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Article

Effects of Aquaculture and Thalassia testudinum on Sediment Organic Carbon in Xincun Bay, Hainan Island

by
Qiuying Han
1,*,†,
Wenxue Che
2,†,
Hui Zhao
2,*,
Jiahui Ye
1,
Wenxuan Zeng
1,
Yufeng Luo
1,
Xinzhu Bai
1,
Muqiu Zhao
1 and
Yunfeng Shi
1
1
Key Laboratory of Utilization and Conservation for Tropical Marine Bioresources of Ministry of Education/Key Laboratory for Coastal Marine Eco-Environment Process and Carbon Sink of Hainan Province, Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya 572022, China
2
College of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang 524000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Water 2024, 16(2), 338; https://doi.org/10.3390/w16020338
Submission received: 22 November 2023 / Revised: 9 January 2024 / Accepted: 14 January 2024 / Published: 19 January 2024
(This article belongs to the Special Issue Conservation and Monitoring of Marine Ecosystem)
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Abstract

:
Eutrophication due to aquaculture can cause the decline of seagrasses and impact their carbon storage capacity. This study explored the effects of aquaculture on the sediment organic carbon (SOC) in Thalassia testudinum seagrass beds using enzyme activity and microorganisms as indicators. Our results showed that the distance to aquaculture significantly increased the SOC and TN of sediments; the C/N ratio of sediments was reduced by the distance to aquaculture. Distance to aquaculture and seagrasses significantly impacted the δ13C of sediments, and their significant interactive effects on the δ13C of sediments were found. Distance to aquaculture and seagrasses had significantly interactive effects on the cellulase activity of sediments. Distance to aquaculture and seagrasses separately reduced the invertase activity of sediments. SOC in the seagrass bed was significantly positively impacted by cellulase activity and polyphenol oxidase activity in sediments. Firmicutes, Desulfobacterota and Chloroflexi were the dominant taxa in the S1 and S2 locations. From the S1 location to the S2 location, the relative abundances of Firmicutes and Desulfobacterota increased. The functional profiles of COG were relatively similar between the S1 and S2 locations. BugBase phenotype predictions indicated that the microbial phenotypes of all the seagrass sediment samples were dominated by anaerobic bacteria in terms of oxygen utilizing phenotypes. FAPROTAX functional predictions indicated that aquaculture affects functions associated with seagrass bed sediment bacteria, particularly those related to carbon and nitrogen cycling. This study can provide an important basis for understanding the response mechanism of global carbon sink changes to human activities such as aquaculture and supply more scientific data for promoting the conservation and management of seagrass beds.

1. Introduction

Seagrass beds provide a variety of ecological functions such as sediment accretion and stabilization [1,2], nursery habitats [3,4,5], and coastal protection [6,7,8,9,10]. Seagrass beds are regarded as one of the major blue carbon sinks that indirectly contribute to climate change mitigation [11,12,13]. In recent years, the total area of seagrass beds has been rapidly declining due to human activities worldwide [14]. Eutrophication is one of the main causes of global seagrass bed degradation [15,16]. Eutrophication can lead to algal blooms and light attenuation, affecting the primary productivity of seagrass beds and reducing their cover area [15,17]. Although seagrass beds cover only 0.1% of the ocean surface [18], they can capture large amounts of carbon [12] and store 10–18% of the ocean’s total carbon mass annually [19,20]. The global average surface SOC content in seagrass beds is 1.8%, with approximately 50% of this SOC coming from seagrasses themselves [21]. Typically, seagrasses fix more carbon than they need for metabolism [22], and most of the excess organic carbon is transported to the roots and rhizomes of the seagrasses, where it is eventually sequestered in the sediment through environmental action [21]. Seagrass beds can release dissolved organic carbon, either through the release of biomass or from macrophytes [23,24], and transport it to other ecosystems through water currents [25]. Annual global seagrass bed export of dissolved organic carbon is as high as (1.6~3.3) × 108 MgC [22], accounting for approximately 46% of the global net primary productivity of seagrasses [26]. Seagrasses, such as Thalassia testudinum, provide habitats for a wide variety of calcifying organisms which contribute to lime mud production, acting as sources of inorganic carbon [27]. For example, algal and epiphytic carbonate production supported sediment production in T. testudinum beds in Eleuthera Bank, Bahamas (range: 1.8–237.3 g CaCO3 m−2 yr−1) [28]. The mechanisms of T. testudinum contributing to carbon banks have not been clarified but are important for evaluating the influences of seagrass calcification to the ocean carbon cycle and the analysis of current and future impacts of ocean acidification.
Sediment enzymes are involved in biogeochemical processes in sediments, and these enzymes are closely related to the degradation of organic matter in sediments, energy transfer, nutrient cycling, and the environmental quality of sediments [29]. The high carbohydrate content in seagrass plants provides the rich substrate for the reaction of oxidoreductases (polyphenol oxidase and peroxidase, etc.) and hydrolases (invertase, cellulase, and amylase, etc.) in the sediments of seagrass beds [30]. However, few studies have reported on the properties of oxidoreductases and hydrolases that are closely related to carbon transformation in the sediments of seagrass beds. It is important to investigate the main enzymatic activities of SOC to understand carbon conversion and storage processes. The long-term storage of organic carbon in the sediments of seagrass beds is mainly due to the anaerobic environment, which is not conducive to microbial growth and where seagrass debris is not easily decomposed [31,32,33]. However, the global loss of organic carbon from seagrass beds is as high as 2.99 × 108 MgC per year [34]. Some studies have shown that eutrophication, global warming, plant invasion, and anthropogenic disturbance may impact the characteristics of microbial communities in seagrass beds [35,36]. SOC storage potential may be positively correlated with seagrass productivity as increased anthropogenic nutrients have altered the sediment carbon storage potential of seagrass beds [37]. In addition, eutrophication can affect the storage capacity of organic carbon by altering its composition and the processes through which microorganisms transform [33,38]. Thus, changes in microbial activity and community structure can affect the stability of blue carbon and increase microbial respiration and activity, which could increase the organic carbon mineralization, thereby accelerating carbon loss [39,40,41]. However, to date, there has been limited research conducted on the impact of microorganisms on the carbon cycle in seagrass beds (but see [42]). Studies on how microorganisms respond to nutrient enrichment and, thus, influence organic carbon transformation processes in seagrass beds still need to be strengthened.
The rapid expansion of aquaculture in coastal areas worldwide has led to a variety of environmental problems [43,44], and the growth of aquaculture areas along the coast of China has increased the risk of degradation of seagrass beds [38]. Xincun Bay, an almost completely enclosed bay, is a key aquaculture area in China, where Thalassia testudinum is the dominant species [38]. In recent years, fish farming by net has caused a significant increase in nutrient concentrations in seawater, resulting in significant degradation of seagrass beds in Xincun Bay [45,46]. The area of aquaculture was approximately 500 m2, and fish aquaculture production was 900 t in 2011 [47]. Since 2017, the area of fish aquaculture has been decreased due to the sustainable management policies of the local government. The tide in Xincun Bay is an irregular diurnal tide, and the average tidal range is 0.63 m [48]. The tidal current at the inlet of the lagoon is a reciprocating current, lasting 15 h for the flood tide (direction to the lagoon) and 10 h for the ebb tide [48]. The average density and biomass of seagrasses separately decreased from 1910 shoots/m2 and 127 g/m2 in 2004 to 775 shoots/m2 and 42.25 g/m2 in 2021. The average coverage rate of seagrasses decreased from 66% in 2006 to 9.9% in 2021 [49]. In Xincun Bay, the highest and lowest value of T. testudinum leaf length was 15.05 ± 6.13 cm in May and 7.19 ± 2.55 cm in September [50]. The highest and lowest value of T. testudinum leaf width was 11.93 ± 1.68 cm in November and 5.22 ± 1.71 cm in September, respectively [50]. The highest value (31.23 ± 0.94%) and the lowest value (24.9 ± 3.48%) of carbon content of the underground T. testudinum tissues was recorded in November and March, respectively [50]. Seawater temperature had significantly positive relationships with the leaf width and carbon content of the underground tissues [50]. This study aims to investigate the distance effects of aquaculture on the organic carbon content, enzymatic activity, and microorganisms in the sediments of T. testudinum seagrass beds in Xincun Bay. Distance is defined as the length (m) from the center of the fish aquaculture area to T. testudinum seagrass meadows. Clarifying the effects of aquaculture on the organic carbon stocks of sediments in T. testudinum beds can provide an important basis for understanding the response mechanisms of global carbon sink changes to anthropogenic eutrophication and support important scientific data for promoting the conservation and management of seagrass beds.

2. Materials and Methods

2.1. Sampling and Sample Preparation

Samples were collected in Xincun Bay (18°24′–18°27′ N, 109°57′–110°02′ E), located in southeastern Hainan Island, South China Sea. Surface layer sediments (0–5 cm) in or out (10 m to the seagrass edge) of the T. testudinum beds at 2 different distances (S1 (500 m), S2 (2000 m)) from fish aquaculture cages were collected (Figure 1). In each sampling location, 3 replicated sediment samples were collected. In total, there were 12 sediment samples obtained. After collection, the samples were quickly put into PE Ziplock bags, stored in an incubator with ice packs, and then quickly brought back to the laboratory. The microbial sequencing samples were immediately placed in a −80 °C refrigerator for storage.

2.2. SOC, TN, C/N Ratio, δ13C, and Enzyme Activity

The sediment samples were freeze-dried, sieved, and then soaked in 1 M hydrochloric acid for 8–10 h to remove inorganic carbon; the SOC and TN content was then measured by a CHN elemental analyzer (Elementar, Vario EL-III, Langenselbold, Germany). Sediment δ13C was measured using a Stable Isotope Analyzer (IRMS, MAT 253Plus, ThermoFisher Scientific, Waltham, MA, USA). The C/N ratio of the sediments was calculated. The activities of polyphenol oxidase, invertase, and cellulase in the supernatants were measured using commercial kits (QS2934, QS2939, and QS2934, Cablebridge Biotechnology, Shanghai, China), following the manufacturer’s recommendations. The concentration of total protein in the supernatant was quantified using a BCA Protein Assay kit (QS3202, Cablebridge Biotechnology, Shanghai, China) and was used to normalize the measured activities of polyphenol oxidase, invertase, and cellulase to U mg−1 prot.

2.3. Bacteria Diversity

Microbial community genomic DNA was extracted from the sediment samples using the FastDNA® Spin Kit for Soil (MP Biomedicals, Solon, OH, USA), according to the manufacturer’s instructions. The hypervariable regions V3-V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler (ABI, Carlsbad, CA, USA). Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA), according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

2.4. Statistical Analysis

To test the effects of aquaculture distance and seagrasses and their interaction (distance × seagrasses) on SOC, TN, the C/N ratio, δ13C, and enzyme activity, we used a two-way ANOVA. The p-value of the univariate responses was used to indicate which of these separate variables gave the most significant response. Those data that deviated from normality (Kolmogorov–Smirnov’s test) or homoscedasticity (Levene’s test) were transformed prior to analysis in order to meet two-way ANOVA assumptions. The correlation between TN, the C/N ratio, enzyme activity, SOC, and δ13C of each sampling location was analyzed according to Pearson’s correlations using SPSS11.5 software. Data were presented as means (±SE), and a significant level of 5% was used in all analyses.
Referring to the method of previous reports [51,52], the fastp version 0.19.6 was used to eliminate the low quality score (<20) sequence. After removing the chimeric sequences, operational taxonomic units (OTUs) with a 97% similarity cutoff [53,54] were clustered using UPARSE version 7.1 [53]. All reads were identified and classified using the RDP Classifier against the Silva 16S rRNA database project, at a bootstrap confidence level of 70%. We calculated the alpha diversity estimators, including Shannon, Simpson, ACE, Chao1, and Coverage. The Bray–curtis distance matrix in Qiime software version 1.9.1 was used to calculate the beta diversity of the microbial communities, and the principal coordinate analysis (PCoA) was used to create the map. A community barplot analysis of the variation of the bacterial community in the different samples was illustrated with the R pheatmap package. We used PICRUSt2 software version 2.2.0 (https://github.com/picrust/picrust2/ accessed on 21 November 2023), the microbiome analysis tool BugBase (https://bugbase.cs.umn.edu/index.html accessed on 21 November 2023), and the FAPROTAX database (http://www.loucalab.com/archive/FAPROTAX accessed on 21 November 2023) for functional prediction.

3. Results

3.1. SOC, TN, C/N Ratio, δ13C, and Enzyme Activity

Distance to aquaculture significantly increased the SOC (p = 0.002) and TN (p < 0.001) of sediments (Figure 2a,b). The highest and lowest SOC separately appeared outside the seagrass bed at the S2 location (0.40 ± 0.04) and in the seagrass bed at the S1 location (0.27 ± 0.01). The highest and lowest TN separately appeared outside the seagrass bed at the S2 location (0.045 ± 0.005%) and in the seagrass bed at the S1 location (0.025 ± 0.002%). The C/N ratio (p < 0.001) of sediments was reduced by the distance to aquaculture (Figure 2c). The highest and lowest C/N ratio separately appeared in the seagrass bed at the S1 location (11.02 ± 0.29) and outside the seagrass bed at the S2 location (8.95 ± 0.50) (Figure 2c). Distance to aquaculture (p = 0.036) and seagrasses (p < 0.001) significantly impacted the δ13C of sediments, and their significant interactive effects on the δ13C of sediments were found (p < 0.001) (Figure 2d).
Significant interactive effects between distance to aquaculture and seagrasses on the cellulase activity of sediments were found (p < 0.001) (Figure 3a). Distance to aquaculture (p = 0.04) and seagrasses (p = 0.002) separately reduced the invertase activity of sediments (Figure 3b). The highest and lowest C/N ratio separately appeared in the seagrass bed at the S1 location (1.83 ± 0.07 mg·g−1·d−1) and outside the seagrass bed at the S2 location (1.39 ± 0.08 mg·g−1·d−1) (Figure 3b). Distance to aquaculture and seagrasses significantly interactively impacting the polyphenol oxidase activity of sediments were found (p < 0.001) (Figure 3c).
TN in sediments had significant positive effects on SOC in or outside the seagrass bed (Table 1). SOC in the seagrass bed was significantly positively impacted by cellulase activity and polyphenol oxidase activity in sediments (Table 1).
Water 16 00338 g002
Figure 2. Responses of sediment SOC (a), TN (b), C/N (c), and δ13C (d) to aquaculture distance and seagrasses. The p-values of the two-way ANOVA (see Table 2) are indicated above the graph.
Figure 2. Responses of sediment SOC (a), TN (b), C/N (c), and δ13C (d) to aquaculture distance and seagrasses. The p-values of the two-way ANOVA (see Table 2) are indicated above the graph.
Water 16 00338 g002
Water 16 00338 g003
Figure 3. Responses of sediment activity of cellulase (a), invertase (b), and polyphenol oxidase (c) to aquaculture distance and seagrasses. The p-values of the two-way ANOVA (see Table 2) are indicated above the graph.
Figure 3. Responses of sediment activity of cellulase (a), invertase (b), and polyphenol oxidase (c) to aquaculture distance and seagrasses. The p-values of the two-way ANOVA (see Table 2) are indicated above the graph.
Water 16 00338 g003
Table 1. Pearson’s correlations between environmental factors and sediment organic carbon and δ13C in T. testudinuma bed at different distances from the aquaculture cage.
Table 1. Pearson’s correlations between environmental factors and sediment organic carbon and δ13C in T. testudinuma bed at different distances from the aquaculture cage.
In the Seagrass BedOutside the Seagrass Bed
SOC Contentδ13CSOC Contentδ13C
Sediment TN0.959 **−0.2130.901 **0.256
Sediment C/N ratio−0.731 **0.465−0.031−0.249
Cellulase activity0.747 **0.126−0.323−0.103
Invertase activity0.0990.515−0.0390.131
Polyphenol oxidase activity0.471 *0.395−0.0990.166
Note(s): * Indicated correlation at 0.05 (double tail). ** Indicated correlation at 0.01 (double tail).
Table 2. ANOVA results showing the effects of seagrasses and distance on sediment variables. Univariate responses are displayed as the average value of sediment variables inside and outside the seagrass bed at different distances (mean ± S.E.). Both the p-values of the two-way ANOVA for seagrasses and distance variables and the p-values of the one-way ANOVA for seagrasses and distance variables are shown.
Table 2. ANOVA results showing the effects of seagrasses and distance on sediment variables. Univariate responses are displayed as the average value of sediment variables inside and outside the seagrass bed at different distances (mean ± S.E.). Both the p-values of the two-way ANOVA for seagrasses and distance variables and the p-values of the one-way ANOVA for seagrasses and distance variables are shown.
S1S2p-Value
VariablesIn the Seagrass BedOutside the Seagrass BedIn the Seagrass BedOutside the Seagrass BedDistanceSeagrassDistance & Seagrass
SOC0.27 ± 0.010.33 ± 0.030.39 ± 0.020.40 ± 0.040.002 **0.3330.407
TN0.025 ± 0.0020.031 ± 0.0020.042 ± 0.0040.045 ± 0.005<0.001 **0.2280.713
C/N11.02 ± 0.2910.52 ± 0.279.52 ± 0.428.95 ± 0.50<0.001 **0.1730.926
δ13C−14.49 ± 0.24−14.71 ± 0.44−15.53 ± 0.42−12.03 ± 0.310.036 *<0.001 **<0.001 **
Cellulase activity0.12 ± 0.000.15 ± 0.010.15 ± 0.010.12 ± 0.000.3820.741<0.001 **
Invertase activity1.83 ± 0.071.65 ± 0.051.75 ± 0.111.39 ± 0.080.04 *0.002 **0.262
Polyphenol oxidase activity7.89 ± 0.3410.85 ± 0.3610.09 ± 0.347.56 ± 0.180.0890.492<0.001 **
Note(s): * Indicated correlation at 0.05 (double tail). ** Indicated correlation at 0.01 (double tail).

3.2. The Microbes in Sediments

3.2.1. Alpha Diversity and Beta Diversity

Each sample was more than 98% of the coverage, indicating that these sequencing results represented the real condition of the samples. The rarefaction curves tended to approach the saturation plateau, showing that the sequencing data were reasonable and that most bacterial species were detected and could reflect bacterial diversity. The microbial diversity index listed in Table 3 comprises community diversity indices (Shannon, Simpson) and community richness indices (Ace, Chao1). The α diversity varied in the seagrass bed and outside the seagrass bed. The Shannon index showed little change in the seagrass bed (6.65 ± 0.23) and outside the seagrass bed (6.65 ± 0.01) at the S1 location, and that outside the seagrass bed (6.69 ± 0.09) was higher than in the seagrass bed (6.11 ± 0.04) at the S2 location; the Simpson index at the S1 location exhibited little change in the seagrass bed (0.0038 ± 0.0011) and outside the seagrass bed (0.0038 ± 0.0001), which at the S2 location outside of the seagrass bed (0.0044 ± 0.0001) was lower than in the seagrass bed (0.0093 ± 0.0004). The Ace and Chao1 indices at both the S1 and S2 locations were higher outside the seagrass bed than in the seagrass bed. The above results indicated that there was little variation in the diversity of community diversity in and outside the seagrass bed, with higher community richness outside the seagrass bed than in the seagrass bed. Distance to aquaculture affected the α diversity of sediments. The Shannon index was lower at the S2 (6.11 ± 0.04) than S1 location (6.65 ± 0.23) in the seagrass bed, which was higher at the S2 (6.69 ± 0.09) than S1 location (6.65 ± 0.01) outside the seagrass bed; the distance to aquaculture resulted in that the Simpson index increased. The Ace index and Chao1 index at the S2 location were lower than the S1 location in the seagrass bed, which at the S2 location were higher than the S1 location outside the seagrass bed. The results showed that interactive effects between distance to aquaculture and seagrasses on the α diversity of sediments.
Principal coordinate analysis (PCoA) measured differences among communities based on beta diversity, which was used to compare bacterial community diversity among the samples (Figure 4). The PCoA results showed that the first two principal components explained 64.97% of the variation in community composition between the samples, with PC1 and PC2 explaining 42.63% and 22.34% of community variance, respectively. Replicated samples of each location were clustered together separately for high reproducibility. The communities at the S1 and S2 locations exhibited changes over distances according to the PCoA. The bacterial communities in the seagrass bed and outside the seagrass bed at both the S1 and S2 locations clustered closer, respectively, although the clusters at the S1 location were farther apart than the clusters from the S2 location. Based on these results, the bacterial communities of the seagrass bed sediments differed by distance from aquaculture, while the more similar the distance from aquaculture, the more similar the species composition of the samples.

3.2.2. Microbial Community Structure

The bacterial community composition at the phylum level is shown in Figure 5. There were 13 bacterial phyla, and their relative abundances were greater than 2% in the samples; the relative abundances of phyla were less than 2% in the samples named as others. Across all the samples, the seagrass sediment community was dominated by Firmicutes, Desulfobacterota, and Chloroflexi, but their relative abundances were different. The relative abundances of these three phyla accounted for 13.57%, 11.70%, and 10.31%, respectively. The highest relative abundances of Firmicutes, Desulfobacterota, and Chloroflexi separately appeared in the seagrass bed at the S2 location (15.98%), in the seagrass bed at the S2 location (13.87%), and outside the seagrass bed at the S1 location (12.67%). The three other phyla, Acidobacteriota (9.12%), Proteobacteria (9.05%), and Bacteroidota (8.87%), also contributed a relatively large percentage of the identified organisms.
At the S1 location, the relative abundances of Firmicutes, Chloroflexi, and Acidobacteriota were higher outside than inside the seagrass bed; the relative abundances of Desulfobacterota, Proteobacteria, and Bacteroidota were lower outside than inside the seagrass bed. At the S2 location, the relative abundances of Chloroflexi and Acidobacteriota were higher outside than inside the seagrass bed; the relative abundances of Firmicutes, Desulfobacterota, Proteobacteria, and Bacteroidota were lower outside than inside the seagrass bed. Distance to aquaculture led to the relative abundances of Firmicutes, Desulfobacterota, and Chloroflexi increased, and the relative abundances of Acidobacteriota, Proteobacteria, and Bacteroidota decreased in the seagrass bed. The relative abundances of Firmicutes, Desulfobacterota, and Bacteroidota were lower at the S1 than S2 location outside the seagrass bed. The relative abundances of Chloroflexi, Acidobacteriota, and Proteobacteria were higher at the S1 than S2 location outside the seagrass bed.
At the genus level, heat maps of the top 20 genera were used for the samples inside and outside the seagrass bed at different sites to analyze the structure of the seagrass sediment community (Figure 6). As shown in Figure 6, norank_f__Desulfocapsaceae, norank_f__norank_o__Actinomarinales and norank_f__norank_o__norank_c__norank_p__Latescibacterota were the dominant species in the seagrass bed at the S1 location. The relative abundances of these three genera accounted for 4.12%, 3.74%, and 2.89%, respectively. Norank_f__norank_o__SBR1031, norank_f__norank_o__norank_c__norank_p__Latescibacterota and norank_f__norank_o__Aminicenantales were the dominant species outside the seagrass bed at the S1 location. The relative abundances of these three genera accounted for 3.88%, 3.68%, and 2.94%, respectively. At the S2 location, sediment communities in the seagrass bed were dominated by Psychrobacter, norank_f__Desulfocapsaceae and norank_f__norank _o__Actinomarinales, and their relative abundances were 5.38%, 4.95%, and 4.17%, respectively. Outside the seagrass bed at the S2 location, sediment communities were dominated by Sulfurovum, norank_f__norank_o__norank_c__norank_p__Latescibacterota and norank_f__norank_o__SBR1031, with the relative abundances of them being 4.51%, 3.12%, and 2.90%, respectively. From the horizontal axis, the samples between in and outside the seagrass bed were generally separated; that is the samples outside the seagrass bed were more similar than in the seagrass bed at both the S1 and S2 locations.

3.2.3. Linear Discriminant Analysis (LDA) Effect Size Analysis

LDA was used to find the potential discriminating taxa with significant differences in different groups. Only statistical analyses from phylum to genus were performed since an analysis of the large number of OTUs detected in this study was too computationally complex.
The LEfSe analysis revealed many specific bacterial groups at both the S1 and S2 locations. The results showed that there were 31 bacterial taxa distinguished between the S1 and the S2 locations with an LDA value of 3 (Figure 7). Three classes, namely, Kiritimatiellae, BD2-11_terrestrial_group, and Thermoleophilia; seven orders, namely, WCHB1-41, Milano-WF1B-44, Arenicellales, norank_c__BD2-11_terrestrial_group, Chromatiales, Gaiellales, and Polyangiales; six families, namely, norank_o__WCHB1-41, norank_o__Milano-WF1B-44, norank_o__norank_c__BD2-11_terrestrial_group, Arenicellaceae, Sedimenticolaceae, and norank_o__Gaiellales; and six genera, namely, norank_f__norank_o__WCHB1-41, norank_f__norank_o__Milano-WF1B-44, Robiginitalea, norank_f__norank_o__norank_c__BD2-11_terrestrial_group, norank_f__norank_o__Gaiellales, and unclassified_f__Sedimenticolaceae were enriched at the S1 location. One class (ABY1), one order (unclassified_c__Bacilli), three families (unclassified_c__Bacilli, unclassified_o__Lachnospirales, and Defluviitaleaceae), and four genera (unclassified_c__Bacilli, unclassified_o__Lachnospirales, Desulfosarcina, and Defluviitaleaceae_UCG-011) were enriched at the S2 location.

3.2.4. Function Prediction Analysis of Bacteria

Function Prediction by PICRUSt

To determine the function of the observed sediment bacteria, a community prediction analysis was carried out using the PICRUSt method. The PICRUSt program enabled the prediction of 16S rRNA based on high-throughput sequencing data and the use of the Cluster of Orthologous Groups (COG) database for further data analysis. The microbial COG profiles are shown in Figure 8; the functional profiles of COG were relatively similar in all the samples. As the distance from aquaculture increased, no major changes were found either in or outside the seagrass bed. The main microbial functional features included: energy production and conversion; amino acid transport and metabolism; carbohydrate transport and metabolism; translation, ribosomal structure, and biogenesis; transcription; cell wall/membrane/envelope biogenesis; inorganic ion transport and metabolism; and signal transduction mechanisms all of which indicate that the samples were dominated by metabolic functions.

Phenotypic Prediction by BugBase

BugBase analysis was used to predict the phenotype of prokaryotic microorganisms in the environmental samples, which included gram-positive, gram-negative, biofilm-forming, pathogenic, mobile element-containing, oxygen-utilizing (aerobic, anaerobic, facultatively anaerobic), and oxidative stress-tolerant phenotypes. Figure 9 shows that the phenotypes of the seagrass sediment microbiota and a large proportion of the microbial phenotypes were gram negative, pathogenic, biofilm forming, and oxidative stress tolerant. In terms of the gram-negative phenotype, the relative abundance was higher at the S2 than S1 location. With similar relative abundances at both the S1 and S2 locations, the gram-positive phenotype was altered compared with the gram-negative phenotype. Distance to aquaculture significantly increased the relative abundances of pathogenic phenotypes (p < 0.05) of sediments. The relative abundances of biofilm-forming, oxidative stress-tolerant, aerobic, and mobile element-containing phenotypes in sediments were reduced by the distance to aquaculture. In terms of the oxygen-utilizing phenotype, all the samples had a predominantly anaerobic microbial phenotype. The facultatively anaerobic microbial phenotype showed the lowest relative abundance in the samples. The relative abundances of sediment anaerobic and facultatively anaerobic phenotypes increased with distance from aquaculture.

Function Prediction by FAPROTAX

FAPROTAX function annotation results showed that the predicted OTUs presented variation within seagrass sediment and were shaped by the distance to aquaculture (Figure 10). Chemoheterotrophy, aerobic chemoheterotrophy, fermentation, phototrophy, and photoautotrophy were the main functions, but their abundances varied inside and outside the seagrass bed. At both the S1 and S2 locations, the abundances of chemoheterotrophy, aerobic chemoheterotrophy, phototrophy, and photoautotrophy were lower outside than inside the seagrass bed, but the fermentation was reversed, with a higher abundance outside than inside the seagrass bed. There was an increasing trend in the abundances of chemoheterotrophy, aerobic chemoheterotrophy, phototrophy, and photoautotrophy inside the seagrass bed from the S1 location to the S2 location. The function abundance of fermentation was lower at the S1 location than the S2 location inside the seagrass bed. Distance to aquaculture reduced the abundances of chemoheterotrophy, aerobic chemoheterotrophy, and fermentation outside the seagrass bed. The distance to aquaculture resulted in the abundances of phototrophy and photoautotrophy increased outside the seagrass bed. The abundance of hydrocarbon degradation in the seagrass bed sediments increased with the increased distance from aquaculture.

4. Discussion

Seagrasses are typically composed of non-structural carbohydrates and structural carbohydrates [55,56]. Non-structural carbohydrates typically include sucrose and glucose, which are more easily decomposed and are a readily mineralized organic carbon, while lignin and cellulose, which are difficult to degrade, are structural carbohydrates and are used to form the tissue structure of seagrasses [57]. The sucrose, cellulose, and lignin content of seagrasses is high in nutrient-loading environments due to the fact that nutrient enrichment is not conducive to seagrass growth and may cause algal blooms [58]. Liu found that nutrient concentrations were directly proportional to the enzymatic activity of the surface sediments in seagrass beds and that higher nutrient loads accelerated the decomposition of organic carbon in the sediments [59]. Our results (Figure 3) showed that invertase activity decreased as the sediments got farther away from aquaculture areas, but SOC, TN and C/N ratio significantly increased, demonstrating that the nutrient-rich effluent from aquaculture may increase enzyme activity and decrease the mineralization rate of SOC in the sediments. The δ13C values of seagrasses range from −23‰ to −3‰, with most seagrasses having δ13C values of approximately −10‰ [60]. The δ13C values of macroalgae, planktonic algae, and epiphytic algae are lower than that of seagrasses at approximately −22‰ to −16.8‰ [61,62,63]. In our study, the lower the δ13C values, the greater the contribution of seagrass tissue to SOC.
Our results showed that invertase activity was significantly decreased by the distance to aquaculture and seagrasses (Table 1), and the distance to aquaculture and seagrasses interactively impacted cellulase and polyphenol oxidase activity (Figure 3). Cellulase and polyphenol oxidase activity, as well as TN, had a positive correlation with SOC content (Table 1), which indicated that the mechanisms of nutrient enrichment from aquaculture to SOC in seagrass beds, by impacting enzyme activity, may be more complicated than what is currently known [29]. We recommend further research involving the combined effects of the mechanisms of nutrients and seagrasses on SOC through a long-term investigation.
Microorganisms are important to the process of SOC conversion in seagrass beds [64]. Microorganisms can directly use low molecular weight organic carbon [65] and can also use extracellular enzymes to degrade less active organic carbon [66]. Higher nutrient concentrations not only have a significant negative impact on the growth and development of seagrasses but also affect bacterial activity and alter microbial communities [67]. Nutrient concentrations can increase the input of unstable organic carbon into the sediment [68], leading to an increased microbial abundance in seagrass bed sediments and altering the composition of microbiomass carbon in SOC [69]. In this study, distance to aquaculture influenced the bacterial α diversity in the seagrass bed sediments, which demonstrated that eutrophication was one of the environmental stressors and had an impact on the bacterial diversity of the seagrass bed sediments. Fahimipour et al. analyzed the diversity of the symbiotic microbial communities of two seagrasses, Halophila ovalis and Posidonia australis, and found that the symbiotic microbial diversity varied between host types [70]. Piñeiro-Juncal et al. also found that bacterial diversity in seagrass beds was influenced by sediment depth and plant cover [71]. Mechanisms by which microoganisms affect SOC in seagrasses in the conditions of eutrophication due to aquaculture require further research.
The structural composition of microbial communities is a key indicator of organic carbon conversion processes in seagrass beds and a basis for understanding the organic carbon storage potential of seagrass beds [72]. In this study, the relative abundance of Desulfobacterota increased from the S1 location to the S2 location. Desulfobacterota is closely related to organic carbon mineralization in seagrass beds, as sulphate reduction is an important pathway for organic carbon mineralization in seagrass beds. Desulfococcus uses sulfate, sulfite, and thiosulfate as electron acceptors for the complete oxidation of different fatty acids and alcohols to CO2 [73]. From the S1 location to the S2 location, the relative abundance of Bacteroidota reduced in the seagrass bed. Some of the microorganisms in the phylum Bacteroidota have unique T9SS secretion systems, which are important for the degradation of polysaccharides in Bacteroidota [74,75,76]. The mechanism of sugar metabolism in Bacteroidota is mainly through the degradation of cellulose and soluble sugars by lytic polysaccharide monooxygenases and the production of superoxide dismutase by interacting with other bacteria to promote lignin breakdown [77,78,79]. In our study, at the genus level, the dominant taxa of bacterial communities in the sediments between in and outside the seagrass bed differed and changed with distance from aquaculture, which indicated that nutrient enrichment may alter the dominant taxa in the bacterial community of seagrass bed sediments. That should be considered in future studies.
Our study found that the microbial phenotypes of all the seagrass sediment samples were dominated by anaerobic bacteria in terms of oxygen-utilizing phenotypes. An important reason for the long-term storage of organic carbon in seagrass bed sediments is that the anaerobic environment is not conducive to the growth of aerobic microorganisms, which are less able to decompose inert organic carbon in the sediment [32,33]. The relative abundance of anaerobic bacteria was higher at the S2 than S1 location, which demonstrated that nutrient enrichment may lead to significant increase in the abundance of aerobic bacteria in seagrass bed sediments, which is clearly detrimental to the storage of SOC. From the S1 location to the S2 location, the relative abundance of bacterial communities in seagrass bed sediments showed a decreasing trend in terms of oxidative stress-tolerant phenotypes, which also further demonstrated that nutrient enrichment could cause an oxidative stress response that affects the microbial conversion process of organic carbon, thus its storage capacity [57].
In this study, FAPROTAX function annotation results showed that the abundances of chemoheterotrophy and aerobic chemoheterotrophy in seagrass bed sediments showed an increasing trend from the S1 location to the S2 location. The aerobic chemoheterotrophic and chemoheterotrophy processes are correlated with the sediment C/N ratio; nutrient enrichment in the cage aquaculture area provides an additional source of nitrogen to the seagrass bed sediment microorganisms, which increases the demand for carbon due to the balance of carbon and nitrogen requirements [80]. The drivers of microbial community assembly are trophic interactions [81], which may be because nutrient enrichment not only causes seagrass beds to decline but also decreases the carbon storage of seagrass beds. The hydrocarbon degradation process was positively correlated with the sediment C/N ratio [80]. The cage aquaculture area caused nutrient enrichment; therefore the abundance of the hydrocarbon degradation function of sediment microorganisms was lower in the seagrass bed closest to the cage aquaculture area.

5. Conclusions

Overall, this study provides evidence that distance to aquaculture significantly increased the SOC and TN of sediments, and the C/N ratio of sediments was reduced by the distance to aquaculture. Distance to aquaculture and seagrasses significantly interactively impacted the δ13C of sediments. Aquaculture and seagrass beds altered the SOC content by impacting enzyme activity and microorganisms, which provides more theoretical support for achieving carbon neutrality in seagrass beds. SOC transformation processes are complex because they may be impacted by environmental variables, such as tidal currents, which could affect sediment composition and seagrass distribution. This complexity should be well considered in future coastal management and seagrass protection, especially in the background of eutrophication. Additional long-term monitoring of global carbon sink changes response mechanisms to biological factors, such as benthos and microorganisms, are also required.

Author Contributions

Methodology, Q.H., W.C., H.Z., J.Y. and Y.S.; Validation, Q.H., H.Z. and Y.L.; Investigation, Q.H., W.C., J.Y., W.Z. and M.Z.; Resources, H.Z.; Data curation, Q.H. and W.C.; Writing—original draft, W.C.; Writing—review & editing, Q.H., W.C. and H.Z.; Visualization, X.B.; Supervision, Q.H. and H.Z.; Project administration, Q.H.; Funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a high-level talented team project of the Natural Science Foundation of Hainan Province (420RC657), the National Natural Science Foundation of China (No. 42076162), the Key Research and Development Project of Hainan Province (ZDYF2023SHFZ100), and the Major Science and Technology Program Project of Yazhou Bay Innovation Research Institute of Hainan Institute of Tropical Oceanography (2022CXYZD002).

Data Availability Statement

All of the research data have been provided in the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sampling sites in Xincun Bay, Hainan Island, South China Sea.
Figure 1. Sampling sites in Xincun Bay, Hainan Island, South China Sea.
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Figure 4. PCoA of the seagrass sediment communities. Red represents sites in the seagrass bed, and green shows sites outside the seagrass bed.
Figure 4. PCoA of the seagrass sediment communities. Red represents sites in the seagrass bed, and green shows sites outside the seagrass bed.
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Figure 5. Bacterial community composition of seagrass sediment (at the phylum level).
Figure 5. Bacterial community composition of seagrass sediment (at the phylum level).
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Figure 6. Bacterial community relative abundances at the genus level in seagrass sediment species (top 20).
Figure 6. Bacterial community relative abundances at the genus level in seagrass sediment species (top 20).
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Figure 7. LDA analysis indicates significantly different distributions between locations S1 and S2.
Figure 7. LDA analysis indicates significantly different distributions between locations S1 and S2.
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Figure 8. PICRUSt function prediction of seagrass sediment bacterial community.
Figure 8. PICRUSt function prediction of seagrass sediment bacterial community.
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Figure 9. Phenotypic prediction of seagrass sediment by BugBase. * Indicated correlation at 0.05.
Figure 9. Phenotypic prediction of seagrass sediment by BugBase. * Indicated correlation at 0.05.
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Figure 10. Abundances of FAPROTAX function annotation for the bacteria from the seagrass sediment.
Figure 10. Abundances of FAPROTAX function annotation for the bacteria from the seagrass sediment.
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Table 3. The α diversity of bacterial community in sediments (mean ± S.E.).
Table 3. The α diversity of bacterial community in sediments (mean ± S.E.).
SamplesShannonSimpsonAceChao1
In the seagrass bedS16.65 ± 0.230.0038 ± 0.00114716.50 ± 802.284650.80 ± 545.54
S26.11 ± 0.040.0093 ± 0.00044310.60 ± 506.394235.80 ± 285.69
Outside the seagrass bedS16.65 ± 0.010.0038 ± 0.00014849.60 ± 152.754759.50 ± 30.23
S26.69 ± 0.090.0044 ± 0.00005003.90 ± 233.424904.10 ± 165.43
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Han, Q.; Che, W.; Zhao, H.; Ye, J.; Zeng, W.; Luo, Y.; Bai, X.; Zhao, M.; Shi, Y. Effects of Aquaculture and Thalassia testudinum on Sediment Organic Carbon in Xincun Bay, Hainan Island. Water 2024, 16, 338. https://doi.org/10.3390/w16020338

AMA Style

Han Q, Che W, Zhao H, Ye J, Zeng W, Luo Y, Bai X, Zhao M, Shi Y. Effects of Aquaculture and Thalassia testudinum on Sediment Organic Carbon in Xincun Bay, Hainan Island. Water. 2024; 16(2):338. https://doi.org/10.3390/w16020338

Chicago/Turabian Style

Han, Qiuying, Wenxue Che, Hui Zhao, Jiahui Ye, Wenxuan Zeng, Yufeng Luo, Xinzhu Bai, Muqiu Zhao, and Yunfeng Shi. 2024. "Effects of Aquaculture and Thalassia testudinum on Sediment Organic Carbon in Xincun Bay, Hainan Island" Water 16, no. 2: 338. https://doi.org/10.3390/w16020338

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