Habitat and Client Diversity Influence the Skin Microbiome of the Caribbean Cleaner Goby Elacatinus Evelynae

Habitat and Client Diversity Influence the Skin Microbiome of the Caribbean Cleaner Goby Elacatinus Evelynae | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Habitat and Client Diversity Influence the Skin Microbiome of the Caribbean Cleaner Goby Elacatinus Evelynae Ana Pereira, Marta C. Soares, Teresa Santos, Ana Poças, Marcos Pérez-Losada, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-828485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Fish associated microorganisms are known to be affected by the environment and other external factors, such as microbial transfer between interacting partners. One of the most iconic mutualistic interactions on coral reefs are the cleaning interactions between cleanerfishes and their clients, during which direct physical contact occurs. Here, we characterized the skin bacteria of the Caribbean cleaner sharknose goby, Elacatinus evelynae , in four coral reefs of the US Virgin Islands using sequencing of the V4 region of the 16S rRNA gene. We specifically tested the relationship between gobies’ level of interaction with clients and skin microbiota diversity and composition. Our results showed differences in microbial alpha- and beta-diversity in the skin of gobies from different reef habitats and high inter-individual variation in microbiota diversity and structure. Overall, the results showed that fish-to-fish direct contact and specifically, access to a diverse clientele, influences the bacterial diversity and structure of cleaner gobies’ skin. Because of their frequent contact with clients, and therefore, high potential for microbial exchange, cleanerfish may serve as models in future studies aiming to understand the role of social microbial transfer in reef fish communities. Behavioral Ecology Skin microbiota microbial diversity cleanerfish Elacatinus evelynae coral reefs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Coral reefs are highly complex marine ecosystems that have been the focus of numerous studies examining the drivers of biodiversity and community dynamics [ 1 ]. As in other ecosystems, coral reef microorganisms are emerging as key members to maintaining reef health and resilience in the face of large-scale degradation due to climate change, human impacts and emerging diseases of corals (reviewed by [ 2 ]). This has resulted in increased efforts to characterize diseases of reef microbial communities, and to identify which organisms influence resilience and recovery (e.g. [ 3 ]). Recent studies have shown the importance of interactions between the coral microbiome and the larger reef community (e.g. between reef-building coral and benthic algae, [ 4 ]). However, relatively few studies have examined the microbiome associated with the most mobile members of the reef community, such as fishes (e.g. [ 5 , 6 ]). Fish microbial communities are known to be affected by multiple biotic and abiotic variables. There is evidence that fish-associated bacteria are organ-specific, species-specific and individual specific, thus comprising highly diverse communities [ 7 – 9 ]. While host factors seem to be the major drivers of fish gut bacterial diversity [ 10 , 11 ], the physicochemical properties of the water exert a considerable effect on the diversity of fish skin-associated microbes (e.g. temperature and salinity, [ 12 ]). Despite the impact of the surrounding environment, the contribution of the water microbiota to the fish skin microbiota composition seems negligible, with fish mucosae being a highly selective environment (e.g. [ 13 ]). However, other external factors, such as direct transfer of microorganisms between fishes, might also play a major and still unexplored role [ 14 – 16 ]. Microbial transfer between interacting partners has been shown to be common in nature, shaping microbial consortia in humans and other animal groups (reviewed in [ 17 , 18 ]), including fish [ 14 ]. Although social microbial transmission could ultimately increase microbiome complexity, which may reduce the abundance of opportunistic and/or pathogenic taxa, as seen in bees [ 19 ] or chimpanzees [ 18 ], social interactions may also facilitate pathogen transmission and consequently increase levels of infection and disease (reviewed by [ 20 ]). One of the most iconic mutualistic interactions on coral reefs is the relationship between cleanerfishes and clients. Cleaners attract individuals from multiple species (clients) to their “cleaning stations”, which are usually fixed territories where they inspect the body of multiple client fishes per day to remove parasites, dead tissue and mucus [ 21 ]. Although many small fish species clean opportunistically, members of two genera are obligate or “dedicated” cleaners [ 22 ]. These include the cleaner wrasses ( Labroides spp.) in the Indo-Pacific and the cleaner gobies ( Elacatinus spp.) in the Caribbean region. Cleaner gobies of the genus Elacatinus reside on benthic substrate, moving only to make contact with client fishes [ 21 , 23 ], which travel and interrupt other activities to visit cleaner gobies [ 24 , 25 ]. Visits to cleaner goby stations can be influenced by multiple factors including location relative to territorial client fish [ 24 – 26 ], local fish abundance [ 27 ], structural complexity [ 28 ], and parasite activity and abundance [ 26 , 29 ]. Consequently, the abundance and diversity of client fishes can vary widely among cleaning stations. Because of their frequent contact with heterospecifics, and therefore, high potential for microbial exchange, cleanerfish may serve as a useful animal model system to understand the role of social microbial transfer in ecological communities [ 16 ]. Indeed, a recent study comparing “cleaner” vs “non-cleaner” ecotypes of E. prochilos from Barbados found that bacterial diversity was significantly increased in “cleaner” ecotypes [ 15 ]. Here, we characterized the skin bacteria of the most ubiquitous Caribbean cleaner goby species, the sharknose goby Elacatinus evelynae , in several reefs within the US Virgin Islands, using 16S rRNA gene (V4 hypervariable region) amplicon sequencing. We specifically studied the relationship between gobies’ level of interaction with clients, and skin microbiota diversity and composition. We expected to find a relationship between microbial diversity and client diversity and geographical differences in the skin microbiota among reefs due to putative socio-environmental differences. Methods Study species, sites, and behavioral observations This study was conducted in the United States Virgin Islands in July 2017. All behavioral observations were performed at four sampling localities: two sites on the Island of St. Thomas: Brewers Bay (18.339998, -64.977417; n = 7 cleaner gobies) and Hull Bay (18.369340, -64.952925; n = 13), and two on the island of St. John: one along the west rim of Greater Lameshur Bay at West Lameshur (18.317928, -64.723946; n = 12) and another along the east rim at Donkey Bight (18.314054, -64.720945; n = 12) (Fig. 1 ). Donkey Bight is dominated by a mix of rocky reef, live and dead coral, sand, and seagrass, while West Lameshur is highly degraded with almost no live coral, and it is located near a mangrove swamp [ 30 ]. We focused on the sharknose goby Elacatinus evelynae , which are small fish (1.2–3.5 cm total length) with a prominent lateral blue and yellow stripe running from the snout to the base of the tail. This species is common across the study reef sites, inhabiting the surface of living coral, usually Siderastrea spp., Orbicella spp., Montastrea spp., Diploria spp., and Pseudodiploria spp. [ 23 ]. Cleaning stations were identified, and a subset was randomly selected for this study. Observations of cleaning interactions were made by two snorkelers positioned as far from the station as possible while still being able to see cleaning interactions (at least 2 m). Individual gobies were observed for 30 min at each location three times a day (at dawn, midday and dusk, totaling 1h30min for each cleaner) encompassing the hours during which E. evelynae are more active [ 25 ]. Observations were registered after a 2–5 min delay to allow the fish to become accustomed to the presence of the observer. During each observation period, the number of clients visiting each cleaning station, number of clients inspected, number of client genera visiting, number of client genera inspected, and average inspection time were recorded. Following the final observation, cleaner gobies were captured using individual hand nets. Nets were submerged in a 30% bleach solution and rinsed with fresh water prior to each use. Upon capture, both sides of each fish were swabbed with tubed sterile cotton swabs (MedicalWire), which were stored at -80°C until DNA extraction. Laboratory procedures Total DNA was extracted using the PowerSoil extraction kit and following manufacturer’s protocol. Extracted DNA was shipped in dry ice to the Centre for Microbial Systems at the University of Michigan Medical School (USA) where the V4 hypervariable region of the 16S rRNA gene (~ 250 bp) was amplified for each sample and controls (i.e. extraction and PCR blanks) with the primers 515F/806R [ 31 ] and sequenced using a dual indexing sequencing strategy [ 32 ]. Libraries were pooled and sequenced in a single Illumina MiSeq sequencing run. Data analysis Raw FASTQ files were analyzed using the DADA2 pipeline [ 33 ] for merging paired end reads, filtering and sequencing error correction using the following parameters: trimLeft = 20, truncLen = c(220, 200), maxN = 0, maxEE = c(2,2), truncQ = 2. Singletons were discarded and reads were collapsed into amplicon sequence variants (ASVs). Taxonomy was determined against the SILVA reference database (release 132) [ 34 ]. ASVs present in PCR and extraction blanks, that remained unclassified or were classified as Mitochondria (identified as Family) and Chloroplast (identified as Class) were removed from the dataset. Archaea were also excluded since the primers used are known to discriminate against this group in the marine environment [ 35 ]. An ASV frequency table was constructed with the R package phyloseq [ 36 ] and read normalized counts were obtained via the negative binomial distribution implemented in DESeq2 [ 37 ]. Raw sequence reads were deposited into NCBI’s Short Read Archive under accession PRJNA756005. Alpha-diversity (intra-sample) was estimated using Shannon, Fisher and Faith’s Phylogenetic Diversity (PD) indices using the R package phyloseq [ 36 ]. Alpha-diversity differences among localities were tested using Generalized Linear Models (GLMs) and post-hoc comparisons were evaluated with Tukey’s HSD test. To test the effect of each cleaning behavior on microbial alpha-diversity, Linear Mixed Models (LME) were performed using the number of clients visiting each cleaning station, number of clients inspected, number of client genera visiting, number of client genera inspected and average inspection time for each model as fixed factors (predictors). Locality was included as a random factor and models were built using the R package lmer. The significance of GLM and LME models was estimated using ANOVA of type III with Satterthwaite approximation for degrees of freedom. Beta-diversity (inter-samples) was estimated using phylogenetic informed weighted and unweighted Unifrac [ 38 ] and Bray Curtis (BC) indices. Dissimilarity in microbial structure among reef locations was visualized using principal coordinates analysis (PCoA). The homogeneity of beta diversity dispersion among localities was also assessed by first calculating the average distance to the sample group centroid using the betadisper function of the vegan R package [ 39 ] and then compared using a permutation test. Tukey’s HSD test was used for post-hoc comparisons. Differences in microbial structure among reefs were then tested with a PERMANOVA with the strata option for locality and 9999 permutations, as implemented in the adonis function of the vegan package. Post-hoc comparisons were performed using a pairwise PERMANOVA with the Bonferroni p-value correction for multiple testing. Additionally, the differences in the abundances of phyla or genera represented by ≥ 3% on average of all sequences were also assessed among reef locations and cleaning activity using the same GLM and LME structure described above. A Venn diagram was used to depict the number of ASVs shared among reef locations. A dissimilarity matrix with the client genera inspected by each goby was constructed and differences among localities were also tested with a PERMANOVA using the BC index. To test the correlation between cleaning activity and the microbial community of each locality, Mantel statistical correlations based on Spearman's rank were performed between the number of clients visiting the cleaning station, number of clients inspected, number of client genera visiting and inspected and the beta-diversity distance matrices using the vegan R package. For all tests, differences were considered significant when P < 0.05. Results Client composition and cleaning activity A total of 44 fish species belonging to 29 genera and 14 families visited the observed cleaning stations (Supplementary Table S1). The striped parrotfish ( Scarus iseri ) was the most common client fish in Brewers Bay (22% of the total visits in that reef), the yellow goatfish ( Mulloidichthys martinicus ) in Hull Bay (33%), the ocean surgeonfish ( Acanthurus bahianus ) in Donkey Bight (20%) and the longfin damselfish ( Stegastes diencaeus ) in West Lameshur (19%) (Supplementary Table S1). Client fish genera composition was significantly different among reefs (PERMANOVA, F = 2.11, p = 0.001), and the pairwise differences were significant between Donkey Bight and West Lameshur (both located in St John) (F = 3.72, p = 0.03) and also between West Lameshur and Hull Bay (F = 2.78, p = 0.03). Cleaner gobies from Donkey Bight and West Lameshur inspected the highest number of client genera (Mean (M) = 4.9, Standard Deviation (SD) = 2.4 and M = 4.8, SD = 2.2 respectively; M = 4.0, SD = 0.8 in Brewers Bay and M = 2.6, SD = 1.6 in Hull Bay). Moreover, dawn was the observation period with the highest cleaning activity (see Supplementary Table S2). The skin microbiome of the cleaner goby Elacatinus evelynae Bacteria-associated 16S rRNA amplicons present in the skin of 44 E. evelynae cleaner gobies from four reefs in the US Virgin Islands (Brewers Bay, Hull Bay, Donkey Bight and West Lameshur) were sequenced, generating 1,245,579 raw reads, 1,099,501 filtered sequences and 1,222 ASVs. From those, 223 ASVs were present in Brewers Bay, 586 ASVs in Donkey Bight, 341 ASVs in West Lameshur, and 457 ASVs in Hull Bay (Fig. 2 ). The most abundant bacterial phyla (≥ 3%) across all samples were Proteobacteria (58%), Bacteroidota (16%), Firmicutes (7%), Actinobacteriota (4%) and Cyanobacteria (4%). Pseudomonas was the most abundant genus in Brewers Bay (17.7%), Ekhidna in Hull Bay (14.8%), and Psychrobacter in Donkey Bight and West Lameshur (15.7% and 15.5% respectively). The most abundant genera (≥ 3%) found for each locality are detailed in Table 1 . Differences in phyla abundance among localities were not found (p > 0.05), while significant differences were found for the genera Alcanivorax , Cloacibacterium , Halomonas , Pseudomonas and an unclassified genus from Pseudomonadaceae family (p < 0.04) (Supplementary Table S3). Table 1 Percentage of the most abundant bacterial taxa collapsed by phyla and genera for each reef location. Values in bold represent an abundance ≥ 3%. % of sequences Bacterial taxa Brewers Bay Hull Bay Donkey Bight West Lameshur Phyla Actinobacteriota 2.2 6.7 4.3 3.7 Bacteroidota 22.0 14.9 6.3 21.8 Bdellovibrionota 10.4 0.1 0.2 0.8 Cyanobacteria 6.8 3.4 3.1 5.1 Firmicutes 3.9 8.2 5.2 9.5 Planctomycetota 3.0 0.1 0.4 0.02 Proteobacteria 48.4 63.4 67.8 48.8 Genera Acinetobacter 1.0 1.8 3.1 4.0 Alcanivorax 0.0 0.0 4.8 2.2 Cloacibacterium 3.9 0.2 6.4 0.8 Ekhidna 12.7 14.8 1.9 0.4 Endozoicomonas 0.0 0.1 0.8 5.8 Enterovibrio 0.0 6.3 0.2 3.0 Halomonas 0.0 0.1 9.6 5.5 Marinobacter 0.1 0.0 2.6 1.1 Mycoplasma 1.7 4.8 0.1 0.0 NS5 marine group (Flavobacteriaceae) 0.1 1.9 0.2 2.9 OM27 (Bdellovibrionaceae) 10.3 0.6 0.0 0.1 Pseudomonas 17.7 10.3 1.9 8.9 Psychrobacter 9.1 3.7 15.7 15.5 Stenotrophomonas 4.3 2.6 0.6 0.8 Synechococcus 4.2 2.8 0.7 2.5 Unclassified Neisseriaceae 0.0 5.2 0.1 0.0 Unclassified Pseudomonadaceae 0.0 0.0 6.3 3.3 Unclassified Rhodobacteraceae 2.7 0.5 0.5 0.1 Although a total of 43 ASVs were common among reefs (Fig. 2 ), no single ASV was present in all sampled fish. Nevertheless, two ASVs identified as Rubrobacter sp. and Cloacibacterium sp. were found in all samples from Donkey Bight (n = 12), therefore constituting the core microbiota in that locality. Variation of the skin microbiome of cleaner gobies with cleaning activity Alpha diversity of the bacterial communities associated with the cleaner goby skin was positively (i.e., increasingly) correlated to the number of clients and client genera visiting the cleaning station and inspected for all reefs except for West Lameshur, which showed an inverted pattern, i.e., higher number and diversity of clients corresponded to lower values of alpha diversity (Fig. 3 ). However, none of the trends were statistically significant (p > 0.24, Supplementary Table S3). Moreover, average inspection time did not correlate with alpha-diversity (p > 0.15, Supplementary Table S3). Beta diversity was not explained by the cleaning activity variables (i.e., the number of clients visiting the cleaning station, number of clients inspected, average inspection time, number of client genera visiting and inspected) with all beta diversity indices (p > 0.08, Supplementary Table S3). However, there was a significant positive correlation between cleaner gobies’ microbial beta-diversity using the Unweighted Unifrac distance and the number of client genera inspected in Brewers Bay and Hull Bay (Mantel test, p < 0.02, Supplementary Table S4). In Donkey Bight, there was also a positive correlation between goby microbial beta-diversity using the BC index and number of client genera visiting and inspected (Mantel test, p < 0.003, Supplementary Table S4) and the same correlation was found in West Lameshur with the Weighted Unifrac distance (Mantel test, p < 0.02, Supplementary Table S4). Diversity of the skin microbiome of cleaner gobies across reef locations Skin bacterial alpha-diversity was significantly different among reef localities (p < 0.02, Supplementary Table S3), with pairwise comparisons showing that cleaner gobies from Donkey Bight harbored significantly higher alpha diversity compared to those from the other localities (p < 0.04; Fig. 4 ). No differences were observed in the alpha diversities of individuals from the remaining locations. Bacterial community structure (beta-diversity) was significantly different amongst reef localities with all beta diversity indices (p < 0.003), with significant pairwise differences between Donkey Bight and each of the other reef sites (p < 0.02; Fig. 5 a,b,c and Supplementary Table S5). Beta dispersion was also significantly different among locations considering the Bray Curtis (F = 5.94, p = 0.002; Fig. 5 d) and weighted Unifrac (F = 6.97, p = 0.001; Fig. 5 e) indices. Pairwise comparisons of beta dispersion for the Bray Curtis index showed differences between Donkey Bight and West Lameshur, as well as between Donkey Bight and Hull Bay (TukeyHSD, p < 0.003; Fig. 5 d). For the weighted Unifrac distance, differences were found between Donkey Bight and all remaining localities (Tukey HSD, p < 0.03; Fig. 5 e). Discussion Cleaning stations have been shown to attract a wide diversity of fish species and thus, enhance local reef fish biodiversity and abundance (e.g. [ 40 ]). Because of the direct physical contact between cleaners and clients, there is the potential for cleaning stations to act as hubs for microbial exchange between fish. In this study, we used a 16S rRNA gene amplicon sequencing approach to test whether clientele diversity was associated with differences in the skin microbiome of the cleaner goby E. evelynae in the US Virgin Islands. Our results showed differences in alpha- and beta-diversity amongst gobies from different sampled reefs with few shared ASVs among them and high inter-individual variation in microbiota diversity and structure. Overall, the results showed increasing bacterial alpha diversity with the number of clients and client genera inspected (except in West Lameshur), as well as a positive correlation between beta-diversity and clientele diversity. Goby cleaning activity impacts skin microbial diversity Access by gobies to different reef fish species might be shaped by the level of reef degradation and therefore influence cleaning interactions and consequently, goby microbiome. Our results showed a positive correlation between goby skin bacterial beta-diversity and the diversity of clientele inspected (i.e., number of client genera) in all sampled reefs. Moreover, differences in clientele diversity visiting cleaning stations were also observed in our study. For example, although diversity of client species was high at West Lameshur, the most common clients were Stegastes damselfish, which are territorial fish only visiting cleaning stations within their territories [ 24 ], but usually less parasitized [ 29 ]. The territorial behavior of these fish can also influence which other potential client species gain access to cleaning stations. By contrast, the remaining sites showed higher visitation rates of larger fish species, such as striped parrotfish ( Scarus iseri ) in Brewers Bay, yellow goatfish ( Mulloidichthys martinicus ) in Hull Bay and ocean surgeonfish ( Acanthurus bahianus ) in Donkey Bight, all of which are “preferred” clients, likely due to larger body size and thus higher parasite burden [ 23 , 41 ]. Larger clients, therefore, engage in longer cleaning interactions [ 41 ], which could increase bacterial transfer. However, in our study only client diversity was positively correlated with bacterial structure in the skin of cleaner gobies, indicating a stronger effect of client diversity compared to duration of inspection. Recent studies have shown microbial changes between fish participants of symbiotic relationships. For example, microbial composition of clownfish mucus changes with contact with its anemone host [ 42 ]. Similarly, a “cleaner” ecotype of the Barbadian broadstripe cleaner goby Elacatinus prochilos harbored higher skin microbiota diversity than “non-cleaner” ecotypes [ 15 ]. Microbial interhost dispersal in zebrafish has also shown to influence diversity and composition of microbial communities affecting host immune system [ 14 ]. However, the mechanisms involved in those changes are not well understood. Despite the high diversity of clients visiting cleaning stations in West Lameshur, not only did cleaners from that location have similar bacterial alpha-diversity levels to the ones from Brewers Bay and Hull Bay, but they also showed a contrasting relation between microbial diversity and cleaning activity when compared to the gobies in the other reefs. A possible explanation for the differences found in West Lameshur might be related to the greater habitat degradation at this sampling locality [ 30 ], which could have altered local reef community dynamics. Although reef animal communities harbor some of the most diverse microbial communities of the marine environment [ 42 ], it is estimated that the changes in host communities in a given location may impact microbial diversity in the entire reef [ 43 ]. Cleaner gobies inspect multiple client fish per day and engage in direct fish-to-fish contact [ 21 , 44 ]. Although this creates the opportunity for exchange of microbes, given the interspecific nature of the interactions, actual exchange and persistence of microbes cannot be assumed. Our data, while correlative, supports this hypothesis (i.e., microbial exchange increases with cleaning activity). The alternative explanation, that cleaners with more diverse microbiota attract a more diverse array of clients, seems less likely but cannot be ruled out without experimental manipulation. Cleanerfish have been shown to remove significant numbers of ectoparasites from hosts [ 45 ], which could otherwise compromise client welfare by causing skin damage, feeding on blood, and acting as vectors for diseases (reviewed by [ 46 ]). Nonetheless, the gain of a seemingly easy meal for cleaners (client-gleaned ectoparasites and mucous) may come with a price: while obviously predatory clients may eat the cleaners [ 23 ], less obvious is the fact that clients may also be vectors of parasites, bacterial contamination, and consequently disease to cleaners [ 47 ]. Although frequent contact with other reef fish seems to potentiate chances of increased microbial diversity and diversity in cleaners, which may protect against infections [ 48 ], further experiments on cleaner fish microbial exchange should be performed to understand the potential risks of cleaning activity and their impacts on reef communities. Goby skin microbiota varies across reef locations Spatial differences in skin microbiota have been reported for vertebrates, such as bats [ 49 ], amphibians [ 50 ], and marine species [ 51 , 52 ], including reef fishes [ 5 , 6 ]. Although our main goal was to examine the relationship between client diversity and microbial diversity, we also observed differences in goby skin microbial composition among reef sites. Interestingly, those differences include contrasting results from fish captured from different reef habitats within less than 500 m of each other. Even though they are located within the same bay, Donkey Bight and West Lameshur differ in coral cover [ 30 ]. Additionally, a mangrove swamp empties out near West Lameshur site, and therefore water quality parameters and reef communities are likely to vary among our sites [ 53 ], leading to differences in fish microbial consortia. In fact, several studies have shown that fish skin microbiota respond to changes in the physicochemical composition of the water [ 11 , 13 ]. Despite host taxonomy being considered one of the main factors influencing fish microbiome [ 5 ], a high skin microbial variability among individuals was found alongside the absence of a core microbiome in the study area for E. evelynae . Indeed, only a small proportion of bacterial taxa (3.5%) was common to all localities (Fig. 2 ), suggesting that local environmental differences might have a significant impact on the structure of goby skin bacteria. Other studies examining microbial composition of marine species between different localities found a core microbiome even between distant locations (e.g., whale blow between Pacific and Atlantic humpback whales, [ 54 ]). The present data shows that cleaner gobies share a considerably low proportion of bacteria even in a small geographic context, suggesting a substantial effect of environment, which may also be responsible for differences in clientele diversity and abundance at each sampled site. Conclusions This study suggests that fish-to-fish direct contact and specifically, access to a diverse clientele, influences the bacterial diversity and structure of cleaner gobies’ skin. However, our study did not control for environmental factors and therefore, the extent to which microbial diversity of cleaner gobies can be influenced by the surrounding environment and social behavior needs to be further explored in controlled experimental conditions. Nonetheless, this study sets the stage for future research using cleaner gobies as models to understand microbial dynamics in coral reefs. Besides the cleaner gobies studied herein, the microbiome of other obligate cleaners such as wrasses in Indo-Pacific reefs and the less studied but highly diverse group of cleaner shrimps [ 22 ], may also be influenced by cleaning behavior, and specifically by client diversity. Given current concerns over reef degradation worldwide and the importance of microbial commensals towards reef resilience, holistic studies examining microbial transfer to and from cleanerfish and other reef fish and the potential cascading effects deriving from such interactions are warranted. Additionally, microbial communities residing in areas surrounding cleaning stations, where fish largely congregate, should also be investigated due to their potential effects to the entire reef holobiont. Declarations Funding Funding was provided by the National Science Foundation awards OCE-2023420 to PCS and OCE-2022955 to AA, and by the European Regional Development Fund (ERDF) through the COMPETE program and by National Funds through Foundation for Science and Technology (project PTDC/BIA-MIC/27995/2017 POCI-01-0145- FEDER-027995) to RX. RX was also supported by Foundation for Science and Technology (FCT) under the Programa Operacional Potencial Humano-Quadro de Referência Estratégico Nacional funds from the European Social Fund and Portuguese Ministério da Educação e Ciência (IF/00359/2015, and 2020.00854.CEECIND). Field data were collected with support from NSF OCE-1536794 to PCS. Conflicts of interest/Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Availability of data and material Raw sequence reads are available in NCBI’s Short Read Archive under accession PRJNA756005. Code availability Not applicable. Ethics approval Fish were collected under permit number DFW18072U from the US Virgin Islands Division of Fish and Wildlife and permit number VIIS-2018-SCI-0008 for sites within the Virgin Islands National Park, and under IACUC ethics protocol number 778227-1, PC Sikkel, PI. Consent to participate Not applicable. Consent for publication Not applicable. Acknowledgements: We thank the Center for Marine and Environmental Studies (CMES) and the Virgin Islands Environmental Resource Station (VIERS) of the University of the Virgin Islands for logistical support. Thanks also to Matthew Nicholson, Gina Hendrick, and Andres Pagan for assisting with field logistics. This work is contribution number 238 from the University of the Virgin Islands Center for Marine and Environmental Studies. References Langmead O, Sheppard C (2004) Coral reef community dynamics and disturbance: a simulation model. Ecological Modelling 175: 271-290. doi: 10.1016/j.ecolmodel.2003.10.019 Vanwonterghem I, Webster NS (2020) Coral Reef Microorganisms in a Changing Climate. iScience 23: 100972. doi: 10.1016/j.isci.2020.100972 Apprill A, Hughen K, Mincer T (2013) Major similarities in the bacterial communities associated with lesioned and healthy Fungiidae corals. Environ Microbiol 15: 2063-2072. doi: 10.1111/1462-2920.12107 Barott KL, Rodriguez-Mueller B, Youle M, Marhaver KL, Vermeij MJ, Smith JE, Rohwer FL (2012) Microbial to reef scale interactions between the reef-building coral Montastraea annularis and benthic algae. Proc Biol Sci 279: 1655-1664. doi: 10.1098/rspb.2011.2155 Chiarello M, Auguet JC, Bettarel Y, Bouvier C, Claverie T, Graham NAJ, Rieuvilleneuve F, Sucre E, Bouvier T, Villeger S (2018) Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome 6: 147. doi: 10.1186/s40168-018-0530-4 Xavier R, Pereira A, Pagan A, Hendrick GC, Nicholson MD, Rosado D, Soares MC, Pérez-Losada M, Sikkel PC (2020) The effects of environment and ontogeny on the skin microbiome of two Stegastes damselfishes (Pomacentridae) from the eastern Caribbean Sea. Marine Biology 167. doi: 10.1007/s00227-020-03717-7 Larsen A, Tao Z, Bullard SA, Arias CR (2013) Diversity of the skin microbiota of fishes: evidence for host species specificity. FEMS Microbiol Ecol 85: 483-494. doi: 10.1111/1574-6941.12136 Chiarello M, Villeger S, Bouvier C, Auguet JC, Bouvier T (2017) Captive bottlenose dolphins and killer whales harbor a species-specific skin microbiota that varies among individuals. Sci Rep 7: 15269. doi: 10.1038/s41598-017-15220-z Chiarello M, Villeger S, Bouvier C, Bettarel Y, Bouvier T (2015) High diversity of skin-associated bacterial communities of marine fishes is promoted by their high variability among body parts, individuals and species. FEMS Microbiol Ecol 91. doi: 10.1093/femsec/fiv061 Huang Q, Sham RC, Deng Y, Mao Y, Wang C, Zhang T, Leung KMY (2020) Diversity of gut microbiomes in marine fishes is shaped by host-related factors. Mol Ecol 29: 5019-5034. doi: 10.1111/mec.15699 Sylvain FE, Holland A, Bouslama S, Audet-Gilbert E, Lavoie C, Val AL, Derome N (2020) Fish Skin and Gut Microbiomes Show Contrasting Signatures of Host Species and Habitat. Appl Environ Microbiol 86. doi: 10.1128/AEM.00789-20 Krotman Y, Yergaliyev TM, Alexander Shani R, Avrahami Y, Szitenberg A (2020) Dissecting the factors shaping fish skin microbiomes in a heterogeneous inland water system. Microbiome 8: 9. doi: 10.1186/s40168-020-0784-5 Rosado D, Perez-Losada M, Pereira A, Severino R, Xavier R (2021) Effects of aging on the skin and gill microbiota of farmed seabass and seabream. Anim Microbiome 3: 10. doi: 10.1186/s42523-020-00072-2 Burns AR, Miller E, Agarwal M, Rolig AS, Milligan-Myhre K, Seredick S, Guillemin K, Bohannan BJM (2017) Interhost dispersal alters microbiome assembly and can overwhelm host innate immunity in an experimental zebrafish model. Proc Natl Acad Sci U S A 114: 11181-11186. doi: 10.1073/pnas.1702511114 Xavier R, Mazzei R, Perez-Losada M, Rosado D, Santos JL, Verissimo A, Soares MC (2019) A Risky Business? Habitat and Social Behavior Impact Skin and Gut Microbiomes in Caribbean Cleaning Gobies. Front Microbiol 10: 716. doi: 10.3389/fmicb.2019.00716 Soares MC, Cable J, Lima‐Maximino MG, Maximino C, Xavier R (2019) Using fish models to investigate the links between microbiome and social behaviour: The next step for translational microbiome research? Fish and Fisheries. doi: 10.1111/faf.12366 Archie EA, Tung J (2015) Social behavior and the microbiome. Current Opinion in Behavioral Sciences 6: 28-34. doi: 10.1016/j.cobeha.2015.07.008 Moeller AH, Foerster S, Wilson ML, Pusey AE, Hahn BH, Ochman H (2016) Social behavior shapes the chimpanzee pan-microbiome. Sci Adv 2: e1500997. doi: 10.1126/sciadv.1500997 Koch H, Schmid-Hempel P (2011) Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc Natl Acad Sci U S A 108: 19288-19292. doi: 10.1073/pnas.1110474108 Schmid-Hempel P (2017) Parasites and Their Social Hosts. Trends Parasitol 33: 453-462. doi: 10.1016/j.pt.2017.01.003 Côté IM, Soares MC (2011) Gobies as cleaners. The Biology of Gobies. Science Publishers, St. Helier, pp. 525 Vaughan DB, Grutter AS, Costello MJ, Hutson KS (2017) Cleaner fishes and shrimp diversity and a re-evaluation of cleaning symbioses. Fish and Fisheries 18: 698-716. doi: 10.1111/faf.12198 Soares MC, Cardoso SC, Côté IM (2007) Client preferences by Caribbean cleaning gobies: food, safety or something else? Behavioral Ecology and Sociobiology 61: 1015-1022. doi: 10.1007/s00265-006-0334-6 Cheney KL, Côté IM (2001) Are Caribbean cleaning symbioses mutualistic? Costs and benefits of visiting cleaning stations to longfin damselfish. Animal Behaviour 62: 927-933. doi: 10.1006/anbe.2001.1832 Sikkel PC, Herzlieb SE, Kramer DL (2005) Compensatory cleaner-seeking behavior following spawning in female yellowtail damselfish. Marine Ecology Progress Series 296: 1-11. doi: 10.3354/meps296001 Sikkel PC, Fuller CA, Hunte W (2000) Habitat/sex differences in time at cleaning stations and ectoparasite loads in a Caribbean reef fish. Marine Ecology Progress Series 193: 191-199. doi: 10.3354/meps193191 Arnal C, Côté IM, Sasal P, Morand S (2000) Cleaner-client interactions on a Caribbean reef: influence of correlates of parasitism. Behavioral Ecology and Sociobiology 47: 353-358. doi: 10.1007/s002650050676 Whittey KE, Dunkley K, Young GC, Cable J, Perkins SE (2021) Microhabitats of sharknose goby ( Elacatinus evelynae ) cleaning stations and their links with cleaning behaviour. Coral Reefs. doi: 10.1007/s00338-021-02105-x Cheney KL, Côté IM (2005) Mutualism or parasitism? The variable outcome of cleaning symbioses. Biol Lett 1: 162-165. doi: 10.1098/rsbl.2004.0288 Artim JM, Nicholson MD, Hendrick GC, Brandt M, Smith TB, Sikkel PC (2020) Abundance of a cryptic generalist parasite reflects degradation of an ecosystem. Ecosphere 11: e03268. doi: 10.1002/ecs2.3268 Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621-1624. doi: 10.1038/ismej.2012.8 Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 79: 5112-5120. doi: 10.1128/AEM.01043-13 Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13: 581-583. doi: 10.1038/nmeth.3869 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41: D590-596. doi: 10.1093/nar/gks1219 Parada AE, Needham DM, Fuhrman JA (2016) Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18: 1403-1414. doi: 10.1111/1462-2920.13023 McMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8: e61217. doi: 10.1371/journal.pone.0061217 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. doi: 10.1186/s13059-014-0550-8 Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5: 169-172. doi: 10.1038/ismej.2010.133 Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2020) vegan: Community Ecology Package. Grutter AS, Bshary R (2003) Cleaner wrasse prefer client mucus: support for partner control mechanisms in cleaning interactions. Proc Biol Sci 270 Suppl 2: S242-244. doi: 10.1098/rsbl.2003.0077 Dunkley K, Ellison AR, Mohammed RS, van Oosterhout C, Whittey KE, Perkins SE, Cable J (2019) Long-term cleaning patterns of the sharknose goby ( Elacatinus evelynae ). Coral Reefs 38: 321-330. doi: 10.1007/s00338-019-01778-9 Pratte ZA, Patin NV, McWhirt ME, Caughman AM, Parris DJ, Stewart FJ (2018) Association with a sea anemone alters the skin microbiome of clownfish. Coral Reefs 37: 1119-1125. doi: 10.1007/s00338-018-01750-z Chiarello M, Auguet JC, Graham NAJ, Claverie T, Sucre E, Bouvier C, Rieuvilleneuve F, Restrepo-Ortiz CX, Bettarel Y, Villeger S, Bouvier T (2020) Exceptional but vulnerable microbial diversity in coral reef animal surface microbiomes. Proc Biol Sci 287: 20200642. doi: 10.1098/rspb.2020.0642 Quimbayo JP, Zapata FA (2018) Cleaning interactions by gobies on a tropical eastern Pacific coral reef. J Fish Biol 92: 1110-1125. doi: 10.1111/jfb.13573 Grutter AS (1996) Parasite removal rates by the cleaner wrasse Labroides dimidiatus . Marine Ecology Progress Series 130: 61-70. doi: 10.3354/meps130061 Sikkel PC, Welicky RL (2019) The Ecological Significance of Parasitic Crustaceans. Parasitic Crustacea, pp. 421-477 Narvaez P, Vaughan DB, Grutter AS, Hutson KS (2021) New perspectives on the role of cleaning symbiosis in the possible transmission of fish diseases. Reviews in Fish Biology and Fisheries 31: 233-251. doi: 10.1007/s11160-021-09642-2 Esteban MÁ (2012) An Overview of the Immunological Defenses in Fish Skin. ISRN Immunology 2012: 1-29. doi: 10.5402/2012/853470 Avena CV, Parfrey LW, Leff JW, Archer HM, Frick WF, Langwig KE, Kilpatrick AM, Powers KE, Foster JT, McKenzie VJ (2016) Deconstructing the Bat Skin Microbiome: Influences of the Host and the Environment. Front Microbiol 7: 1753. doi: 10.3389/fmicb.2016.01753 Ellison S, Rovito S, Parra-Olea G, Vasquez-Almazan C, Flechas SV, Bi K, Vredenburg VT (2019) The Influence of Habitat and Phylogeny on the Skin Microbiome of Amphibians in Guatemala and Mexico. Microb Ecol 78: 257-267. doi: 10.1007/s00248-018-1288-8 Apprill A, Robbins J, Eren AM, Pack AA, Reveillaud J, Mattila D, Moore M, Niemeyer M, Moore KM, Mincer TJ (2014) Humpback whale populations share a core skin bacterial community: towards a health index for marine mammals? PLoS One 9: e90785. doi: 10.1371/journal.pone.0090785 Chiarello M, Paz-Vinas I, Veyssiere C, Santoul F, Loot G, Ferriol J, Bouletreau S (2019) Environmental conditions and neutral processes shape the skin microbiome of European catfish ( Silurus glanis ) populations of Southwestern France. Environ Microbiol Rep 11: 605-614. doi: 10.1111/1758-2229.12774 Becker CC, Weber L, Suca JJ, Llopiz JK, Mooney TA, Apprill A (2020) Microbial and nutrient dynamics in mangrove, reef, and seagrass waters over tidal and diurnal time scales. Aquatic Microbial Ecology 85: 101-119. doi: 10.3354/ame01944 Apprill A, Miller CA, Moore MJ, Durban JW, Fearnbach H, Barrett-Lennard LG (2017) Extensive Core Microbiome in Drone-Captured Whale Blow Supports a Framework for Health Monitoring. mSystems 2. doi: 10.1128/mSystems.00119-17 Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Sep, 2021 Reviewers invited by journal 24 Aug, 2021 Editor assigned by journal 20 Aug, 2021 First submitted to journal 18 Aug, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-828485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":48169031,"identity":"1d9e321e-0142-4ad5-8895-09d32c76ca6f","order_by":0,"name":"Ana Pereira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYPADAwYGfhCdUEBA4QFkLZINIC0GRGsB6ToA1YsL8M9ufvb4Y9sdOfnZzcc+fCi4J298fnXihwcGDPL8YgewapG4c8zc4GDbM2ODO8eSZ84wKDbcduPtZgmgwwxnzk7Abs2NBDOJg22HEzdI5Bgz8xgkMG67cXYDSEuCwW3sWuRvpH8DaamfPyP/M/MfgwT7zTPObv6BT4vBjRywLQkMN3KYmRkMEhI38Pduw2uL4Y2cMokz5w4bbrhzzJixxyAhecYN3m0WCQYSOP0idyN9m0RF2WF5YIg9ZvjxJ8G2v//s5ps/Kmzk+aVxeB8OJOCMBBQuMVr4DxChehSMglEwCkYSAADuN2Zp35OvsAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5328-1668","institution":"CIBIO: Universidade do Porto Centro de Investigacao em Biodiversidade e Recursos Geneticos","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"","lastName":"Pereira","suffix":""},{"id":48169032,"identity":"bdd48245-49a3-4ccd-a192-1ad76eed2900","order_by":1,"name":"Marta C. Soares","email":"","orcid":"","institution":"University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"C.","lastName":"Soares","suffix":""},{"id":48169033,"identity":"fec60d36-8784-488f-8022-7a3384b6a94c","order_by":2,"name":"Teresa Santos","email":"","orcid":"","institution":"University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Santos","suffix":""},{"id":48169034,"identity":"360e2752-8550-448c-b23d-0a8cfac58177","order_by":3,"name":"Ana Poças","email":"","orcid":"","institution":"University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Poças","suffix":""},{"id":48169035,"identity":"09c5d993-4946-43ad-863e-28916c33f3f5","order_by":4,"name":"Marcos Pérez-Losada","email":"","orcid":"","institution":"University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Pérez-Losada","suffix":""},{"id":48169036,"identity":"6dd94c9a-ec2d-4917-af64-852da3c25e9d","order_by":5,"name":"Amy Apprill","email":"","orcid":"","institution":"Woods Hole Oceanographic Institution, Woods Hole","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Apprill","suffix":""},{"id":48169037,"identity":"9660931c-03f9-42f5-b280-839a04c76f13","order_by":6,"name":"Paul C. Sikkel","email":"","orcid":"","institution":"Rosenstiel School of Marine and Atmospheric Sciences","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"C.","lastName":"Sikkel","suffix":""},{"id":48169038,"identity":"d9e9b25f-8a8d-439b-805a-1a55ecf9936d","order_by":7,"name":"Raquel Xavier","email":"","orcid":"","institution":"University of Porto","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Xavier","suffix":""}],"badges":[],"createdAt":"2021-08-20 00:49:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-828485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-828485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12845413,"identity":"2d20bcc8-afcd-45b7-a86b-93a916b944ba","added_by":"auto","created_at":"2021-08-27 20:30:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2483800,"visible":true,"origin":"","legend":"Sampling sites located in the US Virgin Islands. Map created on ArcGIS software by Esri","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/5b4c042eefde1377f7e35156.png"},{"id":12845469,"identity":"7fe818c2-ee5e-4f63-a681-0d5d5de585b8","added_by":"auto","created_at":"2021-08-27 20:33:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":484669,"visible":true,"origin":"","legend":"Venn diagram with the number of shared and unique ASVs amongst localities","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/8d8e631ce18c8ad02eb45d02.png"},{"id":12845416,"identity":"ffbfdcef-b704-4217-820a-da873af7b575","added_by":"auto","created_at":"2021-08-27 20:30:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":976354,"visible":true,"origin":"","legend":"Boxplots of the alpha-diversity measures for each locality with Tukey's HSD significance for pairwise differences. * indicates significant differences","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/760f584282857f8fc53181bf.png"},{"id":12845418,"identity":"27984871-aedb-437a-beb3-c3e7065eda38","added_by":"auto","created_at":"2021-08-27 20:30:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110229,"visible":true,"origin":"","legend":"Linear regression plots depicting alpha diversity measures versus each of the observed cleaning variables","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/4eb8fdb317325e369836d3fe.png"},{"id":12845415,"identity":"a254b9b8-bd36-4c42-ba1b-dae5802ba7ee","added_by":"auto","created_at":"2021-08-27 20:30:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":427777,"visible":true,"origin":"","legend":"a), b), c) PCoA plots with beta-diversity distances grouped by locality with 95% confidence interval ellipse, d), e), f) beta dispersion represented by distance to centroid for each beta diversity measure","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/0e529f3fca66a820f89aed53.png"},{"id":15675104,"identity":"add6a533-bd3b-4eaa-a4ea-c004d1a636cb","added_by":"auto","created_at":"2021-11-18 14:26:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2175860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/27dc7b08-e799-4a27-baa4-9a312e25052b.pdf"},{"id":12845419,"identity":"fb644421-f277-4363-940c-5db8c202eef2","added_by":"auto","created_at":"2021-08-27 20:30:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":56670,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-828485/v1/8632495bfd4651391941b69d.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHabitat and Client Diversity Influence the Skin Microbiome of the Caribbean Cleaner Goby \u003cem\u003eElacatinus Evelynae\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoral reefs are highly complex marine ecosystems that have been the focus of numerous studies examining the drivers of biodiversity and community dynamics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As in other ecosystems, coral reef microorganisms are emerging as key members to maintaining reef health and resilience in the face of large-scale degradation due to climate change, human impacts and emerging diseases of corals (reviewed by [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). This has resulted in increased efforts to characterize diseases of reef microbial communities, and to identify which organisms influence resilience and recovery (e.g. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]). Recent studies have shown the importance of interactions between the coral microbiome and the larger reef community (e.g. between reef-building coral and benthic algae, [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]). However, relatively few studies have examined the microbiome associated with the most mobile members of the reef community, such as fishes (e.g. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eFish microbial communities are known to be affected by multiple biotic and abiotic variables. There is evidence that fish-associated bacteria are organ-specific, species-specific and individual specific, thus comprising highly diverse communities [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While host factors seem to be the major drivers of fish gut bacterial diversity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the physicochemical properties of the water exert a considerable effect on the diversity of fish skin-associated microbes (e.g. temperature and salinity, [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]). Despite the impact of the surrounding environment, the contribution of the water microbiota to the fish skin microbiota composition seems negligible, with fish mucosae being a highly selective environment (e.g. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]). However, other external factors, such as direct transfer of microorganisms between fishes, might also play a major and still unexplored role [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Microbial transfer between interacting partners has been shown to be common in nature, shaping microbial consortia in humans and other animal groups (reviewed in [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), including fish [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although social microbial transmission could ultimately increase microbiome complexity, which may reduce the abundance of opportunistic and/or pathogenic taxa, as seen in bees [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or chimpanzees [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], social interactions may also facilitate pathogen transmission and consequently increase levels of infection and disease (reviewed by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eOne of the most iconic mutualistic interactions on coral reefs is the relationship between cleanerfishes and clients. Cleaners attract individuals from multiple species (clients) to their \u0026ldquo;cleaning stations\u0026rdquo;, which are usually fixed territories where they inspect the body of multiple client fishes per day to remove parasites, dead tissue and mucus [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although many small fish species clean opportunistically, members of two genera are obligate or \u0026ldquo;dedicated\u0026rdquo; cleaners [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These include the cleaner wrasses (\u003cem\u003eLabroides\u003c/em\u003e spp.) in the Indo-Pacific and the cleaner gobies (\u003cem\u003eElacatinus\u003c/em\u003e spp.) in the Caribbean region. Cleaner gobies of the genus \u003cem\u003eElacatinus\u003c/em\u003e reside on benthic substrate, moving only to make contact with client fishes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which travel and interrupt other activities to visit cleaner gobies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Visits to cleaner goby stations can be influenced by multiple factors including location relative to territorial client fish [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], local fish abundance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], structural complexity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and parasite activity and abundance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, the abundance and diversity of client fishes can vary widely among cleaning stations. Because of their frequent contact with heterospecifics, and therefore, high potential for microbial exchange, cleanerfish may serve as a useful animal model system to understand the role of social microbial transfer in ecological communities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Indeed, a recent study comparing \u0026ldquo;cleaner\u0026rdquo; vs \u0026ldquo;non-cleaner\u0026rdquo; ecotypes of \u003cem\u003eE. prochilos\u003c/em\u003e from Barbados found that bacterial diversity was significantly increased in \u0026ldquo;cleaner\u0026rdquo; ecotypes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we characterized the skin bacteria of the most ubiquitous Caribbean cleaner goby species, the sharknose goby \u003cem\u003eElacatinus evelynae\u003c/em\u003e, in several reefs within the US Virgin Islands, using 16S rRNA gene (V4 hypervariable region) amplicon sequencing. We specifically studied the relationship between gobies\u0026rsquo; level of interaction with clients, and skin microbiota diversity and composition. We expected to find a relationship between microbial diversity and client diversity and geographical differences in the skin microbiota among reefs due to putative socio-environmental differences.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy species, sites, and behavioral observations\u003c/h2\u003e \u003cp\u003eThis study was conducted in the United States Virgin Islands in July 2017. All behavioral observations were performed at four sampling localities: two sites on the Island of St. Thomas: Brewers Bay (18.339998, -64.977417; n\u0026thinsp;=\u0026thinsp;7 cleaner gobies) and Hull Bay (18.369340, -64.952925; n\u0026thinsp;=\u0026thinsp;13), and two on the island of St. John: one along the west rim of Greater Lameshur Bay at West Lameshur (18.317928, -64.723946; n\u0026thinsp;=\u0026thinsp;12) and another along the east rim at Donkey Bight (18.314054, -64.720945; n\u0026thinsp;=\u0026thinsp;12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Donkey Bight is dominated by a mix of rocky reef, live and dead coral, sand, and seagrass, while West Lameshur is highly degraded with almost no live coral, and it is located near a mangrove swamp [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe focused on the sharknose goby \u003cem\u003eElacatinus evelynae\u003c/em\u003e, which are small fish (1.2\u0026ndash;3.5 cm total length) with a prominent lateral blue and yellow stripe running from the snout to the base of the tail. This species is common across the study reef sites, inhabiting the surface of living coral, usually \u003cem\u003eSiderastrea\u003c/em\u003e spp., \u003cem\u003eOrbicella\u003c/em\u003e spp., \u003cem\u003eMontastrea\u003c/em\u003e spp., \u003cem\u003eDiploria\u003c/em\u003e spp., and \u003cem\u003ePseudodiploria\u003c/em\u003e spp. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cleaning stations were identified, and a subset was randomly selected for this study. Observations of cleaning interactions were made by two snorkelers positioned as far from the station as possible while still being able to see cleaning interactions (at least 2 m). Individual gobies were observed for 30 min at each location three times a day (at dawn, midday and dusk, totaling 1h30min for each cleaner) encompassing the hours during which \u003cem\u003eE. evelynae\u003c/em\u003e are more active [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Observations were registered after a 2\u0026ndash;5 min delay to allow the fish to become accustomed to the presence of the observer. During each observation period, the number of clients visiting each cleaning station, number of clients inspected, number of client genera visiting, number of client genera inspected, and average inspection time were recorded.\u003c/p\u003e \u003cp\u003eFollowing the final observation, cleaner gobies were captured using individual hand nets. Nets were submerged in a 30% bleach solution and rinsed with fresh water prior to each use. Upon capture, both sides of each fish were swabbed with tubed sterile cotton swabs (MedicalWire), which were stored at -80\u0026deg;C until DNA extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory procedures\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted using the PowerSoil extraction kit and following manufacturer\u0026rsquo;s protocol. Extracted DNA was shipped in dry ice to the Centre for Microbial Systems at the University of Michigan Medical School (USA) where the V4 hypervariable region of the 16S rRNA gene (~\u0026thinsp;250 bp) was amplified for each sample and controls (i.e. extraction and PCR blanks) with the primers 515F/806R [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and sequenced using a dual indexing sequencing strategy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Libraries were pooled and sequenced in a single Illumina MiSeq sequencing run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eRaw FASTQ files were analyzed using the DADA2 pipeline [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] for merging paired end reads, filtering and sequencing error correction using the following parameters: trimLeft\u0026thinsp;=\u0026thinsp;20, truncLen\u0026thinsp;=\u0026thinsp;c(220, 200), maxN\u0026thinsp;=\u0026thinsp;0, maxEE\u0026thinsp;=\u0026thinsp;c(2,2), truncQ\u0026thinsp;=\u0026thinsp;2. Singletons were discarded and reads were collapsed into amplicon sequence variants (ASVs). Taxonomy was determined against the SILVA reference database (release 132) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. ASVs present in PCR and extraction blanks, that remained unclassified or were classified as Mitochondria (identified as Family) and Chloroplast (identified as Class) were removed from the dataset. Archaea were also excluded since the primers used are known to discriminate against this group in the marine environment [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. An ASV frequency table was constructed with the R package \u003cem\u003ephyloseq\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and read normalized counts were obtained via the negative binomial distribution implemented in DESeq2 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Raw sequence reads were deposited into NCBI\u0026rsquo;s Short Read Archive under accession PRJNA756005.\u003c/p\u003e \u003cp\u003eAlpha-diversity (intra-sample) was estimated using Shannon, Fisher and Faith\u0026rsquo;s Phylogenetic Diversity (PD) indices using the R package \u003cem\u003ephyloseq\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Alpha-diversity differences among localities were tested using Generalized Linear Models (GLMs) and post-hoc comparisons were evaluated with Tukey\u0026rsquo;s HSD test. To test the effect of each cleaning behavior on microbial alpha-diversity, Linear Mixed Models (LME) were performed using the number of clients visiting each cleaning station, number of clients inspected, number of client genera visiting, number of client genera inspected and average inspection time for each model as fixed factors (predictors). Locality was included as a random factor and models were built using the R package \u003cem\u003elmer.\u003c/em\u003e The significance of GLM and LME models was estimated using ANOVA of type III with Satterthwaite approximation for degrees of freedom. Beta-diversity (inter-samples) was estimated using phylogenetic informed weighted and unweighted Unifrac [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Bray Curtis (BC) indices. Dissimilarity in microbial structure among reef locations was visualized using principal coordinates analysis (PCoA). The homogeneity of beta diversity dispersion among localities was also assessed by first calculating the average distance to the sample group centroid using the \u003cem\u003ebetadisper\u003c/em\u003e function of the \u003cem\u003evegan\u003c/em\u003e R package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and then compared using a permutation test. Tukey\u0026rsquo;s HSD test was used for post-hoc comparisons. Differences in microbial structure among reefs were then tested with a PERMANOVA with the strata option for locality and 9999 permutations, as implemented in the \u003cem\u003eadonis\u003c/em\u003e function of the \u003cem\u003evegan\u003c/em\u003e package. Post-hoc comparisons were performed using a pairwise PERMANOVA with the Bonferroni p-value correction for multiple testing. Additionally, the differences in the abundances of phyla or genera represented by \u0026ge;\u0026thinsp;3% on average of all sequences were also assessed among reef locations and cleaning activity using the same GLM and LME structure described above. A Venn diagram was used to depict the number of ASVs shared among reef locations.\u003c/p\u003e \u003cp\u003eA dissimilarity matrix with the client genera inspected by each goby was constructed and differences among localities were also tested with a PERMANOVA using the BC index. To test the correlation between cleaning activity and the microbial community of each locality, Mantel statistical correlations based on Spearman's rank were performed between the number of clients visiting the cleaning station, number of clients inspected, number of client genera visiting and inspected and the beta-diversity distance matrices using the \u003cem\u003evegan\u003c/em\u003e R package. For all tests, differences were considered significant when P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eClient composition and cleaning activity\u003c/h2\u003e\n \u003cp\u003eA total of 44 fish species belonging to 29 genera and 14 families visited the observed cleaning stations (Supplementary Table S1). The striped parrotfish (\u003cem\u003eScarus iseri\u003c/em\u003e) was the most common client fish in Brewers Bay (22% of the total visits in that reef), the yellow goatfish (\u003cem\u003eMulloidichthys martinicus\u003c/em\u003e) in Hull Bay (33%), the ocean surgeonfish (\u003cem\u003eAcanthurus bahianus\u003c/em\u003e) in Donkey Bight (20%) and the longfin damselfish (\u003cem\u003eStegastes diencaeus\u003c/em\u003e) in West Lameshur (19%) (Supplementary Table S1). Client fish genera composition was significantly different among reefs (PERMANOVA, F\u0026thinsp;=\u0026thinsp;2.11, p\u0026thinsp;=\u0026thinsp;0.001), and the pairwise differences were significant between Donkey Bight and West Lameshur (both located in St John) (F\u0026thinsp;=\u0026thinsp;3.72, p\u0026thinsp;=\u0026thinsp;0.03) and also between West Lameshur and Hull Bay (F\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;=\u0026thinsp;0.03). Cleaner gobies from Donkey Bight and West Lameshur inspected the highest number of client genera (Mean (M)\u0026thinsp;=\u0026thinsp;4.9, Standard Deviation (SD)\u0026thinsp;=\u0026thinsp;2.4 and M\u0026thinsp;=\u0026thinsp;4.8, SD\u0026thinsp;=\u0026thinsp;2.2 respectively; M\u0026thinsp;=\u0026thinsp;4.0, SD\u0026thinsp;=\u0026thinsp;0.8 in Brewers Bay and M\u0026thinsp;=\u0026thinsp;2.6, SD\u0026thinsp;=\u0026thinsp;1.6 in Hull Bay). Moreover, dawn was the observation period with the highest cleaning activity (see Supplementary Table S2).\u003c/p\u003e\n \u003ch2\u003eThe skin microbiome of the cleaner goby \u003cem\u003eElacatinus evelynae\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eBacteria-associated 16S rRNA amplicons present in the skin of 44 \u003cem\u003eE. evelynae\u003c/em\u003e cleaner gobies from four reefs in the US Virgin Islands (Brewers Bay, Hull Bay, Donkey Bight and West Lameshur) were sequenced, generating 1,245,579 raw reads, 1,099,501 filtered sequences and 1,222 ASVs. From those, 223 ASVs were present in Brewers Bay, 586 ASVs in Donkey Bight, 341 ASVs in West Lameshur, and 457 ASVs in Hull Bay (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The most abundant bacterial phyla (\u0026ge;\u0026thinsp;3%) across all samples were Proteobacteria (58%), Bacteroidota (16%), Firmicutes (7%), Actinobacteriota (4%) and Cyanobacteria (4%). \u003cem\u003ePseudomonas\u003c/em\u003e was the most abundant genus in Brewers Bay (17.7%), \u003cem\u003eEkhidna\u003c/em\u003e in Hull Bay (14.8%), and \u003cem\u003ePsychrobacter\u003c/em\u003e in Donkey Bight and West Lameshur (15.7% and 15.5% respectively). The most abundant genera (\u0026ge;\u0026thinsp;3%) found for each locality are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Differences in phyla abundance among localities were not found (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while significant differences were found for the genera \u003cem\u003eAlcanivorax\u003c/em\u003e, \u003cem\u003eCloacibacterium\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e and an unclassified genus from Pseudomonadaceae family (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04) (Supplementary Table S3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePercentage of the most abundant bacterial taxa collapsed by phyla and genera for each reef location. Values in bold represent an abundance\u0026thinsp;\u0026ge;\u0026thinsp;3%.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e% of sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacterial taxa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrewers Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHull Bay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonkey Bight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Lameshur\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhyla\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eActinobacteriota\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroidota\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBdellovibrionota\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCyanobacteria\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePlanctomycetota\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenera\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAlcanivorax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCloacibacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEkhidna\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEndozoicomonas\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterovibrio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHalomonas\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMarinobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMycoplasma\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS5 marine group (Flavobacteriaceae)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOM27 (Bdellovibrionaceae)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychrobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStenotrophomonas\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSynechococcus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified Neisseriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified Pseudomonadaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified Rhodobacteraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAlthough a total of 43 ASVs were common among reefs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), no single ASV was present in all sampled fish. Nevertheless, two ASVs identified as \u003cem\u003eRubrobacter\u003c/em\u003e sp. and \u003cem\u003eCloacibacterium\u003c/em\u003e sp. were found in all samples from Donkey Bight (n\u0026thinsp;=\u0026thinsp;12), therefore constituting the core microbiota in that locality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eVariation of the skin microbiome of cleaner gobies with cleaning activity\u003c/h2\u003e\n \u003cp\u003eAlpha diversity of the bacterial communities associated with the cleaner goby skin was positively (i.e., increasingly) correlated to the number of clients and client genera visiting the cleaning station and inspected for all reefs except for West Lameshur, which showed an inverted pattern, i.e., higher number and diversity of clients corresponded to lower values of alpha diversity (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). However, none of the trends were statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.24, Supplementary Table S3). Moreover, average inspection time did not correlate with alpha-diversity (p\u0026thinsp;\u0026gt;\u0026thinsp;0.15, Supplementary Table S3).\u003c/p\u003e\n \u003cp\u003eBeta diversity was not explained by the cleaning activity variables (i.e., the number of clients visiting the cleaning station, number of clients inspected, average inspection time, number of client genera visiting and inspected) with all beta diversity indices (p\u0026thinsp;\u0026gt;\u0026thinsp;0.08, Supplementary Table S3). However, there was a significant positive correlation between cleaner gobies\u0026rsquo; microbial beta-diversity using the Unweighted Unifrac distance and the number of client genera inspected in Brewers Bay and Hull Bay (Mantel test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02, Supplementary Table S4). In Donkey Bight, there was also a positive correlation between goby microbial beta-diversity using the BC index and number of client genera visiting and inspected (Mantel test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.003, Supplementary Table S4) and the same correlation was found in West Lameshur with the Weighted Unifrac distance (Mantel test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.02, Supplementary Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eDiversity of the skin microbiome of cleaner gobies across reef locations\u003c/h2\u003e\n \u003cp\u003eSkin bacterial alpha-diversity was significantly different among reef localities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02, Supplementary Table S3), with pairwise comparisons showing that cleaner gobies from Donkey Bight harbored significantly higher alpha diversity compared to those from the other localities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). No differences were observed in the alpha diversities of individuals from the remaining locations.\u003c/p\u003e\n \u003cp\u003eBacterial community structure (beta-diversity) was significantly different amongst reef localities with all beta diversity indices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.003), with significant pairwise differences between Donkey Bight and each of the other reef sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea,b,c and Supplementary Table S5). Beta dispersion was also significantly different among locations considering the Bray Curtis (F\u0026thinsp;=\u0026thinsp;5.94, p\u0026thinsp;=\u0026thinsp;0.002; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed) and weighted Unifrac (F\u0026thinsp;=\u0026thinsp;6.97, p\u0026thinsp;=\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee) indices. Pairwise comparisons of beta dispersion for the Bray Curtis index showed differences between Donkey Bight and West Lameshur, as well as between Donkey Bight and Hull Bay (TukeyHSD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.003; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). For the weighted Unifrac distance, differences were found between Donkey Bight and all remaining localities (Tukey HSD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.03; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCleaning stations have been shown to attract a wide diversity of fish species and thus, enhance local reef fish biodiversity and abundance (e.g. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]). Because of the direct physical contact between cleaners and clients, there is the potential for cleaning stations to act as hubs for microbial exchange between fish. In this study, we used a 16S rRNA gene amplicon sequencing approach to test whether clientele diversity was associated with differences in the skin microbiome of the cleaner goby \u003cem\u003eE. evelynae\u003c/em\u003e in the US Virgin Islands. Our results showed differences in alpha- and beta-diversity amongst gobies from different sampled reefs with few shared ASVs among them and high inter-individual variation in microbiota diversity and structure. Overall, the results showed increasing bacterial alpha diversity with the number of clients and client genera inspected (except in West Lameshur), as well as a positive correlation between beta-diversity and clientele diversity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGoby cleaning activity impacts skin microbial diversity\u003c/h2\u003e \u003cp\u003eAccess by gobies to different reef fish species might be shaped by the level of reef degradation and therefore influence cleaning interactions and consequently, goby microbiome. Our results showed a positive correlation between goby skin bacterial beta-diversity and the diversity of clientele inspected (i.e., number of client genera) in all sampled reefs. Moreover, differences in clientele diversity visiting cleaning stations were also observed in our study. For example, although diversity of client species was high at West Lameshur, the most common clients were \u003cem\u003eStegastes\u003c/em\u003e damselfish, which are territorial fish only visiting cleaning stations within their territories [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], but usually less parasitized [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The territorial behavior of these fish can also influence which other potential client species gain access to cleaning stations. By contrast, the remaining sites showed higher visitation rates of larger fish species, such as striped parrotfish (\u003cem\u003eScarus iseri\u003c/em\u003e) in Brewers Bay, yellow goatfish (\u003cem\u003eMulloidichthys martinicus\u003c/em\u003e) in Hull Bay and ocean surgeonfish (\u003cem\u003eAcanthurus bahianus\u003c/em\u003e) in Donkey Bight, all of which are \u0026ldquo;preferred\u0026rdquo; clients, likely due to larger body size and thus higher parasite burden [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Larger clients, therefore, engage in longer cleaning interactions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which could increase bacterial transfer. However, in our study only client diversity was positively correlated with bacterial structure in the skin of cleaner gobies, indicating a stronger effect of client diversity compared to duration of inspection.\u003c/p\u003e \u003cp\u003eRecent studies have shown microbial changes between fish participants of symbiotic relationships. For example, microbial composition of clownfish mucus changes with contact with its anemone host [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similarly, a \u0026ldquo;cleaner\u0026rdquo; ecotype of the Barbadian broadstripe cleaner goby \u003cem\u003eElacatinus prochilos\u003c/em\u003e harbored higher skin microbiota diversity than \u0026ldquo;non-cleaner\u0026rdquo; ecotypes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Microbial interhost dispersal in zebrafish has also shown to influence diversity and composition of microbial communities affecting host immune system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the mechanisms involved in those changes are not well understood. Despite the high diversity of clients visiting cleaning stations in West Lameshur, not only did cleaners from that location have similar bacterial alpha-diversity levels to the ones from Brewers Bay and Hull Bay, but they also showed a contrasting relation between microbial diversity and cleaning activity when compared to the gobies in the other reefs. A possible explanation for the differences found in West Lameshur might be related to the greater habitat degradation at this sampling locality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which could have altered local reef community dynamics. Although reef animal communities harbor some of the most diverse microbial communities of the marine environment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], it is estimated that the changes in host communities in a given location may impact microbial diversity in the entire reef [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Cleaner gobies inspect multiple client fish per day and engage in direct fish-to-fish contact [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although this creates the opportunity for exchange of microbes, given the interspecific nature of the interactions, actual exchange and persistence of microbes cannot be assumed. Our data, while correlative, supports this hypothesis (i.e., microbial exchange increases with cleaning activity). The alternative explanation, that cleaners with more diverse microbiota attract a more diverse array of clients, seems less likely but cannot be ruled out without experimental manipulation.\u003c/p\u003e \u003cp\u003eCleanerfish have been shown to remove significant numbers of ectoparasites from hosts [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which could otherwise compromise client welfare by causing skin damage, feeding on blood, and acting as vectors for diseases (reviewed by [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]). Nonetheless, the gain of a seemingly easy meal for cleaners (client-gleaned ectoparasites and mucous) may come with a price: while obviously predatory clients may eat the cleaners [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], less obvious is the fact that clients may also be vectors of parasites, bacterial contamination, and consequently disease to cleaners [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Although frequent contact with other reef fish seems to potentiate chances of increased microbial diversity and diversity in cleaners, which may protect against infections [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], further experiments on cleaner fish microbial exchange should be performed to understand the potential risks of cleaning activity and their impacts on reef communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGoby skin microbiota varies across reef locations\u003c/h2\u003e \u003cp\u003eSpatial differences in skin microbiota have been reported for vertebrates, such as bats [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], amphibians [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and marine species [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], including reef fishes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although our main goal was to examine the relationship between client diversity and microbial diversity, we also observed differences in goby skin microbial composition among reef sites. Interestingly, those differences include contrasting results from fish captured from different reef habitats within less than 500 m of each other. Even though they are located within the same bay, Donkey Bight and West Lameshur differ in coral cover [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, a mangrove swamp empties out near West Lameshur site, and therefore water quality parameters and reef communities are likely to vary among our sites [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], leading to differences in fish microbial consortia. In fact, several studies have shown that fish skin microbiota respond to changes in the physicochemical composition of the water [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite host taxonomy being considered one of the main factors influencing fish microbiome [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], a high skin microbial variability among individuals was found alongside the absence of a core microbiome in the study area for \u003cem\u003eE. evelynae\u003c/em\u003e. Indeed, only a small proportion of bacterial taxa (3.5%) was common to all localities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that local environmental differences might have a significant impact on the structure of goby skin bacteria. Other studies examining microbial composition of marine species between different localities found a core microbiome even between distant locations (e.g., whale blow between Pacific and Atlantic humpback whales, [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]). The present data shows that cleaner gobies share a considerably low proportion of bacteria even in a small geographic context, suggesting a substantial effect of environment, which may also be responsible for differences in clientele diversity and abundance at each sampled site.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study suggests that fish-to-fish direct contact and specifically, access to a diverse clientele, influences the bacterial diversity and structure of cleaner gobies\u0026rsquo; skin. However, our study did not control for environmental factors and therefore, the extent to which microbial diversity of cleaner gobies can be influenced by the surrounding environment and social behavior needs to be further explored in controlled experimental conditions. Nonetheless, this study sets the stage for future research using cleaner gobies as models to understand microbial dynamics in coral reefs. Besides the cleaner gobies studied herein, the microbiome of other obligate cleaners such as wrasses in Indo-Pacific reefs and the less studied but highly diverse group of cleaner shrimps [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], may also be influenced by cleaning behavior, and specifically by client diversity. Given current concerns over reef degradation worldwide and the importance of microbial commensals towards reef resilience, holistic studies examining microbial transfer to and from cleanerfish and other reef fish and the potential cascading effects deriving from such interactions are warranted. Additionally, microbial communities residing in areas surrounding cleaning stations, where fish largely congregate, should also be investigated due to their potential effects to the entire reef holobiont.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by the National Science Foundation awards OCE-2023420 to PCS and OCE-2022955 to AA, and by the European Regional Development Fund (ERDF) through the COMPETE program and by National Funds through Foundation for Science and Technology (project PTDC/BIA-MIC/27995/2017 POCI-01-0145- FEDER-027995) to RX. RX was also supported by Foundation for Science and Technology (FCT) under the Programa Operacional Potencial Humano-Quadro de Refer\u0026ecirc;ncia Estrat\u0026eacute;gico Nacional funds from the European Social Fund and Portuguese Minist\u0026eacute;rio da Educa\u0026ccedil;\u0026atilde;o e Ci\u0026ecirc;ncia (IF/00359/2015, and 2020.00854.CEECIND). Field data were collected with support from NSF OCE-1536794 to PCS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequence reads are available in NCBI\u0026rsquo;s Short Read Archive under accession PRJNA756005.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish were collected under permit number DFW18072U from the US Virgin Islands Division of Fish and Wildlife and permit number VIIS-2018-SCI-0008 for sites within the Virgin Islands National Park, and under IACUC ethics protocol number 778227-1, PC Sikkel, PI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank the Center for Marine and Environmental Studies (CMES) and the Virgin Islands Environmental Resource Station (VIERS) of the University of the Virgin Islands for logistical support. Thanks also to Matthew Nicholson, Gina Hendrick, and Andres Pagan for assisting with field logistics. This work is contribution number 238 from the University of the Virgin Islands Center for Marine and Environmental Studies.\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLangmead O, Sheppard C (2004) Coral reef community dynamics and disturbance: a simulation model. Ecological Modelling 175: 271-290. doi: 10.1016/j.ecolmodel.2003.10.019\u003c/li\u003e\n\u003cli\u003eVanwonterghem I, Webster NS (2020) Coral Reef Microorganisms in a Changing Climate. iScience 23: 100972. doi: 10.1016/j.isci.2020.100972\u003c/li\u003e\n\u003cli\u003eApprill A, Hughen K, Mincer T (2013) Major similarities in the bacterial communities associated with lesioned and healthy Fungiidae corals. Environ Microbiol 15: 2063-2072. doi: 10.1111/1462-2920.12107\u003c/li\u003e\n\u003cli\u003eBarott KL, Rodriguez-Mueller B, Youle M, Marhaver KL, Vermeij MJ, Smith JE, Rohwer FL (2012) Microbial to reef scale interactions between the reef-building coral Montastraea annularis and benthic algae. Proc Biol Sci 279: 1655-1664. doi: 10.1098/rspb.2011.2155\u003c/li\u003e\n\u003cli\u003eChiarello M, Auguet JC, Bettarel Y, Bouvier C, Claverie T, Graham NAJ, Rieuvilleneuve F, Sucre E, Bouvier T, Villeger S (2018) Skin microbiome of coral reef fish is highly variable and driven by host phylogeny and diet. Microbiome 6: 147. doi: 10.1186/s40168-018-0530-4\u003c/li\u003e\n\u003cli\u003eXavier R, Pereira A, Pagan A, Hendrick GC, Nicholson MD, Rosado D, Soares MC, P\u0026eacute;rez-Losada M, Sikkel PC (2020) The effects of environment and ontogeny on the skin microbiome of two \u003cem\u003eStegastes damselfishes\u003c/em\u003e (Pomacentridae) from the eastern Caribbean Sea. Marine Biology 167. doi: 10.1007/s00227-020-03717-7\u003c/li\u003e\n\u003cli\u003eLarsen A, Tao Z, Bullard SA, Arias CR (2013) Diversity of the skin microbiota of fishes: evidence for host species specificity. FEMS Microbiol Ecol 85: 483-494. doi: 10.1111/1574-6941.12136\u003c/li\u003e\n\u003cli\u003eChiarello M, Villeger S, Bouvier C, Auguet JC, Bouvier T (2017) Captive bottlenose dolphins and killer whales harbor a species-specific skin microbiota that varies among individuals. Sci Rep 7: 15269. doi: 10.1038/s41598-017-15220-z\u003c/li\u003e\n\u003cli\u003eChiarello M, Villeger S, Bouvier C, Bettarel Y, Bouvier T (2015) High diversity of skin-associated bacterial communities of marine fishes is promoted by their high variability among body parts, individuals and species. FEMS Microbiol Ecol 91. doi: 10.1093/femsec/fiv061\u003c/li\u003e\n\u003cli\u003eHuang Q, Sham RC, Deng Y, Mao Y, Wang C, Zhang T, Leung KMY (2020) Diversity of gut microbiomes in marine fishes is shaped by host-related factors. Mol Ecol 29: 5019-5034. doi: 10.1111/mec.15699\u003c/li\u003e\n\u003cli\u003eSylvain FE, Holland A, Bouslama S, Audet-Gilbert E, Lavoie C, Val AL, Derome N (2020) Fish Skin and Gut Microbiomes Show Contrasting Signatures of Host Species and Habitat. Appl Environ Microbiol 86. doi: 10.1128/AEM.00789-20\u003c/li\u003e\n\u003cli\u003eKrotman Y, Yergaliyev TM, Alexander Shani R, Avrahami Y, Szitenberg A (2020) Dissecting the factors shaping fish skin microbiomes in a heterogeneous inland water system. Microbiome 8: 9. doi: 10.1186/s40168-020-0784-5\u003c/li\u003e\n\u003cli\u003eRosado D, Perez-Losada M, Pereira A, Severino R, Xavier R (2021) Effects of aging on the skin and gill microbiota of farmed seabass and seabream. Anim Microbiome 3: 10. doi: 10.1186/s42523-020-00072-2\u003c/li\u003e\n\u003cli\u003eBurns AR, Miller E, Agarwal M, Rolig AS, Milligan-Myhre K, Seredick S, Guillemin K, Bohannan BJM (2017) Interhost dispersal alters microbiome assembly and can overwhelm host innate immunity in an experimental zebrafish model. Proc Natl Acad Sci U S A 114: 11181-11186. doi: 10.1073/pnas.1702511114\u003c/li\u003e\n\u003cli\u003eXavier R, Mazzei R, Perez-Losada M, Rosado D, Santos JL, Verissimo A, Soares MC (2019) A Risky Business? Habitat and Social Behavior Impact Skin and Gut Microbiomes in Caribbean Cleaning Gobies. Front Microbiol 10: 716. doi: 10.3389/fmicb.2019.00716\u003c/li\u003e\n\u003cli\u003eSoares MC, Cable J, Lima‐Maximino MG, Maximino C, Xavier R (2019) Using fish models to investigate the links between microbiome and social behaviour: The next step for translational microbiome research? Fish and Fisheries. doi: 10.1111/faf.12366\u003c/li\u003e\n\u003cli\u003eArchie EA, Tung J (2015) Social behavior and the microbiome. Current Opinion in Behavioral Sciences 6: 28-34. doi: 10.1016/j.cobeha.2015.07.008\u003c/li\u003e\n\u003cli\u003eMoeller AH, Foerster S, Wilson ML, Pusey AE, Hahn BH, Ochman H (2016) Social behavior shapes the chimpanzee pan-microbiome. Sci Adv 2: e1500997. doi: 10.1126/sciadv.1500997\u003c/li\u003e\n\u003cli\u003eKoch H, Schmid-Hempel P (2011) Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc Natl Acad Sci U S A 108: 19288-19292. doi: 10.1073/pnas.1110474108\u003c/li\u003e\n\u003cli\u003eSchmid-Hempel P (2017) Parasites and Their Social Hosts. Trends Parasitol 33: 453-462. doi: 10.1016/j.pt.2017.01.003\u003c/li\u003e\n\u003cli\u003eC\u0026ocirc;t\u0026eacute; IM, Soares MC (2011) Gobies as cleaners. The Biology of Gobies. Science Publishers, St. Helier, pp. 525\u003c/li\u003e\n\u003cli\u003eVaughan DB, Grutter AS, Costello MJ, Hutson KS (2017) Cleaner fishes and shrimp diversity and a re-evaluation of cleaning symbioses. Fish and Fisheries 18: 698-716. doi: 10.1111/faf.12198\u003c/li\u003e\n\u003cli\u003eSoares MC, Cardoso SC, C\u0026ocirc;t\u0026eacute; IM (2007) Client preferences by Caribbean cleaning gobies: food, safety or something else? Behavioral Ecology and Sociobiology 61: 1015-1022. doi: 10.1007/s00265-006-0334-6\u003c/li\u003e\n\u003cli\u003eCheney KL, C\u0026ocirc;t\u0026eacute; IM (2001) Are Caribbean cleaning symbioses mutualistic? Costs and benefits of visiting cleaning stations to longfin damselfish. Animal Behaviour 62: 927-933. doi: 10.1006/anbe.2001.1832\u003c/li\u003e\n\u003cli\u003eSikkel PC, Herzlieb SE, Kramer DL (2005) Compensatory cleaner-seeking behavior following spawning in female yellowtail damselfish. Marine Ecology Progress Series 296: 1-11. doi: 10.3354/meps296001\u003c/li\u003e\n\u003cli\u003eSikkel PC, Fuller CA, Hunte W (2000) Habitat/sex differences in time at cleaning stations and ectoparasite loads in a Caribbean reef fish. Marine Ecology Progress Series 193: 191-199. doi: 10.3354/meps193191\u003c/li\u003e\n\u003cli\u003eArnal C, C\u0026ocirc;t\u0026eacute; IM, Sasal P, Morand S (2000) Cleaner-client interactions on a Caribbean reef: influence of correlates of parasitism. Behavioral Ecology and Sociobiology 47: 353-358. doi: 10.1007/s002650050676\u003c/li\u003e\n\u003cli\u003eWhittey KE, Dunkley K, Young GC, Cable J, Perkins SE (2021) Microhabitats of sharknose goby (\u003cem\u003eElacatinus evelynae\u003c/em\u003e) cleaning stations and their links with cleaning behaviour. Coral Reefs. doi: 10.1007/s00338-021-02105-x\u003c/li\u003e\n\u003cli\u003eCheney KL, C\u0026ocirc;t\u0026eacute; IM (2005) Mutualism or parasitism? The variable outcome of cleaning symbioses. Biol Lett 1: 162-165. doi: 10.1098/rsbl.2004.0288\u003c/li\u003e\n\u003cli\u003eArtim JM, Nicholson MD, Hendrick GC, Brandt M, Smith TB, Sikkel PC (2020) Abundance of a cryptic generalist parasite reflects degradation of an ecosystem. Ecosphere 11: e03268. doi: 10.1002/ecs2.3268\u003c/li\u003e\n\u003cli\u003eCaporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621-1624. doi: 10.1038/ismej.2012.8\u003c/li\u003e\n\u003cli\u003eKozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol 79: 5112-5120. doi: 10.1128/AEM.01043-13\u003c/li\u003e\n\u003cli\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13: 581-583. doi: 10.1038/nmeth.3869\u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41: D590-596. doi: 10.1093/nar/gks1219\u003c/li\u003e\n\u003cli\u003eParada AE, Needham DM, Fuhrman JA (2016) Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18: 1403-1414. doi: 10.1111/1462-2920.13023\u003c/li\u003e\n\u003cli\u003eMcMurdie PJ, Holmes S (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8: e61217. doi: 10.1371/journal.pone.0061217\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. doi: 10.1186/s13059-014-0550-8\u003c/li\u003e\n\u003cli\u003eLozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5: 169-172. doi: 10.1038/ismej.2010.133\u003c/li\u003e\n\u003cli\u003eOksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O\u0026apos;Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2020) vegan: Community Ecology Package. \u003c/li\u003e\n\u003cli\u003eGrutter AS, Bshary R (2003) Cleaner wrasse prefer client mucus: support for partner control mechanisms in cleaning interactions. Proc Biol Sci 270 Suppl 2: S242-244. doi: 10.1098/rsbl.2003.0077\u003c/li\u003e\n\u003cli\u003eDunkley K, Ellison AR, Mohammed RS, van Oosterhout C, Whittey KE, Perkins SE, Cable J (2019) Long-term cleaning patterns of the sharknose goby (\u003cem\u003eElacatinus evelynae\u003c/em\u003e). Coral Reefs 38: 321-330. doi: 10.1007/s00338-019-01778-9\u003c/li\u003e\n\u003cli\u003ePratte ZA, Patin NV, McWhirt ME, Caughman AM, Parris DJ, Stewart FJ (2018) Association with a sea anemone alters the skin microbiome of clownfish. Coral Reefs 37: 1119-1125. doi: 10.1007/s00338-018-01750-z\u003c/li\u003e\n\u003cli\u003eChiarello M, Auguet JC, Graham NAJ, Claverie T, Sucre E, Bouvier C, Rieuvilleneuve F, Restrepo-Ortiz CX, Bettarel Y, Villeger S, Bouvier T (2020) Exceptional but vulnerable microbial diversity in coral reef animal surface microbiomes. Proc Biol Sci 287: 20200642. doi: 10.1098/rspb.2020.0642\u003c/li\u003e\n\u003cli\u003eQuimbayo JP, Zapata FA (2018) Cleaning interactions by gobies on a tropical eastern Pacific coral reef. J Fish Biol 92: 1110-1125. doi: 10.1111/jfb.13573\u003c/li\u003e\n\u003cli\u003eGrutter AS (1996) Parasite removal rates by the cleaner wrasse \u003cem\u003eLabroides dimidiatus\u003c/em\u003e. Marine Ecology Progress Series 130: 61-70. doi: 10.3354/meps130061\u003c/li\u003e\n\u003cli\u003eSikkel PC, Welicky RL (2019) The Ecological Significance of Parasitic Crustaceans. Parasitic Crustacea, pp. 421-477\u003c/li\u003e\n\u003cli\u003eNarvaez P, Vaughan DB, Grutter AS, Hutson KS (2021) New perspectives on the role of cleaning symbiosis in the possible transmission of fish diseases. Reviews in Fish Biology and Fisheries 31: 233-251. doi: 10.1007/s11160-021-09642-2\u003c/li\u003e\n\u003cli\u003eEsteban M\u0026Aacute; (2012) An Overview of the Immunological Defenses in Fish Skin. ISRN Immunology 2012: 1-29. doi: 10.5402/2012/853470\u003c/li\u003e\n\u003cli\u003eAvena CV, Parfrey LW, Leff JW, Archer HM, Frick WF, Langwig KE, Kilpatrick AM, Powers KE, Foster JT, McKenzie VJ (2016) Deconstructing the Bat Skin Microbiome: Influences of the Host and the Environment. Front Microbiol 7: 1753. doi: 10.3389/fmicb.2016.01753\u003c/li\u003e\n\u003cli\u003eEllison S, Rovito S, Parra-Olea G, Vasquez-Almazan C, Flechas SV, Bi K, Vredenburg VT (2019) The Influence of Habitat and Phylogeny on the Skin Microbiome of Amphibians in Guatemala and Mexico. Microb Ecol 78: 257-267. doi: 10.1007/s00248-018-1288-8\u003c/li\u003e\n\u003cli\u003eApprill A, Robbins J, Eren AM, Pack AA, Reveillaud J, Mattila D, Moore M, Niemeyer M, Moore KM, Mincer TJ (2014) Humpback whale populations share a core skin bacterial community: towards a health index for marine mammals? PLoS One 9: e90785. doi: 10.1371/journal.pone.0090785\u003c/li\u003e\n\u003cli\u003eChiarello M, Paz-Vinas I, Veyssiere C, Santoul F, Loot G, Ferriol J, Bouletreau S (2019) Environmental conditions and neutral processes shape the skin microbiome of European catfish (\u003cem\u003eSilurus glanis\u003c/em\u003e) populations of Southwestern France. Environ Microbiol Rep 11: 605-614. doi: 10.1111/1758-2229.12774\u003c/li\u003e\n\u003cli\u003eBecker CC, Weber L, Suca JJ, Llopiz JK, Mooney TA, Apprill A (2020) Microbial and nutrient dynamics in mangrove, reef, and seagrass waters over tidal and diurnal time scales. Aquatic Microbial Ecology 85: 101-119. doi: 10.3354/ame01944\u003c/li\u003e\n\u003cli\u003eApprill A, Miller CA, Moore MJ, Durban JW, Fearnbach H, Barrett-Lennard LG (2017) Extensive Core Microbiome in Drone-Captured Whale Blow Supports a Framework for Health Monitoring. mSystems 2. doi: 10.1128/mSystems.00119-17\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Skin microbiota, microbial diversity, cleanerfish, Elacatinus evelynae, coral reefs","lastPublishedDoi":"10.21203/rs.3.rs-828485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-828485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFish associated microorganisms are known to be affected by the environment and other external factors, such as microbial transfer between interacting partners. One of the most iconic mutualistic interactions on coral reefs are the cleaning interactions between cleanerfishes and their clients, during which direct physical contact occurs. Here, we characterized the skin bacteria of the Caribbean cleaner sharknose goby, \u003cem\u003eElacatinus evelynae\u003c/em\u003e, in four coral reefs of the US Virgin Islands using sequencing of the V4 region of the 16S rRNA gene. We specifically tested the relationship between gobies\u0026rsquo; level of interaction with clients and skin microbiota diversity and composition. Our results showed differences in microbial alpha- and beta-diversity in the skin of gobies from different reef habitats and high inter-individual variation in microbiota diversity and structure. Overall, the results showed that fish-to-fish direct contact and specifically, access to a diverse clientele, influences the bacterial diversity and structure of cleaner gobies\u0026rsquo; skin. Because of their frequent contact with clients, and therefore, high potential for microbial exchange, cleanerfish may serve as models in future studies aiming to understand the role of social microbial transfer in reef fish communities.\u003c/p\u003e","manuscriptTitle":"Habitat and Client Diversity Influence the Skin Microbiome of the Caribbean Cleaner Goby Elacatinus Evelynae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-27 20:30:51","doi":"10.21203/rs.3.rs-828485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-09-10T08:15:38+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-08-25T00:30:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-08-20T04:37:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2021-08-18T17:57:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d32505bf-bd03-494d-b221-aa0cb1f511cc","owner":[],"postedDate":"August 27th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":6764127,"name":"Behavioral Ecology"}],"tags":[],"updatedAt":"2022-02-17T14:56:25+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-27 20:30:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-828485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-828485","identity":"rs-828485","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}