and Tamoutounour pou! 2016), and support antagonistic effects against pathogens (Cogen et al. 2010). However the pov! functional diversity of the skin microbiota of marine mammals has never been assessed, as pow! well as its congruence with its phylogenetic diversity has never been assessed (Nelson et al. pox! 2015). ppo! Recent advances in bioinformatics (e.g. PICRUSt (Langille et al. 2013)) allow predicting ppp! metagenome functional content from 16S rDNA data and hence to assess simultaneously the ppq! taxonomic, phylogenetic and potential functional diversities of microbial communities. ppr! pps! Here, using high-throughput sequencing, we assessed the taxonomic and phylogenetic ppt! diversities of the skin microbiota from 4 body zones (i.e. the dorsal, anal and pectoral fins, ppu! and its anal zone) of 8 individuals of two emblematic Odontoceti (toothed whales) species, ppv! the bottlenose dolphin (Tursiops truncatus ) and the killer whale (Orcinus orca ), housed in ppw! controlled conditions. We also predicted the functional facet of microbiota diversity using ppx! PICRUST software. We first measured the similarity between the microbiota of the two pqo! species. Second, we quantified the magnitude of intraspecific variability of microbiota, i.e. pqp! between individuals of each species and between their body parts. Third, we analyzed the pqq! similarity between the skin microbiota of cetaceans and the one of terrestrial mammals and pqr! non-mammal vertebrates. pqs! pqt! MATERIAL AND METHODS pqu! Sampling of skin and planktonic microbiotas pqv! We sampled skin microbiota of four killer whales ( Orcinus orca ) and four bottlenose dolphins pqw! (Tursiops truncatus ) housed at Marineland (Antibes, France) in accordance with European pqx! laws (Directive EC 1999/22 and EU CITES 338/97). Animals were manipulated by their pro! caretakers, in accordance with internal practices of the park. Sampling was done using a non- prp! invasive method (swabbing a small surface for 1 minute). All manipulations were approved prq! by Marineland’s scientific committee. prr! prs! Killer whales and dolphins were aged from 13 to more than 30 years at the time of sampling

! ppw! prt! (Table 1). Contrary to dolphins, killer whales were affiliated, with the younger ones being pru! siblings or half-siblings, and the older one (Freya) being the mother of the older male prv! (Valentin). All animals but one (i.e. Valentin which received an antifungal treatment that prw! ended two weeks before the day of sampling) did not receive any antibiotics during the 6 prx! months before sampling. pso! Individuals of the two species were kept in two separated pools, which are filled by the same psp! seawater circulation system. Seawater is pumped from 600-meters offshore and 68-meters psq! deep in the Mediterranean Sea, and filtered through sand. Water flux is set so that the water of psr! each pool is renewed every 2 hours. pss! pst! Table 1: Animals included in this study . The age of each animal at sampling time is indicated, as psu! well as their kinship, when known. Animals older than 30 years old were captured from the wild psv! during the early 1980s; therefore their exact age is unknown. Species Individual Age (years) Sex Complementary Information

Freya >30 Female Valentin’s mother

Antifungal treatment ended 15 days before Valentin 18 Male sampling K. whales Wikie 13 Female Valentin’s half sister, sister of Inouk

Inouk 15 Male Valentin’s half brother, brother of Wikie

Sharki >30 Female

Lotty >30 Female Dolphins Dam 17 Male

Rocky 15 Male psw! psx! pto! The day of sampling, each animal was asked by its caretaker to raise successively 4 body ptp! zones (i.e. the dorsal, caudal and pectoral fins, and anal zone) outside of water. These four ptq! zones could be considered as distinct patches for microbiotas (i.e. distant to each other by > ptr! 30 cm) and experience different micro-environmental conditions (e.g. the anal zone because pts! of release of feces and urine). After briefly rinsing the skin using 100-mL autoclaved

! ppx! ptt! seawater, skin microbiota was sampled by swabbing a 63-cm 2 circular surface using sterile ptu! foam-tipped applicators from Whatman (GE Healthcare) during 30 seconds on each side of ptv! the swab. For the caudal and pectoral fins, only the upper side of the fin was sampled. We ptw! then cut the tip of the swab using ethanol-rinsed scissors and placed the sponge part of the ptx! swab into sterile cryotubes. puo! pup! For each species, three 100-mL pool and input water (i.e. exit of pipe from filtering system) puq! samples were collected and filtrated through a 47 mm diameter, 0.2 µm pore size, pur! polycarbonate membrane (Whatman, Clifton, USA). The membranes were then placed in pus! sterile cryotubes. All samples were immediately snap-frozen in liquid nitrogen, transported to put! the laboratory and stored at -80°C before DNA extraction. puu! puv! 16S rDNA amplification and sequencing puw! DNA was extracted using the DNeasy Blood & Tissue kit (Qiagen, ID 69504) using the pux! manufacturer’s protocol with a few modifications. Briefly, swabs were placed in 2mL sterile pvo! microtubes, and 260 µL of enzymatic lysis buffer were added. After a 30-minutes incubation pvp! at 37°C, 50 µL of proteinase K and 200 µL of AL buffer were added before the incubation at pvq! 56°C for 30 minutes. The elution step was done twice in 100 µL of elution buffer. The two pvr! eluates were pooled to obtain a single 200 µL DNA sample per swab. DNA quality and pvs! quantity was assessed by spectrophotometry (NanoDrop 1000, Thermo Fisher Scientific, pvt! USA). pvu! The V3-V4 region of the 16S rDNA gene was amplified using bacterial primers modified for pvv! Illumina sequencing 341F (5’-CTTTCCCTACACGACGCTCTTCCGATCT- pvw! ACGGRAGGCAGCAG- 3’) (Liu et al. 2007) and 784R (5’ - pvx! GGAGTTCAGACGTGTGCTCTTCCGATCT-TACCAGGGTATCTAATCCT- 3’) pwo! (Andersson et al. 2008). Amplification was very difficult due to the low DNA concentration, pwp! and possible contamination by keratinocytes in skin samples. Consequently skin and water pwq! samples were amplified using two different PCR kits and conditions, which are provided in pwr! Supplementary Information S1. Both sample types were amplified in triplicates. After PCR, pws! the success of amplification was verified by migration on agarose gels, and equal volumes of pwt! three PCR products were pooled for each sample. After pooling, final concentration measured pwu! by Nanodrop (Wilmington, USA) averaged 14 ng . µL-1 (±17, n=43). After amplification, ! pwv! >JNBFHE:K! :FHNGML! H?! :EE! .!0! IKH=NK>! IHHE>=! :G=! :G>=! NI! NLBG@! <:EB;K:M>=!

! pqo! pww! 2FINK>!6.!;>:=L by an external laboratory (MR DNA, Shallowater, USA) and sequenced pwx! on a single run of Illumina platform using the 2 x 250 bp MiSeq chemistry. To check biases pxo! induced by the two different PCR protocols, we amplified 2 water DNA samples using both pxp! PCR kits and compared them after sequencing. They showed similar community structure pxq! (see S1). The nucleotide sequence data is available in the NCBI SRA database under the pxr! biosample numbers SAMN07278850-SAMN07278894. pxs! pxt! Sequence processing and phylogenetic analyses pxu! Assembly of paired reads was performed by the sequencing platform. All subsequent steps of pxv! sequence processing were performed following the SOP of Kozich et al for MiSeq (Kozich et pxw! al. 2013), https://www.mothur.org/wiki/MiSeq_SOP, 2016) using Mothur (Schloss et al. pxx! 2009). After removing sequences with an irregular length (i.e. outside a range of 420-460 pb), qoo! sequences were aligned along the SILVA reference database (Quast et al. 2013) (release 123). qop! Unaligned sequences were removed from the final alignment during this process. Chimeras qoq! were removed using UCHIME (Edgar et al. 2011). Filtered sequences were then classified qor! using the SILVA reference taxonomy and the non-bacterial reads were removed. After these qos! steps, we obtained a total of 2,198,758 sequences from our 43 samples, with 51,133±20,883 qot! (expressed as Mean±SD) sequences per sample. The number of sequences read for each qou! sample is unlikely correlated with total abundance of bacteria in sample, while it could bias qov! assessments of microbial biodiversity. Therefore, to ensure that further diversity assessments qow! were not biased by the uneven sequencing efficiency among samples, 10,000 sequences were qox! sub-sampled within each sample (Supplementary Information S2). Non-parametric Chao’s qpo! coverage estimator was computed in each community to assess effect of subsampling level qpp! using “Coverage” function provided in entropart R-package (Marcon and Hérault 2014). This qpq! index averaged 0.98±0.008 among microbial communities testifying for the accuracy of qpr! further diversity analyzes. qps! Sequences were then clustered into OTUs with 99% sequence identity, and the dominant qpt! sequence for each OTU was selected as reference and aligned against the SILVA reference qpu! database using Mothur for subsequent phylogenetic tree reconstruction. An outgroup was qpv! defined using a set of archaeal sequences obtained from SILVA database and re-aligned qpw! against the previous alignment of reference sequences using the MAFFT v7 with –add option qpx! (Katoh et al. 2002) before tree reconstruction using Fasttree (Price, Dehal, and Arkin 2010).

! pqp! qqo! To estimate the potential functions of microbial OTUs based on 16S rDNA data, we used qqp! PICRUST software (Langille et al. 2013) on reference sequences, using KEGG orthologs qqq! (Kanehisa et al. 2004) grouped into pathways (function categorize_by_function.py , level =3). qqr! A matrix containing 329 pathways was obtained. We then removed all eukaryotic functions, qqs! for instance genes related to cardiovascular diseases and categories grouped as “organismal qqt! systems”. NSTI values averaged 0.04±0.02 and 0.12±0.02 respectively in skin-associated and qqu! planktonic communities, indicating that OTUs sequences were close enough to the nearest qqv! 16S rDNA of reference genomes to infer functions. qqw! qqx! Investigating the possible presence of pathogens qro! Two additional phylogenetic analyses were performed separately for the two genera qrp! Staphylococcus and Streptococcus to look for putative pathogenic bacteria on cetacean skin. qrq! Near full-length 16S rDNA sequences of well-known pathogenic and non-pathogenic species qrr! of these genera were downloaded from the SILVA database (ACC number provided in qrs! Supplementary S3). Reference sequences of the most abundant OTUs belonging to these two qrt! genera (i.e. 35 Staphylococci and 31 Streptococci sequences), as well as the SILVA qru! sequences were aligned against the SILVA reference database using Mothur, and added into qrv! the SILVA reference phylogenetic tree using ARB software (Ludwig et al. 2004). The full qrw! phylogenetic tree was then pruned using the ape R-package to remove all but the added qrx! sequences, while keeping the topology of the tree. We then visualized the phylogenetic tree to qso! determine if OTUs from this study were close to the pathogenic species considered. qsp! qsq! Assessing diversity of and dissimilarity between skin microbiotas qsr! Four complementary diversity indices were computed to assess the taxonomic and qss! phylogenetic facets of diversity, including their respective compositional and structural qst! components (Chiarello et al. 2015). qsu! The compositional diversity accounts only for the presence/absence of OTUs or phylogenetic qsv! lineages (here defined as subsets of the phylogenetic tree, containing OTUs and their qsw! associated branch lengths). Compositional taxonomic diversity was measured by counting the qsx! number of OTUs in a sample (OTUs or functional richness). The phylogenetic compositional qto! diversity (i.e. the phylogenetic richness) was measured as Faith’s PD (Faith 1992) divided by qtp! the total PD of the tree (to scale values between 0 and 1). The structural diversity accounts for qtq! the relative abundance of OTUs or phylogenetic lineages, based on the number of sequences qtr! represented by each OTU.

! pqq! qts! The taxonomic structural diversity was computed using the Shannon index (Shannon 1948), qtt! expressed in Hill numbers (Jost 2007) on abundance of OTUs. The phylogenetic structural qtu! diversity was measured using the Allen index (Allen, Kon, and Bar -Yam 2009). All diversity qtv! indices were computed using R software. The taxonomic alpha diversity indices were qtw! computed using our own functions (available at https://github.com/marlenec/chao), while the qtx! Faith PD and Allen index were calculated respectively using the picante and entropart quo! packages (Kembel et al. 2010; Marcon and Hérault 2014). qup! quq! Similarly, we used four complementary beta-diversity indices to assess the taxonomic and qur! phylogenetic dissimilarity between pairs of microbiotas, according to their composition or qus! structure. The compositional taxonomic dissimilarity was assessed based on presence/absence qut! of OTUs, using the Sorensen index (Sørensen 1948) computed with betapart package quu! (Baselga and Orme 2012). The structural taxonomic dissimilarity, taking into account the quv! relative abundance of OTUs, was measured using the multiplicative decomposition of the quw! Shannon index (Chiarello et al. 2015). The phylogenetic compositional and structural qux! dissimilarities were computed using the unweighted and weighted versions of the Unifrac qvo! index (Lozupone and Knight 2005; Lozupone et al. 2007), respectively, from the GUniFrac qvp! package (Chen and ORPHANED 2012). qvq! qvr! Kruskal-Wallis tests (KW) were performed on alpha-diversity indices to assess the effect of qvs! sample type (i.e. water vs. skin samples), species, individual, sex, or body zone on microbial qvt! alpha-diversity. When significant, the KW was followed by post-hoc pairwise comparisons qvu! among groups using the pgirmess package, which includes the correction for multiple tests qvv! from Siegel and Castellan (Siegel and Castellan 1988; Giraudoux 2011). The correlation qvw! between the age of the individual and its associated alpha-diversity was assessed using a qvx! Spearman’s correlation test using stats R-package. Beta-diversity values were visualized on qwo! PCoA plots using the ape package (Paradis, Claude, and Strimmer 2004). The effect of qwp! sample type, species, individual, age, sex, and body zone on the structure and composition of qwq! microbial communities was assessed by performing separated one-factor PERMANOVAs qwr! with 999 permutations on beta-diversity values using vegan package (Dixon 2003). The qws! number of identical OTUs between skin microbiota and planktonic communities was qwt! analyzed using an Euler Diagramm computed with venneuler R-package (Wilkinson and qwu! Urbanek 2011). To assess how each microbial clade contributed to the dissimilarity between

! pqr! qwv! planktonic and skin microbiotas, as well as between microbiotas of cetacean species, we qww! performed a LefSe analysis (Segata et al. 2011). LefSe provides Linear Discriminant Analysis qwx! (LDA) scores for the bacteria clades contributing the most to the differences between qxo! cetacean species. qxp! qxq! Comparing skin microbiota of cetaceans and other vertebrates qxr! The skin microbiota of dolphins and killer whales was compared to the published skin qxs! microbiota of 11 terrestrial and marine vertebrates, namely Human (Grice et al. 2009; qxt! Staudinger, Pipal, and Redl 2011; Oh et al. 2014), pig (McIntyre et al. 2016), humpback qxu! whale (Apprill et al. 2014) and eight teleostean fish species (Larsen et al. 2013; Chiarello et qxv! al. 2015). Due to the different primers that were used for these different species, we could not qxw! directly reanalyze sequences from studies to assess OTUs abundance. Therefore, we extracted qxx! clades relative abundance from published figures and averaged across all individuals (i.e. 36 roo! humans, 4 pigs and 57 humpback whales) for each mammalian species. In the case of marine rop! fishes, as individual data was not available for all species, we chose to average clades relative roq! abundances of all species to make a single “fish” category. The most abundant clades ror! colonizing the animals were averaged for each animal; and a Bray-Curtis dissimilarity index ros! (Bray and Curtis 1957) (BC) between the different microbiotas was computed based on the rot! relative abundance of the different clades. A BC index of 1 indicates that microbiotas are rou! maximally dissimilar, i.e. that they are dominated by different clades while a BC=0 indicates rov! that the two microbiotas have the same taxonomic structure (i.e. same clades with same row! abundances). rox! rpo! RESULTS rpp! Diversity of skin and planktonic microbiotas rpq! We recovered a total of 7,287 OTUs among our 43 samples, with OTU richness ranging from rpr! 210 to 606 across samples. Water samples (481±64 OTUs, n=11 samples) were significantly rps! richer than skin samples (332±84 OTUs, n=32 samples) (Kruskal-Wallis, P<0.0001, Fig. 1). rpt! However when considering the relative abundance of OTUs, skin samples were significantly rpu! more diverse than water samples (KW, P=0.001), with a Shannon index of 30.4±23.6 and rpv! 9.2±2.5 equivalent number of species, respectively (Supplementary Information S4). rpw! Phylogenetic richness did not significantly differ between planktonic and skin-associated rpx! communities (KW, P=0.06, S4). However, when taking into account the relative abundance of rqo! phylogenetic lineages, skin samples were significantly more diverse than water samples (KW,

! pqs! rqp! P<0.0001), with Allen index being ca. 1.8 times higher in skin-associated communities than rqq! in the planktonic ones (Fig. 1). rqr! rqs! Skin-associated microbial alpha-diversity did not significantly differ between species (KW, rqt! P>0.05). At intraspecific level, there was no effect of individuals or body zones on OTUs rqu! richness and phylogenetic richness (KW, P>0.05, Fig. 1 and S4). However a significant effect rqv! of individual on taxonomic diversity was found for both species (Shannon index, KW, P=0.02 rqw! and 0.01 for dolphins and killer whales, respectively), which was not explained by age rqx! (Spearman’s correlation test, P>0.05) or sex (KW, P>0.05). Post-hoc pairwise comparisons rro! showed that the dolphins Sharki and Rocky hosted significantly different levels of taxonomic rrp! diversity; and that Valentin hosted significantly lower taxonomic diversity than Freya, Inouk rrq! and Wiki (P<0.05, S4). The microbiotas of these individuals also have contrasted levels of rrr! phylogenetic diversity (P<0.05). rrs! rrt! rru! rrv! rrw! rrx! rso! rsp! rsq! rsr!

! pqt! rss! rst! Fig. 1: Biodiversity of skin microbial communities from captive killer whales (A and D) and common rsu! bottlenose dolphins (B and E), and planktonic communities (C and F). The first row of plots (A-C) rsv! illustrates taxonomic richness, i.e. number of OTUs observed in a sample and the second row of plots rsw! (D-F) illustrates phylogenetic diversity, measured using Allen’s index (i.e. accounting for relative rsx! abundance of phylogenetic lineages). Total diversity of each individual (i.e. accounting for all body rto! zones sampled) is illustrated with larger light-gray bars. Bars on panels C and F represent the mean rtp! (and associated standard deviation) of OTU richness and phylogenetic diversity for planktonic rtq! communities (n=3 water samples). “Pool” refers to animal’s surrounding water, and “Input” refers to rtr! the water sampled at the exit of filtering system. rts! rtt! rtu! Dissimilarity between microbiotas rtv! Water communities and skin-associated microbial communities significantly differed for all rtw! facets of biodiversity considered (Table 2, Fig. 2, Supplementary Information S6). For rtx! instance, only 12% of OTUs found on cetacean were also present in surrounding planktonic ruo! communities (Fig. 3). Taxonomic (0.61±0.17) and phylogenetic (0.43±0.19) dissimilarities rup! reached their higher level between planktonic and skin-associated communities (S5). ruq! Dolphin- and killer whale-associated communities significantly differed for both taxonomic rur! and phylogenetic diversity, as revealed by PERMANOVA (Table 2). Within each species, rus! individuals had skin microbiotas with significantly different phylogenetic structure (Table 2).

! pqu! rut! The age of the individual, and in a lesser extent, its sex, had a significant effect on diversity of ruu! dolphin skin microbiota when considering relative abundances of OTUs or phylogenetic ruv! lineages (PERMANOVA, Table 2). For killer whales, neither age nor sex had a significant ruw! effect on skin-associated microbiota. Microbiotas from the four studied body zones were not rux! significantly different (PERMANOVA, Table 2). rvo! rvp!

rvq! rvr! Fig. 2: Phylogenetic dissimilarity between microbial communities illustrated along the two first axes rvs! from Principal Coordinates Analyses computed on weighted Unifrac dissimilarity index. rvt! All samples (i.e. skin-associated communities of 4 body zones of 4 captive common bottlenose rvu! dolphins and of 4 killer whales, and planktonic communities from respective pools and exit of filtering rvv! system) are present on panel (A). Panels (B) and (C) represent only captive killer whales or common rvw! bottlenose dolphin skin-associated communities with the 4 body zones of each individuals being rvx! delimitated by a polygon. rwo! rwp! rwq! rwr! rws! rwt!

! pqv! rwu! Table 2: Determinants of biodiversity of skin microbial communities.

Biodiversity facets Taxonomic Phylogenetic

Dissimilarity indices Sorensen Beta-Shannon U-Unifrac W-Unifrac

Factor R2 P R2 P R2 P R2 P Biotic vs. abiotic 0.09 0.001 0.36 0.001 0.12 0.001 0.46 0.001 Dolphins vs. 0.06 0.001 0.14 0.001 0.06 0.001 0.10 0.032 K. whales Killer whales Individuals 0.23 0.010 0.32 0.026 0.24 0.018 0.48 0.002 Body zones 0.19 0.683 0.15 0.805 0.20 0.420 0.10 0.957 Age 0.07 0.105 0.07 0.421 0.08 0.116 0.07 0.316 Sex 0.08 0.092 0.09 0.163 0.07 0.311 0.10 0.159 Dolphins Individuals 0.22 0.035 0.51 0.001 0.21 0.102 0.60 0.002 Body zones 0.20 0.569 0.10 0.961 0.20 0.663 0.08 0.975 Age 0.07 0.211 0.16 0.044 0.07 0.234 0.36 0.003 Sex 0.07 0.332 0.14 0.068 0.07 0.377 0.31 0.007 rwv! Effect of each factor was tested using permutational ANOVAs (PERMANOVAS, 999 permutations) rww! on dissimilarity matrices with for each facet of biodiversity, two indices: one index accounting only rwx! for composition of OTUs or phylogenetic lineages ( i.e. Sorensen or Unweighted Unifrac), and one rxo! index accounting for relative-abundance of OTUs or phylogenetic lineages ( i.e. beta-Shannon or rxp! weighted Unifrac). Bold P-values (<0.05) indicate a significant effect of the tested factor. Partial R- rxq! squared (R 2) is the proportion of variation in the dissimilarity matrix explained by the tested factor. rxr! rxs! rxt! rxu! Fig. 3: Euler diagram representing the number of skin- rxv! associated OTUs from each species (killer whales and rxw! common bottlenose dolphins) and planktonic rxx! communities that are shared between the three soo! conditions and unique ones. sop! soq! sor! sos! sot! sou!

! pqw! sov! Composition of bacterial communities sow! Planktonic communities were dominated by Alphaproteobacteria (95.9±0% of sequences), sox! especially Hyphomonadaceae , Rhodospirillaceae and Rhodobiaceae (71.4±0.1%, 7.6±0.1% spo! and 6.6±0.1% of all Alphaproteobacteria , respectively) (Fig 4 and S6). Ca. 90% of planktonic spp! OTUs could not be identified at genus level, excepted Anderseniella sp . spq! [Alphaproteobacteria ], which contributed to 6.3±0.1% of sequences in water samples (S6). spr! Cetacean skin microbiota was mostly composed of Gammaproteobacteria (57.5±27%), sps! Alphaproteobacteria (22.4±18.8%), Actinobacteria (7.9±0.1%) and Bacilli (7.3±0.1%) (Fig spt! 4). The most abundant genus on both species was Psychrobacter sp. [Gammaproteobacteria ], spu! which dominated skin samples, making 30.1±31% of total abundance on killer whales’ skin spv! and 45.2±28% on dolphins’ skin, while they represented a small fraction of sequences spw! (1.1±0.2%) in planktonic communities (S6). Other genera were punctually abundant in skin spx! samples, including Enhydrobacter (8.4±11.9% in both species), Staphylococcus (4.6±7.5%), sqo! Sphingomonas (3.5±5.9%), Paracoccus (2.8±4.9%) and Gardnerella (0.4±0.7%) (S6). sqp! Families and genera revealed by LefSe analysis for the two host species were mostly scarse sqq! and are not visible in S6. Four biomarkers were found for killer whales’ skin, belonging to sqr! Alphaproteobacteria : Phyllobacteriaceae (log10 effect size=4.5), Rubellimicrobium and sqs! Ruegeria (Rhodobacteraceae ) (3.6 and 3.5), and Microvirga (Methylobacteriaceae ) (3.4). sqt! Three biomarkers were significantly more abundant on dolphin’s skin: Nocardiaceae (3.4), squ! Enterobacteriaceae (3.2) and Caulobacter (Caulobacteraceae ) (3.1). sqv! Among OTUs from skin microbiotas, 237 were identified as Staphylococcus sp ., with the sqw! most abundant one averaging 4.4% of sequences in dolphin-associated communities. The sqx! phylogenetic analysis of the 35 most abundant ones showed that none of them was related to sro! the recognized marine mammal pathogen Staphylococcus delphini or other pathogenic srp! staphylococci (Supplementary S3). The most abundant Staphylococcus was closely related to srq! the opportunist Staphyloccocus warneri . 45 OTUs identified as Streptococcus were recovered srr! in skin-associated communities, with the most abundant one averaging 0.5% of sequences of srs! both species’ microbiotas. None of the 40 most abundant OTUs were related to the srt! pathogenic hemolytic Streptococci . sru! srv! srw! srx!

! pqx! sso! ssp! Fig. 4: Mean relative abundance of bacterial classes in skin-associated communities of common ssq! bottlenose dolphin and killer whales, and planktonic communities. P: upper side of pectoral fin, D: ssr! dorsal fin, C: upper side of caudal fin, A: anal zone. “Pool” refers to animal’s surrounding water, and sss! “Input” refers to the water sampled from the exit of pipe from filtering system. sst! ssu! Potential functional diversity of planktonic and skin microbiota ssv! LefSe analysis identified 19 functional biomarkers of planktonic communities, with strongest ssw! effect sizes for pathways involved in environmental information processing (Supplementary ssx! Information S7). Other functional biomarkers of planktonic communities were pathways sto! related to cellular processes, especially those related to motility (flagellar assembly pathway stp! and motility proteins, being respectively twice and 60% more abundant in planktonic stq! communities) and cell cycle, principally reflected by Caulobacter cell cycle pathways. str! Cetacean skin microbiota was characterized by functions involved in genetic information sts! processing, especially pathways related to DNA repair and recombination proteins stt! (Supplementary Information S7), DNA replication and translation (especially from 0.9 to stu! 1.3% of proteins involved in ribosome biogenesis in skin communities).

! pro! stv! Comparison of cetacean microbiota with other vertebrate microbiotas stw! Predominant clades in skin microbiota of dolphins and killer whales sampled for this study stx! were distinct from skin microbiota reported for terrestrial and marine vertebrates (Fig. 5). suo! Dissimilarity in relative abundance of major microbial clades between the two toothed whale sup! species studied here was twice lower than dissimilarity between the toothed whales and the suq! baleen whale (free-ranging Humpback whales) or the teleostean fishes (Fig. 5). Humpback sur! whale hosts higher proportions of Bacteroidetes than toothed whales, while fishes host more sus! Firmicutes and Betaproteobacteria. Skin microbiota of toothed whale was highly dissimilar sut! from the skin microbiota of pig and human (Bray-Curtis >0.95, Fig. 5). suu!

suv! suw! Fig. 5: Comparison of cetacean microbiota with microbiotas of other vertebrates. Mean relative sux! abundance of predominant microbial clades in marine and terrestrial animals (A) and associated svo! pairwise Bray-Curtis dissimilarity computed on mean relative abundance of these clades (B). In panel svp! B, the error bars associated to the 3 top bars are the standard deviation across the two Bray-Curtis svq! values obtained from the separated comparison of dolphins and killer whales skin-associated svr! microbiota with human, fish and pig’s microbiota, respectively. svs! svt! svu!

! prp! svv! DISCUSSION svw! The skin microbiotas of the captive dolphins and killer whales were distinct from their svx! surrounding planktonic communities. Indeed, while 100 mL of water contained nearly 1.5 swo! times more OTUs than a single sample corresponding to a 63 cm 2 swabbing of an animal’s swp! skin, the taxonomic diversity, accounting for OTUs relative abundances, was three times swq! higher in skin samples than in water samples (S4). Therefore, skin of cetacean host less OTUs swr! than the surrounding seawater, but due to a higher evenness of OTUs abundances, skin sws! microbiota is indeed more diverse than planktonic communities. Accordingly, phylogenetic swt! diversity, taking into account the relative abundance of phylogenetic lineages, was also up to swu! three times higher in skin-associated communities. Hence, OTUs dominating skin microbiota swv! were distributed among distant phylogenetic lineages while OTUs dominating planktonic sww! communities were clustered into a few lineages. These contrasted patterns were reported for swx! teleostean fishes raised in controlled conditions (Chiarello et al. 2015) and free-ranging sxo! humpback whales (Apprill et al. 2014). sxp! Besides differences in level of diversity, planktonic and skin microbial communities also host sxq! different OTUs and phylogenetic lineages (Table 2, Fig. 2-4). Planktonic communities were sxr! indeed dominated by Hyphomonadaceae and Rhodospirillaceae , which contain several genera sxs! typical of marine environements (Abraham and Rohde 2014; Baldani et al. 2014), and sxt! Anderseniella (Rhobdobacteriaceae ), that was firstly isolated from marine sediment, and is sxu! present in marine aerosols (Brettar et al. 2007; Seifried, Wichels, and Gerdts 2015). By sxv! contrast, skin-associated microbial communities were dominated by Psychrobacter sp. sxw! [Gammaproteobacteria ], a genus that was previously shown to be predominant on the skin of sxx! humpback whales (Apprill et al. 2014), and was also isolated from the skin and muscle too! biopsies of Weddel seals (Mellish et al. 2010), and from the skin of teleostean fishes (Lowrey top! et al. 2015). Since Psychrobacter sp. could act as an opportunistic pathogen in skin lesions of toq! sea lions (Alvarez-Perez et al. 2010), it should be looked for on skin of other marine animals, tor! and particularly on endangered mammals. tos! Other predominant genera which were found on both killer whales and dolphins skin tot! (Enhydrobacter , Staphylococcus , Sphingomonas, Paracoccus ) are known commensals of the tou! human skin (Perez et al. 2016). Such genera were not detected in healthy free-ranging whales tov! (Apprill et al. 2014), suggesting transfer from caretakers (e.g. during medical examinations or tow! training). The skin of marine mammals has key similarities with the skin of terrestrial tox! mammals, characterized by a very thick epidermidis composed of keratinocytes (Berta, tpo! Sumich, and Kovacs 2015), with specificities, for instance an incomplete process of

! prq! tpp! cornification, referred as ‘parakeratosis’, which also naturally occurs in mammalian mucosa tpq! (Cozzi, Huggenberger, and Oelschläger 2016; Delport et al. 2016). These skin characteristics tpr! may explain the presence of such human-associated genera on skin of captive dolphins and tps! killer whales’ skin through transfer from their caretakers, as it has been observed in other tpt! animals maintained in captivity (Delport et al. 2016; Clayton et al. 2016). However, skin tpu! microbiota from free-ranging dolphin and killer whale has to be analyzed before excluding tpv! the possibility that such genera might naturally occur in Odontoceti. tpw! Additionally, cetacean skin surface is covered by a biogel that smoothed its surface and tpx! prevents the attachment of settling organisms (Baum et al. 2001). This property, together with tqo! animal’s behavior (swimming and jumping which favoring particles detachments), and skin tqp! sloughing, may induce a constant shedding of skin-associated microorganisms. Captive tqq! animals may perform these behaviors less frequently than wild animals, which may ultimately tqr! favor the growth of opportunistic bacteria. Moreover, while all but one animal did not receive tqs! antibiotics for at least 6 months (15 days in the case of one killer whale) the occasional use of tqt! antibiotics on such captive animals may also modify their microbiota. For instance, killer tqu! whales and dolphins inhabiting industrialized coastal zones, hence likely confronted to tqv! antibiotics released to the sea through wastewater, were shown to host antibiotic-resistant tqw! bacteria in their pulmonary system and gut (Kueneman et al. 2014; Council et al. 2016). tqx! However, long-term effects of occasional antibiotic use on skin microbiota, especially in tro! marine mammals, still need to be investigated. trp! trq! The different phylogenetic lineages present in planktonic and skin-associated communities trr! could perform different functions (Table 2), with a higher proportion of biochemical trs! pathways related to motility and membrane transport in planktonic communities. A trt! metagenomics approach in surface seawater communities showed a similar trend towards tru! dominance of flagellum assembly pathway and of membrane transporters, which is consistent trv! with the motile heterotrophic lifestyle of surface planktonic communities competing for trw! nutrients (DeLong et al. 2006). By contrast, skin-associated communities contained higher trx! proportions of functions involved in protein folding, DNA replication, reparation and tso! translation. Such functions may be driven by the need for skin-associated bacterial cells to tsp! grow rapidly on the skin of the animal to counter sloughing (Apprill et al. 2014). However, in tsq! this study, microbial functions were estimated with the PICRUST software using tsr! phylogenetic affiliation of OTUs and a reference genome database. This assessment is limited tss! to previously annotated genes (ignoring undiscovered functional genes), and do not account

! prr! tst! for potential differences in gene expression. Therefore, the high similarity in functional tsu! diversity between animals found in this study should be confirmed by further metagenomics tsv! and metatranscriptomics studies. tsw! tsx! Skin microbiota was species-specific (Table 2). Host-species specificity of skin microbiota tto! had already been evidenced in other marine (Larsen et al. 2013) and terrestrial animals ttp! (Kueneman et al. 2014; Council et al. 2016; Avena et al. 2016). The major contributors in this ttq! interspecific difference were several Alphaproteobacterial families, namely ttr! Phyllobacteriaceae and Rhodobacteraceae , that were more abundant on killer whale’s skin, tts! and Nocardiaceae [Actinobacteria ] and Enterobacteriaceae [Gammaproteobacteria ] that had ttt! higher abundances on dolphin’s skin (Fig 4 and S6). ttu! This difference of microbial lineages between host species living in similar conditions has ttv! also been found for amphibians in natural pounds (McKenzie et al. 2012), and fishes raised in ttw! controlled conditions (Chiarello et al. 2015). These findings reinforce the hypothesis that even ttx! in aquatic environment were microbes are highly abundant and diverse; the unique features of tuo! animal’s skin shape its microbiota. Further studies are needed to determine which factors (e.g . tup! differences in immune system, skin structure, and/or pH and body temperature) on cetacean tuq! skin may promote this species effect. A likely important one is the differential expression of tur! antimicrobial peptides between species, that were found to be secreted in the skin of several tus! Delphinidae (Meyer and Seegers 2004), and which were shown to determine interspecific tut! microbial differences on invertebrate model species (Franzenburg et al. 2013). tuu! tuv! Within each species, individuals showed contrasted levels of OTUs and phylogenetic tuw! diversity (Fig 1 & S4) as well as dissimilarities in abundances of taxonomic clades and tux! phylogenetic lineages (Fig 4 & S6, Table 2). Hence, individual features seem to play an tvo! important role for shaping diversity of skin microbiota even when individuals have been tvp! living in the same environment and have frequent social interactions including direct skin tvq! contact (Dudzinski 1998; Dudzinski et al. 2009), which should favor homogenization of skin tvr! microbiota among individuals (as observed for humans (Meadow et al. 2013)). This is well tvs! illustrated by the mother killer whales Freya and her young son Valentin (Table 1). Both are tvt! in continual contact, but did not have closer skin-associated microbial structures (Fig. 2). tvu! Inter-individual variability was already documented for the pulmonary microbiota of tvv! bottlenose dolphins housed in SeaWorld (QLD, Australia) (Lima et al. 2012) and was shown tvw! to be consistent over time. Moreover, in the same study, authors confirmed this variability in

! prs! tvx! a total of 24 free-ranging dolphins of two species (Tursiops truncatus and T. aduncus ), two! without being able to detect any influence of age or sex of the animals. Our results for captive twp! animals highlight the importance of intraspecific variability of skin microbiome as well as twq! correlation between these differences and individual traits, e.g. sex and age as shown for twr! dolphins. Further studies are needed to unravel the proximal drivers, such as immunity or tws! physiology, of skin microbiota variability within a species. twt! twu! Body zones did not have replicable different skin microbiota (Table 2, Fig 2), contrary to the twv! patterns observed in humans (Grice et al. 2009; Perez et al. 2016). This absence of difference tww! in skin microbiota between body zones suggests that environmental conditions are more twx! homogeneous throughout the body of cetaceans than humans, probably due to several reasons txo! that are not mutually exclusive: the absence of hair follicules and sebaceous and sweat glands txp! in the dermis of cetaceans (Cozzi, Huggenberger, and Oelschläger 2016), the absence of moist txq! vs. dry microenvironments differentiation due to the aquatic habitat, and/or the leaching effect txr! of swimming that would homogenize physicochemical conditions at skin surface. However, txs! marine mammals could also harbor unique skin microbiome in other micro-niches that we did txt! not sample in our study, and which may provide different nutrient sources and protection, by txu! the presence of mucus in the eyelids (Berta, Sumich, and Kovacs 2015) or pulmonary txv! surfactant in the blowhole (Berta, Sumich, and Kovacs 2015), or which may retain particles txw! and microbes more easily, e.g. at fin folding. txx! uoo! Studies focusing on gut microbiota of insects (Brucker and Bordenstein 2012; Brooks et al. uop! 2016) and terrestrial mammals (Phillips et al. 2012; Brooks et al. 2016; Groussin et al. 2017) uoq! found a correlation between hosts phylogeny and microbiota and suggested that microbiota uor! result from ‘phylosymbiosis’ (Brucker and Bordenstein 2012; Brooks et al. 2016), partly due uos! to co-speciation of hosts and microbes that are vertically transmitted (Groussin et al. 2017). In uot! the case of skin-associated microbiota, and more importantly in the case of those of animals uou! living in seawater, there is still no test if this microbiota is vertically or horizontally uov! transmitted between individuals, and if differences in skin microbiota are correlated to host’s uow! phylogeny. Here, using previously published microbiota of 11 vertebrate species (Fig. 5), we uox! showed that the microbiota of captive bottlenose dolphins and killer whales is twice closer to upo! humpback whale and marine teleostean fishes than to terrestrial mammals (human and pig). upp! This suggests that the marine environment has a strong impact on the composition of skin upq! microbiota compared to evolutionary legacies within mammals. The specificity of marine

! prt! upr! skin microbiotas may be related to the aqueous conditions (Torsvik, Øvreås, and Thingstad ups! 2002), as well as the salinity experienced by skin-associated microbial cells, which are major upt! structuring factors of prokaryotic communities in other environments (Lozupone and Knight upu! 2007; Auguet, Barberan, and Casamayor 2009). Assessing skin microbiotas of more marine upv! vertebrates, including fishes from several orders, pinnipeds and sirenians as well as reptiles, is upw! thus needed to confirm this hypothesis. Such assessments should include both metagenomics upx! and metatranscriptomics approaches to unravel drivers and roles of skin microbiotas. uqo! uqp! ACKNOWLEDGEMENTS uqq! We acknowledge the “Fondation Marineland” for its financial support, and Marineland staff uqr! for their technical assistance during sampling. We also acknowledge three anonymous uqs! reviewers who helped improving this manuscript. uqt! uqu! AUTHORS CONTRIBUTIONS uqv! MC, TB and SV conceived the study. MC, SV and CB collected samples. MC performed uqw! DNA extraction and PCR, and MC and JCA analyzed results. MC drafted the manuscript, and uqx! all authors (MC, TB, SV, CB and JCA) contributed to the final version. uro! urp! ADDITIONAL INFORMATION urq! This project was funded by the “Marineland Foundation” (3,000 €). The funders had no role urr! in study design, data collection and analysis decision to publish, or preparation of the urs! manuscript. Authors declare no conflicts of interests.

! pru! p! p`p ,8JRP;OEQ!"! q! r! s! Skin microbiome of coral reef fishes is diversified, species-specific, not phylogenetically t! conserved and vulnerable to human activities u! v! w! Marlène Chiarello 1, Jean-Christophe Auguet 1, Yvan Bettarel 1, Corinne Bouvier 1, Thomas x! Claverie 1, Nicholas AJ Graham 2, Fabien Rieuvilleneuve 1, Elliot Sucré 1, Thierry Bouvier 1#, and po! Sébastien Villéger 1 pp! pq! pr! 1Marine Biodiversity, Exploitation and Conservation (MARBEC), Université de Montpellier, ps! CNRS, IRD, IFREMER, Place Eugène Bataillon, Case 093, 34 095 Montpellier Cedex 5, pt! France pu! 2Lancaster Environment Centre, Lancaster University, Lancaster, UK pv! pw! px! En préparation qo! qp! Des informations supplémentaires complètent ce manuscrit, disponibles en section 7.5 qq! qr! qs! qt! qu! qv! qw! qx! ro! rp! rq! rr!

! prv! rs! SUMMARY rt! All animals are associated with microbial communities, called microbiomes (Grice et al. ru! 2009; Wahl et al. 2012; Larsen et al. 2013; Apprill et al. 2014; Bourne, Morrow, and Webster rv! 2016). Studies on terrestrial vertebrates found that this microbiome varies across species, and rw! that these differences are related to host ecological traits ( e.g. diet), and evolutionary legacy rx! (i.e. related species tend to host more similar microbiome, a pattern called “phylosymbiosis”) so! (Ochman et al. 2010; Phillips et al. 2012; Groussin et al. 2017). However, these studies sp! focused on intestinal microbiome of terrestrial vertebrates. The existence of such patterns on sq! skin microbiome, which is crucial for host fitness in marine environment (Efrony et al. 2007; sr! Gómez and Balcázar 2008; Krediet et al. 2013; Lowrey et al. 2015), is underexplored. More ss! specifically, the skin microbiome of marine fishes, which constitute the most diverse group of st! vertebrates (Nelson, Grande, and Wilson 2016), remain largely unknown, while many species su! are under increasing threat especially on coral reefs (Graham et al. 2011). sv! We analyzed the prokaryotic microbiome of 44 fish species of the Western Indian Ocean. We sw! found that prokaryotes living on fish skin are highly diverse, contain numerous unknown sx! clades, and are highly variable between species. However, these interspecific differences were to! not coupled to phylosymbiosis and only weakly explained by fish diet. Finally, we found that tp! fish species hosting the highest microbial diversity are also the most vulnerable to fishing. tq! tr! RESULTS AND DISCUSSION ts! Skin of reef fishes is a microbial hotspot tt! We sampled the skin microbiome of 138 individuals of 44 species from the coral reefs around tu! Mayotte Island (France) (Fig 1). These species represented 5 orders and 22 families and the tv! main ecological groups dominating coral reefs (see supplementary Information S1). tw! Biodiversity of microbiome was assessed using 16S rDNA high-throughput sequencing, using tx! universal prokaryotic primers (Caporaso et al. 2011; Apprill et al. 2015, 2011) and applying uo! two complementary indices of phylogenetic diversity, the first one based on presence/absence up! data, which is an index of phylogenetic richness (Faith’s PD), and the second one taking uq! account for microbial abundances and which is referred as phylogenetic entropy (Allen ur! index).We assessed the dissimilarity between microbial communities considering only the us! presence of OTUs (U-Unifrac) or accounting for their abundance (W-Unifrac) (See ut! supplementary methods for more details). uu! uv!

! prw! uw! ! prx! ux! A total of 10,430 OTUs were found on fishes, representing 34 archaeal and bacterial classes vo! and 19 phyla, while 2,210 OTUs representing 17 classes and 11 microbial phyla were found vp! in planktonic communities (Figure 1). Fish skin microbiome was highly variable, as no OTU vq! was recovered in all individuals. Fish skin microbiome was significantly distinct from vr! surrounding planktonic communities, both in term of composition in microbial lineages and vs! of their abundances (Table 1). vt! vu! vv! Table 1: Determinants affecting microbial community structure Unweighted Unifrac Weighted Unifrac

Factor P R2 P R2

Fish vs. water 0.001 0.08 0.001 0.14

Fish species 0.001 0.39 0.001 0.48 vw! Effect of each factor was tested using two separated permutational ANOVAs (PERMANOVAS, 999 vx! permutations) on relative abundance weighted and unweighted Unifrac dissimilarity matrices. wo! wp! wq! Fish skin microbiome was significantly enriched in Gammaproteobacteria (14±12% of wr! abundance in water column vs. 38±24% on fish surface), especially Vibrionaceae (1±3% vs. ws! 7±11%) and Altermonodales (8±10% vs. 10±13%), Rhizobiales (0.01±0.03% vs. 3±5%) and wt! Clostridiales (0.03±0.04% vs. 3±4%) compared to planktonic communities that were enriched wu! in Cyanobacteria ( 24±12% of abundance in water column vs. 4±8% on fish surface), wv! Rhodobacteraceae (7±4% vs. 6±9%) and Flavobacteriaceae (9±4% vs. 5±7%) (Fig 1 and ww! S2). Bacteria largely dominated both planktonic and skin-associated communities, as the 75 wx! OTUs identified as Archaea averaged 1.1% of abundance in planktonic communities and xo! 0.8% in skin-associated communities. 37% of Archaeal OTUs were affiliated to the phylum xp! Thaumarchaeota , 17% to the phylum Euryarchaeota , and all other OTUs remained xq! unclassified. Thaumarchaeota were mostly affiliated to the Marine Group I (9 OTUs out of xr! 28 Thaumarchaeota) and South African Gold Mine Group 1 (4 OTUs). Euryarchaeota were xs! mostly classified into Thermoplasmata (5 OTUs out of 13 Euryarchaeota), Methanomicrobia xt! (4 OTUs) and Halobacteria (3 OTUs). While not represented in the figure in S2 because of xu! their small effect size, Thaumarcheaota , Thermoplasmata and Halobacteria were xv! significantly more abundant in fish samples (see S2 for their respective effect sizes). These xw! archaeal clades were already found in seawater and in sponges (Polónia et al. 2016) and in

! pso! xx! surface mucus of scleratinian corals (Kellogg 2004). To our knowledge, this is one of the first poo! records of Archaea on teleost fish surface. pop! poq! Fish skin microbiome and planktonic communities harboured high proportions of unclassified por! microbial taxa. Using the Mothur taxonomic affiliation method, as many as 60% of the pos! 11,583 recovered OTUs recovered in both fish skin microbiome and planktonic communities pot! could not be classified at class level (Fig 1) and 46% even could not be classified at phylum pou! level. These OTUs ranged from 0 to 34% of total abundance in a sample. We refined the pov! taxonomic affiliation of the most frequent unclassified OTUs ( i.e. 93 OTUs that were pow! recovered in at least 15 samples, see Supplementary Methods) using the Arb parsimony pox! insertion tool and the Silva backbone tree (v128). Twenty-three of them belonged to classes ppo! that were not detected during OTU classification by Mothur, so the actual diversity of tropical ppp! microbiome is likely higher than the one reported here (Fig 1, S3). These findings on only a ppq! small subset of fish species suggests that the thousands of tropical fishes certainly hosts a ppr! large number of unknown microbes, which highlights the need for larger screening of marine pps! vertebrate microbiome. ppt! ppu! In addition to the differences between fish skin microbiome and planktonic communities in ppv! terms of composition and structure, phylogenetic richness present on a fish species ppw! (accounting only for presence/absence of microbial phylogenetic lineages) was on average 1.2 ppx! times higher on the skin of one fish than in 200-mL of seawater (Kruskal Wallis test on pqo! Faith’s PD, S4). This higher richness of skin-associated microbiome compared to surrounding pqp! planktonic communities is contrasting from other studies on farmed teleostean fishes pqq! (Chiarello et al. 2015) and cetaceans (Chiarello et al., in revision ; Apprill et al. 2014), which pqr! found higher phylogenetic richness in seawater than in skin microbiome. Phylogenetic pqs! entropy (measured with Allen index which accounts for the relative abundance of prokaryotic pqt! phylogenetic lineages) of fish skin microbiome was also 1.4 times higher than phylogenetic pqu! entropy of planktonic communities (S4), and suggests that skin-associated microbiome of pqv! tropical fishes was not only richer, but also characterized by a higher evenness of OTUs pqw! abundances than surrounding planktonic communities. pqx! pro! Phylogenetic richness of skin microbiome varied significantly among fish species (S4). In prp! addition, phylogenetically related fish species had on average similar levels of prokaryotic prq! phylogenetic richness at their surface ( Moran’s I autocorrelation tests, P<0.05 in 96% of

! psp! prr! subsamples, n=999, see Supplementary Methods and S4) For instance, Chaetodon falcula and prs! Chaetodon auriga shared very similar levels of phylogenetic richness at their surface (Fig 1, prt! respectively 1.9±0.8 and 1.7±1.3, making a 10% difference), while Pterois miles and Pterois pru! radiata hosted a phylogenetic richness of 5.7±1.49 and 5.88±00 respectively (also 10% of prv! difference between both Pterois ). Phylogenetic entropy of microbiomes, which accounts for prw! dominance of phylogenetic lineages, was also significantly different among species (S4) and prx! these differences were also related to phylogenetic distances between fishes, with a weaker pso! signal (Moran’s I, P<0.05 in 88% of subsamples, n=999, S4). For instance the difference psp! between phylogenetic entropies hosted Chaetodon falcula and Chaetodon auriga was higher psq! than the one between phylogenetic richness (38 % of difference), while Pterois miles and psr! Pterois radiata hosted very similar levels of phylogenetic richness, with a 2.4% difference pss! between them. pst! Therefore, fish species host different levels of microbial diversity, including richness of psu! microbial phylogenetic lineages, as well as diversity of the most abundant ones, and these psv! differences are related to their phylogeny. psw! psx! Assessing dissimilarity between fish skin microbiome and looking for its determinants pto! Phylogenetic dissimilarity between individuals’ microbiome from the same species was ptp! overall high (0.76±0.07 for U-Unifrac, and 0.56±0.15 for W-Unifrac, respectively; S5) ptq! especially when compared to the dissimilarity between water samples (S5). Such high ptr! intraspecific variability of skin microbiome confirms findings reported for other fish species pts! (Larsen et al. 2013; Chiarello et al. 2015). Nevertheless, variability in phylogenetic structure ptt! of microbiome was significantly higher between fish species than between individuals from ptu! the same species (PERMANOVA, Table 1, S5). This confirms that fish species host distinct ptv! microbial phylogenetic lineages, as it was previously reported for Teleostei (Larsen et al. ptw! 2013; Chiarello et al. 2015). ptx! puo! However, these high interspecific differences in skin microbiome where not significantly pup! correlated with phylogenetic distance between fish species (Mantel tests on W-Unifrac and U- puq! Unifrac, P<0.05 in 8.4 and 0% of subsamples, respectively, n=999, Fig 2, S6). This absence pur! of phylosymbiosis was persistent even when considering clustering levels for defining OTUs pus! higher than 97% (see Supplementary Methods and S7). Hence, two fish species that are put! phylogenetically close did not host more similar microbiomes than two distant ones (Fig 2). puu! For instance, the dissimilarity values between the two angelfishes Pygoplites diacanthus and

! psq! puv! Pomacanthus imperator (Perciformes) (U-Unifrac=0.85±0.11 and W-Unifrac=0.80±0.17) puw! were higher to the ones obtained between Pygoplites diacanthus and the scorpionfish Pterois pux! miles (Scorpaeniformes) (U-Unifrac=0.84±0.07 and W-Unifrac=0.73±0.10). Even within the pvo! order Perciformes, which contained most species included in this study, no phylosymbiosis pvp! was detected and correlation between phylogenetic distance of hosts and phylogenetic pvq! dissimilarity of microbiome was even lower than 0.1 (Mantel tests performed on W-Unifrac pvr! & U-Unifrac P<0.05 in 0.7 and 0% of subsamples respectively, n=999, Fig 2 & S6). For pvs! instance, the dissimilarity values between Chaetodon falcula and the phylogenetically distant pvt! Pterocaesio trilienata (U-Unifrac=0.83±0.06 and W-Unifrac=0.75±0.06) were nearly pvu! identical to the dissimilarity values between the two phylogenetically close species pvv! Chaetodon falcula and Chaetodon lunula (U-Unifrac=0.84±0.06 and W-Unifrac=0.74±0.17). pvw!

pvx! Fig 2: Phylogenetic dissimilarity between skin-associated microbiomes of fishes belonging to the pwo! same taxonomic order (dots) and from fishes belonging to different orders (‘+’ sign), along with the pwp! divergence times between fish species. For clarity, only one individual per fish species is represented, pwq! and thus intraspecific dissimilarities are not shown. pwr! pws!

! psr! pwt! This absence of phylosymbiosis in fish skin microbiome contrasts with previous studies that pwu! reported phylosymbiosis for gut microbiome of terrestrial animals (mammals (Groussin et al. pwv! 2017), hominids (Ochman et al. 2010), insects (Brooks et al. 2016)) and for microbiome of pww! tropical sponges (Easson and Thacker 2014) . However, whether gut microbiome is correlated pwx! with host phylogeny has never been tested for teleostean fishes. pxo! Teleostean fishes are the most diverse groups of vertebrates and comprise 40 orders, 448 pxp! families, and more than 30,000 species (Nelson, Grande, and Wilson 2016). According to pxq! times estimates from Rabosky et al. (Rabosky et al. 2013), the main fish families included in pxr! our study diverged from 53 Mya to 129 Mya. Such divergence times are much larger than pxs! those of clades in studies that detected a phylosymbiosis signal, e.g . terrestrial mammals (0.8 pxt! to 150 Mya) (Groussin et al. 2017), hominids (15-20 Mya) (Ochman et al. 2010), and other pxu! terrestrial clades (0.2-100 Mya) (Brooks et al. 2016), suggesting that phylosymbiosis pattern pxv! may be more detectable in phylogenetically close organisms. Still, phylosymbiosis signal was pxw! detected among sponge species that diverged 680 Mya (Easson and Thacker 2014). pxx! qoo! The absence of a significant phylogenetic signal among the marked interspecific differences qop! in fish skin microbiome could be explained by the role of three drivers: (i) environmental qoq! factors, (ii) fish ecological traits, and/or (iii) fish immune system. First, fishes included in this qor! study were sampled at less than 15 km of distance away from each other, and reef type qos! (fringing vs barrier) explained less than 3% of dissimilarity in skin microbiome of fishes qot! found in both habitats, while fish species explained around 30% (PERMANOVAs performed qou! on fish species recovered in both sites, P=0.001 for fish species and 0.002 for reef type, S8). qov! For comparison, microbial phylogenetic entropy of plankton showed higher variability qow! between reef types (PERMANOVA performed on W-Unifrac, P=0.001 R 2=0.27). Hence the qox! variability in skin microbiome found within and among species is unlikely driven by qpo! environmental factors and is thus rather driven by host-specific drivers. qpp! qpq! We tested whether phylogenetic structure of skin microbiome could be predicted by key qpr! ecological traits of fishes, including body size, schooling, period of activity, diet, mobility, qps! and position in the water column (S9 and Supplementary Methods). The only trait that yielded qpt! a significant, although weak explanatory effect (R 2=0.18 when tested with W-Unifrac) was qpu! diet (Table 2 and S10). Such an effect was not due to a transfer of microbial cells from sessile qpv! invertebrates to sessile invertebrates-eating species (S11). Although it has been proven that qpw! diet shapes gut microbiome of other vertebrates, including teleostean fishes, at both

! pss! qpx! interspecific (Ley et al. 2008; Muegge et al. 2011; Sullam et al. 2012; Sanders et al. 2015; qqo! Groussin et al. 2017) and intraspecific scales (Wu et al. 2011; Muegge et al. 2011; Sullam et qqp! al. 2015; Williams et al. 2016), this is the first report of an effect of species diet on its skin qqq! microbiome. An explanation would be a transfer from fishes’ feces to their skin, explaining an qqr! indirect effect from their diet. However gut microbiome of coral fish are still largely unknown qqs! (but see (Miyake, Ngugi, and Stingl 2015) on Acanthuridae from Red Sea). qqt! qqu! Table 2: Effect of fish ecological traits on the structure of skin microbial communities

Unweighted Unifrac Weighted Unifrac

Ecological Trait % of sign. P-values Mean R 2 % of sign. P-values Mean R 2

Diet 97.9 0.16 ± 0.01 88.1 0.18 ± 0.01

Size 23.2 0.10 ± 0.00 8.8 0.10 ± 0.01

Schooling 5.1 0.09 ± 0.00 24.4 0.10 ± 0.01

Mobility 4.7 0.05 ± 0.00 2.1 0.05 ± 0.01

Position 8.8 0.05 ± 0.00 4.6 0.04 ± 0.01

Activity 0 0.04 ± 0.00 0.1 0.04 ± 0.01 qqv! Results of the PERMANOVAs-based analysis assessing the effect of fish ecological traits performed qqw! on 999 subsampling replicates containing one individual per fish species (see Supplementary Methods qqx! for more details). Mean and associated standard deviation of R-squared values on 999 subsamples are qro! provided, as well as the percentage of subsamples where the correlation was significant (P<0.05). qrp! qrq! Third, fish-associated microbial communities closely interact with immune system of their qrr! host, elicitating immune responses and educating immune system (Kelly and Salinas 2017). qrs! Malmstrom et al. (Malmstrøm et al. 2016) recently revealed that the number of copies of qrt! histo-incompatibility genes MHCI and MHCII, which encode proteins that detect non-self qru! antigens and trigger an immune response, varies drastically among teleost fishes and do not qrv! perfectly fit phylogeny. In addition, differences in skin immunology could occur between qrw! individuals ( e.g. between starved and nourished individuals (Caruso et al. 2010), between qrx! healthy and infected individuals (Lindenstrøm, Secombes, and Buchmann 2004), and between qso! juveniles and adults (Grøntvedt and Espelid 2003)). Therefore, difference in immune system qsp! of fish could explain the high levels of both intra- and interspecific variability in skin qsq! microbiome as well as the absence of phylogenetic signal. qsr!

! pst! qss! Microbial diversity hosted by fishes is vulnerable to global change qst! Climate change and fishing pressure have been the main drivers of decline of marine fish qsu! populations for the last 30 years (Jones et al. 2004; Graham et al. 2008; Bellwood et al. 2004; qsv! Myers et al. 2007). Therefore, any microbial diversity associated to fish species that are qsw! vulnerable two these threats is vulnerable too. We found that phylogenetic richness of skin qsx! microbiome was significantly correlated to host’s vulnerability to fishing (as estimated by qto! (Cheung, Pitcher, and Pauly 2005) ; Spearman’s correlation test, P<0.05 in 95.1% of qtp! subsamples, rho=0.43± 0.08, n=999). Hence, the 10% most vulnerable fish species included qtq! in our study (vulnerability score ranging from 36 for Pterois radiata to 79 for Sphyranea qtr! barracuda ) hosted a phylogenetic richness 1.5 times higher than the 10% less vulnerable qts! species (vulnerability ranging from 10 for Corythoichthys flavofasciatus to 25 for Myripristis qtt! violacea ). However, phylogenetic entropy of microbiome was not significantly correlated to qtu! vulnerability to fishing (P<0.05 in 47.9% of subsamples, rho=-0.33±0.07, n=999). Thus, the qtv! fish species that are the most threatened by fishing host OTUs that are phylogenetically qtw! unique and rare. Decline of these fishes would therefore be accompanied by the loss of such qtx! microbial diversity from coral reef. Neither phylogenetic richness, nor phylogenetic entropy, quo! was significantly correlated to vulnerability to habitat loss (S12). qup! quq! We here report that the high macroscopic fish biodiversity in a coral reef supports a high qur! biodiversity of microbial species because each fish species host a high and unique diversity of qus! microbes. In addition, the absence of phylosymbiosis pattern related to these differences has qut! important consequences for conservation of microbial diversity associated to fishes since quu! protecting a few species of each clade does not prevent from losing a unique fraction of quv! microbial diversity. Such a loss is even more likely under current fishing effort, which target quw! species with the highest phylogenetic richness of microbes. These findings raise the need for a qux! comprehensive assessment of the whole microbial biodiversity associated to coral reefs that qvo! are vanishing at an accelerated rate. qvp! qvq! Authors’ contribution statement qvr! Conceptualization, M. C., T.B. and S.V.; Methodology, M.C., C.B. N.A.G., T.B. and S.V.; qvs! Investigation, M.C., J-C.A., C.B., T.C, F.R., E.S. and S.V; Formal Analysis, M.C.; Writing– qvt! Original Draft, M.C.; Writing–Review & Editing, M.C., J-C.A., Y.B., C.B., T.C., N.A.G., qvu! F.R., E.S., T.B and S.V.; Data Curation, M.C.; Funding Acquisition, T.B.; Supervision, T.B. qvv! and S.V.

! psu! qvw! qvx! Conflict of Interest qwo! Authors have no conflict of interest to disclose. qwp! qwq! Additional Information qwr! This project was funded by the TOTAL Foundation. The funders had no role in study design, qws! sampling and analysis decision to publish, or preparation of the manuscript.

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! pts! p! q`p ,8JRP;OEQ!#! q! r! Macroscopic biodiversity of coral reefs supports a high, unique and vulnerable s! microscopic biodiversity t! u! Chiarello M. 1, Auguet J-C. 1, Claverie T. 1,2 , Sucré E. 1,2 , Bouvier C. 1, Rilleuvineuve F. 1 , v! Nicholas AJ Graham 3, Bettarel Y 1. Villéger S. 1 and Bouvier T. 1 w! x! po! 1Marine Biodiversity, Exploitation and Conservation (MARBEC), Université de Montpellier, pp! CNRS, IRD, IFREMER, Place Eugène Bataillon, Case 093, 34 095 Montpellier Cedex 5, pq! France pr! 2Centre Universitaire de Formation et de Recherche de Mayotte, Route nationale 3, BP53, ps! 97660 Dembeni, France pt! 3Lancaster Environment Centre, Lancaster University, Lancaster, UK pu! pv! En préparation pw! px! Des informations supplémentaires complètent ce manuscrit, disponibles en section 7.6 qo! qp! qq! qr! qs! qt! qu! qv! qw! qx! ro! rp! rq! rr!

! ptt! rs! ABSTRACT rt! Marine environments host a huge diversity and biomass of microorganisms especially within ru! the water column as well as on surfaces and in gut of macro-organisms. While microbial rv! communities inhabiting living surfaces have crucial roles for host’s fitness, most of their rw! diversity, including variability between species and animals groups, is still unknown rx! especially in species-rich ecosystems such as coral reefs. Here, we assessed the contribution so! of various living surfaces, consisting in 138 fish samples, 84 hard, soft corals and anemones, sp! and 43 other mobile and sessile invertebrates on the total microbial diversity in a well- sq! preserved coral reef ecosystem, as well as vulnerability of this microbial diversity to loss of sr! macroscopic diversity due to anthropic changes. We demonstrated that animal surfaces host ss! most of total reef microbial diversity because surface microbiomes are rich and species- st! specific. Hence, a removal of all macro-organisms included in our study would induce a loss su! of more than 80% of prokaryotic phylogenetic richness in the ecosystem. sv! sw! sx! to! tp! tq! tr! ts! tt! tu! tv! tw! tx! uo! up! uq! ur! us! ut! uu! uv!

! ptu! uw! INTRODUCTION ux! Marine ecosystems provide habitats supporting a high abundance and high diversity of micro- vo! organisms, including viruses, Archaea, bacteria and microeucaryotes (Kirchman 2010). The vp! numerous studies of eukaryotic and prokaryotic microbial diversities from the water column vq! (Bork et al. 2015) have demonstrated that marine microbes are functionally diverse, including vr! auto- and heterotrophic organisms, primary producers and grazers and viruses, and are vs! consequently key players of biogeochemical cycles occurring in the oceans (Kirchman 2010). vt! vu! However marine ecosystems also include diverse biotic microhabitats were microbes are vv! abundant, that are surface of living animals, including sessile invertebrates such as corals, and vw! mobile vertebrates like fishes (McFall-Ngai et al. 2013). Scleratinian corals, for instance, vx! besides their microalgal symbionts, host from 10 6 to 10 8 microbial cells per milliliter of their wo! surface mucus, which can represent thousands of bacterial Operational Taxonomic Units wp! (OTUs) (Bourne, Morrow, and Webster 2016). Corals also host Archaea and viruses, wq! demonstrating the high phylogenetic diversity of coral-associated microbial communities wr! (Nguyen-Kim et al. 2015; Frade et al. 2016). Those communities play crucial roles for their ws! hosts, especially mediating resistance to pathogens (Krediet et al. 2013) and tolerance to wt! environmental perturbations (Glasl, Herndl, and Frade 2016), likely including thermal stress wu! (Ziegler et al. 2017). Moreover, members of coral-associated microbial communities include wv! several ecosystem-significant genera involved in N, C and S cycling, which are participating ww! to coral’s nutrition, and hence to marine ecosystem functioning (Bourne, Morrow, and wx! Webster 2016). xo! xp! Despite the potentially significant roles of animal surfaces-associated communities in marine xq! ecosystems, they have been scarcely investigated to date compared to planktonic and benthic xr! microbial communities. In addition, most studies focused on corals and sponges (Sunagawa, xs! Woodley, and Medina 2010; Thomas et al. 2016), and to a lesser extent to some mollusks of xt! economical interest (e.g. (Lokmer et al. 2016)). The microbiome of other invertebrates, i.e. xu! echinoderms, crustaceans and cnidarians other than scleratinian corals, is still largely xv! unknown. Similarly, if several studies have investigated the microbiome of teleostean fishes xw! (Ghanbari, Kneifel, and Domig 2015), most of them focused on the gut microbiome (Miyake, xx! Ngugi, and Stingl 2015; Givens et al. 2015). By contrast, the surface microbiome of fishes has poo! been poorly investigated ( e.g. (Larsen et al. 2013; Chiarello et al. 2015) for temperate pop! ecosystems) and never for species inhabiting tropical reefs. Like surface microbiome of

! ptv! poq! marine invertebrates, surface microbiome of teleostean fishes is distinct from planktonic por! communities (Chiarello et al. 2015), diverse and species-specific (Larsen et al. 2013). pos! pot! Most of marine ecosystems host high biodiversity of marine macro-organisms, which have pou! diverse biology (presence of scales or not, nature of the skin, composition of the mucus, pov! release of nutrients and antimicrobial compounds) and ecology ( e.g. mobility, diet), and hence pow! contrasted habitats for micro-organisms which could eventually promote a global high pox! surface-associated microbial diversity at the ecosystem level. Therefore, it is urgent to test ppo! which proportion of marine microbial biodiversity is actually living on macro-organisms. ppp! This statement is particularly true for coral reefs, which host more than two thirds of the ppq! marine teleostean biodiversity (Kulbicki et al. 2013; Nelson, Grande, and Wilson 2016), and ppr! >1 millions invertebrates species (Stella et al. 2011). These ecosystems play crucial roles for pps! global oceanic ecosystem services, generating primary production sustaining more than 25% ppt! of all marine species and providing food and protection for millions of people (Peterson and ppu! Lubchenko 2012). These ecosystems are vulnerable to anthropic changes, especially ocean ppv! warming and acidification, which induced massive coral bleaching events all around the ppw! world during the last two years (Hughes et al. 2017). While a few studies have investigated ppx! the possible direct effects of anthropic pressures on host-associated microbiome in coral reefs pqo! (especially in corals, e.g. (Webster et al. 2016; Zaneveld et al. 2016)), the effect of an erosion pqp! of macroscopic biodiversity on ecosystem’s microbial biodiversity is still unknown. pqq! pqr! The objective of this study was therefore to assess the contribution of host-associated surface pqs! microbiomes to the total microbial diversity of a healthy coral reef and the potential effects of pqt! an erosion of macroscopic diversity due to anthropic pressures on reef microbial diversity. pqu! pqv! MATERIAL AND METHODS pqw! Study area pqx! Sampling was conducted on November 2015 (17th to 27th) on coral reefs around Mayotte pro! Island (France), located in the Western part of the Indian Ocean. Mayotte lagoon is the third prp! largest lagoon in the world and houses 195 km of coral reefs and more than 700 fish species prq! (Wickel et al. 2014). Fish were sampled from two sites in the South West of the lagoon: a prr! fringing reef (S12°54’17.46’’, E44°58’15.72’’), and the inner slope of the barrier reef prs! (S12°57’33.72’’, E45°04’49.38’’). Both sites are far from cities, were at a good ecological prt! state at the time of sampling with more than >50% of coral cover and abundant fish

! ptw! pru! communities including predators such as groupers and barracudas. prv! prw! Sampling procedure prx! Sampling objective was to target the most abundant organisms from each of main animal pso! groups within a radius of 50m around each site. Sampling was authorized by the Mayotte’s psp! maritime authority (permit N°12/UTM/2015). psq! psr! In order avoid contamination during sampling; fishes were caught using speargun and pss! hookline and killed immediately after capture by cervical dislocation (following the European pst! directive 2010/63/UE). Fishes were handled by the mouth using a clamp and all participants psu! wear gloves. After death, the fish was laid down, and skin microbiome was sampled by psv! swabbing the entire untouched side of the animal using buccal swabs (SK-2S swabs, Isohelix, psw! UK), with the exception of the head. Surface microbiomes of Anthozoa were sampled by psx! taking out a 1 to 10 cm fragment of a colony out of the water and exposing to air, which pto! causes mucus secretion. This mucus dripping from the coral surface contains the microbiota ptp! and was directly collected (Leruste, Bouvier, and Bettarel 2012). For the few corals that don’t ptq! secrete mucus when exposed to air, surface microbiome was sampled by swabbing the entire ptr! untouched surface of the specimen. Other invertebrates were caught on the bottom using pts! gloves, and entirely swabbed on the boat for 1 minute. In the case of clams and hermit crabs, ptt! the swab was carefully inserted inside the shell before gently rubbing the surface of the living ptu! tissues of the animal for 30 s. All invertebrates were released after microbiome sampling. ptv! ptw! A total of 138 fishes from 44 species and 22 families were sampled on the two sites. 84 ptx! colonies of hard and soft corals, gorgonians and anemones (referred collectively as Anthozoa ) puo! were sampled, belonging to 14 scleratinian genera, 3 octocorallians (soft corals and pup! gorgonians) and one anemone species. We also sampled 43 other invertebrates, which puq! belonged to crustaceans, echinoderms, mollusks and sponges. The number of individuals (or pur! colony) sampled in each site varies from 1 to 9 (see Table 1 and Supplementary Information pus! S1). put! puu! puv! puw! pux!

! ptx! pvo! Table 1: Microbial communities sampled Coral reef Animal Number of Number of Details compartment group species or taxa samples Teleost fish 44 138 5 orders and 22 families 14 hard coral genera, 2 soft coral genera, 1 Anthozoa 18 84 Animal gorgoniidae and 1 surface anemone 2 crustaceans, 7 Other 12 43 echinoderms, 1 mollusk, invertebrates and 2 sponges 17 surface samples, and 18 samples from the Planktonic - - 35 bottom of the water column Total 300 pvp! Details about animal taxa and number of individuals sampled for each taxon are provided in pvq! Supplementary Information S1. pvr! pvs! pvt! Fishes were identified at species level. Corals were identified at genus level. Classification of pvu! other invertebrates was made at species level when possible (for crustacean, starfish, urchin, pvv! anemone), or at higher taxonomic levels for a few clades (one sea cucumber, comatule, hermit pvw! crab, brittle stars and giant clams). pvx! pwo! Each day of sampling 200-mL seawater samples were collected at sea surface and at 30 cm pwp! from the bottom of the sea, stored in an electric cooler, and filtrated at the end of each day pwq! through a 47 mm 0.2 µm polycarbonate membrane (Whatman, Clifton, USA). The pwr! membranes were then placed in sterile cryotubes. A total of 35 water samples were collected pws! during the campaign. pwt! pwu! All samples were stored at -5°C in an electric cooler during the day and remained frozen until pwv! DNA extraction. pww! pwx! 16S rDNA amplification and sequencing pxo! Swabs and water membranes were incubated during 30 minutes at 37°C in 570 µL of lysis pxp! buffer from Maxwell® Buccal Swab LEV DNA kits (Promega Corporation, Madison, USA) pxq! and 2 µL of 37.5-KU.µL -1 Ready-Lyse lysozyme TM (Epicentre Technologies, Madison, USA). pxr! Then, 30 µl of proteinase K (from manufacturer’s kit) were added and tubes were incubated

! puo! pxs! overnight at 56°C. The totality of the solution was then placed in the kit for extraction. In the pxt! case of coral mucus, two 570-µL mucus samples were incubated separately with 570 µL of pxu! lysis buffer, following the same protocol and extracted using two kits before pooling. pxv! pxw! DNA extraction was performed using the Maxwell® 16 Bench-top extraction system pxx! following manufacturer’s instructions, and eluted in 50 µL of elution buffer. qoo! The V3-V4 region of the 16S rDNA gene was amplified using the prokaryotic primers qop! modified for Illumina sequencing 515F (5’-C TTT CCC TAC ACG ACG CTC TTC CGA qoq! TCT -!GTG CCA GCM GCC GCG GTA A - 3’) (Caporaso et al. 2011) and the modified qor! version of 806R by Apprill et al. (Apprill et al. 2015) (5’ – G GAG TTC AGA CGT GTG qos! CTC TTC CGA TCT - GGA CTA CNV GGG TWT CTA AT - 3’), with PuRe Taq Ready- qot! To-Go PCR Beads (Amersham Biosciences, Freiburg, Germany) using 1µL of extracted DNA qou! and 10µM.µL -1 of each primer as follows: initial denaturation at 94 ◦C for 1 min, followed by qov! 35 cycles of 94°C for 1 min, 55°C for 1 min and 72°C for 1 min, ending with a final extension qow! at 72 ◦C for 10 min. Equimolar amounts of sample DNA extracted from each reef type were qox! separately pooled and sequenced in two separated runs by an external laboratory (INRA GeT- qpo! PlaGE platform, Toulouse, France) on an Illumina platform using the 2x 250 bp MiSeq qpp! chemistry. 7 PCR blanks were included in each sequencing run in order to assess the presence qpq! of contaminants, which were removed during subsequent steps of sequence processing. qpr! qps! Sequence processing and phylogenetic analyses qpt! Sequence processing was performed following the Standard Operating Procedure of Kozich et qpu! al for MiSeq (Kozich et al. 2013), https://www.mothur.org/wiki/MiSeq_SOP , 2017) using qpv! Mothur (Schloss et al. 2009). After assembly of paired reads from each run, sequences of both qpw! runs were merged and sequences with an abnormal length (outside a range of 250-300 pb) qpx! were removed. Sequences were aligned along the SILVA reference database (Quast et al. qqo! 2013) release 128. Chimeras were removed using UCHIME (Edgar et al. 2011). Sequences qqp! were then classified using the SILVA reference taxonomy and the non-prokaryotic ones were qqq! removed. 5,828,480 sequences from 300 samples were kept after cleaning process, ranging qqr! from 2,443 to 43,504 sequences per sample. After this, 2,000 sequences were sub-sampled qqs! within each sample (as done in (Caporaso et al. 2011)) in order to correct the uneven qqt! sequencing efficiency among samples and hence carry further analyzes on unbiased estimates qqu! of microbial diversity. Sequences were then grouped into OTUs using a 97% cutoff parameter

! pup! qqv! to obtain 18,486 OTUs from 300 samples. Chao’s non-parametric coverage computed using qqw! entropart R-package averaged 93±5% across all samples (Chao et al. 1988; Marcon and qqx! Hérault 2014). qro! qrp! The most frequent sequence for each OTU was selected as reference and added to the SILVA qrq! reference phylogenetic tree using ARB software (Ludwig et al. 2004). The full phylogenetic qrr! tree was then pruned using the ape R-package to remove all but the added sequences, while qrs! keeping the topology of the reference SILVA tree. A chronogram was then adjusted to the qrt! phylogenetic tree using PATHd8 (Britton et al. 2007). The divergence time between Archaea qru! and Bacteria was fixed at 3.8 Ga. The minimum divergence time between Euryarchaeota and qrv! other Archaea was set to 2.7 Ga (Blank 2009), and the maximum age of apparition of qrw! Thermoplamatales was set to 2.32 Ga (Blank 2009). The minimum age of apparition of qrx! Cyanobacteria was set to 2.5 Ga (Schirrmeister et al. 2013). The minimum divergence time qso! between Rickettsiales and the rest of Alphaproteobacterial sequences was set at 1.6 Ga, as qsp! done by Groussin et al. (Groussin et al. 2017). Finally the divergence times between qsq! Chromatiaceae and other Gammaproteobacteria , was set to minimum 1.64 Ga (Brocks et al. qsr! 2005). qss! qst! Assessing microbial biodiversity qsu! The overall surface microbiome of each fish species, Anthozoa genus, or other invertebrates’ qsv! taxa, was computed as the mean relative abundance of each OTU across individuals from the qsw! same taxon (see S1). Phylogenetic diversity of each microbiome was described with two qsx! complementary indices (Chao, Chiu, and Jost 2010). Phylogenetic richness of each species qto! was measured with Faith’PD which accounts only for position of OTUs on the phylogenetic qtp! tree (Faith 1992) using picante R-package (Kembel et al. 2010), and then corrected by the qtq! total height of the prokaryotic tree in order to scale values between 0 and 1. Phylogenetic qtr! entropy, taking into account the relative abundance of OTUs as well as their position on the qts! phylogenetic tree, was measured using Allen’s index (Allen, Kon, and Bar ‐Yam 2009), qtt! which is a phylogenetic extension of Shannon’s entropy. Allen index was computed using our qtu! own R-function (https://github.com/marlenec/chao, q=1) adapted from entropart R-package qtv! (Marcon and Hérault 2014). Faith’s index increases when communities are composed of qtw! OTUs that are phylogenetically distant, while Allen’s index increases when their most qtx! abundant OTUs are phylogenetically distant.

! puq! quo! qup! Phylogenetic dissimilarity between all pairs of microbiomes was assessed using two quq! complementary indices: the relative abundance weighted and unweigthed versions of the qur! Unifrac index using the GUniFrac R-package (Chen and ORPHANED 2012). Abundance qus! unweighted phylogenetic dissimilarity indices ranges from 0 when assemblages share the qut! same phylogenetic lineages to 1 when assemblages are made of phylogenetically distant quu! OTUs, while weighted dissimilarity indices tend to 0 when assemblages share the same quv! dominant phylogenetic lineages and tend to 1 when assemblages are dominated by quw! phylogenetically distant OTUs. ! qux! qvo! Statistical tests qvp! The comparison of phylogenetic richness and entropy values obtained from each ecosystem qvq! compartment (planktonic and animal surface microbiome) was done using a Kruskal-Wallis qvr! test (999 permutations) in vegan R-package. qvs! To assess the contribution of each compartment to global microbial diversity in the qvt! ecosystem, two analyses were performed. The first analysis consisted in selecting OTUs that qvu! were unique from each compartment, i.e. recovered in only in planktonic or animal surface- qvv! associated communities, and calculating the phylogenetic richness associated to these OTUs. qvw! The second analysis consisted in accumulation curves, which were obtained by randomly qvx! picking from 1 to 75 animal taxa and 1 to 35 planktonic communities and computing the total qwo! phylogenetic richness for each set of communities. This procedure was performed 100 times qwp! and permitted to compare level of phylogenetic richness for a given number of communities. qwq! Additionally to this visual analysis, we also estimated the total richness of planktonic and qwr! animal surface-associated communities, by computing the Chao1 estimator on the pooled qws! OTUs raw counts across all communities in both compartments (Chao 1984) using fossil R- qwt! package. qwu! Significant difference in microbial structure and composition between planktonic and animal qwv! surface-associated microbial communities was assessed using two Permutational multivariate qww! ANOVAs (PERMANOVAs) performed respectively on abundance unweighted phylogenetic qwx! dissimilarities (U-Unifrac) and weighted dissimilarities (W-Unifrac). To assess if planktonic qxo! communities were more variable than surface-associated communities, a permutation analysis qxp! of dispersion (PERMDISP) was performed on both weighted and unweighted dissimilarities qxq! among planktonic communities and among animal surface-associated communities using the qxr! vegan R-package.

! pur! qxs! Significant differences between surface microbiomes associated to the three major animal qxt! types (44 teleostean fish species, 24 Anthozoan genera and 12 other invertebrates’ taxa) were qxu! assessed using PERMANOVAs performed on both unweighted and weighted dissimilarities. qxv! Last, the effect of animal’s taxon to its associated surface microbiome was assessed on the qxw! full dataset of a given animal type (138 fishes, 84 Anthozoan colonies, or 43 individuals in qxx! “other invertebrates” group) using a separated PERMANOVAs for each animal type roo! (teleostean fishes, Anthozoa and other invertebrates) performed on both unweighted and rop! weighted dissimilarities. roq! ror! In order to identify bacterial clades that are different between planktonic and animal surface ros! associated communities, and consistent among water samples or animal taxa ( i.e. consistently rot! retrieved in all subclasses of the same class), we performed a LefSe analysis (Segata et al. rou! 2011), using respectively water samples and animal taxa as subclasses. rov! Then, to identify biomarkers for main animal groups (teleostean fishes, Anthozoa and other row! vertebrates), a LefSe analysis was performed using these groups as main classes, and the rox! different animal taxa belonging to these groups (Fish species, Anthozoan genera and other rpo! invertebrates’ taxa) as subclasses. rpp! rpq! Tests performed on abundance weighted and unweighted diversity and dissimilarity indices rpr! revealed overall congruent results (see S2). rps! rpt! Vulnerability of microbial diversity scenarios rpu! To assess the vulnerability of reef microbial diversity to the loss of macro-organisms we rpv! simulated an extinction scenario combining the effects of global warming and overfishing. rpw! More precisely, macro-organisms species were removed proportionally to their respective rpx! vulnerability to heat stress for hard corals, and their vulnerability to habitat loss (due to coral rqo! bleaching) plus their vulnerability to fishing for fishes, using 100 replicates. rqp! Vulnerability to overfishing (Cheung, Pitcher, and Pauly 2005) was obtained from Fishbase rqq! (http://www.fishbase.org/ , 2017). Cheung’s vulnerability to fishing is based on fish’s total rqr! length, life history traits (including age at first maturity, longevity, growth rate, fecundity, rqs! spatial behaviour), and geographical range. Vulnerability to habitat loss due to global change rqt! in coral reefs was computed as done in (Graham et al. 2011) using data from Fishbase.org and rqu! expert knowledge for input variables, which were diet specialization, habitat specialization, rqv! recruitment specialization for live coral and body size.

! pus! rqw! Vulnerability of scleratinian coral genus to global warming was based on their bleaching rqx! response in the Western Indian Ocean (McClanahan et al. 2007). The genus Isopora , for rro! which we had no data, was excluded from the extinction scenario. Similarly, as no data was rrp! available for the other Anthozoa , as well as all other invertebrates, we excluded them from the rrq! scenario as well. Therefore, the vulnerability of microbial diversity reported hereafter is based rrr! on microbiome of 44 fish species and 13 genera of scleratinian corals. rrs! At each level of extinction (5 to 100% of the most vulnerable scleratinian an fishes removed), rrt! the percentage of remaining diversity was compared to the one obtained from a random loss rru! of the same number of corals and fishes, computed 100 times. A significant deviation from rrv! this random loss was assessed by computing P-value, calculated as the rank of the mean rrw! diversity value in the 100 replicates of the extinction scenario, among the increasingly sorted rrx! diversity values of all 100 replicates of the random scenario. We considered that the deviation rso! from the random scenario was significant when P<0.05 , meaning that the diversity value in rsp! the observed community was lower than the 5% lowest diversity values following the random rsq! loss of animal taxa. rsr! rss! RESULTS rst! Comparison of animal surface microbiomes and planktonic communities rsu! On average, we recovered 201±33 (mean±SD) and 253±159 OTUs in planktonic and animal rsv! surface-associated communities, respectively. Animal surface-associated communities were rsw! the phylogenetically richer ones (Kruskal-Wallis, P<0.001 and associated post-hoc tests rsx! P<0.05) with one individual containing on average 6.6±1.2% of the total branch length of the rto! phylogenetic tree grouping all OTUs recovered in the ecosystem, and planktonic community rtp! recovered in one 200-mL water sample contained and 3.2±1.4% of the total height of the tree, rtq! respectively (Figure 1 A, S2-Fig 1). rtr! rts! rtt! rtu! rtv! rtw! rtx! ruo! rup!

! put! ruq! rur! Fig 1: Phylogenetic richness of reef microbial communities rus! (A): Phylogenetic richness measured using Faith’s PD (expressed as proportion of total phylogenetic rut! richness present in all samples) of animal surface-associated microbiomes ( e.g. on one fish or coral ruu! colony) and of planktonic communities ( i.e. in each water sample) Boxes represent the interquartile ruv! range of phylogenetic richness among communities. Thick bars represent the median of phylogenetic ruw! richness, and vertical segments extend to the most extreme data point that is no more than 1.5 times rux! the length of the box away from the box. (B): total phylogenetic richness of all animal surface- rvo! associated microbiomes ( i.e. after pooling all samples) and of all planktonic communities, and rvp! contribution of the unique ( i.e. those recovered only on animals or only in water samples) and shared rvq! OTUs to phylogenetic richness. rvr! rvs! rvt! The pool of all animal surface-associated communities contained higher OTU richness and rvu! phylogenetic richness than all planktonic communities combined (Figure 1 B, accumulation rvv! curves, S3). Chao’s richness estimator reached 29,321 OTUs in animal surface microbiome, rvw! which was 4.6 times the estimation for planktonic communities (6,395.4). rvx! More importantly, the 13,825 OTUs that could not be found in planktonic communities made rwo! 75% percent of the total phylogenetic richness (Fig 1B, Unique fraction). By contrast, the 938 rwp! OTUs found only in planktonic communities made 8.8% of the total phylogenetic richness, rwq! which accounted for half of planktonic phylogenetic richness (Fig 1B). 2.5% of OTUs that rwr! were found only in animal surface-associated microbiomes belonged to the 5% most abundant rws! OTUs in the same compartment, while 11.1% were moderately abundant OTUs (belonging to rwt! the 5-20% most abundant OTUs), and 56.5% were rare OTUs (belonging to the 50% less rwu! abundant OTUs). By contrast, none of the OTUs found only in planktonic communities rwv! belonged to the 5% most abundant ones. 3.1% belonged to the 5-20% most abundant OTUs, rww! and 65.2% belonged to the 50% less abundant OTUs (Fig 2-A).

! puu! rwx! rxo! Fig 2: Dominance and taxonomic identity of OTUs unique to animal surface-associated rxp! microbiomes or planktonic communities . (A) Rank of abundance of unique OTUs from each rxq! compartment. Each bar represents the proportion of unique OTUs within each group of dominance rxr! (rank of abundance in decreasing order). (B) Heatmap representing the relative abundance of unique rxs! OTUs in each compartment (Animal surface and plankton). OTUs were clustered at family-level, and rxt! only families that made more than 1% abundance in at least one compartment are displayed. rxu! rxv! rxw! OTUs unique to planktonic communities were affiliated to Flavobacteriaceae (0.15%), rxx! Rhodobacteraceae (0.06%), and the cyanobacterial Family I (0.01%). OTUs unique to soo! animal surface-associated microbiomes were mostly affiliated to Flavobacteriaceae (1.61%), sop! Rhodobacteraceae (0.58%) and Shewanellaceae (0.57%) (Fig 2-B). Overall most of OTUs soq! unique to a type of community remained unclassified given current reference database, even sor! at class level (65% of unique OTUs). sos! sot! Variability among planktonic communities (U-Unifrac = 0.61±0.05) was significantly lower sou! than variability among animal surface-associated communities (U-Unifrac=0.78±0.05, sov! PERMDISP, P<0.001, and Fig 3 and S2). sow! sox! Despite this high variability between taxa, the composition of animal surface-associated spo! communities significantly differed from the planktonic ones (PERMANOVA, animal surfaces

! puv! spp! vs. planktonic communities, P=0.001, R 2=0.15) (Fig 3-A). Subsequent LefSe analyses spq! identified 101 significant biomarkers discriminating planktonic vs. animal surfaces-associated spr! communities. Animal surface were especially enriched in unclassified Gammaproteobacteria , sps! Alphaproteobacteria , and Firmicutes , while planktonic communities showed significantly spt! higher abundances of Cyanobacteria (S4). spu! spv!

spw! spx! Fig 3: Phylogenetic dissimilarity between microbial communities sqo! (A) Microbial communities plotted on the two first axes from a PCoA computed on pairwise sqp! unweighted Unifrac dissimilarities between all communities, the closer two points the lowest the sqq! dissimilarity in phylogenetic composition of microbial communities, and (B) average intra-and inter- sqr! group variability of biotic surfaces associated microbiome, measured with unweighted Unifrac. See sqs! S2-Fig 2 for analyzes of dissimilarity in phylogenetic structure using abundance-weighted Unifrac sqt! index. squ! sqv! Phylogenetic richness and dissimilarity between reef organisms sqw! Variability among microbiomes of each group of macro-organisms averaged 0.76±0.05 sqx! between different fish species, 0.74±0.04 between Anthozoa genera, and 0.79±0.05 between sro! other invertebrates’ taxa (U-Unifrac, KW and associated post-hoc tests, P<0.05 in all pairwise srp! comparisons). Among each main animal group, there was a significant effect of fish species, srq! Anthozoa genera and, to a lesser extent, other invertebrates’ taxa on surface-associated srr! microbial community structure and composition (PERMANOVA, Table 2). Microbiomes of srs! fishes, Anthozoa and other invertebrates were significantly different when tested by srt! PERMANOVA (P=0.001, R 2=0.06) (Fig3-A). However, the variability between fish- sru! Anthozoa - and other invertebrates-associated microbiomes was only 1.02 times higher than

! puw! srv! the variability between microbiomes from the same group of animals (U-Unifrac, Fig 3-B and srw! W-Unifrac, S2-Fig 2). Accordingly LefSe could not identify any significant biomarkers for srx! the surface microbiomes of these three main animal groups, which shared similar class-level sso! composition (S5). ssp! ssq! ssr! Table 2: Phylogenetic dissimilarity of surface-associated microbiomes U-Unifrac W-Unifrac Factor P R2 P R2 Fish species 0.001 0.39 0.001 0.41 Anthozoa Genera 0.001 0.39 0.001 0.46 Other invertebrates taxa 0.001 0.27 0.001 0.29 sss! The effect of fish species, Anthozoa genera, and other invertebrates taxa were tested using three sst! separated Permutational Multivariate ANOVAs (PERMANOVAs, 999 permutations) on surface- ssu! associated microbiomes of (i) the 138 teleostean fish samples, (ii) the 82 Anthozoa samples, and (iii) ssv! the 43 other invertebrates’ samples. Bold P-values indicate a significant effect of the tested factor. ssw! ssx! sto! Unraveling the core surface microbiome of reef organisms stp! No OTU was common to all animal taxa studied. Among the 17,483 OTUs found on animal stq! surfaces, 62% were recovered in the surface microbiome of only one taxon, and only 121 str! OTUs (0.7% of OTUs) were found in at least half of animal taxa (38 taxa or more). These sts! “core” OTUs were mostly affiliated to Gammaproteobacteria , especially Alteromonadaceae , stt! Halomonadaceae and Vibrionaceae , Alphaproteobacteria , especially Rhodobacteraceae , and stu! Flavobacteriaceae (S6), and ranged between 0 and 42% of abundance in a taxon. The 5 most stv! abundant “core” OTUs across taxa were identified as Catenococcus (Vibrionaceae ) (0-23% of stw! abundance in a taxon), Alteromonadales including Aestuariibacter (0-17%), Cyanobacteria stx! (0-42%) and unclassified Gammaproteobacteria (0-29%). suo! sup! Vulnerability of reef microbial diversity to loss of macro-organisms suq! The most vulnerable fish species to the combined overfishing and habitat loss pressures were sur! Chaetodon trifascialis , Chaetodon meyeri and Sphyranea barracuda . The most vulnerable sus! coral genus to ocean warming in our dataset was Montastrea (McClanahan et al. 2007). sut! There was no correlation between habitat loss of fish species and their surface OTU richness, suu! phylogenetic richness or phylogenetic entropy (Spearman’s correlation test, P=0.77, 0.57 and suv! 0.44, respectively). There was a marginally significant correlation between vulnerability to

! pux! suw! fishing and fish surface microbial diversity (P=0.09, 0.09 and 0.07 and rho=0.25, 0.25 and sux! 0.27 respectively for OTU richness and phylogenetic richness and entropy). However, there svo! was no correlation between the combined vulnerability of fishing and habitat loss on fish svp! surface microbial diversity (P=0.36, 0.20 and 0.70, respectively for OTU richness and svq! phylogenetic richness and entropy). There was no correlation between coral’s vulnerability to svr! warming and its associated OTU richness, and phylogenetic richness and entropy (P= !0.25, svs! 0.48 and 0.64, respectively). svt! Extinction of the 5% most vulnerable coral and fish species would erase 2.6±1.2% of svu! prokaryotic OTUs (S7) and 1.9±0.9% of microbial phylogenetic richness (Figure 4). A loss svv! of the 50% most vulnerable coral and fish species would induce a loss of 22.4±3.3% of OTUs svw! and 17±2.5% of phylogenetic richness. The slope of microbial diversity extinction increased svx! slightly with increasing the loss of species (Fig 4 and S7). The local extinction of the 13 swo! Scleratinians included in the analysis and the 44 fish species would lead to a remaining swp! microbial OTU richness of 45% and a phylogenetic richness of 54% of their respective initial swq! value at the ecosystem level (Fig 4 and S7). Simulated loss of microbial diversity following swr! loss of the most vulnerable animals was however not significantly different from loss sws! expected under random extinction of fish species and corals’ genera (Fig 4 and S7). swt! swu! swv! sww! swx! sxo! sxp! sxq! sxr! sxs! sxt! sxu! sxv! sxw! sxx! too! top!

! pvo! toq! tor! Fig 4: Vulnerability of coral reef microbial diversity to loss of fishes and scleratinian corals. (A) tos! Black points and bars represent mean (±SD) of remaining microbial phylogenetic richness on the tot! studied coral reef for a given proportion of coral and fish taxa lost. Fishes and corals loss were tou! simulated according to their decreasing vulnerability to habitat loss and fishing, and bleaching, tov! respectively (100 replicates). When 100% of coral and fish species are lost, the remaining microbial tow! phylogenetic richness corresponds to the one of planktonic communities, and communities associated tox! to the surface of animal taxa that were not included in the extinction scenario because their tpo! vulnerability was unknown ( i.e. the scleratinian Isopora , soft corals, gorgonians, anemone and all non- tpp! anthozoan invertebrates). scenario simulating a random loss of fishes and corals ( i.e. species randomly tpq! removed independently from their vulnerability), is illustrated with the grey area that represent range tpr! of remaining phylogenetic richness among 100 replicates. tps! (B) Slope of the loss in phylogenetic richness for the “warming+fishing” scenario, is represented as tpt! the mean !± SD of slopes calculated at each level of extinction on all 100 replicates. tpu!

! pvp! tpv! DISCUSSION tpw! Microbial diversity hosted by animal surfaces contributed to 93% of the whole prokaryotic tpx! OTUs richness found on studied coral reefs. Animal surface-associated communities reached tqo! a ca. 6-fold higher phylogenetic richness than planktonic communities (Fig 1-B). Prokaryotic tqp! OTUs found only on biotic surfaces covered 75% of the phylogenetic tree grouping all OTUs tqq! recovered from the ecosystem and were abundant on animal surfaces, while OTUs found only tqr! in seawater were mostly rare in the corresponding communities and phylogenetically tqs! redundant (Figure 2-A). tqt! These results confirmed that the variety of reef animals’ surfaces provide habitats for a high tqu! prokaryotic diversity including for microbial lineages that are not detected in the water tqv! column. tqw! tqx! Microbial communities covering abiotic surfaces (e.g. rocks) are usually distinct from those tro! present in surrounding water column (Roth-Schulze et al. 2016). Indeed, colonizing surfaces trp! requires several features, such as capacity of detecting molecular signals from such surfaces trq! and motility to reach them (Dunne 2002), capacity of attachment to a surface (pili or trr! extracellular polysaccharides synthesis) (Van Houdt and Michiels 2005), and capacity to grow trs! as a biofilm, involving cell-to-cell signaling and differential gene expression (Vlamakis et al. trt! 2013). Many bacterial species are able to switch between free-living and attached lifestyles tru! (Grossart 2010), and some authors even argue that virtually all bacteria are able to grow as trv! biofilms, which allows a higher activity compared to the planktonic phenotype (Donlan 2002; trw! Dunne 2002; McDougald et al. 2011). trx! tso! Marine animals have developed strategies to limit microfouling, using specific skin texture tsp! limiting bio-adhesion (Scardino and de Nys 2011), secreting a constantly renewing mucus tsq! that embed microbial cells and particles (Scardino and de Nys 2011; Ángeles Esteban 2012), tsr! or a gel that smooths the surface of the animal and dissolves bacterial adhesins (Baum et al. tss! 2001), sloughing (Amy Apprill et al. 2014), and secreting antimicrobial compounds (Rakers tst! et al. 2013). Therefore, to establish on a biotic surface, prokaryotes need to pass through these tsu! defenses from host, e.g. by degrading host’s surface mucus (Balebona et al. 1998), biding tsv! specific interactions (Grze śkowiak et al. 2011) and by evading host’s immune defense (Ryu et tsw! al. 2014). These characteristics could select specific prokaryotic clades, adapted to the tsx! interaction with their macroscopic host. Accordingly, prokaryotic communities recovered on tto! animal surfaces in our study were significantly distinct from planktonic communities (Fig3-

! pvq! ttp! A). They were mostly enriched in Gammaproteobacteria , especially Alteromonadales and ttq! Vibrionales (S4). Vibrionales and Alteromonadales (in our study mostly Aestuariibacter , S4) ttr! are both marine bacteria, commonly found on corals, fish, crustaceans, shellfish and sponges tts! (Nicole S Webster et al. 2010; Lee et al. 2012; Givens et al. 2013; Lokesh and Kiron 2016; ttt! Lokmer et al. 2016). The Vibrionales , particularly, are able to stick to and degrade chitin and ttu! mucin (Szabady et al. 2011; Boyd et al. 2015), both molecules that are frequent on shellfish ttv! and crustacean surfaces, and in fish and coral mucus. ttw! ttx! Additionally to their overall high phylogenetic diversity, animal surface-associated tuo! communities were overall 1.3 times more variable than planktonic communities (Fig3-A). tup! The three major animal groups sampled, i.e. teleostean fishes, Anthozoa and other tuq! invertebrates hosted significantly different prokaryotic communities at their surface, but this tur! factor only explained 6% of abundance unweighted phylogenetic dissimilarity (U-Unifrac) tus! (see S2 for W-Unifrac). As a consequence no significant biomarker was identified for each of tut! these groups, indicating that there is no typical microbiome for corals or teleostean fishes (Fig tuu! 3-A). For instance the two corals Goniopora and Echinopora were as dissimilar (U- tuv! Unifrac=0.76 and W-Unifrac=0.60) as Echinopora and the parrotfish Scarus russelii (U- tuw! Unifrac=0.75 and W-Unifrac=0.59). Nevertheless, despite high variability, fish species tux! explained 39% of prokaryotic composition (tested on U-Unifrac) and 41% of prokaryotic tvo! structure (tested on W-Unifrac). Similarly, Anthozoa genera explained 39% of prokaryotic tvp! composition and 46% of prokaryotic structure (Table 2). tvq! tvr! Hence, despite an absence of a specific microbiome for the three main groups of animal tvs! sampled, within each group, species did have significantly different microbiome. While tvt! species-specificity of surface microbiome has been already reported for species from the same tvu! groups of animals, including corals and teleostean fishes (Larsen et al. 2013; Frade et al. tvv! 2016), here we report for the first time that this interspecific variability within a clade is as tvw! high as between species from different clades that diverged ~800 Mya (divergence time tvx! estimate between Teleostei and Anthozoa according to http://www.timetree.org/). For two! instance, abundance weighted dissimilarity between the two parrotfishes Chlorurus sordidus twp! and Scarus caudofasciatus (W-Unifrac=0.47) was higher than the one between Chlorurus twq! sordidus and the scleratinian coral Platygyra sp (W-Unifrac=0.41). twr! Accordingly to the high variability of animal surface microbiome, rarefaction curve based on tws! animal surface microbiomes showed that sampled microbial diversity is far from reaching its

! pvr! twt! asymptote (S3). Hence, reef microbial diversity is certainly even higher than the one reported twu! here based on a small subset of macroscopic diversity living on the studied coral reef twv! ecosystem (Mayotte lagoon hosts more than 700 fish species (Wickel et al. 2014)). tww! twx! The loss of an animal species due to environmental disturbances at a given location will txo! induce the loss of its associated microbial diversity. Scenario of microbial diversity erosion txp! following macroscopic extinctions revealed that the loss of the 5% most vulnerable fish and txq! scleratinian corals would induce a loss of only ~2% of the total reef prokaryotic OTUs and txr! phylogenetic richness. A loss of the 50% most vulnerable coral and fish species induced txs! equivalent losses of OTUs and phylogenetic richness (18 and 21%), indicating a moderate txt! phylogenetic redundancy between animal surface-associated OTUs. A loss of all scleratinian txu! corals (excepted Isopora ) and fishes would leave 54% of total prokaryotic phylogenetic txv! richness, corresponding to prokaryotic communities associated with seawater and txw! invertebrates other than hard corals. These levels of prokaryotic diversity loss were not txx! significantly different from the one expected under a random extinction scenario (S7). Indeed, uoo! the most vulnerable macro-organisms to the combined “fishing+warming” pressure, do not uop! host a higher proportion of microbial diversity, while the most vulnerable fishes to fishing uoq! host phylogenetically richer microbiomes. For instance, the 10 most vulnerable species to uor! fishing and habitat loss hosted a phylogenetic richness only slightly higher than the 10 less uos! vulnerable fishes (7.7±3.9 vs. 6.9±4.1). uot! uou! To our knowledge, no quantified vulnerability measures exist for invertebrates other than uov! sessile Anthozoa, so we did not include them in our extinction scenario. Due to their wide uow! phylogenetic range, spanning from crustaceans, echinoderms, mollusks, to sponges, uox! microbiomes of invertebrates other than Anthozoa showed the highest variability (Fig3-B and upo! S2), which suggests that loss of warming sensitive taxa of sponges, starfishes and sea urchin upp! (Przeslawski et al. 2008; Byrne et al. 2009), or taxa targeted by humans such as crustaceans upq! and mollusks (Polovina et al. 1995), would induce a more severe erosion of microbial upr! diversity than the one simulated here. For instance in our system, a total removal of all living ups! species would result to a remaining 16% of initial microbial phylogenetic richness in the reef, upt! corresponding to the microbial diversity hosted by planktonic communities. upu! upv! While the overall functional diversity supported by animal surface microbiome still needs to upw! be addressed, it should be highlighted that some major members of their core microbiomes

! pvs! upx! have important functional roles, as marine sulfur cycling ( Catenococcus , (Podgorsek and uqo! Imhoff 1999; Sorokin 2003)), hydrocarbon degrading ( Alteromonadaceae , (López-Pérez and uqp! Rodriguez-Valera 2014)) and photosynthesis ( Cyanobacteria ). Further metagenomic studies uqq! are therefore needed to investigate the functional roles of these communities associated to uqr! biotic surfaces in the ecosystem, and the potential impacts of macroscopic diversity loss to uqs! global microbial functional diversity. uqt! uqu! Acknowledgments uqv! We thank Emily Darling and Jérémie Vidal-Dupiol for helping us for identifying hard corals, uqw! and Frederic Ducarme for his help identifying echinoderms. uqx! uro! Conflict of Interest urp! Authors have no conflict of interest to disclose. urq! urr! Additional Information urs! This project was funded by the TOTAL Foundation. The funders had no role in study design, urt! sampling and analysis decision to publish, or preparation of the manuscript. uru! urv! urw! urx! !

! pvt!

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! qsu! t`o (JBKOI8QEKJP!PRMMH@I>JQ8EO>P!8R!I8JRP;OEQ!/! ! SUPPLEMENTARY INFORMATION

Contrasted performance of taxonomic and phylogenetic diversity indices to detect assembly rules and dissimilarities between microbial communities

Chiarello M. *, Bouvier T. and Villéger S.

! !

! qsv! ! Supplementary Information S1: Illustration of the method used to assess the performance of alpha-diversity indices to detect phylogenetic clustering. Clustering detection was performed

by comparing Dclustering : value of the tested index of a given clustered community, to the distribution of all 500 Drandom : values of the tested index in randomly assembled communities.

! qsw! !

! qsx! Supplementary Information S2: Properties of the communities simulated, with 4 richness values ( S, panels titles) and 4 levels of unevenness (σ, abscisses). The level of unevenness was controlled by the log(sd) ( σ) parameter of the log-normal distribution (see Material &

Methods) and resulted in a strong difference in terms of community structure. Communities computed with a σ=2 were characterized by high dominances, while communities computed with a σ of 0.25 had a very even distribution of abundances. Left panels : Relative abundance of the most abundant OTU of the community, expressed in percentage; right panels : proportion of OTUs needed to make more than 95% of cumulated relative abundance.

! qto! ! Supplementary Information S3 : Illustration of the method used to assess the performance of beta-diversity indices to detect the difference between community types. For each (S* σ) scenario, 5 community types were defined, composed of 6 communities each, with a certain level of intra-environmental variability. For clarity, only two communities were shown for each site. Dots represent OTUs defined on tips of the phylogenetic tree; the size of each dot is proportional to the relative abundance of the OTU. We assessed the performance of detection of significant differences between communities belonging to different types by calculating beta-diversity values between all pair of communities in the two compared types, and performing a one-way PERMANOVA.

Supplementary Information S4: R function to calculate Chao’s indices. Available at https://github.com/marlenec/chao .

! qtp! ! Supplementary Information S5: Phylogenetic alpha-diversity values for Chao’s (left panels) and Leinster & Cobbold’s indices (right panels) in randomly assembled communities, for each richness ( S, panels titles) and unevenness values ( σ, in abscisses).

! qtq! !

Supplementary Information S6: Efficiency of clustering detection by Leinster & Cobbold’s indices. Values are expressed as percentage of significant P-values (<0.05) across the 500 replicates, for each level of OTUs richness ( S, titles of panels) and each level of unevenness

σ of their abundances ( , abscises). For 1,000 and 2,000-OTUs communities, PD LC (q=1) and

PD LC (q=2) are superposed and thus only PD LC (q=2) is visible.

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SUPPLEMENTARY INFORMATION

Captive bottlenose dolphins and killer whales harbor a species-specific skin microbiota that varies among individuals

Chiarello M., Villéger S., Bouvier C., Auguet JC., and Bouvier T.

! qtu! Supplementary Information S1: Description of the two PCR protocols used in this study and comparison of bacterial composition on water samples

Skin samples Water samples

Kit Phusion High-Fidelity PuRe Taq Ready-To-Go PCR Beads Total vol. (µL) 20 25 DNA vol. (µL) 2 5 Initial denaturation 1 min 98°C 2 min 94°C PCR cycle 1 min 94°C; 40s 57.8°C; 30s 72°C 1 min 94°C; 40s 57.8°C; 30s 72°C Nb. of cycles 35 35 Final extension 10 min 72°C 10 min 72°C S1-Table 1: PCR reagents and conditions used for the two sample types studied. Skin DNA and water DNA were respectively amplified using the Phusion High-Fidelity DNA polymerase (Biolabs, Ipswich, USA) and PuRe Taq Ready-To-Go PCR Beads (Amersham Biosciences, Freiburg, Germany) following manufacturer’s instructions.

! qtv!

S1-Fig 1: Most abundant classes and families in planktonic communities analyzed using Phusion and Ready-To-Go kits. Both PCR types were performed on the same DNA extracted from animals’ surrounding water. Class-level bacterial composition was very similar between both PCR types.

! qtw!

S1-Fig 2: PCoAs based on Weighted Unifrac, showing planktonic communities analyzed using both PCR types. On (A) panel, all samples included in this study plus water replicates that could be amplified using Phusion kit. On (B) panel, only planktonic communities were displayed. Water replicates from both animals’ pools clustered together, whatever the PCR type used for amplification.

! qtx! Supplementary Information S2: Assessing the effect of sequences subsampling

S2-Figure 1: Rarefaction curves obtained on raw sequencing outputs ( i.e. with uneven number of sequences read for each sample) (A) and after random subsampling of 10,000 reads for each sample, and (B) number of OTUs at each rarefaction size. Subsampling did not affect the ranking of OTUs richness between planktonic and skin-associated communities (See results in main text).

! quo! Supplementary Information S3: phylogenetic analysis of Staphylococcus sp .

Salinicoccus alkaliphilus Salinicoccus albus Macrococcus caseolyticus Macrococcus bovicus Staphylococcus lentus Staphylococcus massiliensis Staphylococcus cohnii subsp. cohnii Otu10176 Staphylococcus succinus subsp. succinus Otu2505 Staphylococcus equorum Staphylococcus gallinarum Staphylococcus arlettae Otu943 Staphylococcus xylosus Staphylococcus saprophyticus subsp. saprophyticus ATCC 15305 Staphylococcus auricularis Staphylococcus simulans Otu660 Staphylococcus piscifermentans Staphylococcus condimenti Staphylococcus carnosus subsp. carnosus Staphylococcus muscae Staphylococcus felis Staphylococcus chromogenes Staphylococcus agnetis Staphylococcus hyicus Staphylococcus pseudintermedius Staphylococcus intermedius Staphylococcus delphini Staphylococcus lugdunensis Otu385 Staphylococcus hominis subsp. hominis Otu2994 Staphylococcus petrasii subsp. jettensis Staphylococcus haemolyticus Otu8305 Staphylococcus devriesei Otu8288 Otu6718 Otu5905 Staphylococcus pasteuri Otu533 Otu784 Otu1685 Otu158 Otu1115 Otu1 Staphylococcus warneri Otu544 Otu649 Otu2360 Staphylococcus simiae Staphylococcus aureus Staphylococcus aureus subsp. anaerobius Otu1862 Otu2415 Otu1629 Otu10070 Otu878 Otu870 Otu9045 Otu808 Otu496 Otu6413 Otu2118 Staphylococcus saccharolyticus Staphylococcus epidermidis Otu7212 Otu237 Otu2 Otu4138 Otu7248 Staphylococcus capitis subsp. capitis Staphylococcus caprae

S3-Fig 1: Phylogenetic relationships of sequences affiliated to Staphylococcus sp . Sequences from this study are labelled as « OTUXX », while other sequences were downloaded from SILVA database. Macrococcus sp and Salinicoccus sp were used as outgroup. Accession numbers from downloaded sequences are available in the table below.

! qup! S3-Table 1: Accession numbers of sequences downloaded from SILVA database used to make phylogenetic analysis of Staphylococcus-affiliated sequences.

Species Accession Macrococcus bovicus Y15714.1.1546 Macrococcus caseolyticus KP191046.1.1466 Salinicoccus albus EF177692.1.1478 Salinicoccus alkaliphilus AF275710.1.1459 Staphylococcus agnetis HM484980.1.1409 Staphylococcus arlettae AB009933.1.1494 Staphylococcus aureus L36472.3194.4748 Staphylococcus aureus subsp. anaerobius D83355.1.1476 Staphylococcus auricularis D83358.1.1475 Staphylococcus capitis subsp. capitis L37599.1.1469 Staphylococcus caprae AB009935.1.1492 Staphylococcus carnosus subsp. carnosus B009934.1.1493 Staphylococcus chromogenes D83360.1.1475 Staphylococcus cohnii subsp. cohnii D83361.1.1477 Staphylococcus condimenti Y15750.1.1545 Staphylococcus delphini AB009938.1.1493 Staphylococcus devriesei FJ389206.1.1537 Staphylococcus epidermidis D83363.1.1475 Staphylococcus equorum AB009939.1.1494 Staphylococcus felis D83364.1.1475 Staphylococcus gallinarum D83366.1.1477 Staphylococcus haemolyticus X66100.1.1544 Staphylococcus hominis subsp. hominis X66101.1.1544 Staphylococcus hyicus D83368.1.1476 Staphylococcus intermedius D83369.1.1476 Staphylococcus lentus D83370.1.1480 Staphylococcus lugdunensis AB009941.1.1492 Staphylococcus massiliensis EU707796.1.1477 Staphylococcus muscae FR733703.1.1537 Staphylococcus pasteuri AB009944.1.1494 Staphylococcus petrasii subsp. jettensis JN092118.1.1444 Staphylococcus piscifermentans Y15754.1.1544 Staphylococcus pseudintermedius AJ780976.1.1512 Staphylococcus saccharolyticus L37602.1.1527 Staphylococcus saprophyticus subsp. saprophyticus AP008934.743716.745270 Staphylococcus simiae AY727530.1.1478 Staphylococcus simulans D83373.1.1476 Staphylococcus succinus subsp. succinus AF004220.1.1548 Staphylococcus warneri L37603.1.1470 Staphylococcus xylosus D83374.1.1477

! quq! Supplementary Information S4: Alpha-diversity recovered from samples.

S4-Fig 1: Diversity of skin-associated communities of killer whales (A and D), dolphins (B and E), and of planktonic communities (C and F). The first row of plots (A-C) illustrates the phylogenetic richness observed in a sample, measured with relative Faith PD, i.e. the sum of the height of all branches from the phylogenetic tree recovered in the sample corrected by the total height of the phylogenetic tree, and converted into percentage. The second row of plots (D-F) illustrates taxonomic diversity, measured by the index of Shannon, which is based on the relative abundances of OTUs recovered in a sample. Total diversity of each individual (i.e. accounting for all body zones sampled) is illustrated with larger light-gray bars. Bars on panels C and F represent the mean (and associated standard deviation) of OTU richness and phylogenetic diversity for planktonic communities (n=3 water samples). “Pool” refers to animal’s surrounding water, and “Input” refers to the water sampled at the exit of filtering system.

! qur! Supplementary Information S5: Dissimilarity between microbiotas

S5-Fig 1: Average of pairwise dissimilarity between sets of microbiotas for two facets and two components of biodiversity. A: Taxonomic (based on OTUs) compositional (insensitive to relative abundances of OTUs) dissimilarity. B: taxonomic structural (weighted by OTUs relative abundances) dissimilarity. C-D: phylogenetic compositional and structural dissimilarities. Error bars represent the standard deviation of dissimilarity indices among pairs of microbiotas.

! qus! Supplementary Information S6: Structure of microbiotas !

! S6-Fig 1: Mean relative abundance of bacterial families in skin-associated communities of common bottlenose dolphin and killer whales, and planktonic communities. P: upper side of pectoral fin, D: dorsal fin, C: upper side of caudal fin, A: anal zone. “Pool” refers to animal’s surrounding water, and “Input” refers to the water sampled at the exit of pipe from filtering system. 1: Gammaproteobacteria; 2: Alphaproteobacteria; 3: Bacilli; 4:Actinobacteria; 5:Fusobacteria; 6: Sphingobacteria; 7: Betaproteobacteria. !

! qut! ! S6-Fig 2: Mean relative abundance of bacterial genera in skin-associated communities of common bottlenose dolphin and killer whales, and planktonic communities. P: upper side of pectoral fin, D: dorsal fin, C: upper side of caudal fin, A: anal zone. “Pool” refers to animal’s surrounding water, and “Input” refers to the water sampled at the exit of pipe from filtering system. 1: Gammaproteobacteria; 2: Alphaproteobacteria; 3: Bacilli; 4:Actinobacteria; 5:Fusobacteria; 6: Sphingobacteria; 7: Betaproteobacteria; 8: Bacteroidetes. a: Moraxellaceae; b: Staphylococcaceae; c: Sphingomonadaceae; d: Rhodobacteraceae; e: Bifidobacteriaceae; f: Rhizobiaceae; g: Pasteurellaceae; h: Micrococcaceae; i: Fusobacteriaceae; j: Pseudomonadaceae; k: Streptococcaceae; l: Corynebacteriaceae; m: Flavobacteriaceae; n: Actinomycetaceae; o: Pseudoalteromonadaceae; p: Halomonadaceae; q: Alteromonadaceae.

! quu! Supplementary Information S7: LefSe analysis performed on KEGG pathways

S7-Fig 1: LefSe analysis performed on KEGG pathways , showing the most discriminating pathways (higher LDA score) between skin-associated and planktonic communities. functional categories associated with highest LDA score for each type of microbial communities are underlined, and pathways belonging to these categories are illustrated with dots of corresponding colors.

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Supplementary Methods

Skin microbiome of coral reef fishes is diversified, species-specific, not phylogenetically conserved and vulnerable to global change

Marlène Chiarello, Jean-Christophe Auguet, Yvan Bettarel, Corinne Bouvier, Thomas Claverie, Nicholas AJ Graham, Fabien Rieuvilleneuve, Elliot Sucré, Thierry Bouvier, and Sébastien Villéger

Sampling area Fish sampling was conducted on November 2015 (17th to 27th) on coral reefs around Mayotte Island (France), located in the Western part of the Indian Ocean. Mayotte lagoon is the third largest lagoon in the world and houses 195 km of coral reefs and more than 700 fish species (Wickel et al. 2014). Fish were sampled from two sites in the South West of the lagoon: a fringing reef (S12°54’17.46’’, E44°58’15.72’’), and the inner slope of the barrier reef (S12°57’33.72’’, E45°04’49.38’’). Both sites are far from cities, were at a good ecological state at the time of sampling with more than >50% of coral cover and abundant fish communities including predators such as groupers and barracudas.

Sampling procedure Sampling objective was to target the most abundant species of major ecologically and phylogenetically contrasted fish families present in each site (within a radius of 50m), including Acanthuridae, Balistidae, Chaetodontidae, Labridae , Pomacanthidae, Pomacentridae, Scaridae and Scorpaenidae. In order to take into account the interspecific variability of skin microbiome, up to 3 adult individuals of each species were sampled in each site.

! quw! In order avoid contamination during sampling, fishes were caught using speargun and hook line and killed immediately after capture by cervical dislocation (following the European directive 2010/63/UE). Fish were handled only by the mouth using a clamp and all participants wear gloves. After death, the fish was laid down on one side along a ruler, measured, and skin microbiome was sampled by swabbing the entire untouched side of the body (from back of operculum to caudal peduncle, i.e. head not included) using buccal swabs (SK-2S swabs, Isohelix, UK). A total of 138 fishes were sampled for their skin microbiome. They belonged to 44 species with 29 species represented by at least three individuals (Supplementary Information S1) and 10 species represented by a single individual. Species belonged to 5 orders and 22 families, with 35 species belonging to Perciformes (S1).

Fish Sampling was authorized by the Mayotte’s directorate of maritime affairs (permit N°12/UTM/2015).

To assess microbial diversity in the water column of the two sites, a total of thirty six 200-mL seawater samples were collected at sea surface (9 samples) and at 30 cm from the bottom of the sea (9 samples), stored in an electric cooler, and filtrated at the end of the day through a 47 mm 0.2 µm polycarbonate membrane (Whatman, Clifton, USA). The membranes were then placed in sterile cryotubes. One surface water sample taken on fringing reef could not be amplified during subsequent steps, and was removed, making a total of 35 water samples included in this study.

All samples were stored in an electric cooler during the day, which was cooled to -20°C before each sampling day, and cooled again during the day if the temperature inside rose over -5°C. At the end of the day, all samples were transported in the lab and stored at -80°C during two weeks, and then transported back by plane in mainland France in the electric cooler for DNA extraction.

16S rDNA amplification and sequencing Swabs and water membranes were incubated during 30 minutes at 37°C in 570 µL of lysis buffer from Maxwell® Buccal Swab LEV DNA kits (Promega Corporation, Madison, USA) and 2 µL of 37.5-KU.µL -1 Ready-Lyse lysozyme TM (Epicentre Technologies, Madison, USA).

! qux! Then, 30 µl of proteinase K (from manufacturer’s kit) were added and tubes were incubated overnight at 56°C. The totality of the solution was then placed in the kit for extraction. DNA extraction was performed using the Maxwell® 16 Bench-top extraction system following manufacturer’s instructions, and eluted in 50 µL of elution buffer. The V3-V4 region of the 16S rDNA gene was amplified using the prokaryotic primers modified for Illumina sequencing 515F (5’-C TTT CCC TAC ACG ACG CTC TTC CGA TCT - GTG CCA GCM GCC GCG GTA A- 3’) (Caporaso et al. 2011) and the modified version of 806R by Apprill et al. (Apprill et al. 2015) (5’ – G GAG TTC AGA CGT GTG CTC TTC CGA TCT - GGA CTA CNV GGG TWT CTA AT - 3’), with PuRe Taq Ready- To-Go PCR Beads (Amersham Biosciences, Freiburg, Germany) using 1µL of extracted DNA and 10µM.µL -1 of each primer as follows: initial denaturation at 94 ◦C for 1 min, followed by 35 cycles of 94°C for 1 min, 55°C for 1 min and 72°C for 1 min, ending with a final extension at 72 ◦C for 10 min. Equimolar amounts of sample DNA extracted from each sample site were separately pooled and sequenced in two separated runs by an external laboratory (INRA GeT- PlaGE platform, Toulouse, France) on an Illumina platform using the 2x 250 bp MiSeq chemistry. 7 PCR blanks were included in each sequencing run in order to assess the presence of contaminants, which were removed during subsequent steps of sequence processing.

Sequence processing to define OTUs and their phylogenetic relationships Sequence processing was performed following the SOP of Kozich et al for MiSeq (2013), https://www.mothur.org/wiki/MiSeq_SOP , 2017) using Mothur (Schloss et al. 2009). After assembly of paired reads in each run, sequences of both runs were merged and sequences with an abnormal length (outside a range of 250-300 pb) were removed. Sequences were aligned along the SILVA reference database (Quast et al. 2013) release 128, with removing of sequences abnormally aligned. Chimeras were removed using UCHIME (Edgar et al. 2011). Filtered sequences were then classified using the SILVA reference taxonomy and the non- prokaryotic ones were removed. 10,877 sequences from 173 samples were kept after cleaning process, ranging from 2,450 to 43,306 sequences per sample. After this, 2,000 sequences were sub-sampled within each sample in order to correct the uneven sequencing efficiency among samples. Sequences were then grouped into Operational Taxonomic Units (OTUs) using a 97% cutoff parameter, and the relative abundance of all OTUs was computed using number of sequences. Relative abundances of OTUs recovered from blank samples were then subtracted

! qvo! to their respective relative abundance in all other samples. Rarefaction curves obtained from all samples are provided in S13.

The dominant sequence for each OTU was selected as reference and added into the SILVA reference phylogenetic tree (release 128) using ARB software (Ludwig et al. 2004). The full phylogenetic tree was then pruned using the ape R-package to remove all but the added sequences, while keeping the topology of the tree. A chronogram was then adjusted to the phylogenetic tree using PATHd8 (Britton et al. 2007). The divergence time between Archaea and Bacteria was fixed at 3.8 Ga. The minimum divergence time between Euryarchaeota and other Archaea was set to 2.7 Ga (Blank 2009), and the maximum age of apparition of Thermoplamatales was set to 2.32 Ga (Blank 2009). The minimum age of apparition of Cyanobacteria was set to 2.5 Ga (Schirrmeister et al. 2013). The minimum divergence time between Rickettsiales and the rest of Alphaproteobacterial sequences was set at 1.6 Ga, as done by Groussin et al. (Groussin et al. 2017). Finally the divergence times between Chromatiaceae and other Gammaproteobacteria, was set to minimum 1.64 Ga (Brocks et al. 2005).

60% of the recovered OTUs were not classified by Mothur at class level using SILVA reference alignment. We refined the taxonomic affiliation of the most frequent unclassified OTUs ( i.e. 93 OTUs that were recovered in at least 15 samples, see Supplementary Methods) using the Arb parsimony insertion tool and the Silva backbone tree. Results are provided in S3.

Computing phylogenetic diversity For each microbial assemblage (i.e. skin microbiome of one fish individual or planktonic microbes from one water sample) we assessed two complementary phylogenetic diversity indices. First we measured phylogenetic richness which accounts only for presence of microbial lineages using Faith’PD index (Faith 1992) computed with picante R-package (Kembel et al. 2010), and then corrected by the sum of all branches lengths in the prokaryotic tree in order to scale values between 0 and 1. Faith’s PD index increases when OTUs are phylogenetically distant. Second, we measured phylogenetic entropy accounting for the relative abundance of OTUs, using the Allen’s index (Allen, Kon, and Bar -Yam 2009), which is a phylogenetic extension of Shannon’s taxonomic entropy index. Allen index was

! qvp! computed using our own R-function (https://github.com/marlenec/chao , q=1) based on entropart R-package (Marcon and Hérault 2014). Allen’s index increases when the most abundant OTUs are phylogenetically distant. Phylogenetic dissimilarity between pairs of microbial assemblages was assessed using two indices accounting only for OTUs presence or their dominance, i.e. unweigthed and abundance-weighted versions of the Unifrac index, respectively, both computed using the GUniFrac R-package (Chen and ORPHANED 2012). Phylogenetic dissimilarity indices accounting for composition (or structure) ranges from 0 when assemblages share the same (dominant) phylogenetic lineages to 1 when assemblages are made of (or dominated by) phylogenetically distant OTUs.

Fish phylogeny and ecological traits Phylogenetic relationships between studied fish species were extracted from a published time- calibrated phylogeny containing 7,822 fish species, covering all Actinopterygian orders (Rabosky et al. 2013). Out of our 44 fish species, 13 were not initially present in the phylogenetic tree, and were manually grafted next to their closest species accordingly to literature. One species ( Cephalopholis argus ) was incorrectly branched next to Scaridae in the initial tree, and was therefore also grafted next to its closest relative (see S14). The ecology of the 44 species was described using a set of 6 categorical traits describing body size at fish maturity, mobility, period of activity, schooling behaviour, position in water column and diet. Values were taken from a global database of functional traits for 6,316 tropical reef fishes (Mouillot et al. 2014). The distribution of trait values among the 44 studied species is described in S9.

Fish vulnerability Vulnerability of each fish species to fishing (Cheung, Pitcher, and Pauly 2005) was obtained from Fishbase ( http://www.fishbase.org/ , 2017). Cheung’s vulnerability to fishing is based on fish’s total length, life history traits (including age at first maturity, longevity, growth rate, fecundity, spatial behaviour), and geographical range. A vulnerability close to 100 means that the vulnerability to extinction is very high, while vulnerability close to 0 means that the vulnerability to extinction is low. Vulnerability to habitat loss due to global change in coral reefs was computed as in (Graham et al. 2011) based on species diet specialization, habitat specialization, recruitment specialization for live coral and body size estimated from data from Fishbase.org and expert knowledge.

! qvq!

Determinants of microbial alpha-diversity The comparison of both diversity facets (phylogenetic richness and phylogenetic entropy) obtained in planktonic communities and fish skin microbiome, as well as between fish species was done using separated Kruskal-Wallis tests (999 permutations) in vegan R-package. To test if closely related fish species have more similar levels of phylogenetic richness and entropy values than expected by chance, we computed Moran’s I which is used as an autocorrelation measure of trait variation along a phylogenetic tree. We thus used the inverse of patristic distances between fish species as a measure of phylogenetic proximity (Münkemüller et al. 2012) and replicate the test on 999 random subsamples of 44 individuals (1 per species) to account for intraspecific variability . For each test, Moran’s I values were than compared to the ones obtained when shuffling diversity values 999 times on the phylogenetic tree using adephylo R-package. To test whether microbial phylogenetic richness and entropy on fish skin was correlated with the vulnerability of fish species to anthropogenic threats, we performed a Spearman’s correlation test between each phylogenetic diversity index (richness and entropy) and each fish vulnerability score (to overfishing and to habitat loss). These analyzes were performed on 999 subsamples of individuals to account for intraspecific variability in skin microbiome.

Determinants of dissimilarity between skin microbiomes The comparison of the structure of fish-associated microbial communities and the planktonic ones was performed on the full dataset using a permutational ANOVA (PERMANOVA) performed on dissimilarity values (Weighted and Unweighted Unifrac) using vegan R- package (Table 1). To assess how each microbial clade contributed to the dissimilarity between planktonic and skin-associated microbial communities, we performed a LefSe analysis (Segata et al. 2011). LefSe provides Linear Discriminant Analysis (LDA) scores for the bacterial clades contributing the most to the differences between communities (S2). The assessment of the effect of fish species was done using a PERMANOVA (Table 1). To assess the effect of reef type on fish skin microbiome, we performed a PERMANOVA on the 16 species for which we sampled at least one representative on both reef types (total of 74 individuals). To compare the effects of reef type and fish species on its microbiome, both factors, as well as the interaction between them, were included in the analysis (S8). The correlation between dissimilarities between microbiomes and hosts’ phylogeny (phylosymbiosis) was measured using Mantel tests based on Pearson’s coefficient, using

! qvr! vegan R-package and 999 permutations. In order to take into account the effect of intraspecific variability 999 random subsamples containing one individual per fish species were considered. To check if the phylogenetic signal was blurred due to very long phylogenetic branches between fish species, separated Mantel tests were also computed on the fish order Perciformes only. In order to assess the effects of host phylogeny at higher phylogenetic scales than OTUs, we used the Beta Diversity Through Time (BTTD) approach developed by Groussin et al. (Groussin et al. 2017), which computes various beta-diversity indices at different time periods (slices) along the bacterial phylogenetic tree. We went back in time this way until 900 Mya, which corresponds approximately to divergence between bacterial orders, and computed Sorensen and Bray-Curtis indices at each slice of 100 Mya. At each slice, correlation between pairwise beta-diversity values and host phylogeny was tested using a Mantel test based on Pearson’s coefficient and 999 permutations. This analysis was performed using 500 subsamples of 44 individuals (1 per species). The effect of fish ecological traits was assessed using PERMANOVAs, using 999 random subsamples containing one individual by fish species. The ecological traits were ordered in the model according to their independent contribution (greatest to least) to the total variability.

As for all analyses involving dataset subsampling, results were reported as the percentage of significant P-values obtained in all subsamples, together with the number of subsamples used in the analysis, and when useful, the mean standard deviation of the statistic among all subsamples.

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Supplementary Information

Skin microbiome of coral reef fishes is diversified, species-specific, not phylogenetically conserved and vulnerable to global change

Marlène Chiarello, Jean-Christophe Auguet, Yvan Bettarel, Corinne Bouvier, Thomas Claverie, Nicholas AJ Graham, Fabien Rieuvilleneuve, Elliot Sucré, Thierry Bouvier, and Sébastien Villéger

! qvv! Supplementary Information S1: Order Family Species Number of individuals Beryciformes Holocentridae Myripristis murdjan 2 Beryciformes Holocentridae Myripristis violacea 1 Perciformes Acanthuridae Acanthurus leucosternon 5 Perciformes Acanthuridae Acanthurus lineatus 3 Perciformes Acanthuridae Ctenochaetus striatus 7 Perciformes Acanthuridae Naso unicornis 1 Perciformes Caesionidae Pterocaesio tile 1 Perciformes Caesionidae Pterocaesio trilienata 2 Perciformes Carangidae Caranx melampygus 3 Perciformes Chaetodontidae Chaetodon auriga 2 Perciformes Chaetodontidae Chaetodon falcula 6 Perciformes Chaetodontidae Chaetodon lunula 4 Perciformes Chaetodontidae Chaetodon meyeri 3 Perciformes Chaetodontidae Chaetodon trifascialis 3 Perciformes Chaetodontidae Forcipiger flavissimus 3 Perciformes Ephippidae Platax orbicularis 5 Perciformes Ephippidae Platax teira 1 Perciformes Kyphosidae Kyphosus vaigiensis 3 Perciformes Labridae Cheilinus fasciatus 3 Perciformes Labridae Hemigymnus fasciatus 3 Perciformes Labridae Thalassoma hebraicum 3 Perciformes Lethrinidae Monotaxis grandoculis 5 Perciformes Mullidae Parupeneus cyclostomus 2 Perciformes Mullidae Parupeneus trifasciatus 3 Perciformes Pinguipedidae Parapercis hexophtalma 3 Perciformes Pomacanthidae Pomacanthus imperator 3 Perciformes Pomacanthidae Pygoplites diacanthus 6 Perciformes Pomacentridae Abudefduf sexfasciatus 3 Perciformes Pomacentridae Abudefduf sparoides 4 Perciformes Pomacentridae Amphiprion akallopisos 3 Perciformes Scaridae Chlorurus sordidus 6 Perciformes Scaridae Scarus caudofasciatus 4 Perciformes Scaridae Scarus russelii 1 Perciformes Serranidae Cephalopholis argus 6 Perciformes Serranidae Cephalopholis boenak 1 Perciformes Sphyraenidae Sphyraena barracuda 1 Perciformes Zanclidae Zanclus cornutus 6 Scorpaeniformes Scorpaenidae Pterois miles 2 Scorpaeniformes Scorpaenidae Pterois radiata 1 Syngnathiformes Syngnathidae Corythoichthys flavofasciatus 3 Tetraodontiformes Balistidae Balistapus undulatus 3 Tetraodontiformes Balistidae Sufflamen chrysopterum 6 Tetraodontiformes Diodontidae Arothron nigropunctatus 1 Tetraodontiformes Monacanthidae Cantherhines pardalis 1 Total Individuals 138 S1: Fish individuals included in this study. Classification is from Nelson, 2016.

! qvw! Supplementary Information S2:

S2-Figure: LefSe analysis showing the most differential microbial taxa between fish skin- associated and planktonic communities. Only microbial taxa that raised an LDA score > 4 are shown. See supplementary methods for information and references about LefSe.

! qvx! S2-Table: Full results of LefSe analysis, showing all significant biomarkers detected in fish surface and in seawater Prokaryotic clades enriched in log10 LDA Prokaryotic clades log10 LDA fish skin Score enriched in seawater Score Proteobacteria 5,12 Cyanobacteria 5.02 Gammaproteobacteria 5.04 unclassified 4.71 Alphaproteobacteria 4.42 Rhodobacteraceae 4.34 Vibrionaceae 4.41 Flavobacteriaceae 4.28 Vibrionales 4.41 Flavobacteriales 4.24 Firmicutes 4.31 Flavobacteriia 4.24 Catenococcus 4.28 Proteobacteria 4.18 Alteromonadales 4.17 Bacteroidetes 4.14 Rhizobiales 4.15 Rhodobacteraceae 4.09 Clostridia 4.09 Rhodobacterales 4.09 Clostridiales 4.09 Aestuariibacter 3.88 Alteromonadales 4.00 Alteromonadaceae 3.88 Methylobacteriaceae 3.94 Rhodospirillaceae 3.59 Methylobacterium 3.94 Rhodospirillales 3.35 Clostridiales 3.91 BD2_7 3.04 Halomonadaceae 3.89 Cellvibrionales 3.02 Oceanospirillales 3.89 OM182_clade 2.66 Betaproteobacteria 3.84 Betaproteobacteria 2.38 Burkholderiales 3.82 ! ! Breoghania 3.75 $ $ Bradyrhizobiaceae 3.73 $ $ Psychrobium 3.73 $ $ Shewanellaceae 3.73 $ $ Fusobacteriaceae 3.70 $ $ Fusobacteriia 3.70 $ $ Fusobacteria 3.70 $ $ Fusobacteriales 3.70 $ $ Aliivibrio 3.68 $ $ Ilyobacter 3.68 $ $ Firmicutes 3.67 Planctomycetales 3.59 Planctomycetaceae 3.59 Planctomycetes 3.59 Planctomycetacia 3.59 Bacilli 3.56 Campylobacterales 3.55 Epsilonproteobacteria 3.55 Actinobacteria 3.54 Campylobacteraceae 3.50 Nesiotobacter 3.50 Oxalobacteraceae 3.43 Campylobacteraceae 3.43 Bacillales 3.37 Burkholderiales 3.33

! qwo! Sphingobacteriia 3.32 Sphingobacteriales 3.32 Rhodospirillum 3.31 Planctomycetaceae 3.31 Vibrionaceae 3.31 Comamonadaceae 3.31 Burkholderiales 3.30 Chitinophagaceae 3.29 Hydrotalea 3.26 Azorhizobium 3.26 Rhodospirillales 3.23 Micrococcales 3.20 Bacillales 3.17 Clostridiaceae_1 3.13 Roseimaritima 3.13 FamilyI 3.11 Acetobacteraceae 3.07 Capnocytophaga 3.06 Bacilli 3.06 Flavihumibacter 3.05 Actinobacteria 3.04 Clostridium_sensu_stricto 3.04 Clostridiaceae_2 3.04 Proteinivorax 3.02 Family_XIV 3.02 Bacillaceae 2.98 Methanomicrobiales 2.98 Lachnospiraceae 2.96 Corynebacteriales 2.96 Acetobacteraceae 2.95 Dermabacteraceae 2.95 Ornithobacterium 2.94 Clostridiaceae_1 2.93 Hyphomonadaceae 2.93 Xanthobacteraceae 2.93 Saccharococcus 2.93 Halobacteriales 2.92 Haematobacter 2.91 Caulobacterales 2.91 Lachnospiraceae 2.91 Halobacteriaceae 2.90 Ruminococcaceae 2.89 Halobacteria 2.88 Desulfotignum 2.88 Sulfurovum 2.88 Deltaproteobacteria 2.88 Hyunsoonleella 2.87 Micrococcaceae 2.86 Thaumarchaeota 2.86 Carnobacteriaceae 2.85

! qwp! Micrococcales 2.85 Micrococcales 2.85 Sungkyunkwania 2.85 Coriobacteriia 2.85 Coriobacteriales 2.84 Planctopirus 2.84 Auritidibacter 2.83 Pibocella 2.83 Coriobacteriaceae 2.82 Lactobacillales 2.80 Clostridium_sensu_stricto_17 2.78 Telluria 2.77 Sphingomonadales 2.77 Croceicoccus 2.77 Erythrobacteraceae 2.77 Zunongwangia 2.74 Nonlabens 2.73 Bacillaceae 2.73 Helicobacteraceae 2.73 Glaciecola 2.72 Desulfobacteraceae 2.71 DEV007 2.71 Cytophagales 2.71 Cytophagia 2.70 Arcobacter 2.70 Prevotellaceae 2.70 Prevotella_7 2.69 Neisseriaceae 2.69 Stenoxybacter 2.69 Cenarchaeales 2.68 Bacteroidia 2.68 Cenarchaeaceae 2.68 Neisseriales 2.68 Methanomicrobia 2.68 DEV007 2.68 Bacteroidales 2.68 Anaerosporobacter 2.67 Cenarchaeum 2.67 Enterobacteriales 2.66 Cyclobacteriaceae 2.66 Moraxellaceae 2.66 Moraxella 2.66 Pseudomonadales 2.66 Pasteurellaceae 2.66 Solirubrobacterales 2.65 Enterobacteriaceae 2.65 Pasteurellaceae 2.65 Rhodococcus 2.65 Marine_Group_I 2.65 Corynebacteriales 2.64

! qwq! Algoriphagus 2.64 Pasteurellales 2.63 Deltaproteobacteria 2.63 Epilithonimonas 2.62 Thermoleophilia 2.62 Nocardiaceae 2.60 Chiayiivirga 2.60 Corynebacterium_1 2.58 Chitinophagaceae 2.56 Thaumarchaeota 2.54 SS1_B_09_64 2.54 Dolosigranulum 2.54 Rickettsiaceae 2.53 Candidatus_Cryptoprodotis 2.53 Rickettsiales 2.53 Thaumarchaeota 2.53 Desulfovibrionales 2.53 SS1_B_09_64 2.52 SEEP_SRB4 2.50 Chlamydiae 2.50 Chlamydiales 2.47 Simkaniaceae 2.47 Verrucomicrobiales 2.47 Simkania 2.46 Sphingobacteriaceae 2.46 Actinobacteria 2.45 Desulfobulbaceae 2.45 Klugiella 2.44 Verrucomicrobiae 2.44 Microbacteriaceae 2.43 Pedobacter 2.42 Desulfovibrionales 2.41 Rhizobium 2.41 Euryarchaeota 2.39 Rhizobiaceae 2.38 Mollicutes_RF9 2.38 Mollicutes 2.37 Thermoplasmata 2.37 Thermoplasmatales 2.37 Thermoplasmatales_Incertae_Sedis 2.37 Spirochaetales 2.36 Mollicutes_RF9 2.36 Spirochaetaceae 2.36 Tenericutes 2.36 Spirochaetes 2.36 Frankiales 2.35 Treponema_2 2.35 Geodermatophilaceae 2.34 Xanthomonadales 2.34 Xanthomonadaceae 2.34

! qwr! Mollicutes_RF9 2.33 Lentisphaeraceae 2.30 Lentisphaera 2.28 Lentisphaerae 2.28 Lentisphaerales 2.28 Lentisphaeria 2.27 Elusimicrobium 2.26 Elusimicrobiales 2.26 Elusimicrobia 2.26 Elusimicrobiaceae 2.26 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! qws! Supplementary Information S3:

%of Range of rel. Range of Kingdom Phylum Class uncl. abundance number OTUs (%) of hosts Verrucomicrobiae 10.75 0-19.0 16-46 Verrucomicrobia WCHB1-41* 1.08 0-10.2 36 R76_B128* 1.08 0-0.6 27 Bacteroidia* 9.68 0-5.3 15-38 Cytophagia 9.68 0-11.4 16-53 Bacteroidetes Sphingobacteriia 3.23 0-8.3 23-53 Flavobacteriia 2.15 0-0.5 18-25 Clostridia 9.68 0-4.2 15-28 Firmicutes Erysipelotrichia* 1.08 0-2.3 22 Alphaproteobacteria 8.60 0-12.6 19-51 Betaproteobacteria 1.08 0-4.1 15 Proteobacteria Deltaproteobacteria 8.06 0-1.0 15-35 Bacteria Epsilonproteobacteria 1.08 0-4.7 15 Gammaproteobacteria 2.15 0-1.2 27-48 Chloroplast 7.53 0-16.5 15-32 Cyanobacteria 2.15 0-1.0 24-24 Cyanobacteria Melainabacteria* 1.08 0-0.6 16 ML635J-21* 1.08 0-0.5 20 Phycisphaera* 3.23 0-1.7 17-79 Planctomycetes OM190* 2.15 0-0.8 23-23 vadinHA49* 1.08 0-0.7 19 Actinobacteria Acidimicrobiia* 1.08 0-4.7 50 Parcubacteria Candidatus_Campbellbacteria* 1.08 0-1.6 19 Lentisphaerae Oligosphaeria* 1.08 0-10.9 28 unclassified unclassified 4.30 0-10.0 15-70 Archaea Euryarchaeota Thermoplasmata 4.30 0-1.0 21-43 S3-Table: Class-level putative affiliation of the most common unclassified OTUs (unclassified OTUs at class level and found in at least 15 samples) found using Arb parsimony insertion tool. %of uncl. OTUs: Percentage of the selected OTUs belonging to a given class. Range of rel. abundance (%) is the minimum and the maximum relative abundance values of OTU(s) belonging to this class. Range of number of hosts is the minimum and the maximum number of individuals where OTUs belonging to this class were recovered. Single values in this last column indicate the number of individual where the only OTU belonging to this class was recovered. *Asterisks indicate classes that were initially not identified by Mothur in the whole dataset.

! qwt! Supplementary Information S4:

S4-Fig: Microbial phylogenetic richness recovered in water samples and on fish surfaces . Boxes represent the interquartile range of alpha diversity values among water samples (blue), among all fish skin microbiomes (white) and among individuals of a given fish family (colored according to the fish order). Thick bars represent the median of alpha- diversity values, and horizontal segments extend the most extreme data point which is no more than 1.5 times the length of the box away from the box. In the case of Sphyraenidae, Monacanthidae and Diodontidae, only one individual was sampled, and is represented by a single dot.

! qwu! S4-Table 1: Determinants of microbial diversity recovered in water and on fish surfaces

Phylogenetic richness Phylogenetic entropy Factor Df P P Water vs. Fish 1 0.003 0.003 Fish Species 43 <0.001 0.018 Effect of sample type (water vs. fish skin) and fish species on each facet of microbial phylogenetic diversity (richness and entropy) were tested using two separated non-parametric Kruskal-Wallis tests on all 173 (fish skin + water) samples, and on all 138 fish individuals, respectively.

S4-Table 2: Effect of fish phylogeny on the diversity of its skin microbiome

Index of alpha-diversity Moran’s I % of sign. P-values (<0.05)

Phylogenetic richness (Faith’s PD) 0.03±0.02 96.2

Phylogenetic entropy (Allen’s index) 0.02±0.02 88.9

The effect of fish phylogeny on its skin-associated microbial diversity was assessed using Moran’s I index of autocorrelation scaling between -1 and 1, 1 meaning that there is a strong correlation between phylogeny and the trait value tested. I values were compared to I values obtained from a null model were diversity values were randomly shuffled on fish phylogeny (999 replicates). Mean and associated standard deviation of Moran’s I on 999 subsamples are provided, as well as the percentage of subsamples where the correlation was significant (P<0.05).

! ! ! ! ! ! ! ! ! ! !

! qwv! Supplementary Information S5:

S5-Fig: Abundance-unweighted (left) and weighted (right) phylogenetic dissimilarity values, among water samples (n=35 samples), between fish samples and water samples (n=173), between individuals of the same fish species (n=34 species with more than 1 individual), and among individuals from different species (n=44 species). Boxes represent the interquartile range dissimilarity values. Thick bars represent the median of dissimilarity values, and horizontal segments extend the most extreme data point which is no more than 1.5 times the length of the box away from the box.

! qww! Supplementary Information S6:

S6-Fig: Weighted-Unifrac between fish skin-associated microbiomes and divergence times between fish species. One individual of each of the 44 fish species was randomly selected ; each point representing one dissimilarity value between two individuals. ‘Intra- order’ means individuals belonging to species from the same taxonomic order; ‘inter-order’ means individuals belonging to species from different taxonomic orders.

S6-Table: Test for phylosymbiosis

Fish species included Dissimilarity index P-value % of sign. P-values

Unweighted Unifrac 0.28 ± 0.19 8.4 All species Weighted Unifrac 0.43 ± 0.18 0 Unweighted Unifrac 0.48 ± 0.23 0.7 Perciformes only Weighted Unifrac 0.68 ± 0.22 0 Phylosymbiosis was tested using a Mantel test (999 permutations) for correlation between hosts’s phylogeny and dissimilarity values between microbiomes. Mean and associated standard deviation of P-values obtained on 999 subsamples are provided, as well as the percentage of subsamples where the correlation was significant (P<0.05).

! qwx! Supplementary Information S7:

S7-Table: Results of BDTT (Beta-Diversity Through Time) assessing the correlation between microbiome variability and hosts’ phylogeny across bacterial phylogenetic tree

Sorensen Index Bray-Curtis Index

Time (Mya) % of sign. P-values R2 % of sign. P-values R2

0 2.2 0.005±0.01 0 0.001±0.00

100 2.8 0.006±0.01 0 0.001±0.00

200 4.8 0.006±0.01 0 0.001±0.00

300 9.6 0.006±0.01 0 0.001±0.00

400 9.8 0.007±0.01 0 0.001±0.00

500 12.2 0.007±0.01 0 0.001±0.00

600 8.6 0.006±0.01 0 0.002±0.00

700 2.8 0.005±0.01 0 0.002±0.00

800 2.2 0.004±0.01 0 0.001±0.00

900 1.6 0.004±0.01 0 0.001±0.00 This method is from Groussin et al. Nature Communication 2016, (see Supplementary Methods) for 9 periods of 100 millions of year from present time (t=0) to 900 Mya in the past. At each period of time, BDTT computes microbiome variability using both abundance- weighted (Bray Curtis dissimilarity) and unweighted dissimilarity indices (Sorensen index) and assesses their correlation with hosts’ divergence times using a Mantel test. Results of the Mantel tests show that even when considering deep prokaryotic phylogenetic clades ( i.e. 900 Mya correspond roughly to the apparition of Bilaterians and divergence of bacterial orders), there is no correlation between host phylogeny and the structure and composition of its associated skin microbiome. Mean and associated standard deviation of R-squared values obtained on 999 subsamples are provided, as well as the percentage of subsamples where the correlation was significant (P<0.05).

! qxo! Supplementary Information S8:

S8-Table: Effect of reef type and fish species on fish skin microbial community structure Unweighted Unifrac Weighted Unifrac Factor P R2 P R2 Reef type 0.001 0.02 0.002 0.03 Fish species 0.001 0.27 0.001 0.32 Reef type * Fish species 0.020 0.20 0.026 0.19 The effect of reef type versus the one of fish species was assessed by computing a permutational ANOVAs (PERMANOVAs) on abundance weighted Unifrac and unweighted Unifrac dissimilarities between the 74 individuals that belonged to the 16 fish species that were sampled on both sites.

! qxp! Supplementary Information S9:

S9-Fig: Distribution of functional trait values among fish phylogeny. Traits are encoded as ordinal variables as indicated in S8-Table.

S9-Table: Encoding of functional traits tested in this study

Functional traits Encoding 1: 7.1-15 cm; 2: 15.1- 30cm; 3: 30.1-50cm; 4: 30.1-50cm; 5: 50.1-80cm; 6: Size at maturity >80cm Mobility 1: sedentary; 2: mobile within a reef; 3: highly mobile, i.e. between reefs Activity 1: diurnal; 2: diurnal and nocturnal; 3: nocturnal Schooling 1: solitary; 2: pairing; 3: small group; 4: medium group; 5: large group Position 1: sea bottom; 2: above sea bottom; 3: pelagic FC : Pelagic macro-organisms, i.e. large organisms living in the water column, as well as benthic fishes IM : Mobile invertebrates, i.e. all-size mobile benthic invertebrates ! IS : Sessile invertebrates, i.e. corals, sponges, ascidians and all other sessile benthic invertebrates ! Diet PK : Planktonivorous fishes are fishes eating planktonic and small benthic organisms that can migrate in the water column (copepods, crustacean larvae…) ! HM : Herbivorous macro-algal fishes are fishes eating macro-algae and sea grass ! HD : Herbivorous-detritivorous fishes eat undefined or detritical material ! OM : Omnivorous fishes regroup fishes that are both herbivorous or detritivorous, and carnivorous ! ! !

! qxq! Supplementary Information S10:

S10-Fig1: Minimum Spanning Trees (MST) showing fish diets based on Unweighted- Unifrac dissimilarity values (A), or abundance weighted-Unifrac dissimilarity values (B) calculated on mean microbiome per fish species. Species were colored according to fish main prey (S8). The closer the species are on the tree, the more similar are their microbiomes. Complete names of each species are provided in Fig 1 of the manuscript, and in S8.

S10-Fig2: PCoA plots representing all individuals included in this study, based on (A) unweighted-Unifrac dissimilarity values and (B) abundance weighted-Unifrac dissimilarity values between microbiomes. Individuals were colored according to their main prey (S8).

! qxr! Supplementary Information S11: Investigation of an eventual transfer from sessile invertebrates-associated cells to fish skin microbiome

At the same time of sampling and on the same sites, 83 sessile invertebrates were sampled, representing a wide range of clades, i.e. scleratinian corals, soft corals, sponges, anemones and gorgonians. Surface microbiome was sampled, either by sampling of surface mucus, or swabbing, and was analyzed using the same methods as for fish skin swabs. We computed the phylogenetic dissimilarity between surface microbiome of these sessile organisms, and fish skin surface microbiome, depending on fish diet.

S11-Fig: Abundance unweighted-Unifrac (A) and weighted-Unifrac (B) dissimilarity values between sessile invertebrates’ microbiomes and fish skin microbiomes , separated by fish diets. Both phylogenetic dissimilarities were significantly different between different fish diets (Kruskal-Wallis test, P<0.001 for both U- and W-Unifrac). Post-hoc pairwise comparisons of dissimilarities among different diets are represented by letters, two different letters indicating a significant difference between dissimilarity values between diets (post-hoc pairwise comparisons available in pgirmess package, P<0.05). Fishes eating sessile invertebrates hosted a microbiome not significantly closer to the one of sessile invertebrates, than fishes having contrasting diets.

! qxs! Supplementary Information S12: Relation between fish vulnerability to anthropic pressures and microbiome diversity

S12-Table: Spearman’s correlation tests between phylogenetic diversity of microbiome and vulnerability of the 44 fish species

Phylogenetic richness Phylogenetic entropy

Anthropic pressure rho % of sign. P-values rho % of sign. P-values

Overfishing 0.43 ± 0.08 95.1 0.29 ± 0.09 47.9

Habitat loss -0.33 ± 0.07 69.8 -0.25 ± 0.07 28.6 999 correlation tests were performed on 999 subsamples of one individual per fish species (See Supplementary Methods). Mean and associated standard deviation of Spearman’s rho values on 999 subsamples are provided, as well as the percentage of subsamples where the correlation was significant (P<0.05).

S12-Fig: Mean phylogenetic richness (measured by relative Faith’s PD) recovered on fish species (n=44 species, 1 individual randomly subsampled), and fish vulnerability to overfishing (left) and to habitat loss (right).

! qxt! Supplementary Information S13: Rarefaction curves

S13-Fig1: Rarefaction curves obtained from each water replicate. Sample names starting by ‘B-‘ were taken on barrier reef, while sample names starting by ‘F-’ were taken on fringing reef.

! qxu!

S13-Fig2: Rarefaction curves for all fish individuals . The most undersampled species (showing a non-flat curve) were also the phylogenetically richer ones ( Pterois miles , Pterois radiata , Parupeneus cyclostomus , Chlorurus sordidus and Cephalophilis boenak , see Fig 1).

! qxv! Supplementary Information S14: Host phylogenetic tree and branching of species not initially included in Rabosky et al. (2013) phylogenetic tree

A. Rabosky et al. (2013) modified phylogenetic tree with added species in Newick format

((Myripristis_murdjan:8.00651,Myripristis_violacea:8.00651)Node3:121.29586,(((((Parapercis_hexophtalma:10 3.0273727,(Kyphosus_vaigiensis:96.902137,((Thalassoma_hebraicum:39.122854,Hemigymnus_fasciatus:39.12 286)Node11:40.852082,(Cheilinus_fasciatus:77.37268,((Chlorurus_sordidus:26.44134,Scarus_russelii:26.44134 )Node15:1.796755,Scarus_caudofasciatus:28.23811)Node14:49.13474)Node12:2.60219)Node10:16.92714)Nod e9:6.12525)Node8:1.96541,((Pterocaesio_trilienata:31.385374,Pterocaesio_tile:31.3854)Node17:65.2044,((Mon otaxis_grandoculis:80.2476,(Platax_orbicularis:11.3302,Platax_teira:11.33021)Node21:68.91726)Node20:14.45 65,((Arothron_nigropunctatus:82.517345,(Cantherhines_pardalis:58.59547,(Balistapus_undulatus:24.27565,Suff lamen_chrysopterum:24.275687)Node25:34.3198)Node24:23.92198)Node23:10.871422,(((Pomacanthus_imper ator:39.592969,Pygoplites_diacanthus:39.593)Node28:45.4561,(((Chaetodon_lunula:17.92216,(Chaetodon_auri ga:9.00917,Chaetodon_falcula:9.009155)Node32:8.91303)Node31:10.92798,(Chaetodon_trifascialis:26.77443, Chaetodon_meyeri:26.77445)Node33:2.07571)Node30:21.89785,Forcipiger_flavissimus:50.74797)Node29:34.3 0103)Node27:3.6752,((Naso_unicornis:49.999943,(Acanthurus_lineatus:21.92091,(Ctenochaetus_striatus:12.48 236,Acanthurus_leucosternon:12.4823649)Node37:9.438535)Node36:28.07914)Node35:3.90017,Zanclus_cornu tus:53.9002)Node34:34.82407)Node26:4.66462)Node22:1.31527)Node19:1.88563)Node16:8.40301)Node7:1.5 4063,((Cephalopholis_boenak:23.55669,Cephalopholis_argus:23.5566)Node39:69.652503,(Pterois_miles:20.23 159,Pterois_radiata:20.23159)Node40:72.97738)Node38:13.3243)Node6:8.62173,(((Abudefduf_sparoides:10.19 924,Abudefduf_sexfasciatus:10.19924)Node43:40.640407,Amphiprion_akallopisos:50.839627)Node42:63.1446 26,(Caranx_melampygus:81.9976,Sphyraena_barracuda:81.99769)Node44:31.98673)Node41:1.17081)Node5:4. 85771,(Corythoichthys_flavofasciatus:112.0186,(Parupeneus_cyclostomus:14.29613,Parupeneus_trifasciatus:14 .29613)Node46:97.722467)Node45:7.9941602)Node4:9.2895)Node1;

! qxw! B. Branching information of added species and references

13 species were not initially present in Rabosky et al. phylogenetic tree, and one (Cephalophilis argus ) was incorrectly branched between Scaridae and Caesionidae . To define the branching emplacement of these species, we searched for resolved phylogenies of fish families, and searched for the species’ closest relative already included in Rabosky’s tree. We then placed our species in replacement to its closest relative. When possible, we favored studies using genetic data based on multiple loci. No published phylogenetic tree of Caesionidae and Mullidae based on genetic data could be found; therefore we used studies based on morphological and anatomical traits:

Species added Branching emplacement Type of data Reference Acanthurus In replacement of Acanthurus Genetic (Sorenson et al. 2013) leucosternon nigricans On a separated branch, Acanthurus lineatus connected to the common node Genetic (Sorenson et al. 2013) of all other Acanthurus sp. In replacement of Caesio Morpho- Caesio xanthonota (Carpenter 1990) caerulaurea anatomy In replacement of (Santini, Sorenson, Cantherhines pardalis Genetic Cantherinhes pullus and Alfaro 2013) Corythoichthys In replacement of No data* flavofasciatus Corythoichthys intestinalis In replacement of (Craig and Hastings Cephalopholis argus Genetic Cephalopholis nigri 2007) Branched to the node Myripristis murdjan separating Myripristis violacea Genetic (Dornburg et al. 2012) from all other species Parapercis In replacement of Parapercis No data* hexophtalma clathrata On a separated branch, Parupeneus Morpho- connected to the common node (Kim 2002) cyclostomus anatomy of all Upeneichthys sp. Branched to the node separating Parupeneus Morpho- Parupeneus trifasciatus (Kim 2002) cyclostomus from all other anatomy species In replacement of Pterocaesio Morpho- Pterocaesio tile (Carpenter 1990) digramma anatomy In replacement of Pterocaesio Morpho- Pterocaesio trillienata (Carpenter 1990) marri anatomy In replacement of Scarus Scarus caudofasciatus Genetic (Choat et al. 2012) spinus In replacement of Scarus Scarus russelii Genetic (Choat et al. 2012) flavipectoralis *To our knowledge, no phylogenetic tree of Syngnathidae including Corythoichthys flavofasciatus has been published yet. Therefore, we placed our species in replacement of one of its congenerics. The same problem appeared for Parapercis hexophtalma , which was

! qxx! placed in replacement of its only congeneric that also occurs in Indian ocean (Taquet and Diringer 2007)

C. References

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