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Article

Effects of Elevated Temperatures and Nutrient Enrichment on Microbial Communities Associated with Turf Algae Under Laboratory Culture

by
Jatdilok Titioatchasai
1,
Anuchit Darakrai
2,
Sinjai Phetcharat
2 and
Jaruwan Mayakun
2,*
1
Faculty of Innovative Agriculture, Fisheries and Food, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand
2
Division of Biological Science, Faculty of Science, Prince of Songkla University, Songkhla 90110, Thailand
*
Author to whom correspondence should be addressed.
Oceans 2025, 6(4), 68; https://doi.org/10.3390/oceans6040068
Submission received: 25 August 2025 / Revised: 5 October 2025 / Accepted: 13 October 2025 / Published: 17 October 2025
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Abstract

Increased seawater temperatures and nutrient loading are stressors that affect coral reefs and their microbiomes. In this study, filamentous algae were collected and exposed to different temperatures and nutrient concentrations through a laboratory experiment. Microbial DNA was extracted and analyzed using amplicon sequencing of the V3-V4 hypervariable region of the 16S rRNA gene. In total, 1 domain, 51 phyla, 131 classes, 335 orders, 549 families, and 1905 species were identified. Proteobacteria and Bacteroidota were the dominant taxa reported. Elevated seawater temperatures and nutrient enrichment impacted microbial communities associated with turf algae under laboratory culture. Bacterial species diversity and abundance differed under different temperature and nutrient conditions. Proteobacteria and Actinobacteria were abundant in lower-temperature conditions, while Desulfobacterota, Spirochaetota, and Firmicutes were abundant in higher-temperature conditions. Ruegeria was abundant in low-temperature conditions, whereas Vibrio abundance was low. Regarding nutrient conditions, Proteobacteria and Cyanobacteria were abundant under high-nutrient conditions, while Firmicutes and Desulfobacterota were abundant under ambient-nutrient conditions. The higher nutrient concentration increased the abundance of pathogenic bacteria, such as Vibrio and Photobacterium, while Pseudoalteromonas, which is beneficial for reefs, was present under ambient nutrient conditions. This study demonstrates that temperature and nutrient enrichment can shape microbial communities under laboratory conditions, providing an experimental setting for further studies of bacterial functions and metabolic processes in natural conditions under thermal and nutrient stresses.

1. Introduction

Climate change and anthropogenic disturbance are major threats to marine ecosystems [1,2,3]. Increasing CO2 emissions from human activities have caused ocean acidification [4] and increased sea temperatures [5]. Increased seawater temperatures are considered the most serious negative influence on marine ecosystems such as seagrass beds [6], kelp forests [7], and coral reefs [8]. In 2019, the IPCC reported that seawater temperatures will be around 2.5 °C higher by the end of the 21st century [9]. Increased seawater temperatures negatively affect several characteristics of marine populations, such as coral and algae. Decreases in photosynthesis rates, growth rates, abundance, survivorship, and distribution have been reported in several studies [6,8,10,11]. Increased seawater temperatures also threaten coral ecosystems by inducing coral bleaching and triggering diseases that lead to coral mortality [12,13].
Coastal development and the rising world population have increased levels of nutrient run-off and overfishing [14,15]. These two factors have been reported as the main causes of coral phase shift [16]. Moreover, if a degraded reef is subjected to bleaching and stress, reef degradation can be exacerbated [17]. Once a coral reef is sufficiently degraded, turf algae become more abundant and occupy more reef space due to their fast growth rate, ranging from 0.36 mm 3 wk−1 to 0.44 mm 3 wk−1, with an overgrowth rate on coral of 0.34 mm 3 wk−1 under nutrient enrichment conditions [18]. This negatively affects the reef by inhibiting coral juvenile settlement (such as Acropora sp.) and inducing coral diseases, such as black band disease [19]. These developments can change a coral-dominated reef community into a macroalgal-dominated community; notably, turf algae can indicate coral disease and degradation [12,20,21].
The impacts of ocean acidification on turf algae communities have been investigated [22,23,24], but the impacts of temperature and nutrient enrichment on these communities and their associated microorganisms are less well known. While macroalgae-associated microorganisms play crucial roles in many processes, such as nutrient cycling and inhibiting the growth of pathogenic pathogens [25,26], some bacteria are themselves potential pathogens that can trigger coral disease, coral bleaching, and coral mortality during coral–algal competition [27,28]. Elevated temperatures and nutrients can shift communities from beneficial to potentially pathogen-dominated. However, our understanding of how increases in these two factors influence the composition of macroalgae-associated microorganisms is insufficient. Moreover, it is unclear how shifts in the composition of these communities affect coral–algal communities.
This study investigates the effects of increased seawater temperatures and nutrient enrichment on algal-associated microbial communities on coral branches using 16S rRNA gene analysis. We hypothesize that these factors can shift the microbial community’s structure by increasing thermotolerant, nutrient-preference bacterial groups and potential opportunistic pathogen groups. Our findings might increase our understanding of how the changing ocean environment influences bacterial communities associated with turf algae, an underexplored research area, and how this putative influence might affect coral reef ecosystems. Additionally, our findings might be crucial for coastal and reef management.

2. Materials and Methods

2.1. Sample Collection

Coral branches supporting turf and benthic macroalgae were collected from a subtidal reef at 1.5–2.0 m depth at Lidee Island (6°47′00″ N, 99°46′13″ E), Satun Province, Thailand, in October 2023. The study site’s dominant coral genera were the hard corals Porites and Acropora. Sixty coral branches of Acropora were collected from the same reef zone, with an approximate distance of five meters between each sample. Ulva and Polysiphonia were the dominant turf algae. The average temperature of this site was 31.52 ± 0.47 °C, and salinity was 30 ppt. Nitrate and phosphate concentrations were around 118.93 ± 0.28 µM and 22.50 ± 4.10 µM, respectively.
Sixty 15 cm coral branches were collected and kept in individual sterile plastic bags with natural seawater collected from the site. To reduce the effect of environmental changes, all coral branches were transported within 2 h to acclimate at the laboratory of the Faculty of Science, Prince of Songkhla University. All samples were acclimated for 3 days in a 70 L glass aquarium under the same environmental conditions measured in the field at a water temperature of 30 °C, pH = 7.9, and 30 ppt salinity seawater. A seawater recirculation system with a flow rate of 400 L h−1 was set up using an aquarium pump (Eheim, 300 W, EHEIM GmbH & Co. KG, Deizisau, Baden-Württemberg, Germany) to create flow-through water conditions. The light intensity was manipulated using LED lamps (100 W flood light LED) under a 12 h photoperiod of approximately 200 µmol photon m−1 s −1.

2.2. Experimental Design and Setting

The experiment used a factorial design. There were four treatments: 30 °C with an ambient nutrient concentration (30A) (control group), 30 °C with a high nutrient concentration (30N), 35 °C with an ambient nutrient concentration (35A), and 35 °C with a high nutrient concentration (35N) (Figure 1).
After 3 days of acclimation, five coral branches with turf algae were placed in experimental chambers (chamber size, 5 × 10 × 15.2 cm3). Six chambers were enriched using sodium nitrate (NaNO3) (Ajax Finechem Pty Ltd., Taren Point, NSW, Australia) to double the level present in the ambient seawater at the collection site (from 118.92 ± 0.28 to 237.22 ± 2.27 µmol/L), and six chambers were maintained at an ambient level. The pH and salinity of the seawater in each chamber were around 7.9 and 30 ppt, respectively. Four glass aquaria (aquarium size, 30.5 × 71.0 × 30.1 cm3) containing 70 L of seawater were prepared. The water circulation system flowed seawater at a rate of 400 L h−1. The light intensity was around 200 µmol photon m−1 s −1 for all treatments. Two aquaria were heated to 35 °C using heaters, and the other two were maintained at 30 °C. Three enriched chambers were placed in the first aquarium at 35 °C, and three enriched chambers were placed in the second aquarium at 30 °C. Similarly, three ambient chambers were placed in the third aquarium at 35 °C, and three ambient chambers were placed in the fourth aquarium at 30 °C. All turf algae on coral branches were set in a seawater tank for seven days. Throughout the experiment, the water temperature and light intensity in the aquaria were measured using a Hobo data logger (Model: UA-002–64, Onset Computer Corp., Bourne, MA, USA). The data logger was placed in the center of an aquarium, and records were created at 30 min intervals. After seven days, all turf algae on coral branches from each chamber (n = 3 for each treatment) were collected and preserved in filtered seawater filtered with a 0.2 µm pore Whatman® nylon filter at −20 °C before DNA extraction.

2.3. DNA Extraction and 16S rRNA Sequencing

After sample defrosting, all turf algae were removed from coral branches with sterile forceps and placed in sterile microcentrifuge tubes. The microbial DNA of all samples was extracted with DNeasy® Plant® kits (QIAGEN Co., Ltd., Hilden, Germany), following the manufacturer’s instructions. The quality and amount of extracted DNA were evaluated with a DS-11 series spectrophotometer (Devonix Inc., Wilmington, NC, USA). DNA samples were sent to GENEWIZ Biological Technology (Suzhou, China) for sequencing. The hypervariable V3-V4 region was amplified with pairing primers designed by GENEWIZ (South Plainfield, NJ, USA). The forward and reverse sequences were 5′-CCTACGGRRBGCASCAGKVRVGAAT-3′ and 5′-GGACTACNVGGGTWTCTAATCC-3′, respectively. To amplify the hypervariable of the target gene, we used a two-phase PCR protocol. The process started with an initial denaturation at 95 °C for 5 min. The first amplification phase consisted of 10 cycles with denaturation at 94 °C (30 s), annealing at 57 °C (45 s), and extension at 72 °C (1 min). This was followed by 15 cycles under the same denaturation and extension conditions but with a reduced annealing temperature of 47 °C. A final extension was carried out at 72 °C for 10 min to complete the amplification. The polymerase chain reaction (PCR) mixture contained a total volume of 25 µL, including 2.5 µL of TransStart Buffer (TransGen, Beijing, China), 2 µL of dNTPs, 1 µL of each primer, and 20–30 ng of template DNA [29]. After the Index PCR product was obtained and quantified, the final libraries were purified with AMPure XP beads (Beckman Coulter, Brea, CA, USA). Sequencing was conducted utilizing the Illumina MiSeq platform (Illumina, San Diego, CA, USA) in a 2 × 300 bp paired-end run.

2.4. Bioinformatic and Statistical Analysis

All sequences were processed using QIIME 2 [30]. The DADA2 package was used to denoise and quality-control the sequenced data [31]. The SILVA database was used to classify marine microbes with 95% similarity using the Naive Bayes Classifier [32]. The relative abundance of the microbiome data is in each replication for all treatments. Simpson’s diversity index, observed features, Faith’s phylogenic diversity, the abundance-based coverage estimator (ACE), and Pielou’s evenness were calculated to measure the alpha diversity of the microbial community. The Kruskal–Wallis test was used to analyze differences in microbial alpha diversity among treatments. Beta diversity among the four treatments was illustrated using Principal Coordinate Analysis (PCoA) with an unweighted UniFrac distance matrix. Permutational multivariate analysis of variance (PERMANOVA) was used to analyze differences in microbial species composition among treatments with permutation-based significance testing (9999 permutations), and the homogeneity of dispersions was determined using PERMDISP. Strong differences in specific microbial taxa in each sample were examined using linear discriminant analysis for effect size (Lefse) [33], with an LDA threshold score of 2.0 and a significance rating of 0.05. A Venn diagram—constructed on the website https://www.bioinformatics.com.cn/static/others/jvenn/example.html (accessed on 20 July 2024)—was used to analyze and visualize overlap and differences in core microbiota among treatments. PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was used to predict the functions of 16S rRNA gene sequencing data [34].

3. Results

We extracted the microbial DNA from turf algae samples under laboratory culture conditions to detect the diversity and composition of a microbial community under elevated-temperature and nutrient enrichment treatments. After demultiplexing the data, 6,601,661 reads were returned. The minimum and maximum read depths per sample were 179,194 and 253,446, respectively. The mean and median were 221,546 and 220,055 reads per sample, respectively. Taxonomic classification using Qiime2 showed 1 domain, 51 phyla, 142 classes, 335 orders, 549 families, 1006 genera, and 1905 species. A small proportion (0.69%) could not be classified into any bacterial phyla. The data set showed differences in microbial community composition among the four treatments. Proteobacteria were the most dominant phylum in all treatments, with an abundance of 25.75–61.85%, followed by Bacteroidota, Firmicutes, Actinobacteriota, and Desulfobacterota with abundances of 12.66–20.65%, 0.23–21.59%, 1.20–9.50%, and 0.50–17.38%, respectively. The phyla Synergistota and Schekmanbacteria exhibited the lowest abundances at 0–0.0087% and 0–0.0075%, respectively (Figure 2).
At the class level, Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia were dominant, with abundances of 14.20–32.46%, 9.57–32.05%, and 9.65–20.02% (Figure 3). At the species level, Rugeria sp., Woeseia sp., and Marivita sp. were dominant, with 2.91–10.51%, 0.49–3.43%, and 0.17–5.51%, respectively.
There was no significant difference in microbial species richness between the seawater temperature treatments (p > 0.05) (Figure 4). However, the post hoc pairwise test indicated that microbial species diversity was significantly lower in the elevated temperature treatment (35A) (ACE = 1660.83 ± 81.20, Faith PD = 119.30 ± 5.05, and observed feature = 1542.17 ± 81.54) than in the ambient temperature treatment (30A) (ACE = 1808.70 ± 108.86, Faith PD = 134.16 ± 6.97, and observed feature = 1753.00 ± 104.95) (p < 0.05) (Figure 4A–C). Bacterial species evenness was higher in the high-seawater-temperature treatments (J’ = 0.856) than in the ambient-temperature treatments (J’ = 0.835). However, there were no significant differences between the high- and ambient-temperature treatments (p > 0.05). In the ambient seawater temperature treatments, Proteobacteria, Campilobacterota, and Actinobacteria were the dominant phyla, while Firmicutes, Desulfobacterota, and Spirochaetota were found with high relative abundance in the higher-seawater-temperature treatments.
At the class level, the heatmap (Figure 3) shows that Alphaproteobacteria, Gammaproteobacteria, and Bacilli were dominant in the ambient-seawater-temperature treatments, while Desulfuromonadia and Clostridia were dominant in the higher-seawater-temperature treatments. There were also differences at the bacterial species level between the seawater temperature treatments. Halarcobacter sp., Marinobacter hydrocarbonoclasticus, Halodesulfovibrio sp., Izimaplasma sp., Roseimarinus sp., Marinifilum sp., Neptuniibacter Caesariensis, Defluviitaleaceae UCG-011, Oceanirhabdus sp., Fusibacter sp., and Vibrio sp. were dominant in the higher-seawater-temperature treatments, while Ruegeria sp., Pseudoalteromonas sp., Mycoplasma sp., Limnobacter sp., EC94 sp., Methylophaga sp., Bacillus sp., Silicimonas sp., Marixanthomonas sp., and Kiloniellaceae bacteria were found with high relative abundance in the ambient-seawater-temperature treatments.
There were no significant differences in microbial species diversity and evenness between the nutrient treatments (p > 0.05). Nevertheless, microbial communities in the high-nutrient treatments showed slightly higher diversity and evenness (ACE = 1727.95 ± 127.62, Faith PD = 126.80 ± 8.01, observed feature = 1680.83 ± 124.32, and J’ = 0.847 ± 0.012) compared with communities in the ambient-seawater treatments (ACE = 1660.83 ± 110.79, Faith PD = 126.74 ± 7.41, observed feature = 1614.33 ± 105.30, and J’ = 0.844 ± 0.011) (Figure 4A,D). At the phylum level, Desulfobacterota, Firmicutes, and Actinobacteriota showed high abundances in the ambient-nutrient treatments, while the high-nutrient treatments were dominated by Proteobacteria and Cyanobacteria (Figure 2). At the class level, Desulfobulbia, Babeliae, Desulfobacteria, Clostridia, Bacilli, and Desulfovibrionia showed high abundances in the ambient-nutrient treatments, while Alphaproteobacteria, Gammaproteobacteria, Actinobacteria, Dojkabacteria, and Cyanobacteriia showed high abundances in the high-nutrient treatments (Figure 3). At the species level, Pseudoalteromonas sp., Mycoplasma sp., Halodesulfovibrio sp., Desulfovibrio sp., Roseimarinus sp., V2072-189E03 sp., Seonamhaeicola sp., Marinifilum sp., Oceanirhabdus sp., Fusibacter sp., Fictibacillus sp., and Desulfopila sp. were found with high relative abundance in the ambient-nutrient treatments, while Vibrio sp., Halarcobacter sp., Sulfurospirillum sp., Leisingera sp., Neptuniibacter Caesariensis, Pontibacterium granulatum, Actibacter sp., Photobacterium sp., Polycyclovorans sp., and Malaciobacter sp. were found with high abundance in the high-nutrient treatments. The Venn diagram depicting the analysis of all 1905 bacterial taxa shows that 173 (9.08%), 236 (12.39%), 120 (6.30%), and 131 (6.88%) unique bacterial taxa were found in the 30A, 30N, 35A, and 35N treatments, respectively. All treatments shared the same 596 (31.29%) taxa (Figure 5).
Ruegeria sp., Woeseia sp., Marivita sp., Escherichia-Shigella sp., and Marinobacter hydrocarbonoclasticus were the dominant bacterial species in all treatments. For beta diversity analysis, a PCoA of the microbial species composition with an unweighted UniFrac distance matrix showed that there was no significant difference in microbial species composition among the treatments (PERMANOVA, p > 0.05, Figure 6).
Lefse analysis at a threshold LDA score of 2.0 showed twelve taxa that significantly discriminated between the treatments. There were high abundances of an uncultured genus (of the family Saprospiraceae) and SJA-15 (of the family SJA-15) in the 35N treatment. The Fusobacteriales (of the Fusobacteriia class), the Rs-M59 termite group (of the order Campylobacterales), and Sva1033 also showed high abundances in the 35N treatment. Terasakiella (of the order Rhodospirillales), Pseudovibrio (of the family Stappiaceae), Aliikangiella (of the family Kangiellaceae), Aureivirga (of Flavobacteriaceae), and Fimbriimonadales (of the phylum Armatimonadota) were the taxa that presented with the highest abundances in the 30N treatment, while Luteibaculum (of the family Cryomorphaceae) and uncultured (of the order Chitinophagales) were found with high abundances in the 30A treatment (Figure 7).
According to the predictions of the functional potentials of the microbial community, PICRUSt 2 showed that rpoE (RNA polymerase sigma-70 factor), fabG (3-oxoacyl-[acyl-carrier protein] reductase), ABC-2.A (ABC-2 type transport system ATP-binding protein), and ABC-2.P (ABC-2 type transport system permease protein) were the four dominant genes, with relative abundances of 0.319 ± 0.003, 0.239 ± 0.004%, 2.245 ± 0.007%, and 0.234 ± 0.005%, respectively (Figure 8). The rpoE gene (KO) showed the highest abundance in all treatments and was most abundant under the high-temperature condition. Genes ABC-2.A (ABC-2 type transport system ATP-binding protein) and ABC-2.P (ABC-2 type transport system permease protein) were more abundant in the ambient-nutrient condition, while mbtN (acyl-ACP dehydrogenase) was more abundant in the high-nutrient-concentration condition. Under the high-seawater-temperature condition, ABC.CD.A (putative ABC transport system permease protein) and ABC.CD.P (ABC-2 type transport system permease protein) were the dominant genes.

4. Discussion

The results of this study demonstrated the impacts of temperature and nutrient concentration on a microbial community associated with turf algae under laboratory culture. A high seawater temperature can shift microbial community composition by enhancing species diversity and the abundance of some microbial groups. However, nutrient concentration did not affect the abundance and species diversity of the microbial community.
The dominant bacterial phyla found in this study were Proteobacteria, Bacteroidota, Firmicutes, Actinobacteriota, and Desulfobacterota. These five phyla are commonly found in marine ecosystems, such as coral reefs, seagrass beds, and mangroves [35,36,37,38], and are associated with macroalgae [36,39]. At the class level, Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia were the three most abundant classes, and all are associated with macroalgae in Thai waters [36,40] and elsewhere [41,42].
Microbial diversity was lower in the high-seawater-temperature treatments, perhaps because high temperatures affect the abundance of macroalgae, influencing photosynthesis, growth, and survival rates and microbial diversity by inducing protein and enzyme denaturation, ribosomal degradation, DNA damage, and growth inhibition [43,44,45]. Increased seawater temperature is a major factor in shifting microbial-dominated communities to pathogenic bacteria-dominated communities [46,47]. In our higher-temperature treatments, Desulfobacterota, Spirochaetota, and Firmicutes were abundant, while in the lower-temperature treatments, Proteobacteria and Actinobacteria were abundant. These findings support the work of [48], who showed that the abundance of Proteobacteria decreased when temperature increased, while the abundance of Firmicutes increased, and that of Zhou et al. [49], who showed that Actinobacteria abundance decreased when seawater temperature increased. Recent studies of microbial communities in extreme terrestrial and underwater environments noted members of the phyla Desulfobacterota and Spirochaetota [49,50,51]. Zhou et al. [49] elucidated the thermophilic adaptation of Desulfobacterota by analyzing metabolism and gene expression. However, there are no reports of this type of adaptation in the phylum Spirochaetota. In this study, we also found that the relative abundances of SJA-15 and Sva1033 were significantly elevated. These two taxa belong to the phyla Chloroflexi and Desulfobacterota, respectively, and have been reported to possess thermophilic adaptations that confer optimal growth at elevated temperatures [52,53]. Their ability to thrive under high-temperature conditions is supported by previous findings [49,54].
Between different nutrient conditions, there were no significant differences in the species diversity or species evenness of the microbial community. However, Proteobacteria and Cyanobacteria showed higher relative abundances in the high-nutrient condition. Many studies have suggested that high nutrient loadings can alter marine microbial communities [55,56,57]. Proteobacteria are normally found in habitats with high nutrient concentrations that induce their growth rate [58], and high nutrient concentrations are factors that increase chlorophyll a levels, growth rates, and abundance among Cyanobacteria [59,60]. In the present study, Firmicutes and Desulfobacterota were abundant under ambient-nutrient conditions, and in natural habitats, they are normally associated with macroalgae with normal nutrient concentrations [36,42,61].
Under high-nutrient conditions, five bacterial groups showed significantly higher abundances: the Fimbriimonadaceae family and the genera SJA-15, Terasakiella, Pseudovibrio, and Aliikangiella. These taxa belong to the classes Alphaproteobacteria (Pseudovibrio and Terasakiella), Gammaproteobacteria (Aliikangiella), Anaerolineae (SJA-15), and Fimbriimonadia (Fimbriimonadaceae). The increased presence of these groups under eutrophic conditions suggests they may exhibit a copiotrophic lifestyle, characterized by high growth and cell division rates in nutrient-rich environments [62]. Consistent with this, these bacterial groups have often been reported in polluted, nutrient-rich, or environmentally stressed marine habitats [63,64]. Several studies have suggested that these taxa play important roles in nutrient cycling, carbon cycling, and organic matter degradation [63,65,66,67]. In the control treatment (ambient temperature and nutrient levels), Luteibaculum was significantly enriched. This may indicate a preference for low-nutrient, lower-temperature conditions. This genus has been frequently observed in the sea surface microlayer and is a common taxon in stable marine environments [68]. Therefore, Luteibaculum may serve as a bioindicator of baseline or unimpacted reef conditions. Overall, the findings from the LEfSe analysis may provide valuable microbial indicators for assessing coral reef health. However, further research is needed to elucidate the ecological functions of certain bacterial taxa identified in this study.
In this study, the pathogenic bacteria Vibrio and Photobacterium presented with high relative abundance in the high-nutrient-concentration treatments. Several studies have suggested that elevated nutrient concentrations can change epilithic algae that host diverse microbial communities, including pathogenic bacteria [69,70], and lead to microbial communities dominated by pathogenic bacteria [71]. Vibrio also showed increased abundance under high-temperature conditions, consistent with the findings of Ritchie et al. [46] and Düsedau et al. [47], who demonstrated that elevated temperatures can promote the proliferation of pathogenic bacterial communities. Others have noted that Vibrio and Photobacterium are important pathogenic bacteria in coral reef ecosystems [72,73,74]. These two pathogens are frequently found on reefs and in turf algae facing a range of anthropogenic disturbances [36,75]. Epilithic algae and microbiota can be vectors promoting coral bleaching and coral disease by transferring pathogenic bacteria to corals [72,74,76]. The low abundance of Vibrio in the lower-temperature treatments might be related to the high abundance of Ruegeria in that condition, which can prevent the growth of Vibrio [25,77]. Pseudoalteromonas was relatively abundant in the ambient nutrient condition. This bacterial genus also plays an important role in inducing coral juvenile settlement and coral juvenile metamorphosis, and it protects coral from pathogens [78,79], macroalgal settlement, and the germination of macroalgal spores [80]. However, Pseudoalteromonas piratica [81] and Pseudoalteromonas piscicida [82] are, respectively, coral and macroalgal pathogens.
In the gene functional predictions, the rpoE gene showed higher relative abundance in the higher-temperature treatments. The rpoE gene is found in many bacteria [83]. A high abundance of the rpoE gene might indicate that the microbial community is suffering from heat shock [83]. Genes related to metabolic, transport, signaling, and cell processes showed higher abundances under the lower-temperature and high-nutrient conditions (30N), which might indicate that the ambient environment was suitable for the microbial community. However, the functional prediction was not derived from actual gene expression. Moreover, the accuracy of function prediction depends on representing the bacterial taxa in reference genome databases, assumptions of phylogenetic conservation of gene content, and correction for 16S rRNA gene copy numbers. To understand the functional shifts within the turf-associated microbial community in increasing seawater temperatures and eutrophication, metatranscriptomic analysis is needed.
In this study, we used relatively few biological replicates, which may reduce statistical power and limit our ability to detect subtle changes in the microbial community associated with turf algae under laboratory conditions. In addition, the experimental period was short, providing only a snapshot of microbial responses that may not capture long-term adaptations to elevated temperature and nutrient enrichment. Furthermore, the lack of site replication may constrain the generalizability of the findings to natural reef ecosystems. Future studies with larger sample sizes, site replication, and longer durations would improve the robustness and enhance the ecological relevance of the findings. Nevertheless, our results provide valuable insights into how increasing seawater temperatures and nutrient availability can shape turf-associated microbial communities under laboratory conditions, with potential implications for coral reef ecosystem functions. Turf algae can harbor coral pathogens such as Vibrio that can be transferred to corals through contact, causing diseases such as black band disease. Elevated seawater temperatures and nutrient enrichment can facilitate pathogens and act synergistically to damage and harm corals. Therefore, management and conservation are needed to mitigate and minimize human impacts and global threats to coral reefs.

5. Conclusions

This study shows that seawater temperatures are the main factor in shaping the microbial community of turf algae, especially in terms of microbial species diversity. This factor was higher under the lower-seawater-temperature conditions but demonstrated no significant difference between high- and low-nutrient concentrations. The higher seawater temperature decreased microbial diversity. Proteobacteria, Bacteroidota, and Actinobacteria were dominant in all treatments. At the class level, Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia had the highest relative abundances. The potential pathogens Vibrio and Photobacterium were abundant under the high-nutrient condition, and these can induce coral disease and bleaching. Pseudoalteromonas, which induces coral settlement and metamorphosis, was abundant under the low-nutrient condition. However, some members of this genus are coral-pathogenic bacteria. In the lower-seawater-temperature treatment—Ruegeria, which inhibits the growth of Vibrio—was abundant. In the higher-seawater-temperature treatments, Desulfobacterota was abundant. This phylum has adapted to survive in high temperatures. The high abundance of the rpoE gene showed that the higher temperature induced heat stress in the microbial community on the turf algae. Our results provide useful information about the microbiome of reef turf algae and an experimental setting for further investigation of bacterial functions and metabolic processes in natural conditions under elevated temperature and nutrient enrichment conditions. Understanding how temperature and nutrient concentrations influence benthic microbial communities might be useful in coastal and reef conservation. However, the influence of other abiotic factors, such as light intensity, pH, and salinity, also needs to be evaluated.

Author Contributions

Conceptualization, J.M.; methodology, A.D., S.P., and J.M.; software, J.T.; validation, J.M.; formal analysis, J.T.; investigation, J.T. and J.M.; resources, J.M., S.P., and A.D.; data curation, J.T. and J.M.; writing—original draft preparation, J.T.; writing—review and editing, J.M.; visualization, J.T.; supervision, J.M.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science and Research and Innovation Fund (NSRF), Prince of Songkla University (SCI6601028S). This research was also supported by the Faculty of Science Research Fund, Faculty of Science, Prince of Songkla University (SCIORP6801).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Design of the laboratory experiment: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). * acclimation for 3 days.
Figure 1. Design of the laboratory experiment: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). * acclimation for 3 days.
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Figure 2. The chart shows relative abundances at the phylum level under four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Each color represents a different bacterial phylum.
Figure 2. The chart shows relative abundances at the phylum level under four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Each color represents a different bacterial phylum.
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Figure 3. The heatmap shows the relative abundances of thirty dominant classes of bacteria under four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). The colors indicate relative abundance levels from low (white) to high (dark red).
Figure 3. The heatmap shows the relative abundances of thirty dominant classes of bacteria under four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). The colors indicate relative abundance levels from low (white) to high (dark red).
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Figure 4. The charts show the abundance-based coverage estimator (ACE) (A), Faith’s phylogenetic diversity (PD) (B), observed features (C), and Pielou’s evenness (D) of microbial communities in four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Kruskal—Wallis test results for alpha diversity indices across all treatments: ACE (p = 0.2815), Faith PD (p = 0.4077), observed features (p = 0.4060), and Pielou’s evenness (p = 0.599). The * symbol is the mean p-value less than 0.05 (p < 0.05).
Figure 4. The charts show the abundance-based coverage estimator (ACE) (A), Faith’s phylogenetic diversity (PD) (B), observed features (C), and Pielou’s evenness (D) of microbial communities in four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Kruskal—Wallis test results for alpha diversity indices across all treatments: ACE (p = 0.2815), Faith PD (p = 0.4077), observed features (p = 0.4060), and Pielou’s evenness (p = 0.599). The * symbol is the mean p-value less than 0.05 (p < 0.05).
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Figure 5. Venn diagram analysis of relative abundance at the genus level between four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Venn diagrams were drawn using the website https://www.bioinformatics.com.cn/static/others/jvenn/example.html (accessed on 27 August 2024).
Figure 5. Venn diagram analysis of relative abundance at the genus level between four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). Venn diagrams were drawn using the website https://www.bioinformatics.com.cn/static/others/jvenn/example.html (accessed on 27 August 2024).
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Figure 6. PCoA of microbiomes of all samples based on an unweighted UniFrac distance matrix at species level. Red = 30 °C with ambient nutrient concentration (30A). Blue = 30 °C with high nutrient concentration (30N). Orange = 35 °C with ambient nutrient concentration (35A). Green = 35 °C with high nutrient concentration (35N).
Figure 6. PCoA of microbiomes of all samples based on an unweighted UniFrac distance matrix at species level. Red = 30 °C with ambient nutrient concentration (30A). Blue = 30 °C with high nutrient concentration (30N). Orange = 35 °C with ambient nutrient concentration (35A). Green = 35 °C with high nutrient concentration (35N).
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Figure 7. Linear discriminant analysis effect size (LEfSe) analysis of microbial abundance among different treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). (A) Cladogram of detected prokaryotic taxa for each treatment. (B) Taxa with significant differences between different treatments were detected using LEfSe analysis with an LDA threshold score of 2.0 and a significance rating of 0.05.
Figure 7. Linear discriminant analysis effect size (LEfSe) analysis of microbial abundance among different treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). (A) Cladogram of detected prokaryotic taxa for each treatment. (B) Taxa with significant differences between different treatments were detected using LEfSe analysis with an LDA threshold score of 2.0 and a significance rating of 0.05.
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Figure 8. The heatmap shows the relative abundance of the predicted top thirty-five dominant genes (KO numbers) among four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). The colors indicate relative abundance levels from low (white) to high (dark red).
Figure 8. The heatmap shows the relative abundance of the predicted top thirty-five dominant genes (KO numbers) among four treatments: 30 °C with ambient nutrient concentration (30A), 30 °C with high nutrient concentration (30N), 35 °C with ambient nutrient concentration (35A), and 35 °C with high nutrient concentration (35N). The colors indicate relative abundance levels from low (white) to high (dark red).
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Titioatchasai, J.; Darakrai, A.; Phetcharat, S.; Mayakun, J. Effects of Elevated Temperatures and Nutrient Enrichment on Microbial Communities Associated with Turf Algae Under Laboratory Culture. Oceans 2025, 6, 68. https://doi.org/10.3390/oceans6040068

AMA Style

Titioatchasai J, Darakrai A, Phetcharat S, Mayakun J. Effects of Elevated Temperatures and Nutrient Enrichment on Microbial Communities Associated with Turf Algae Under Laboratory Culture. Oceans. 2025; 6(4):68. https://doi.org/10.3390/oceans6040068

Chicago/Turabian Style

Titioatchasai, Jatdilok, Anuchit Darakrai, Sinjai Phetcharat, and Jaruwan Mayakun. 2025. "Effects of Elevated Temperatures and Nutrient Enrichment on Microbial Communities Associated with Turf Algae Under Laboratory Culture" Oceans 6, no. 4: 68. https://doi.org/10.3390/oceans6040068

APA Style

Titioatchasai, J., Darakrai, A., Phetcharat, S., & Mayakun, J. (2025). Effects of Elevated Temperatures and Nutrient Enrichment on Microbial Communities Associated with Turf Algae Under Laboratory Culture. Oceans, 6(4), 68. https://doi.org/10.3390/oceans6040068

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