Are habitat changes driving protist community shifts? A case study in Daya Bay, China

Estuarine, Coastal and Shelf Science 227 (2019) 106356

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Are habitat changes driving protist community shifts? A case study in Daya Bay, China

T

Chuanxin Qina,b,c,∗, Wentao Zhua,b,c,d, Hongmei Maa,b,c, Dingyu Duana,b,c,d, Tao Zuoa,b,c, Shigai Xia,b,c, Wanni Pana,b,c,d a

South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, 510300, China Scientific Observing and Experimental Station of South China Sea Fishery Stock and Environment, Ministry of Agriculture, Guangzhou, 510300, China c Key Laboratory of Marine Ranching, Chinese Academy of Fishery Sciences, Guangzhou, 510300, China d Shanghai Ocean University b

A R T I C LE I N FO

A B S T R A C T

Keywords: Protists Coral reefs Artificial reef Environmental influence Habitat

Protists play an important role in regulating marine ecosystems, and their diversity and community structure differ both temporally and spatially. Little research has been conducted on the structure and function of protists associated with coral reefs and artificial reefs, which are important and unique habitats for marine life. In this study, based on Illumina sequencing analysis of 18S rDNA, the diversity and community structure of protists in coral reef, artificial reef and open-water areas in Daya Bay were described for the first time. The biodiversity of protists in open-water areas was higher than that in artificial reefs and slightly higher than that in coral reefs, but there were no significant differences in protist alpha diversity among the different habitats or depths in Daya Bay. The depth of Daya Bay was not sufficient to significantly affect the distribution of the protist communities, while pH and salinity had the strongest impacts. Furthermore, the correlations between protist abundance and environmental variables of the water mass were much weaker than those between habitat and protist abundance. PERMANOVA revealed significant differences in protist diversity among the habitats. The interactions of living organisms and differences among habitats play key roles in the formation of protist communities, and artificial reefs were found to change the community structure of protists, suggesting that artificial reefs play a role similar to that of coral reefs to improve ecological functions and restore ecosystems.

1. Introduction Sea water is a habitat with an irregular supply of nutrients that is affected by environmental fluctuations, such as upwelling and currents (Polónia et al., 2016), and coral reefs and artificial reefs are two particular habitats in marine ecosystems. Coral reefs are structures that have accumulated over generations from the exoskeletons of coral, which excrete calcium carbonate by absorbing calcium and carbon dioxide. The intricate structure of coral reefs provides habitats for marine organisms, allows the reefs to host a high degree of microbial diversity, and affects microbial community changes (Ainsworth et al., 2010). Although coral reef ecosystems account for a small proportion of the global marine system, they serve as habitat for a quarter of marine fish and other organisms (Rogers et al., 2015). Artificial reefs are artificial structures deployed underwater at the sea bottom near the coast to attract and concentrate fish by imitating some characteristics of natural coral reefs, and they can also improve and restore the aquatic

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ecosystem (Baine, 2001). Such reefs affect the surrounding waters and change the local flow pattern. They also form a unique sediment environment, creating a new local artificial ecosystem (Miller, 2002; Seaman, 2000). Furthermore, the abundance, species richness and biomass of the benthic fauna increase in areas where artificial reefs are constructed (Brown et al., 2014), and the biomass and diversity of fishes and coral benthic communities also change (Feary et al., 2011). Protists, a general term for a large class of unicellular eukaryotes, include more than 20,000 free-living species, including heterotrophic flagellates, ciliates and carnivores, more than half of which live in the marine environment (Appeltans et al., 2012). Protists in marine ecosystems play fundamental ecological roles, mainly as primary producers, and a large part of marine productivity is mediated by photosynthetic protists (Field, 1998). Furthermore, protists are particularly important producers in aquatic food webs (Massana et al., 2011). Protist communities also play key roles in ecosystems, acting as consumers and decomposers, and have other trophic functions (Caron

Corresponding author. South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, 510300. China. E-mail address: [email protected] (C. Qin).

https://doi.org/10.1016/j.ecss.2019.106356 Received 6 June 2019; Received in revised form 9 August 2019; Accepted 26 August 2019 Available online 27 August 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.

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collected from the coral reef areas (less than 5 m) because the water is shallow at these locations, while the water in the artificial reef and open-water areas (water depth greater than 5 m) was sampled at the surface (AR-S and OW-S, respectively) and subsurface (AR-B and OWB). Twenty-five water samples were collected and transferred to sterile cryogenic bottles. After passage through a 200-μm mesh-size pre-filter to reduce the contribution of metazoa to the nucleic acid extracts, the samples were filtered through a Millipore membrane (0.2-μm pore diameter) using a suction device to filter 2 L of seawater. The membranes with the filtered samples were then preserved at −20 °C until further processing. Measurements of environmental variables, including water temperature (T), salinity (Sal), pH, and dissolved oxygen (DO), were obtained using a YSI Professional Plus water quality meter (YSI, USA). Sampling depth (D) was measured using a temperature and salinity probe (CTD, USA).

et al., 2012; Davidson et al., 2010; Sherr and Sherr, 2002). In short, protists are the greatest contributors to living matter (Davidson et al., 2010) and regulate global biogeochemical cycles in marine ecosystems (Caron et al., 2016). Knowledge of the species diversity, community structure and ecosystem functions of protists in natural environments is limited by the shortcomings of traditional microscopic methods and morphological identification (Caron et al., 2012; Mitra et al., 2016). Recently, molecular approaches have enhanced the understanding of microbial diversity, and this technology has been successfully applied to the study of protist communities, revealing high diversity. Protist abundance in the ocean is sensitive to environmental changes, and the diversity and community structure of protists vary among locations due to variation in environmental factors (Lallias et al., 2015). Most studies using molecular approaches in China have been carried out in coastal waters, such as in the South China Sea, the Pearl River, the East China Sea, and the Yellow Sea (R. Li et al., 2018a; Sun et al., 2017; Xu et al., 2017; Yun et al., 2017). There have been far fewer studies on protist abundance in coral reef and artificial reef areas. Most microbial studies on coral reefs have focused on symbiotic bacteria, coral-related bacteria and endophytic algae (Ainsworth et al., 2017; Clerissi et al., 2018), and the knowledge regarding protists in such areas is fragmentary. For artificial reefs and adjacent natural habitats, most studies assess highertrophic-level organisms, such as fish communities (Folpp et al., 2013; Whitmarsh et al., 2008), while ecological studies of artificial reefs at small spatial scales are limited (Yang et al., 2019). Assessing ecosystem function based on species characteristics provides general and predictable rules for understanding community dynamics (Hooper et al., 2005). Considering species identity is critical to understanding ecosystem structure and function, which are intrinsically linked to the characteristics of the species. For example, coral reefs can regulate the functional structure of ecological communities at different spatial scales, and reef functioning depends on the configuration of coral communities and the dominance patterns of key reef-building corals (González-Barrios and Álvarez-Filip, 2018). Therefore, studying the diversity of protists helps us understand the ecological functions of different habitats, such as coral reefs and artificial reefs. In this study, we investigated the protist communities of coral reef, artificial reef and open-water areas in semi-enclosed bays on the eastern side of the Pearl River Estuary in the northern South China Sea by high-throughput amplicon sequencing of the V4 region of 18S rDNA. The main aims of this study were to evaluate the biodiversity of protists in different habits, distinguish spatial changes in community composition, identify changes in the protist community at different depths, determine the similarities and differences among the protist populations in different environments and elaborate on the factors affecting protist diversity and communities. Finally, the ecological effects of artificial reefs were explored by comparing the protist community structure of these reefs with that in natural coral reefs and the open sea.

2.2. Extraction of genomic DNA The sample filters were cut with sterile scissors, and total DNA was extracted according to the instructions for the Power Soil DNA kit (MOBIO, USA). A NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA) was used to determine the DNA concentration and purity. DNA concentration and purity were monitored on 1% agarose gels, and the DNA was diluted to 1 ng/μl using sterile water according to the measured concentration. The extracted DNA was stored in a freezer at −80 °C. 2.3. PCR amplification and illumina sequencing Fifty nanograms of purified DNA was used as the amplification template, and the V4 region of the 18S rDNA gene was amplified using the universal primer set TAReuk454FWD1 (5′-CCA GCA SCY GCG GTA ATT CC-3′; Saccharomyces cerevisiae position 565–584) and TAReukREV3 (5′-ACT TTC GTT CTT GAT YRA-3′; S. cerevisiae position 964–981) (Stoeck et al., 2010). All PCRs were carried out in 30-μL reactions with 15 μL of Phusion® High-Fidelity PCR MasterMix (Biolabs, New England), 0.2 μM forward and reverse primers, and approximately 10 ng of template DNA. Thermal cycling consisted of initial denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 60 s and a final step of 72 °C for 5 min. The resulting PCR products were purified with a GeneJET Gel Extraction Kit (Thermo Scientific, USA) according to the manufacturer's protocol. Sequencing of pooled triplicates was performed on the MiSeq Illumina sequencing platform, and an Illumina PE250 library was constructed for sequencing. 2.4. Sequence analysis Sequences were quality filtered and processed with QIIME version 1.8.0. Usearch software was used in conjunction with the GOLD database to remove chimaeras with a combination of de novo and referencebased methods. Operational taxonomic unit (OTU) clustering at a 97% similarity was performed with non-repetitive sequences (excluding single sequences), and chimaeras were removed during clustering to obtain representative OTU sequences. The remaining reads were clustered into OTUs at the ≥97% similarity level by means of UCLUST (Edgar, 2010), and taxonomic names were assigned to OTUs by comparison with the Silva database. Then, the sequences of the protists were selected for further data analyses (Yun et al., 2017).

2. Materials and methods 2.1. Sampling and environmental measurements Daya Bay is a semi-enclosed subtropical bay surrounded by mountains on three sides that is located in the north of the South China Sea and east of the Pearl River Estuary. There are many islands in the bay, and its coastline is tortuous. The bay includes a variety of natural habitats, including coral reefs, mangroves, rocks and other reefs. Fig. 1 shows the locations of the 15 sampling points in the coral reef (CR1CR5), artificial reef (AR1-AR5) and open-water (OW1-OW5) areas of Daya Bay. The artificial reef area is located near Yangmeikeng in the southwest of Daya Bay (Jia et al., 2011). The water depth of the artificial reef and open-water areas reaches 15 m, while that of the coral reef areas reaches 4 m. Water samples were collected with a water extractor from the surface layer (0.5 m from the surface) and the bottom layer (0.5 m from the bottom). Only water from the surface layer was

2.5. Data analysis Alpha diversity (Shannon diversity index, Chao1 index and Simpson's diversity index) was calculated using mothur (http://www. mothur.org). Based on the standardized Bray-Curtis dissimilarity matrix, principal coordinate analysis (PCoA) was conducted to compare 2

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Fig. 1. Sampling sites in Daya Bay: coral reef (CR1-CR5), artificial reef (AR1-AR5), and open-water (OW1-OW5) areas.

3.2. Beta diversity of protists

community composition among the samples. Restricted-condition principal coordinate axis analysis (CPCoA) based on Bray-Curtis differences was also performed. Permutational ANOVA and multivariate ANOVA (MANOVA) (PERMANOVA) with two factors (habitat and sampling depth) were used to test whether the significant variation in protist diversity was due to habitat (coral reef, artificial reef, and open water) or sampling depth (surface and bottom). A column chart was drawn to show the community structure at the phylum level, and Venn diagrams were created to show the numbers of unique and shared OTUs across all samples. The numbers of unique OTUs for the habitat types were compared with a column chart. Redundancy analysis (RDA) was used to explore the relationships between environmental variables and the protist community, and the Pearson correlation coefficients between environmental variables and the protist community were calculated. Then, the influence of the environmental factors on community composition was evaluated with a Mantel test. Data analyses and visualization were completed in R software. LEfSe analysis was conducted using the online tool LEfSe (http://huttenhower.sph.harvard.edu/ galaxy/root?Tool_id=lefse_upload) to identify communities or species with significant differences among groups.

The PCoA based on Bray-Curtis distances revealed a significant difference in the composition of the protist community (R2 = 0.29, P = 0.001). At the OTU level, PC1 explained 20.36% of the total variance, while PC2 explained 19.11% (Fig. 3a). Usually, PCoA assesses the overall differences among all the samples evaluated in a study, and restricted CPCoA is needed to identify the differences between pairs of groups. Fig. 4b was created with a constrained ordering method combined with Bray-Curtis distances. CPCoA provides a projection plane of the largest difference of each group according to sampling depth. The top of the figure shows the currently displayed plane coordinate system, which explains 24.9% of the total variation among all samples, and there were significant differences among the groups (P < 0.01). The surface layer (AR-S) and bottom layer (AR-B) of the artificial reef area clustered together, and the surface layer (OW–S) and bottom layer (OW–B) of the open-water area clustered together, while the artificial reef bottom layer, the open-water bottom layer and the coral reefs were separated. PERMANOVA was used to quantify the effects of habitat and depth on differences in the protist communities (Table 1).There was no significant difference between OW-S and OW-B (P > 0.05) or between AR-S and AR-B (P > 0.05), while OW-S, AR-S, and CR were significantly different (R2 = 0.277, P < 0.01); there were also significant differences among OW-B, AR-B, and CR (R2 = 0.289, P < 0.01). The variation among the artificial reef (AR), coral reef (CR) and open-water (OW) areas was the most obvious (R2 = 0.237, P < 0.01). Furthermore, PERMANOVA based on Bray-Curtis similarity revealed a significant difference among habitats, indicating that habitat had a greater impact than water layer on the protist community.

3. Results 3.1. Alpha diversity of protists Good coverage and rarefaction curves indicated that the depth of sequencing was sufficient to describe the alpha diversity of the protists. The sequences were divided into different OTUs at a 97% similarity, and the protist diversity and abundance in the samples were calculated based on the analysis of OTUs (Fig. 2). The number of OTUs per sample ranged from 331 to 1104. The OTU richness in OW-B was the highest (923 ± 99), while that in AR-B was the lowest (749 ± 219), but there were no significant differences among the five sample groups (Fig. 2a). The total number of species was greatest in OW-B and lowest in AR-B, but there were again no significant differences among the sample groups (Fig. 3b). The variation in the Shannon index showed the same trend as that in OTU richness and the Chao1 index. From high to low, the order of the groups was OW-B, AR-S, OW-S, CR, and AR-B, with no significant differences among them (Fig. 2c). The Simpson index was highest for OW-S and lowest for AR-S, and there were no differences among the five habitats (Fig. 2d). Overall, the OTU richness, Chao1 index and Shannon index were highest for OW-B and lowest for AR-B. However, alpha diversity did not significantly differ among the 5 habitats.

3.3. Community composition The main protists with an average relative abundance greater than 1% from high to low were as follows: Ochrophyta, Ciliophora, Phragmoplastophyta, Cryptomonadales, Dinoflagellata, Cercozoa, Protalveolata, Rotifera, Eukaryota_uncultured, Chytridiomycota, Cryptomycota, Choanoflagellida, Bicosoecida, Ichthyosporea, Discosea, Rigifilida, Tubulinea, Apicomplexa and Others. Among these taxa, the phyla Ochrophyta (25.88%), Ciliophora (16.76%), Phragmoplastophyta (14.15%) and Cryptomonadales (9.58%) were the most important in the 25 samples. Ochrophyta was highest in OW-S and lowest in CR; Ciliophora was highest in AR-S and lowest in OW-S; Phragmoplastophyta was highest in OW-B and lowest in AR-S; and Cryptomonadales was highest in AR-B and lowest in OW-B. At the phylum level, ANOVA showed no differences in these four main taxonomic groups among the five sampling habitats (Fig. 4). 3

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Fig. 2. Alpha diversity estimates for the protist communities. a OTU richness (number of observed OTUs), b Chao1 index, c Shannon diversity index, d Simpson's diversity index. The groups are the sampling habitats: AR-B, subsurface of the Yangmeikeng artificial reef area; AR-S, surface of the Yangmeikeng artificial reef area; CR, coral reefs; OW-B, subsurface of open-water areas and OW-S, surface of open-water areas.

Cyclotella, and Cercozoa (including Silicofilosea, Vampyrellidae, Vampyrella, Euglyphida, Euamoebida, Chromadorea, and Amphitraemidae) were biomarkers in OW-B.

Among all OTUs detected in Daya Bay at a 97% similarity level, 726 (23.14%) shared OTUs were found in CR, OW-S, OW-B, AR-S and AR-B. There were 3126 OTUs (99.64%) in the surface layer and bottom layer of OW and 3050 OTUs (97.23%) in the surface layer and bottom layer of AR. This finding validated the effects of sampling depth and habitat on the Bray-Curtis dissimilarity in community composition detected in the beta diversity analysis, indicating that the surface and bottom layers of the AR and OW areas did not significantly differ (Fig. 5a). The AR-B samples showed the highest abundance of unique taxa, with Dinoflagellata and Protalveolata being the main representatives. Unique OTUs in OW-S were mainly assigned to Ciliophora or Bicosoecida (Fig. 5b).

3.5. Environmental drivers The environmental factors differed significantly among the sites (PERMANOVA, P < 0.01). To assess the role of environmental variables in shaping protist community composition, a restricted ordination analysis was conducted (Fig. 7). First, the 97% similarity-OTU tables were analysed with detrended correspondence analysis (DCA). The DCA indicated that the maximum gradient length was less than 3, which met the redundancy analysis (RDA) condition. pH and Sal had the strongest influence (P < 0.001) on the species distribution and composition of protists. The correlations (Pearson coefficients) between protist abundance and physicochemical parameters measured in the habitats were calculated. For example, temperature was negatively correlated with Gyrodinium relative abundance (P < 0.05), depth was negatively correlated with Ochromonas and Paraphysomonas relative abundances (P < 0.05), and dissolved oxygen was positively correlated with Strobilidium, Pseudovorticella and Sphaeroeca relative abundances (P < 0.05). Pearson correlation analysis showed that R ranged from −0.50 to 0.42, with the value for approximately 12.75% of the protists and environmental parameters being significant (P < 0.05). The results of the Mantel test verified the results of the correlation analysis between protist relative abundance and associated environmental factors (Table 2).

3.4. LEfSe analysis The LEfSe analysis identified the differentially abundant taxa (biomarkers) in the samples from different habitats and depths. These results were consistent with the overall distribution of average relative abundance of the main taxa of protists identified in the five groups of samples (see above). Forty-five protozoan taxa at different levels with a relative abundance greater than 1% were found in at least one sample (Fig. 6). Cryptophyta (including Goniomonas, Gonyaulacales, and CH1_2B_3), Halteria and Ochromonas were indicative taxa of coral reefs (CR). The groups with the highest relative abundance in AR-S were mainly in Ochrophyta (including Bacillariophytina, Diatomea, Skeletonema, and Ulnaria). Diatomea (including Coscinodiscophytina, Aulacoseira, and Woloszynskia), Gymnodiniphycidae, and Chytridiales were all abundant in AR-B. In OW-S, Choanoflagellida, Salpingoecidae, Codonosigidae, Sphaeroeca, Heteromita, Craspedida, Labyrinthulaceae, and Discosea were relatively abundant indicators. Phragmoplastophyta (including Chlorodendrophyceae, Chlorodendrales, Tetraselmis, Stephanodiscus, Chlorellales, Chloromonas, Katablepharis, and Polytoma), 4

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Fig. 3. Evaluation of beta diversity based on Bray-Curtis dissimilarities. Notes: a PCoA plot of Bray-Curtis distances for areas and depths. b Constrained PCoA plot of Bray-Curtis distances between samples. The groups are the sampling habitats: AR-B, subsurface of the Yangmeikeng artificial reef area; AR-S, surface of the Yangmeikeng artificial reef area; CR, coral reefs; OW-B, subsurface of openwater areas and OW-S, surface of open-water areas.

4. Discussion

2010). Coral polyps in coral reefs mainly feed on small zooplankton, and they also feed on some bacteria and phytoplankton (Qian et al., 2015). The food chain relationships among phytoplankton, zooplankton and corals in coral reef ecosystems differ from those in other sea areas. At the same time, coral reef ecosystems are usually surrounded by water with very low nutrient concentrations. Tides, waves and currents can easily bring nutrients to open-sea areas (Erez, 1990), which means that nutrients in coral reef waters are usually poor. The results of this study are also consistent with the low nutrient availability and

4.1. Diversity of protists in different habitats The biodiversity of OW was higher than that of AR and slightly higher than that of CR (Fig. 2). Protists are among the most important components of plankton in water, and the consumption of bacteria and microalgae by protists in marine microbial food loops is the most basic process of nutrient regeneration in planktonic ecosystems (Tan et al., 5

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Fig. 4. Relative abundances of protists in the layers of the habitats at the phylum level. The groups are the sampling habitats: AR-B, subsurface of the Yangmeikeng artificial reef area; AR-S, surface of the Yangmeikeng artificial reef area; CR, coral reefs; OW-B, subsurface of open-water areas and OW-S, surface of open-water areas.

correlation analysis revealed that only approximately 12.75% of protists were significantly affected by the environmental parameters, which indicated that the correlations between protist relative abundance and environmental variables were much weaker than those between protist relative abundance and habitat. In general, the characteristics of water masses are the main driving factors of prokaryotic diversity in the middle and deep seas. Different prokaryotic communities are found in different water masses, and the prokaryotic community within a single water mass remains similar, even at thousands of kilometres (Zoccarato et al., 2016). Environmental conditions or regional factors may be the main drivers of microbial diversity patterns, but the importance of regional factors may be underestimated (Lindström and Langenheder, 2012). For example, Hardge et al. (2017) used Illumina sequencing to study the protist communities of water masses in different habitats, such as deep chlorophyll maximum water, under-ice water, sea ice and melt pond water, in the central Arctic Ocean. The authors identified characteristic communities associated with specific habitats and indicated that ubiquitous taxa in all water masses often exhibited different habitat preferences. Similarly, the results of the current study clearly showed significant differences among the protist communities of different water masses associated with coral reefs, artificial reefs and open-water habitats. Biogeochemical characteristics of water masses play a minor role in the formation of protist communities, while habitat differences (local factors) play a key role. The results of the beta diversity analysis indicated significant differences among the habitats (artificial reef, coral reef and open-water areas), while the influence of sampling depth was very small (Fig. 3, Table 1). RDA indicated that depth was only negatively correlated with two genera of Phaeophyta (Fig. 7, Table 2). The vertical distribution of marine bacteria in the Atlantic Ocean varies, with distinct combinations at the surface and at 200 m (Morris et al., 2005). Similar patterns exist among the protozoa. Although there were no significant differences in protist assemblage between adjacent depths from the surface to the bottom near Long Beach/San Pedro, there were significant differences among the samples when they were grouped according to depth (< 150 and ≥150 m) (Schnetzer et al., 2011). On a larger depth scale, protists show differential diversity across the vertical distribution of the permanent thermocline (500 m) and deep ocean (3000 m) in the Sargasso Sea (Fabrice et al., 2010). These studies showed that the distributional

Table 1 Quantitative effects of different grouping factors on sample differences based on PERMANOVA. Both R2 and P values are bolded when the correlation is significant. The groups are the sampling habitats: AR-B, subsurface of the Yangmeikeng artificial reef area; AR-S, surface of the Yangmeikeng artificial reef area; CR, coral reefs; OW-B, subsurface of open-water areas and OW-S, surface of open-water areas. Groups

R2

P

AR-S vs. AR-B OW-S vs. OW-B CR vs. AR-S vs. OW-S CR vs. AR-B vs. OW-B CR vs. AR vs. OW

0.075 0.066 0.277 0.289 0.237

0.514 0.624 0.008 0.001 0.001

extremely high productivity of coral reefs. Artificial reefs can provide habitat for feeding and spawning of marine organisms, and they are very conducive to the growth of planktonic communities (Zhang et al., 2016). Medium-sized zooplankton feed on micro-phytoplankton through the classical food chain (Y. Li et al., 2018b) and protozoans through the micro-food chain (Azam et al., 1983); thus, a higher abundance of zooplankton feeding in artificial reefs reduces the diversity of protists. Ciliophora was found to have the highest relative abundance in the artificial reef habitat and the lowest in the open-water areas (Fig. 4b); members of this phylum prey on bacteria, fungi and other protists in micro-food webs (Hlaili et al., 2008). In addition, summer is the flood season for Daya Bay, and the resultant runoff with increased coastal input brings abundant nutrients to the open-sea area (Qiu et al., 2005). At the same time, the higher water temperature in summer provides conditions for a greater diversity of protists in the open-sea area. 4.2. Influence of environmental factors, depth and habitat on protist community shifts Among the environmental factors measured, pH and salinity had the strongest impact (Fig. 7). For example, changes in sea water pH may limit the photosynthesis and growth of marine phytoplankton (Yun et al., 2017), and salinity is a major factor controlling the distributional pattern of ciliates in subtropical estuaries (Sun et al., 2017). Pearson 6

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Fig. 5. Shared OTUs and unique OTUs in CR, OW-S, OW-B, AR-S and AR-B. Notes: a Venn diagram showing the number of unique and shared OTUs for all samples; b Number of unique OTUs of protist taxa. The groups are the sampling habitats: AR-B, subsurface of the Yangmeikeng artificial reef area; AR-S, surface of the Yangmeikeng artificial reef area; CR, coral reefs; OW-B, subsurface of openwater areas and OW-S, surface of open-water areas.

affect the zooplankton community distribution (Huang et al., 2010).

patterns of protists differ with depth, which may be due to the obvious changes in environmental factors, such as the occurrence of chemical/ physical gradients (e.g., light and oxygen declines) at these depth boundaries, which create a niche-rich environment that can support the unique ecological role of protist taxa (Schnetzer et al., 2011). In this study, the sampling depth of Daya Bay was below 15 m, and the difference in protists between the bottom layer and the surface layer was found to be small, which may have been due to the small influence of sampling depth. In a study on the vertical distribution of zooplankton in representative waters in Daya Bay, water temperature, salinity and chlorophyll a were found to have relatively little effect on the changes in zooplankton biomass in the water column and were insufficient to

4.3. Change in biomarkers according to the deployment of artificial reefs Results of the LEfSe analysis showed that the relative abundances of Diatomea (including Bacillariophytina, Skeletonema, Ulnaria, Coscinodiscophytina, and Aulacoseira), Dinoflagellata (Gymnodiniphycidae and Woloszynskia), and Chytridiales in artificial reefs differed significantly. There were more algal biomarkers in artificial reef areas than in the other tested habitats, which may be related to the biological effects of the artificial reefs in the sea area. For example, shellfish attached to artificial reefs can absorb excess nutrients 7

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Fig. 6. Clustering tree. The biomarkers in the five groups are represented by different colours, and yellow nodes represent protozoan taxa that do not play an important role in the five groups. The name of the taxa represented by the letters is shown in the legend on the right. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Lei (Lei et al., 2009) found that the artificial reef increased algal biomass and diversity. Skeletonema is a widely distributed phytoplanktonic taxon in coastal estuaries and marine environments. It is a broad-temperature and low-salinity phytoplanktonic diatom that often forms

from the environment and promote nutrient cycling during their growth, which facilitates the growth of phytoplankton (Zhang et al., 2010). In addition, by comparing the biomass and species of phytoplankton between an artificial reef area and a control area in Daya Bay,

Fig. 7. Distance-based RDA. Numbers represent sample names; different colours or shapes represent sample groups in different environments or under different conditions; arrows represent environmental factors; blue triangles represent different protist taxa; the angles between environmental factors represent positive and negative correlations between environmental factors (acute angle: positive correlation; obtuse angle: negative correlation; right angle: no correlation); and longer arrows represent environmental factors. The closer the projection points and arrows are, the closer the relationship between the sample or species and the environmental factor is. The closer the projection points are, the more similar the attribute values of the environmental factors among samples or species are, that is, the environmental factors have the same influence on the samples or species. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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exist in artificial reefs, there are more nutrients and more active feeding activities than in open-water areas. The greater abundance of zooplankton feeding in artificial reefs also reduces the diversity of protists. The diversity of the bottom layer in the open-water areas was higher than that of the surface layer, while the pattern in the artificial reef was reversed (Fig. 3). For example, red dinoflagellates in open-water areas exhibit a vertical distributional pattern and migrate to the bottom of the water body, which is rich in nutrients, at night (Suzuki et al., 1999). In addition, the enhancement of seawater stratification affects the upward transport of nutrients from the bottom, resulting in a lack of nutrients at the surface and a decrease in primary productivity (Boyce et al., 2010). Conversely, artificial reefs change the flow field near the sea area and produce complex flow patterns, such as upwelling and eddy currents (Chen et al., 2002). The upwelling caused by artificial reefs brings nutrients and organic matter originally deposited onto the bottom into the upper and middle water layers, and it can help supplement nutrients at the surface and optimize nutrient structure, thus affecting the protozoan community at the surface. Natural coral reefs are threatened as a result of human impacts throughout the tropics and subtropics (HoeghGuldberg et al., 2018), and constructing artificial habitats is a viable alternative management strategy. Artificial reefs can mitigate habitat loss and degradation (Fabi et al., 2011). The scope of artificial reef research has expanded from reef design and deployment to improving fisheries and focusing on changes in the community structure or composition associated with such reefs, which indicates that the purpose of artificial reef research has shifted from improving fisheries as a resource to restoring marine ecosystems (Lee et al., 2018). On the smaller scale of protists, the current study shows that artificial reefs can change biodiversity and improve the ecological environment to a certain extent. Similarly, the community structure and composition of temperate reef fishes converge to be more similar to those associated with more complex natural reefs over time (Komyakova et al., 2019). Research shows that artificial reefs can not only protect and increase the biodiversity of reef areas but also improve and restore the marine ecological environment (Powers et al., 2003; Relini et al., 2002; Zalmon et al., 2002). Thus, it is impossible and unrealistic for artificial reefs to be the same as natural coral reefs, but they can imitate some important aspects of natural coral reef structure and perform an ecological function similar to that of coral reefs.

Table 2 Mantel test of community variability (Bray-Curtis dissimilarity) in terms of pH, Sal, DO, temperature and depth among sampling sites. Factor

pH Sal DO T D

Community distance R2

P

0.152 0.173 0.120 0.032 0.030

0.027 0.025 0.072 0.288 0.287

dense, large, monospecific blooms (Celussi et al., 2015). The flow field effect of artificial reefs causes the water to mix more fully than it does in association with other habitat types, and Skeletonema is positively correlated with nutrient level (Wang and Huang, 2008). Dinoflagellates can use their ability to dissolve organic matter to compensate for deficiencies in inorganic nutrients in the environment (Smayda, 1997), and their mixed nutrition strategy can also promote competitive population growth (Johnson, 2015). For example, Gymnodiniphycidae can penetrate the cell membrane of prey for nutrition, which can promote their growth in nutrient-restricted environments (Jeong et al., 2010). It is not surprising that the relative abundance of Chytridiales significantly differed in this study because members of this group produce zoospores and often parasitize other algae, such as Bacillariophyta (Gerphagnon et al., 2015). Artificial reefs produce flow fields, which allow the full exchange of water in all layers and accelerate nutrient cycling; thus, the construction of artificial reefs improves water quality and planktonic abundance (Dai et al., 2018). In the marine microbial food loop, zooplankton and microzooplankton are utilized by larger protozoa (mainly ciliates) (Ko et al., 2011), causing fewer protozoan biomarkers associated with artificial reefs. 4.4. Artificial reefs play an ecological role similar to that of coral reefs With an increase in aquaculture area and the influence of other human activities, some oceanic areas in Daya Bay have become polluted and have begun to deteriorate (Chen et al., 2010). In addition, the ecosystem of Daya Bay is undergoing rapid degradation, and the zooplankton show a trend of individual miniaturization (Wang et al., 2003). The various protozoa in seawater are important components of the micro-plankton. The term protozoa refers to heterotrophic, singlecelled eukaryotes, including flagellates, foraminifera and ciliates, which are generally micro-zooplankton (Matsuno, 1989). The number of protozoans differing in relative abundance in the open-water areas was significantly greater than that in the coral and artificial reefs. The coralzooplankton symbiotic system in coral reefs can effectively facilitate the recycling of nutrients and energy, not only through the absorption of dissolved and granular nutrients but also through the predation of primary producers by zooplankton (Rosenfeld, 2010). Numerous minute corals use their tentacles and gastrointestinal cavity to continuously swallow various microorganisms and phytoplankton in sea water. They filter various particles from the sea water for nutrition and help keep the sea water clean (Cai, 2018). For example, protozoan ciliates serve as a food source for both copepods and corals. Thus, these ciliates enter the coral reef food chain through their trophic relationships (Tan et al., 2010). In addition, the quantities and densities of photosynthetic algae in coral reef ecosystems are very important in regard to the maintenance of the coral reef ecosystem. The shallowwater and oligotrophic environment in such ecosystems is conducive to photosynthesis (Zhao, 2000). Furthermore, the large amount of light and small size of most plants in coral reefs lead to high primary productivity among autotrophs, facilitating carbon sequestration (Birkeland, 1997). Although the phytoplankton-zooplankton-coral food chain does not

Authors’ contributions CQ and WZ conceived the study. HM, DD, TZ, SX and WP led the data collection. WZ performed the analyses and drafted the manuscript. CQ framed the manuscript and contributed to revisions. All authors gave final approval for publication. Acknowledgements Funding for this project was provided by the National Key R&D Program of China (2018YFD0900905), National Natural Science Foundation of China (grant no. 41206119), Central Public-interest Scientific Institution Basal Research Fund, South China Sea Fisheries Research Institute, CAFS (grant nos. 2017YD04, 2019ZD1101) and China Scholarship Council. In addition, the authors would like to thank the anonymous reviewers, who helped improve the paper. References Ainsworth, T.D., Thurber, R.V., Gates, R.D., 2010. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240. Ainsworth, T.D., Fordyce, A.J., Camp, E.F., 2017. The other microeukaryotes of the coral reef microbiome. Trends Microbiol. S966842X–S1730152X. Appeltans, W., Ahyong, S., Anderson, G., Angel, M., Artois, T., Bailly, N., Bamber, R., Barber, A., Bartsch, I., Berta, A., 2012. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202. Azam, F., Fenchel, T., Field, J.G., Gray, J.S., Meyer-Reil, L.A., Thingstad, F., 1983. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10,

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