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Heat‐induced shift in coral microbiome reveals several members of the Rhodobacteraceae family as indicator species for thermal stress in Porites lutea

Abstract The coral holobiont is a complex ecosystem consisting of coral animals and a highly diverse consortium of associated microorganisms including algae, fungi, and bacteria. Several studies have highlighted the importance of coral‐associated bacteria and their potential roles in promoting the host fitness and survival. Recently, dynamics of coral‐associated microbiomes have been demonstrated to be linked to patterns of coral heat tolerance. Here, we examined the effect of elevated seawater temperature on the structure and diversity of bacterial populations associated with Porites lutea, using full‐length 16S rRNA sequences obtained from Pacific Biosciences circular consensus sequencing. We observed a significant increase in alpha diversity indices and a distinct shift in microbiome composition during thermal stress. There was a marked decline in the apparent relative abundance of Gammaproteobacteria family Endozoicomonadaceae after P. lutea had been exposed to elevated seawater temperature. Concomitantly, the bacterial community structure shifted toward the predominance of Alphaproteobacteria family Rhodobacteraceae. Interestingly, we did not observe an increase in relative abundance of Vibrio‐related sequences in our heat‐stressed samples even though the appearance of Vibrio spp. has often been detected in parallel with the increase in the relative abundance of Rhodobacteraceae during thermal bleaching in other coral species. The ability of full‐length 16S rRNA sequences in resolving taxonomic uncertainty of associated bacteria at a species level enabled us to identify 24 robust indicator bacterial species for thermally stressed corals. It is worth noting that the majority of those indicator species were members of the family Rhodobacteraceae. The comparison of bacterial community structure and diversity between corals in ambient water temperature and thermally stressed corals may provide a better understanding on how bacteria symbionts contribute to the resilience of their coral hosts to ocean warming.

Intact microbial communities offered coral holobionts resilience to thermal stress and increased the rate of recovery after bleaching events (Gilbert et al., 2012). More recently, dynamics of coral-associated microbiomes have been demonstrated to be linked to patterns of coral heat tolerance (Ziegler, Seneca, Yum, Palumbi, & Voolstra, 2017). Ziegler et al. (2017) showed that the composition of Acropora hyacinthus-associated bacterial community was different across thermally variable habitats and adapted to the new environment when corals were reciprocally transplanted. Subsequent exposure of those transplanted corals to thermal stress conditions changed the bacterial community of heat-sensitive corals from a more stable, cooler environment, whereas heat-tolerant corals from a highly variable, warmer environment harbored a stable bacterial community .
Branching coral species, especially acroporids, are generally fast-growing and known to be more susceptible to thermal bleaching compared to massive Porites corals, which are slow-growing and more "stress-tolerant" (Darling, Alvarez-Filip, Oliver, Mcclanahan, & Côté, 2012;Loya et al., 2001;Pisapia et al., 2016). Shifts in coralassociated microbiomes during bleaching events have been documented mostly in Acropora species: Acropora millepora and Acropora tenuis from the Great Barrier Reef, Acropora millepora from Fiji and Acropora muricata in Nan-wan, Taiwan (Bourne, Iida, Uthicke, & Smith-Keune, 2008;Grottoli et al., 2018;Lee, Davy, Tang, & Kench, 2016Littman, Willis, Pfeffer, & Bourne, 2009). Here, we investigated the dynamics of bacterial communities associated with a massive stony coral Porites lutea, a dominant reef-builder widely distributed in the Indo-Pacific, under short-term thermal stress. Our survey of P. lutea microbiomes utilized a long-read PacBio SMRT sequencing technology to capture full-length 16S rRNA sequences in order to examine the shifts in coral-associated bacterial community structure and diversity. Full-length 16S rRNA sequences have a benefit of spanning all hypervariable regions, enabling high-resolution taxonomic classification of bacterial species (Pootakham et al., 2017). The investigation of coral-microbial interactions and their dynamics during environmental stress will help further our understanding of the involvement of bacterial symbionts in the acclimatization and resilience of coral holobiont to climate change.

| Sample collection and DNA extraction
Five P. lutea colonies were collected from the Maiton Island in the Andaman Sea (7°45′42.5″N 98°28′51.3″E) at the depth of 7-10 m and immediately placed in containers with aerated seawater. We chose colonies that were at least 10 m apart from each other to reduce the possibility of collecting clonal colonies. P. lutea colonies were transported back to shore (Phuket Marine Biological Center) within an hour of collection and transferred to flow-through aquaria (transparent glass tanks with the dimension of 120 × 25 × 25 cm, holding approximately 75 L), which circulated seawater pumped from the reef to the Phuket Marine Biological Center Research Station.
Each colony was fragmented into four nubbins using a hammer and chisel and acclimated for 14 days in flow-through aquaria at ambient temperature (29°C) with a 12-hr light-dark cycle and irradiance of 150 μmol m −2 s −1 . Two nubbins from each colony were placed in the "control" aquarium, and the other two were placed in the "heated" aquarium. The position of ten nubbins (two from each of the five colonies) in the aquarium was randomized. During the experiment, one aquarium was maintained at the ambient water temperature while the other aquarium was exposed to a heat treatment, which involved an incremental ramping of seawater temperature at a rate of 0.5°C/ day for six consecutive days using one 100-W submersible aquarium heater (Eheim). From day 7 to day 13, the temperature was maintained at 34°C in the "heat treatment" aquarium ( Figure 1a). Coral nubbins were removed from the aquaria for sampling 6 (D6C and D6H) and 13 (D13C and D13H) days after the onset of the thermal stress treatment (Figure 1b). Since the coral nubbins in the control and heated aquaria were from the same parent colonies (one fragment from each colony in each tank), changes in the microbiome were due to treatment effects and not genetic differences between colonies. It is worth noting that we used one aquarium for each condition (control and heated); hence, there was no technical replicate in this study. However, we closely monitored the temperature in both aquaria to ensure that it remained stable in the control aquarium (29°C) and was steadily increasing until it reached 34°C in the heated aquarium ( Figure 1a).
Coral tissues were collected using scalpel blades, placed in sterile screw-capped tubes, and immediately frozen in liquid nitrogen.
DNA extraction was performed using the CTAB protocol reported in Pootakham et al. (2017). DNA samples were quantified using the NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific) and diluted to 50 ng/μl for PCR amplification.

| 16S rRNA amplification and PacBio sequencing
To obtain full-length 16S rRNA amplicons, we carried out the PCR using 50 ng of genomic DNA template and two bacterial-specific primers 27F (5′-AGAGTTTGATCMTGGCTCAG) and 1492R (5′-TACGGYTACCTTGTTACGACTT). To multiplex amplicons from nine samples into one SMRTbell library, we used 27F and 1492R primers tailed with 16-base PacBio barcodes at their 5' ends (https ://github. com/Pacif icBio scien ces/Bioin forma tics-Train ing/blob/maste r/barco ding/pacbio_384_barco des.fasta ). Amplification reactions were carried out using barcoded forward (27F) and reverse (1492R) primers in a final volume of 20 μl consisting of 0.4 U of Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific), 0.2 mM dNTPs, 1× Phusion HF Buffer, 0.1 μM of each primer and 1.5 mM MgCl 2 . The PCR conditions used were the same as those reported in Pootakham et al. (2017). Since we were unable to amplify 16S rRNA fragments from samples D13C-5 and D13H-5 due to their low DNA quality, the number of independent data points for each condition is as follows: D6C n = 5; D6H n = 5; D13C n = 4; and D13H n = 4. PCR products were subsequently purified with Agencourt AMPure XP magnetic beads (Beckman Coulter) and eluted in 20 μl elution buffer (EB). The Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific) was used to quantify the purified PCR products. Barcoded 16S amplicons were multiplexed in equimolar concentrations, and a total of 500 ng of pooled DNA was used for library preparation. SMRTbell libraries were constructed (each containing amplicons from nine samples) and sequenced on a PacBio RSII system using the P6-C4 chemistry with a 6-hr movie time (Pacific Biosciences).
F I G U R E 1 Temperature logs and physiological response of Porites lutea subjected to a heat treatment. (a) Records of daily water temperatures in control (blue line) and heated (red line) aquaria during the 13-day experiment. Vertical dotted lines indicate the days on which the samples were collected (day 6 and day 13). (b) Pictures of representative coral fragments from the control (~29°C) and heated (~34°C) aquaria taken prior to (day 0) and following the imposition of short-term thermal stress (day 6 and day 13)

| 16S rRNA sequence data analysis
To obtain the circular consensus sequences (CCS) with a minimum of five full passes, PacBio raw reads were demultiplexed and processed using the RS-ReadsOfInsert protocol (SMRT Analysis software version 2.3). Consensus 16S rRNA sequences that were shorter than 1,000 nt (likely generated as a result of partial amplification) were removed from further analyses. Based on the CCS read length distribution ( Figure A1), we arbitrarily chose 1,000 nt as a cutoff to avoid using sequences that contained nonoverlapping hypervariable regions for downstream analyses since they might affect the taxonomic classification as well as the diversity index calculation. Both strands of the consensus sequences were screened for the presence of chimeric sequences using the abundance-based algorithm in UCHIME (Edgar, 2010) and a reference dataset from Ribosomal Database Project (RDP) (Cole et al., 2014). Filtered sequences were analyzed as previously described in Pootakham et al. (2017) using QIIME software version 1.9.1 (Caporaso et al., 2010). Sequences classified as unknown or chloroplasts were removed prior to subsequent analyses.
Alpha and beta diversity analyses were performed using the QIIME pipeline based on sequence data that had been clustered into OTUs at a 97% similarity level. The OTU table was rarefied to an even depth of 5,652 sequences per sample corresponding to the lowest number of sequences present in a sample. Taxonomic classification was assigned to the representative sequence of each OTU using the RDP classifier retrained toward the Greengenes database V13.8 (DeSantis et al., 2006). Since we had full-length 16S rRNA sequence information, "unassigned" OTUs were subsequently classified based on their representative sequences using local sequence alignment (BLAST search) against the nt database. If the representative sequences of the OTUs returned the best hits with known taxonomy, those OTUs were classified accordingly. However, if the representative sequences of the OTUs returned "uncultured environmental sample" as their best hits, those OTUs remained categorized as "unassigned." We further investigated the nature of "unassigned" sequences by constructing the maximum-likelihood phylogenetic tree using MEGA7 software (Kumar, Stecher, & Tamura, 2016). The clustering of the sequences was tested by a bootstrap approach using 1,000 replications. Comparison between bacterial communities associated with P. lutea samples in this study (maintained in the aquarium) and those associated with P. lutea samples collected in situ from the same site (MT1-MT3 samples from Pootakham et al. (2017) and MT-Mar samples from Pootakham et al. (2018)) were analyzed as mentioned above using QIIME software version 1.9.1 (Caporaso et al., 2010).
White's nonparametric t test was used to test for differences in relative abundance of associated bacteria between samples collected in control and heated aquaria (White, Nagarajan, & Pop, 2009). To compare bacterial community compositions, we applied a nonparametric permutational multivariate analysis of variance (PERMANOVA) using the weighted UniFrac distance matrix as implemented in the vegan function Adonis (permutation = 1,000) (Lozupone & Knight, 2005;Oksanen, Blanchet, Kindt, Legendre, & Minchin, 2013). The UniFrac method measures the distance between communities based on the lineages they contain (Lozupone & Knight, 2005). The representative OTU sequences were aligned to generate a phylogenetic tree using the command pick_open_ref-erence_otus.py (http://qiime.org/scrip ts/pick_open_refer ence_otus. html). We used the weighted version of the UniFrac metric, which accounts for the relative abundance of each of the taxa within the communities (Lozupone & Knight, 2005).

| Core microbiome and indicator species identification
For the identification of P. lutea core microbiome members and the identification of indicator species, we employed the information from the full-length 16S rRNA sequences and followed the analysis pipeline reported in Pootakham et al. (2017). Members of the P. lutea core microbiome were identified at 100% sample coverage (the minimal percentage of all samples in which an OTU must be present to be considered part of the core microbiome) based on previous studies (Lawler et al., 2016;van de Water et al., 2016;Hadaidi et al., 2017;Pootakham et al., 2017).

| Performance comparison between full-length 16S rRNA and short hypervariable fragments
To compare the performance of short hypervariable regions (V3-V4 and V5-V6) and full-length 16S rRNA sequences in resolving taxonomic classification, we obtained the V3-V4 and V5-V6 sequences in silico from their respective full-length reads using 5′-ACTCCTACGGGAGGCAGCAG and 5′-CTACCAGGGTATCTAATC for the V3-V4 region and 5′-GGATTAGATACCCTGGTAGTCC and 5′-CTCACGRCACGAGCTGACG for the V5-V6 region. The extracted in silico V3-V4 and V5-V6 fragments and their counterpart full-length sequences were aligned to the RDP type strain reference sequences using BLAST (e-value cutoff of 10 -10 ). Any sequences that yielded a minimum of two best hits from different species with identical evalues, aligned sequences, and bit-scores were categorized as "ineffective" for the species-level classification. For classification at the genus level, any query sequence that had two or more best hits belonging to different genera was considered "unassigned."

| Evaluation of coral-associated bacterial diversity during thermal stress using full-length 16S rRNA gene sequences
The degree of bleaching was monitored using the Coral Health Chart from Coral Watch (https ://coral watch-old.org/web/guest/ coral-health-chart ). Coral samples that had experienced thermal stress were partially bleached (color scale: E3) after six days, and pigments were completely lost 13 days after the onset of heat treatment (color scale: E1; Figure 1b). Coral samples that were maintained in the control aquarium at 29°C remained pigmented throughout the experiment (color scale: E4-E5) and did not show any sign of bleaching. Full-length 16S rRNA genes were amplified from coral-associated bacteria and sequenced using the PacBio platform. A total of 1,416,896 PacBio reads totaling 29.5 Gb were obtained from 16 SMRT cells. Polymerase reads were assembled and demultiplexed into 357,254 circular consensus sequencing (CCS) reads with a minimum of five full passes (Table 1). After removing 16S rRNA CCS reads shorter than 1,000 nucleotides and chimeric sequences, we obtained a total of 248,950 reads. The number of processed reads ranged from 5,652 to 26,091 per sample, with an average of 13,831 reads/sample (Table 1).
A series of alpha diversity indices were calculated for each sample in order to evaluate the diversity of coral-associated bacterial communities present within the samples. A wide range in numbers of operational taxonomic units (OTUs) at a 3% dissimilarity level was observed, with the lowest number of OTUs (172) associated with D6C-3 sample and the highest number of OTUs (1,602) associated with D13H-1 sample (Table 1). Good's coverage indices along with the rarefaction curves that have plateaued off for most samples indicated that sufficient sampling has been achieved to capture the total diversity of the bacterial communities under investigation (Table 1, Figure A2). The Shannon indices ranged from 2.43 (D6C-2) to 7.71 (D13H-1), similar to those reported for P. lutea from the South China Sea, Indo-Pacific, and West Indian Ocean (Li et al., 2013;McKew et al., 2012;Sere et al., 2016;Shannon, 1948). A significant difference between the Shannon indices associated with samples from control and heated aquaria was

| Structure and composition of coral-associated microbiomes under thermal stress
Using a confidence threshold of 80%, 220,698 out of 284,950 filtered reads from all 18 samples were assigned based on the RDP TA B L E 1 Number of circular consensus sequence (CCS) reads, OTUs, and alpha diversity indices of Porites lutea microbiome under thermal stress. D6C and D13C indicate that samples were taken from the control aquarium on day 6 and day 13, respectively. D6H and D13H indicate that samples were taken from the heated aquarium on day 6 and day 13, respectively classifier (Wang, Garrity, Tiedje, & Cole, 2007). CCS reads were classified into 22 phyla (15 known and 7 candidate phyla), 60 classes, and 122 orders (Table S1). Proteobacteria represented a ubiquitous and dominant taxon in all P. lutea samples irrespective of the heat treatment ( Figure 3). Microbial communities associated with P. lutea also consisted of members of Cyanobacteria and Bacteroidetes. We compared bacterial communities associated with P. lutea samples in the control aquarium with previously reported communities associated with P. lutea samples collected in situ from the same sampling site (Pootakham et al., 2017(Pootakham et al., , 2018 and noticed that bacterial communities associated with coral samples that had been brought into aquaria harbored a significantly higher proportion of Cyanobacteria ( Figure A3; White's nonparametric t test, p-value = .013).
We observed a significant alteration in the composition of coral- Tropicibacter phthalicicus (2.51%) were among the least abundant species in the core microbiome ( Figure 5). We also compared the data from this study to those from previous reports (Pootakham et al., 2017(Pootakham et al., , 2018 and found one bacterial species, Endozoicomonas elysicola, to be exceptionally persistent in all 30 P. lutea colonies investigated across different geographical locations and seawater temperatures. Most of the species identified as putative members of P. lutea core microbiome were not present in high abundance.
In fact, only three members had a relative abundance higher than 10% within the whole community while five had a relative abundance between 1% and 6%, and the remaining was found to have lower than 1% relative abundance within the community.

| Indicator species associated with heatstressed corals
We examined the microbial communities from both untreated and heat-stressed samples to see whether any specific bacterial species were significantly associated with corals that had been exposed to elevated water temperature. We applied the indicator value  Table S2. Bacterial species that were identified as indicator species (for either control or heat-stressed condition) on both day 6 and day 13 are considered strong indicator species and listed in Table 2.
All six indicator species for corals maintained at ambient temperature (D6C and D13C) belonged to Gammaproteobacteria family Endozoicomonadaceae, consistent with the information that members of this family were significantly more abundant in the microbiomes associated with corals from control aquarium ( Figure 4 and

| Taxonomic classification at species resolution using full-length 16S rRNA sequence information
Classifying the taxonomy of 16S rRNA sequences based on short hypervariable fragments of the 16S rRNA gene can be difficult. In this study, we adopted the PacBio CCS technology to obtain fulllength 16S rRNA sequences covering all hypervariable regions of the 16S rRNA genes. In order to demonstrate the benefit of utilizing full-length sequences in 16S rRNA gene-based community surveys, we extracted the commonly used V3-V4 and V5-V6 regions from the full-length reads and aligned both of the in silico simulated 420-nucleotide V3-V4 and 255-nucleotide V5-V6 amplicons and their respective full-length counterparts against reference sequences in the RDP (Cole et al., 2014). A much higher proportion of full-length 16 rRNA sequences could be classified at the species level compared to those of V3-V4 and V5-V6 fragments ( Figure 6).

F I G U R E 3
Dynamics of coral-associated bacteria during a thermal stress. Bar charts displaying the composition of bacterial communities associated with Porites lutea in control and heated aquaria at a group level (left) and an individual colony level (right). Taxonomic classification of OTUs present in each sample at the phylum/class/family levels was based on the Greengenes database. Eleven most abundant families were depicted, and the remaining taxa were grouped together under "others." We were able to assign taxonomic classification at the species level for 99.8%, 86.2%, and 82.0% for the full-length 16S rRNA sequences, V3-V4 hypervariable fragments, and V5-V6 hypervariable fragments, respectively. Furthermore, we assigned taxonomy (at the genus level) to each in silico (V3-V4 and V5-V6) amplicon extracted from the full-length sequence and compared taxonomic profiles obtained from short amplicons to those obtained from the full-length 16S sequences (Table S3). Taxonomic classification based on hypervariable V3-V4 or V5-V6 regions yielded different microbiome profiles from the classification based on full-length 16S rRNA sequences. For example, Roseovarius was one of the two most prevalent genera associated with heat-stressed corals (D6H and D13H) when using full-length 16S rRNA sequences for classification (Table S3). However, when V5-V6 fragments were used instead of their full-length counterparts, Pseudoruegeria was erroneously represented as the most abundant genus affiliated with heat-stressed microbiomes (when the taxonomic classification was performed with full-length 16S data, the relative abundance of Pseudoruegeria in the heat-stressed microbiomes was <2%; Table S3). The proportion of unassigned sequences was also considerably higher when partial 16S fragments were used instead of their full-length counterparts, suggesting that the commonly used hypervariable V3-V4 and V5-V6 regions were identical among several bacterial 16S rRNA sequences in the reference database (Table S3).

| Thermal stress affects the structure and diversity of coral-associated microbiomes
A diverse group of bacteria are known to associate with corals, and dynamics of bacterial community have recently been shown to be linked to patterns of heat tolerance in their hosts . Here, we investigated the effects of elevated water temperature on the structure and diversity of microbiomes associated with P. lutea using full-length 16 rRNA gene sequences. We employed the aquarium system since it allowed us to control other environmental factors (i.e., the only difference between controlled and heated aquaria was the water temperature). The microbiomes of corals have been known to shift their compositions when brought into aquaria.
Kooperman, Ben-Dov, Kramarsky-Winter, Barak, and Kushmaro (2007) previously reported a decrease in relative abundance of Cyanobacteria in Fungia granulosa that had been maintained in the aquarium (Kooperman et al., 2007). Our observation with P. lutea revealed the opposite trend ( Figure A3), with Cyanobacteria being more abundant in aquarium samples. The relative abundance of Cyanobacteria appeared to increase with the amount of time that P. lutea samples had been maintained in the aquarium (White's nonparametric t test; D6 vs. D13, p-value = .023; Figure 3). However, the shift in the relative abundance of Cyanobacteria was independent of an increase in water temperature since it was observed in both control and heat-treated samples ( in Acropora millepora , Acropora muricata (Lee, Davy, Tang, & Kench, 2016;Shiu et al., 2017), and Pocillopora damicornis (Tout et al., 2015). A number of studies have provided emerging evidence that environmental stressors including elevated water temperature led to an increase in alpha diversity within coral-associated microbiomes Lee et al., 2016;McDevitt-Irwin, Baum, Garren, & Vega Thurber, 2017;Meron et al., 2011;Shiu et al., 2017;Tout et al., 2015). Meron et al. (2011) demonstrated that corals exposed to environment with lower pH exhibited higher microbial diversity (Meron et al., 2011). Corals experiencing a high level of anthropogenic disturbance have also been shown to harbor microbiomes with higher diversity than those farther away from the disturbance (Morrow, Moss, Chadwick, & Liles, 2012). Furthermore, a major coral microbiome restructuring and a marked increase in microbial diversity were evident when F. granulosa corals were exposed to long-term high salinity (Röthig, Ochsenkühn, Roik, Merwe, & Voolstra, 2016).
Besides an increase in the alpha diversity observed, the community composition also changed dramatically especially after 13 days of heat treatment (Figure 2). There was a notable increase in the Endozoicomonadaceae abundance, which became more pronounced after 13 days of heat treatment (Figure 4). Similar to our results, a marked decline in the proportion of Endozoicomonadaceae in the coral-associated microbiome during thermal stress has been reported in P. damicornis (Tout et al., 2015) as well as in P. lutea (Pootakham et al., 2018). Endozoicomonadaceae has been reported to inhabit both coral mucus and coral tissue of several scleractinian species including Acropora cervicornis, P. astreoides, and P. furcate (Ainsworth et al., 2015;Bayer et al., 2013;Chu & Vollmer, 2016). Fluorescent in situ hybridization (FISH) experiments conducted by Bayer et al. (2013) showed that members of the Endozoicomonadaceae family (genus Endozoicomonas) resided in close proximity to Symbiodiniaceae cells within the coral endoderm, suggesting an intimate association with their coral hosts. Researchers have proposed Endozoicomonas to be beneficial symbionts (McDevitt-Irwin et al., 2017), playing an important role in sulfur recycling (Raina et al., 2009) and protein/carbohydrate transport for the host (Neave, Michell, Apprill, & Voolstra, 2017). The decline in relative abundance of these potentially symbiotic taxa was also induced by other environmental stressors besides elevated water temperature. Previous studies have found that the relative occurrence of sequences associated with Endozoicomonadaceae decreased in Acropora and Porites corals in response to ocean acidification (Morrow et al., 2015) and in Porites and Paramuricea corals in response to diseases (Meyer, Paul, & Teplitski, 2014;Vezzulli, Pezzati, Huete-Stauffer, Pruzzo, & Cerrano, 2013).
The structural abundance of dominant microbiome members under normal condition did not seem to be maintained under thermal stress. While there was a significant reduction in the relative abundance of Endozoicomonadaceae following the exposure to thermal stress, we observed a concomitant increase in the relative abundance of Rhodobacteraceae, Alteromonadaceae, and Saprospiraceae (Figures 3 and 4). thermal stress, which primarily involved an increase in the relative abundance of Rhodobacterales, was also observed in P. damicornis (Tout et al., 2015). Similarly, a study by van de Water et al. (2018) showed that members of Rhodobacteraceae (genera Rhodovulum and Dinoroseobacter) became more abundant in Montipora aequituberculata-associated microbiome after an extended period of elevated water temperature treatment (van de Water et al., 2018).

Patterns of increased Rhodobacterales-affiliated sequences in
the tissue layer have also been reported when A. muricata corals were exposed to high temperature for three days (Lee, Davy, Tang, & Kench, 2017). The order Rhodobacterales appears to be opportunistic bacteria, flourishing and increasing their relative abundance when coral hosts experience biotic (e.g., white band and white plaque diseases) and abiotic (e.g., thermal stress and ocean acidification) stress during which there may be open niche space (Cardenas, Rodriguez, Pizarro, Cadavid, & Arevalo-Ferro, 2012;Meron et al., 2011;Roder et al., 2014;Welsh et al., 2017).
Interestingly, even though Rhodobacteraceae became more abundant in coral-associated communities under thermal stress (Tout et al., 2015;Lee et al., 2017;van de Water et al., 2018) and low pH (Meron et al., 2011), Grottoli et al. (2018) observed a decrease in sequences affiliated with this bacterial family when coral hosts were simultaneously exposed to both elevated seawater temperature and ocean acidification (Grottoli et al., 2018).

Several studies have reported an increase in relative abundance
of Rhodobacteraceae in the microbiomes of diseased corals (Kellogg et al., 2013;Pollock, Wada, Torda, Willis, & Bourne, 2016;Sunagawa et al., 2009). Using PhyloChip™ G3, Kellogg et al. (2013) showed as Vibrio spp (Welsh et al., 2017). The appearance of Vibrio-related sequences has been observed in parallel with the increase in the relative abundance of Rhodobacteraceae during thermal bleaching in A. millepora , A. muricata (Lee et al., 2016), A.
Interestingly, despite the similarity in the bacterial profile shifts associated with diseases and thermal stress, we did not observe an increase in relative abundance of Vibrio-related sequences in our heat-treated samples. Indeed, we hardly detected any Vibrio OTUs in our samples across treatments and time periods. The absence of Vibrio species in both healthy and thermally bleached P. lutea colonies was consistent with previous study (Pootakham et al., 2017(Pootakham et al., , 2018.

| Porites lutea core microbiome
The concept of core microbiome has recently been introduced in coral-microbial studies (Ainsworth et al., 2015;Chu & Vollmer, 2016;Hernandez-Agreda, Leggat, Bongaerts, & Ainsworth, 2016;Lawler et al., 2016;Robertson, Haltli, Mccauley, Overy, & Kerr, 2016;van de Water et al., 2016;Hernandez-Agreda, Gates, & Ainsworth, 2017;Sweet & Bulling, 2017), and it is characterized as the stable populations consistently present across complex microbial assemblages (Shade & Handelsman, 2012). Members of a core microbiome are likely to be important for the development, health, and functioning of their hosts (Ley, Turnbaugh, Klein, & Gordon, 2006;Round & Mazmanian, 2009). We stringently defined the members of the P. lutea core microbiome as bacterial species that were present in 100% of all samples based on the percentages of sample coverage previously used in coral microbiome studies (Lawler et al., 2016;van de Water et al., 2016). With full-length 16S rRNA sequence data, we were able to achieve the taxonomic classification of the core microbiome members at the species resolution. Most of P. lutea core microbiome members belonged to Alphaproteobacteria family Rhodobacteraceae and Gammaproteobacteria family Endozoicomonadaceae ( Figure 5). Rhodobacterales are thought to be among the most abundant and diverse bacteria in marine environments, accounting for a quarter of the total bacteria present in coastal and polar regions (Wagner-Dobler & Biebl, 2006). They are functionally diverse and involved in a range of biological activities from the production of antibiotic compounds to the reduction of trace metals (Brinkhoff et al., 2004). They also contribute to ocean carbon and sulfur cycling through the oxidation of carbon monoxide and the production of dimethylsulfide (Wagner-Dobler & Biebl, 2006).
Another prominent taxon that constituted the majority of P. lutea core microbiome was the family Endozoicomonadaceae, which has been reported as dominant members of the core microbiomes in P. furcate, A. cervicornis, P. verrucosa, P. astreoides, and Stylophora pistillata (Chu & Vollmer, 2016;Glasl, Webster, & Bourne, 2017). Notably, all five Endozoicomonadaceae species designated as core microbiome members in this study have previously been identified in the P. lutea core microbiome (Pootakham et al., 2017(Pootakham et al., , 2018. Moreover, E. elysicola was identified in all 30 P. lutea samples collected from different sampling sites, during different seasons and under varying temperature conditions ( Figure 5).

Members of the family Endozoicomonadaceae, especially genus
Endozoicomonas, are ubiquitous and highly abundant in the marine F I G U R E 6 Performance comparison between full-length 16S rRNA and hypervariable fragments (V3-V4 or V5-V6). A bar graph illustrating proportions of sequence reads from each treatment that are classifiable at the species level using full-length (FL; green) or partial 16S rRNA gene fragments (V3-V4, orange; V5-V6, blue).
environment. While they do not appear to be common in deepsea corals, members of this family are frequently found associated with tropical and temperate coral species (Cardenas et al., 2012;La Rivière, Garrabou, & Bally, 2015;Lee et al., 2012;Morrow et al., 2012;Sunagawa et al., 2009). Based on their widespread abundance in marine invertebrates and their dominance in the microbiomes of several coral species (Chu & Vollmer, 2016;Glasl et al., 2017), Endozoicomonas species are likely to play important biological and ecological roles in coral holobionts. Endozoicomonas has been detected on the surface mucus layer and in coral skeleton of Acropora and Porites hosts, and it has been suggested that members of this taxon may be involved in biofilm production that promotes surface colonization of other bacteria (Jessen et al., 2013;Speck & Donachie, 2012). The Endozoicomonas genomes encoded a complete set of putative proteins in the synthetic pathways for various amino acids including alanine, cysteine, glycine, lysine, methionine, serine, and threonine, raising the possibility that the microbial symbionts, especially members of the core microbiome, may provide essential amino acids that cannot be synthesized by the host (Neave et al., 2017;Price et al., 2014).

| Members of the Rhodobacteraceae family are indicator species for thermal stress in Porites lutea
To determine whether specific microbial taxa were significantly as- Rhodobacteraceae genera (Table 2; Table S2). On the contrary, nearly all indicator species for normal water temperature were Endozoicomonadaceae (genera Endozoicomonas and Kistimonas).

Members of Rhodobacteraceae have previously been reported as
indicator taxa for polluted environment and sites impacted by sedimentation and local sewage or municipal wastewater (Ziegler et al., 2016). Several OTUs with sequences related to Rhodobacteraceae have also been identified as indicative microbial taxa for diseased coral tissues (Roder et al., 2014;Sere et al., 2013;Sunagawa et al., 2009). The results presented in this work suggested that members of Rhodobacteraceae family were robust indicator species for thermally stressed P. lutea colonies.
Scleractinian corals are among the largest producers of DMSP (Broadbent & Jones, 2004;Broadbent, Jones, & Jones, 2002), a compound that plays a role in alleviating cellular oxidative stress as DMSP and its breakdown products (dimethylsulfide [DMS] and dimethylsulfoxide [DMSO]) can readily scavenge hydroxyl radicals and other reactive oxygen species (Sunda, Kieber, Kiene, & Huntsman, 2002;Yoch, 2002). Interestingly, Garren et al. (2014) found that coral hosts under thermal stress exude mucus that was richer in DMSP, which appeared to trigger heightened pathogen response, suggesting that pathogenic (e.g., Vibrio coralliilyticus) or opportunistic (e.g., Rhodobacteraceae) bacteria may use this chemical cue to target stressed hosts (Garren et al., 2014). Ruegeria pomeroyi, one of the indicator species for heat-stressed P. lutea, was capable of metabolizing DMSP utilizing both the demethylation pathway (releasing methanethiol) and the cleavage pathway (producing DMS) (Reisch et al., 2013). The ability to utilize DMSP exuded by stressed corals may allow R. pomeroyi to thrive and expand its niches within the coral holobiont especially during thermal stress. Another indicator species for thermal stressed microbiome was Aliiroseovarius crassostreae, a marine Rhodobacter that has been shown to be a causative agent of Roseovarius oyster disease, which resulted in high mortality rates in juvenile eastern oysters in the United States (Boettcher, Geaghan, Maloy, & Barber, 2005). To this point, there has been no report linking R. pomeroyi or A. crassastreae to disease conditions in P. lutea.
Future studies are required to determine whether these two indicator species are capable of inducing signs of coral diseases.

| Full-length 16S rRNA sequences enable taxonomical classification at the species resolution
Classifying the taxonomy of 16S rRNA sequences based on short hypervariable fragments of the 16S rRNA gene can be challenging.
Our results showed that the number of "unassigned" sequences was remarkably higher when partial 16S fragments were used instead of the full-length sequences (Table S3). Taxonomical classification at the genus level yielded noticeably different microbiome profiles when short fragments encompassing hypervariable regions, especially the V5-V6 regions, were used instead of the full-length 16S rRNA sequences (Table S3)
Several studies have highlighted the significance of coral-associated microorganisms and their roles in fitness and survival of the host animals (Ainsworth et al., 2015;Bourne et al., 2008;Gilbert et al., 2012;Lesser, Mazel, Gorbunov, & Falkowski, 2004;Shieh & Lin, 1992;Tout et al., 2015). Our work examined the effect of thermal stress on the composition and structure of bacterial communities associated with P. lutea, one of the dominant reef-builders widely distributed across the Indo-West Pacific, including the Gulf of Thailand and Andaman Sea (Yeemin et al., 2009). We observed a significant increase alpha diversity indices and a prominent shift in microbiome composition during thermal stress. This is one of the first coral-associated microbiome studies that utilized the long-read PacBio SMRT sequencing technology to capture full-length 16S rRNA gene, which allowed over 99.8% of the sequences to be classified at the species level. The ability to achieve species-resolution classification enabled us to identify members of P. lutea core microbiome and a group of indicator species associated with thermal stress. The knowledge on how thermal stress impacts the diversity and composition of coral microbiomes may help us gain a better understanding of how these associated bacteria contribute to the acclimatization and resilience of the coral holobiont to thermal bleaching.

ACK N OWLED G M ENTS
We thank the following funding bodies: National Science and Technology Development Agency (NSTDA), Thailand (Grant Number P-16-50720), and UNESCO-L'Oreal For Women in Science Fellowship (awarded to Pootakham). We would also like to thank the reviewers and editors whose comments and suggestions improved the manuscript.

CO N FLI C T O F I NTE R E S T
None declared.

AUTH O R CO NTR I B UTI O N S
WP and ST conceptualized the study and acquired funding. WP, TY, LP, NJ, and ST carried out investigation. WP, WM, CS, CN, and WK carried out formal analysis. WM and WK carried out visualization experiments. WP wrote the original draft of the manuscript. WP, WM, and WK reviewed and edited the manuscript.

E TH I C A L A PPROVA L
None required.

DATA AVA I L A B I L I T Y S TAT E M E N T
The PacBio 16S rRNA sequence data were deposited in the GenBank F I G U R E A 2 Rarefaction curves of OTUs for the coral-associated bacterial community samples from control and heated aquaria F I G U R E A 3 A bar chart showing the composition of bacterial communities associated with P. lutea in this study (aquarium) and those associated with P. lutea collected directly from the field (environment). Taxonomic classification of OTUs present in each sample at the phylum level was based on the Greengenes database F I G U R E A 4 A maximum-likelihood phylogenetic tree generated using MEGA7, showing the relationship of four "unassigned" OTUs with known bacterial sequences. The clustering of the sequences was tested by a bootstrap approach using 1,000 replications