Diet composition and feeding habits of Meretrix meretrix and Mactra veneriformis in the Northern Bohai Sea based on high- throughput sequencing

Diet composition and feeding habits of Meretrix meretrix and Mactra veneriformis in the Northern Bohai Sea based on high- throughput sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diet composition and feeding habits of Meretrix meretrix and Mactra veneriformis in the Northern Bohai Sea based on high- throughput sequencing Ang Li, Yongan Bai, Ling Zhu, Suyan Xue, Jiaqi Li, Xianglun Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4903946/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 May, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Understanding the diet composition and feeding habits of bivalve shellfish is crucial for developing conservation measures to enhance their resources. This is particularly important for the main economic species in shellfish-producing regions. In this study, we analyzed the stomach contents composition of the two main economic shellfish in Geligang, specifically Meretrix meretrix and Mactra veneriformis, using high-throughput sequencing. The results revealed that 956 operational taxonomic units (OTUs) were common to both M. meretrix and M. veneriformis, with 1117 OTUs unique to M. meretrix and 412 OTUs unique to M. veneriformis. We identified a total of 50 bait organisms from 11 phyla. The main taxa in the stomach contents of M. meretrix were Chlorophyta, Cryptophyta, Pyrrophyta and Bacillariophyta, while Cryptophyta, Chlorophyta, Pyrrophyta and Chrysophyta dominated the stomach contents of M. veneriformis. Non-metric multidimensional scaling (NMDS) analysis indicated less compositional variety in the stomach contents of M. meretrix compared to M. veneriformis. Additionally, the Linear Discriminant Analysis Effect Size (LEfSe) results showed a significant difference in food composition between the two species. Specifically, M. meretrix and M. veneriformis preferred feeding on Bacillariophyta, Chlorophyta, and Cryptophyta, while M. veneriformis favored Chrysophyta. Overall, our study provides fundamental insights for ecological research on feeding habits and resource conservation of M. meretrix and M. veneriformis in Geligang, which can inform the development of effective conservation measures for the shellfish resources. Biological sciences/Molecular biology Biological sciences/Zoology/Animal behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Bivalves are an important part of the marine ecosystem and have been widely used as food, providing a valuable source of protein for humans 1 , 2 . Global aquaculture reached a record high of 130.9 million tons in 2022, with 94.4 million tons coming from aquaculture animals, and 16.6% of this being aquaculture bivalves produced in China 3 . Global climate change, caused by emissions of greenhouse gases (especially carbon dioxide) from human activities in the past century, is one of the most significant challenges facing humanity in the twenty-first century 4 . Reducing emissions and enhancing carbon sink are crucial ways to mitigate the rise of atmospheric carbon dioxide levels. The oceans, being the largest active carbon reservoir on Earth, absorb about 30% of anthropogenic carbon dioxide emissions annually 5 , 6 . Bivalve shellfish contribute to carbon sequestration in two main ways. Firstly, they convert bicarbonate (HCO 3 - ) in seawater into calcium carbonate (CaCO 3 ) shells through calcification. Secondly, they filter-feed on particulate organic carbon in the water column to synthesize their own organic material and increase their carbon content 7 . The faeces and pseudofaeces produced by these organisms are deposited on the seafloor, accelerating the transport of organic carbon to the seafloor. As a result, bivalve shellfish can sequester blue carbon and act as carbon sinks 8 , 9 . Furthermore, due to their widespread distribution, low mobility, filter-feeding capabilities, and their role as traditional model organisms and economically important seafood products, bivalves have been proposed as global or regional indicator animals for monitoring marine microplastic contamination 10 – 12 . The river contributes significantly to the estuary by depositing a substantial amount of silt, which results in a large expanse of shallow water. Additionally, the river discharges considerable organic matter and nutrients into the sea, enriching the estuary area with nutrients 13 . Geligang, located adjacent to the estuary of the Liao River and Shuangtaizi River, covers an area of approximately 10,000 hectares. During high tide, the entire region of Geligang is submerged, while certain portions become exposed during low tide 14 . Estuarine intertidal mudflats are crucial habitats for macrobenthos, serving as growth locations and breeding grounds 15 . Meretrix meretrix and Mactra veneriformis , belonging to the Mollusca, Lamellibranchia, and Veneroida orders, are common benthic economic shellfish species. They primarily inhabit the intertidal mudflats and shallow marine sediments 16 . These two species, M. meretrix and M. veneriformis , are widely distributed in both the intertidal and subtidal areas of Geligang and represent the dominant shellfish species in the region’s mudflats 17 . Consequently, Geligang has emerged as a significant breeding site for northern mudflat shellfish in China. However, in recent years, the quantity of shellfish resources for M. meretrix and M. veneriformis in Geligang has been declining annually due to the increasingly detrimental effects of human activity M. meretrixM. veneriformis 18 . This decline underscores the need for sustainable management practices to protect and preserve these valuable marine resources. M. meretrix and M. veneriformis are filter-feeding shellfish that consume a variety of suspended particles, including inorganic particulate matter, organic detritus, biological waste, bacteria, phytoplankton, and zooplankton in the water column 19 . The knowledge of the diet composition and feeding habits of aquatic species is essential for their resource conservation and ecological studies 20 , 21 . In recent years, Illumina Miseq-based high-throughput sequencing technology has been extensively applied to investigate the diet composition and feeding habits of aquatic species, such as Notorynchus cepedianus 22 , Crassostrea gigas 23 , Apostichopus japonicus 24 , Acanthopagrus schlegelii 25 , and Anguilla Anguilla 26 . With the ability to analyze food with varying degrees of degradation and species categorization at the species level, high-throughput sequencing offers greater sensitivity and accuracy compared to traditional morphological identification. This enables more comprehensive and in-depth dietary assessments 27 , 28 . Information on food composition forms the basis for feeding ecology research and is a critical component in constructing high-resolution food webs 29 , 30 . Therefore, it is crucial from an ecological perspective to understand the dietary characteristics of M. meretrix and M. veneriformis in Geligang and to provide fundamental knowledge for the developing their resource enhancement and conservation strategies. In this study, we employed 23S rDNA as a marker gene to characterize the food composition of M. meretrix and M. veneriformis by Illumina Miseq high-throughput sequencing. The results obtained from this analysis will provide a theoretical foundation for the conservation of their resources in Geligang. By employing this advanced sequencing technique, we aim to gain valuable insights into the dietary preferences and ecological roles of these shellfish species, ultimately contributing to their sustainable management and protection. Results Quantitative traits and delineation of OTUs. The dimensions of the M. meretrix were (5.36 ± 0.14) cm for shell length, (2.35 ± 0.16) cm for shell width, (4.34 ± 0.22) cm for shell height, and (42.29 ± 4.88) g for weight. The dimensions of the M. veneriformis were (3.25 ± 0.32) cm for shell length, (2.21 ± 0.30) cm for shell width, (3.20 ± 0.38) cm for shell height, and (15.86 ± 2.41) g for weight. A total of 1,423,459 high-quality sequence fragments were obtained from 20 samples by Illumina high-throughput sequencing. The lowest sequence number of the samples was 36,957, the maximum was 173,507, and the average number of sequences was (71,173 ± 28,892). The 20 samples were clustered into OTUs after 97% concordance of the sequences, and a total of 2,485 OTUs were obtained. The results of OTUs identification and classification status are shown in Table 1. The number of OTUs shared by the M. meretrix and M. veneriformis was 956 (Fig. 1), of which 1,117 were unique to the M. meretrix and 412 to the M. veneriformis . The dilution curves of every sample leveled off as the sequence depth increased (Fig. 2), suggesting that there were enough samples in this study to capture the great majority of the information. The results of the alpha indices are shown in Fig. 3, with Chao1 and Observed species indices of M. meretrix significantly higher than those of M. veneriformis . Good's coverage of M. meretrix was significantly lower than that of M. veneriformis , while the Shannon, Simpson and Pielou's evenness indices of M. meretrix and M. veneriformis were not significantly different. Identification of eukaryotic species in stomach contents. A total of 50 bait organisms from 11 phyla were identified in the total sample, along with a few OTUs that were unrelated to any known taxonomic groups. The primary taxa of the stomach contents in the M. meretrix were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta, with relative mean abundances of 44.66%, 20.36%, 12.31%, and 9.22%, respectively. Similarly, the primary taxa of the stomach contents in the M. veneriformis were Cryptophyta, Chlorophyta, Pyrrophyta, and Chrysophyta, with relative mean abundances of 44.66%, 20.36%, 12.31%, and 9.22%, respectively (Fig. 4). Euglenozoa, Haptophyta, Xanthophyta, Streptophyta, Chordata, and Cercozoa make up a smaller proportion. The main groups in the Chlorophyta are Haematococcus, Nephroselmis, Pyramimonas, and Chlorella; the main group in the Cryptophyta is Cryptomonas; the main group in the Pyrrophyta is Dinophysis; Bacillariophyta with Thalassiosira as the major group; Chrysophyta with Ochromonas. Compositional characteristics of stomach contents. NMDS analyses based on Bray-Curtis distances found that samples from M. meretrix were grouped together, indicating low variability in the stomach contents composition, while samples from M. veneriformis were found to be more distant from one another, indicating greater variability in the stomach contents composition (Fig. 5). Adonis test results showed significant differences (R 2 = 0.263, P < 0.05) in the composition of the stomach contents of M. meretrix and M. veneriformis . With six groups at the 18% similarity level, Fig. 6 displays the results of the clustering heat map analysis. Samples of M. meretrix were grouped together, but samples of M. veneriformis were separated into six groups. LEfSe analysis allowed the identification of species with significant differences in abundance between groups 31 . The results of LEfSe analyses showed that the stomach contents of M. meretrix had seven orders with LDA values greater than 2 (Fig. 7), namely Bacillariophyceae, Coscinodiscophyceae, and Fragilariophyceae, Glaucocystophyceae, Pedinophyceae, Trebouxiophyceae, and Cryptophyceae; while the stomach contents of M. veneriformis had only two, Chrysophyceae and Dictyochophyceae. Discussion Filter-feeding shellfish are ecologically significant species that not only provide immense economic benefits but also play an important ecological role in driving the transport and transformation of various forms of carbon 32 , 33 . Filter-feeding shellfish consume a wide range of suspended particles, including organic debris, phytoplankton, and zooplankton, from the water column. The distribution of phytoplankton in the water column will be directly impacted by the filter-feeding behaviors of shellfish, which will, in turn, affects the level of marine primary production and ultimately influences the flow of energy and materials throughout the marine ecosystem 34 . Filter-feeding shellfish have selective feeding on phytoplankton. Zhang et al. 35 discovered that Chlamys farreri preferred Bacillariophyta to Pyrrophyta, Bougrier et al. 36 discovered that Crassostrea gigas was able to selectively excrete Skeletonema costatum , Chaetoceros calcirruns , and Nitzschia closterium via pseudofaeces, and Jiang et al. 37 discovered that Argopecten irradians favored ingesting micro- and nano- phytoplankton with larger particle sizes. However, it has been proposed that selective feeding characteristics of filter-feeding shellfish may be affected by the geographical environment. Laboratory experiments often fail to accurately capture the way in which shellfish consume aquatic phytoplankton in their natural environment 38 , 39 . In this study, we selected in situ M. meretrix and M. veneriformis from Geligang for high-throughput sequencing to investigate their feeding ecological characteristics. This approach aims to provide a more accurate understanding of how these shellfish species interact with their natural environment and contribute to marine ecosystem dynamics. Microscopic examination is a widely used technique to determine the diet of organisms; however, it faces challenges in identifying smaller individuals such as pico- and nano-phytoplankton, and in distinguishing digested and semi-digested food in stomach contents 27 , 40 , 41 . While fatty acid and stable carbon and nitrogen isotopic analyses can characterize the extent of the food source, they often fall short in determining the precise composition of the food 28 . High-throughput sequencing provides a solution to these problems by identifying not only the major food components but also tiny, highly digestible bait organisms 42 , 43 . With advancements in high-throughput sequencing technology, its application has expanded widely in feeding ecology, food web structure, species identification, and classification of aquatic organisms 44 – 49 . For example, Qiao et al. 50 used the 23S rRNA gene as a molecular marker to explore differences in the feeding selectivity of Mercenaria mercenaria , M. meretrix and Ruditapes philippinarum in natural waters. Their results showed that these three filter-feeding shellfish exhibite different preferences for phytoplankton genera, with some picophytoplankton dominating the hepatopancreas samples. Similarly, Chen Xiaolei et al. 51 utilized the mitochondrial cytochrome c oxidase subunit I (CO Ⅰ) as a molecular marker to identify the food composition of Diaphus splendidus in the South China Sea. They identified Ostracoda, Copepoda, and Amphipoda as the dominant groups in its food composition, and also discovered jellyfish that were not identified through traditional morphological identification. Li Yulong et al. 52 employed the 18S rDNA high-throughput sequencing method to study the food composition and feeding selection of Hyporhamphus sajori juveniles at different developmental stages under pond culture in the northern Yellow Sea. Their study revealed differences in the food composition of H.sajori juveniles at different developmental stages and detected Fungi, Intramacronucleata, and Streptophyta, which were not identified by traditional morphological identification. In this study, we used 23S rDNA as a molecular marker to investigate the diet of M. meretrix and M. veneriformis . Our findings showed that the primary taxa in the stomach contents of M. meretrix were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta. For M. veneriformis , the primary taxa were Cryptophyta, Chlorophyta, Methanophyta, and Chrysophyta. Liu et al. 53 employed the 18S rDNA high-throughput sequencing method to study the feeding selectivity of R.philippinarum and found that R.philippinarum actively avoided Bacillariophyta, Chrysophyta, and Cryptophyta, while it preferentially selected Pyrrophyta and Chlorophyta. Similarly, Zhang et al. 54 also employed the 18S rDNA high-throughput sequencing method and noted that Bellamya aeruginosa preferred feeding on Chlorella and Euglena, showing lower selectivity for Cryptophyta and Oscillatoriales. In this study, there was a significant difference in food composition between M. meretrix and M. veneriformis . M. meretrix preferred Bacillariophyta, Chlorophyta and Cryptophyta, whereas M. veneriformis favored Chrysophyta. The filter-feeding shellfish employ two primary feeding mechanisms: one relies on the coordinated movement of gill filaments and cilia to capture food particles 55 , hydrodynamic transport of water with different food concentrations for bivalve filtration. While the other utilizes water flow to transport food particles through the gill filaments to the back of the gills and along the dorsal grooves into the labellum 56 , selective feeding by bivalves. Both species are passive filter feeders, The ecological niches occupied by M. meretrix and M. veneriformis in this study are basically the same, so their feeding selectivity may be related to differences in their gill structures. Filter-feeding shellfish with large anterolateral cilia, such as M.mercenaria , retained microalgae larger than 4 µm with 100% efficiency. In contrast, species with small or no anterolateral cilia, like Crassostrea virginica and A. irradians , have retention efficiencies of only 75%-85% 57 . The gill structure of the M. meretrix belongs to the heterorhabdic gill type. The gill filaments are divided into main gill filaments and common gill filaments. The common gill filaments have dense anterior and lateral cilia on the surfaces, which can effectively retain particles larger than 8 µm in size 58 . On the other hand, the gills of M. veneriformis are divided into inner and outer gills, which are petal-shaped. The inner gills are larger than the outer gills and have dense cilia, allowing them to feed on small nutrients flowing into the outer coat cavity. Moreover, the concentration of phytoplankton and their nutritional content can also impact the filter-feeding shellfish's ability to selectively feed 59 , 60 . High-throughput sequencing can detect DNA sequences present in low quantities at a reduced cost. The number of DNA sequences can reflect the relative amount of bait ingestion 61 , 62 . However, it cannot provide absolute quantification due to the overestimation of OTUs for species 63 . Additionally, a portion of the OTUs in this study were not annotated to the species level. There is still much taxonomic information need to be added to the present incomplete 23S rDNA database 64 . In addition to phytoplankton, filter-feeding shellfish also consume zooplankton and organic detritus. However, there are few studies on the potential diets of these filter-feeding shellfish 65 . In high-density aquaculture, the selective feeding of filter-feeding shellfish on different groups of phytoplankton can lead to changes in the structure of the phytoplankton community 66 . In future study, it is also necessary to improve the coverage of taxonomic information, enhance the 23S rDNA database, and increase the identification of potential food sources for zooplankton and other filter-feeding shellfish. Additionally, establishing a long-term monitoring mechanism to observe the succession mechanism of phytoplankton communities is crucial. Furthermore, improving the analysis of the feeding habits of M. meretrix and M. veneriformis is essential to provide valuable data for the study of their feeding ecology and aid in their scientific management and conservation. Conclusion In this study, we employed high-throughput sequencing of 23S rDNA as a molecular marker to analyze the diet composition and feeding habits of M. meretrix and M. veneriformis in Geligang. Our findings revealed that the main taxa in the stomach contents of M. meretrix were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta. For M. veneriformis , the predonminant taxa were Cryptophyta, Chlorophyta, Pyrrophyta, and Chryptophyta. The adonis test results indicated significant differences in the composition of the stomach contents between the two species. M. meretrix showed a preference for Bacillariophyta, Chlorophyta, and Cryptophyta, while M. veneriformis favored Chrysophyta. These feeding preferences may be related to different gill structures. This study provides fundamental insights for research on the feeding ecology and resource conservation of M. meretrix and M. veneriformis in Geligang. Materials and methods Sample collection. As seen in Fig. 8, the M. meretrix and M. veneriformis were randomly collected from the Geligang of Liaodong Bay in June 2023. A total of 10 M. meretrix and 10 M. veneriformis were chosen for predation investigation. Following the collection of the shellfish samples, they were immediately cleaned with water and subjected to standard biological measurements (shell length, shell width, shell height and weight). The stomach contents were sampled, immediately preserved in liquid nitrogen and returned to the laboratory for DNA extraction. DNA extraction. The stomach contents were homogenized after being washed with phosphate buffer (pH 7.2–7.6). The whole DNA was extracted according to the instructions using the Marine DNA Extraction Kit (Tiangen, Beijing, China). The acquired total DNA was measured using a UV spectrophotometer, identified by electrophoresis using 0.8% agarose gel, and the qualified DNA was kept at -20°C. High-Throughput Sequencing. The PCR reaction was conducted in a 20 µL system and sample DNA was amplified by PCR using the 23S rDNA primers p23SrV_f1 (5′-GGA CAG AAA GAC CCT ATG AA-3′) and p23SrV_r1 (5′-TCA GCC TGT TAT CCC TAG AG-3′) 67 . 10 ng of DNA template, 0.5 µL of Q5 high-fidelity Taq enzyme (NEB, Beijing, China), 1 µL of each of the positive and negative primers (5 µmol/L), 4 µL of 5× buffer, and 2 µL of dNTPs (2.5 mmol/L) were used. With a reaction cycle of 29, the PCR process was as follows: pre-denaturation at 95°C for 7 min, denaturation at 95°C for 45 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, and final extension at 72°C for 10 min. Then the PCR products were identified using 2% agarose gel electrophoresis, and purified by, and purified by AxyPrep DNA Gel Extraction Kit (Axygen, Silicon Valley, USA) before being submitted to bipartite sequencing on the Illumina Miseq sequencing platform (Illumina, San Diego, USA). Data analysis. Firstly, the primer fragments of the sequence were removed using cutadapt (v2.3, http://cutadapt.readthedocs.io/en/stable/ ) 68 , and then the raw sequencing data were analyzed by Vsearch (v2.13.4, https://github.com/torognes/vsearch ) 69 for quality control procedures such as quality filtering, noise reduction, splicing and chimera removal to obtain high-quality sequences. QIIME2 (v2019.4) was used to combine the sequences and classify them into operational taxonomic units (OTUs) based on 97% sequence similarity. Representative sequences within the OTUs were chosen for species annotation analysis using the NCBI locally-accounted sequence database (v20230626, ncbi.nlm.nih.gov/). OTU analyses can provide information on the diversity of bait organisms and the relative abundance of different species. The alpha diversity index (Chao1 index, Good's coverage, Simpson index, Pielou's evenness, Shannon index, Observed species index) was calculated using QIIME2, and the Wilcoxon test was used to determine whether group differences were statistically significant. Non-metric multidimensional scaling (NMDS) and hierarchical clustering plots based on Bray-Curtis similarity coefficients were plotted using the vegen package of R 3.6.2 and combined with permutational multivariate analysis of variance (Adonis) to analyze the significance of the differences between the stomach content composition of the M. meretrix and M. veneriformis . Linear discriminant analysis Effect Size (LEfSe) was performed by the microeco package of R 3.6.2 to identify differential bait organisms in M. meretrix and M. veneriformis , with the LDA threshold set at 2. P < 0.01 indicates highly significant differences, and P < 0.05 indicates a significant difference. Declarations Acknowledgments This work was supported by the National Key R&D Program of China (2023YFD2400800), Central Public-interest Scientific Institution Basal Research Fund, YSFRI, CAFS (NO.2020TD50, NO.20603022023007), the Marine Economy Development Project of Liaoning Province (20224722). Author contribution A.L.: Conceptualization, Methodology, Investigation, Software analysis, Writing. Y.B. : Conceptualization, Methodology. L.Z. : Conceptualization, Methodology, Writing- review & editing. S.X.: Investigation, Data curation, Writing- review & editing. J.L. : Methodology, Writing- review & editing. X.L. : Investigation. L.L. : Conceptualization, Data curation, Supervision. L.L. : Investigation. Y.M. : Conceptualization, Methodology, Writing- review & editing, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Correspondence and requests for materials should be addressed to Y.M. References Huang, S., Edie, S.M., Collins, K.S., Crouch, N.M.A., Roy, K. & Jablonski, D . Diversity, distribution and intrinsic extinction vulnerability of exploited marine bivalves. 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PeerJ 4 , e2584 (2016). https://doi.org/10.7717/peerj.2584 Table 1 Table 1 Statistics of operational taxonomic units (OTUs) identification and classification status Species Sample number Phylum Class Order Family Genus Species M. meretrix W_1 9 21 40 54 82 125 W_2 10 23 35 48 67 102 W_3 9 20 31 41 55 83 W_4 9 23 39 51 69 102 W_5 9 21 42 57 83 126 W_6 6 15 26 35 43 64 W_7 9 22 36 51 70 109 W_8 10 21 39 53 78 111 W_9 10 22 37 53 79 112 W_10 8 20 35 47 67 99 M. veneriformis S_1 9 20 36 45 64 96 S_2 8 18 29 36 46 68 S_3 7 17 28 38 51 71 S_4 7 17 26 29 39 51 S_5 7 19 30 35 47 68 S_6 9 22 39 57 76 110 S_7 8 20 40 55 76 113 S_8 8 18 32 44 62 91 S_9 9 21 36 47 64 96 S_10 9 19 35 48 63 89 Additional Declarations No competing interests reported. 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10:54:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":287227,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies composition of eukaryotes for \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/559958cd3084ccf5608583da.png"},{"id":65074302,"identity":"08a0a000-5afa-450e-a29c-ce80535b35a2","added_by":"auto","created_at":"2024-09-23 10:38:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25709,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS analysis of stomach contents for \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/0b2771d41ea184736754517a.png"},{"id":65074307,"identity":"9cdf33d6-f4f0-47ef-85f7-bd891aa163aa","added_by":"auto","created_at":"2024-09-23 10:38:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":467659,"visible":true,"origin":"","legend":"\u003cp\u003eCluster heat map of species composition of stomach contents for \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/517370c7fb88c3a7f8bbf38e.jpg"},{"id":65074308,"identity":"1b4af2ea-c28b-48b0-99a1-bac0b4cfec8c","added_by":"auto","created_at":"2024-09-23 10:38:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":242726,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe analysis of species composition of stomach contents for \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/0cf9b8ecfc02266970a2e527.png"},{"id":65074309,"identity":"c5dc82ce-d843-4381-8cc6-cbb3f52c7a29","added_by":"auto","created_at":"2024-09-23 10:38:11","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":10388854,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of Geligang\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/4f4eaecfaa31f15064d6834d.jpg"},{"id":82537819,"identity":"f84f2886-cfda-47e9-8259-29f473a4a3a5","added_by":"auto","created_at":"2025-05-12 16:10:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13448442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4903946/v1/671c099d-4df7-4f32-906e-f4df462923fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diet composition and feeding habits of Meretrix meretrix and Mactra veneriformis in the Northern Bohai Sea based on high- throughput sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBivalves are an important part of the marine ecosystem and have been widely used as food, providing a valuable source of protein for humans\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Global aquaculture reached a record high of 130.9\u0026nbsp;million tons in 2022, with 94.4\u0026nbsp;million tons coming from aquaculture animals, and 16.6% of this being aquaculture bivalves produced in China\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Global climate change, caused by emissions of greenhouse gases (especially carbon dioxide) from human activities in the past century, is one of the most significant challenges facing humanity in the twenty-first century\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Reducing emissions and enhancing carbon sink are crucial ways to mitigate the rise of atmospheric carbon dioxide levels. The oceans, being the largest active carbon reservoir on Earth, absorb about 30% of anthropogenic carbon dioxide emissions annually\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Bivalve shellfish contribute to carbon sequestration in two main ways. Firstly, they convert bicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) in seawater into calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e) shells through calcification. Secondly, they filter-feed on particulate organic carbon in the water column to synthesize their own organic material and increase their carbon content\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The faeces and pseudofaeces produced by these organisms are deposited on the seafloor, accelerating the transport of organic carbon to the seafloor. As a result, bivalve shellfish can sequester blue carbon and act as carbon sinks\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, due to their widespread distribution, low mobility, filter-feeding capabilities, and their role as traditional model organisms and economically important seafood products, bivalves have been proposed as global or regional indicator animals for monitoring marine microplastic contamination\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe river contributes significantly to the estuary by depositing a substantial amount of silt, which results in a large expanse of shallow water. Additionally, the river discharges considerable organic matter and nutrients into the sea, enriching the estuary area with nutrients\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Geligang, located adjacent to the estuary of the Liao River and Shuangtaizi River, covers an area of approximately 10,000 hectares. During high tide, the entire region of Geligang is submerged, while certain portions become exposed during low tide\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Estuarine intertidal mudflats are crucial habitats for macrobenthos, serving as growth locations and breeding grounds\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMeretrix meretrix\u003c/em\u003e and \u003cem\u003eMactra veneriformis\u003c/em\u003e, belonging to the Mollusca, Lamellibranchia, and Veneroida orders, are common benthic economic shellfish species. They primarily inhabit the intertidal mudflats and shallow marine sediments\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These two species, \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e, are widely distributed in both the intertidal and subtidal areas of Geligang and represent the dominant shellfish species in the region’s mudflats \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Consequently, Geligang has emerged as a significant breeding site for northern mudflat shellfish in China. However, in recent years, the quantity of shellfish resources for \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e in Geligang has been declining annually due to the increasingly detrimental effects of human activity\u003cem\u003eM. meretrixM. veneriformis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This decline underscores the need for sustainable management practices to protect and preserve these valuable marine resources.\u003c/p\u003e\u003cp\u003e\u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e are filter-feeding shellfish that consume a variety of suspended particles, including inorganic particulate matter, organic detritus, biological waste, bacteria, phytoplankton, and zooplankton in the water column\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The knowledge of the diet composition and feeding habits of aquatic species is essential for their resource conservation and ecological studies\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In recent years, Illumina Miseq-based high-throughput sequencing technology has been extensively applied to investigate the diet composition and feeding habits of aquatic species, such as \u003cem\u003eNotorynchus cepedianus\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eCrassostrea gigas\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eApostichopus japonicus\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eAcanthopagrus schlegelii\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and \u003cem\u003eAnguilla Anguilla\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. With the ability to analyze food with varying degrees of degradation and species categorization at the species level, high-throughput sequencing offers greater sensitivity and accuracy compared to traditional morphological identification. This enables more comprehensive and in-depth dietary assessments\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Information on food composition forms the basis for feeding ecology research and is a critical component in constructing high-resolution food webs\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Therefore, it is crucial from an ecological perspective to understand the dietary characteristics of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e in Geligang and to provide fundamental knowledge for the developing their resource enhancement and conservation strategies.\u003c/p\u003e\u003cp\u003eIn this study, we employed 23S rDNA as a marker gene to characterize the food composition of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e by Illumina Miseq high-throughput sequencing. The results obtained from this analysis will provide a theoretical foundation for the conservation of their resources in Geligang. By employing this advanced sequencing technique, we aim to gain valuable insights into the dietary preferences and ecological roles of these shellfish species, ultimately contributing to their sustainable management and protection.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eQuantitative traits and delineation of OTUs.\u003c/b\u003e The dimensions of the \u003cem\u003eM. meretrix\u003c/em\u003e were (5.36 ± 0.14) cm for shell length, (2.35 ± 0.16) cm for shell width, (4.34 ± 0.22) cm for shell height, and (42.29 ± 4.88) g for weight. The dimensions of the \u003cem\u003eM. veneriformis\u003c/em\u003e were (3.25 ± 0.32) cm for shell length, (2.21 ± 0.30) cm for shell width, (3.20 ± 0.38) cm for shell height, and (15.86 ± 2.41) g for weight.\u003c/p\u003e\u003cp\u003eA total of 1,423,459 high-quality sequence fragments were obtained from 20 samples by Illumina high-throughput sequencing. The lowest sequence number of the samples was 36,957, the maximum was 173,507, and the average number of sequences was (71,173 ± 28,892). The 20 samples were clustered into OTUs after 97% concordance of the sequences, and a total of 2,485 OTUs were obtained. The results of OTUs identification and classification status are shown in Table\u0026nbsp;1. The number of OTUs shared by the \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e was 956 (Fig.\u0026nbsp;1), of which 1,117 were unique to the \u003cem\u003eM. meretrix\u003c/em\u003e and 412 to the \u003cem\u003eM. veneriformis\u003c/em\u003e. The dilution curves of every sample leveled off as the sequence depth increased (Fig.\u0026nbsp;2), suggesting that there were enough samples in this study to capture the great majority of the information. The results of the alpha indices are shown in Fig.\u0026nbsp;3, with Chao1 and Observed species indices of \u003cem\u003eM. meretrix\u003c/em\u003e significantly higher than those of \u003cem\u003eM. veneriformis\u003c/em\u003e. Good's coverage of \u003cem\u003eM. meretrix\u003c/em\u003e was significantly lower than that of \u003cem\u003eM. veneriformis\u003c/em\u003e, while the Shannon, Simpson and Pielou's evenness indices of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e were not significantly different.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of eukaryotic species in stomach contents.\u003c/b\u003e A total of 50 bait organisms from 11 phyla were identified in the total sample, along with a few OTUs that were unrelated to any known taxonomic groups. The primary taxa of the stomach contents in the \u003cem\u003eM. meretrix\u003c/em\u003e were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta, with relative mean abundances of 44.66%, 20.36%, 12.31%, and 9.22%, respectively. Similarly, the primary taxa of the stomach contents in the \u003cem\u003eM. veneriformis\u003c/em\u003e were Cryptophyta, Chlorophyta, Pyrrophyta, and Chrysophyta, with relative mean abundances of 44.66%, 20.36%, 12.31%, and 9.22%, respectively (Fig.\u0026nbsp;4). Euglenozoa, Haptophyta, Xanthophyta, Streptophyta, Chordata, and Cercozoa make up a smaller proportion. The main groups in the Chlorophyta are Haematococcus, Nephroselmis, Pyramimonas, and Chlorella; the main group in the Cryptophyta is Cryptomonas; the main group in the Pyrrophyta is Dinophysis; Bacillariophyta with Thalassiosira as the major group; Chrysophyta with Ochromonas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCompositional characteristics of stomach contents.\u003c/b\u003e NMDS analyses based on Bray-Curtis distances found that samples from \u003cem\u003eM. meretrix\u003c/em\u003e were grouped together, indicating low variability in the stomach contents composition, while samples from \u003cem\u003eM. veneriformis\u003c/em\u003e were found to be more distant from one another, indicating greater variability in the stomach contents composition (Fig.\u0026nbsp;5). Adonis test results showed significant differences (R\u003csup\u003e2\u003c/sup\u003e = 0.263, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) in the composition of the stomach contents of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eWith six groups at the 18% similarity level, Fig.\u0026nbsp;6 displays the results of the clustering heat map analysis. Samples of \u003cem\u003eM. meretrix\u003c/em\u003e were grouped together, but samples of \u003cem\u003eM. veneriformis\u003c/em\u003e were separated into six groups.\u003c/p\u003e\u003cp\u003eLEfSe analysis allowed the identification of species with significant differences in abundance between groups\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The results of LEfSe analyses showed that the stomach contents of \u003cem\u003eM. meretrix\u003c/em\u003e had seven orders with LDA values greater than 2 (Fig.\u0026nbsp;7), namely Bacillariophyceae, Coscinodiscophyceae, and Fragilariophyceae, Glaucocystophyceae, Pedinophyceae, Trebouxiophyceae, and Cryptophyceae; while the stomach contents of \u003cem\u003eM. veneriformis\u003c/em\u003e had only two, Chrysophyceae and Dictyochophyceae.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFilter-feeding shellfish are ecologically significant species that not only provide immense economic benefits but also play an important ecological role in driving the transport and transformation of various forms of carbon\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Filter-feeding shellfish consume a wide range of suspended particles, including organic debris, phytoplankton, and zooplankton, from the water column. The distribution of phytoplankton in the water column will be directly impacted by the filter-feeding behaviors of shellfish, which will, in turn, affects the level of marine primary production and ultimately influences the flow of energy and materials throughout the marine ecosystem\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Filter-feeding shellfish have selective feeding on phytoplankton. Zhang et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e discovered that \u003cem\u003eChlamys farreri\u003c/em\u003e preferred Bacillariophyta to Pyrrophyta, Bougrier et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e discovered that \u003cem\u003eCrassostrea gigas\u003c/em\u003e was able to selectively excrete \u003cem\u003eSkeletonema costatum\u003c/em\u003e, \u003cem\u003eChaetoceros calcirruns\u003c/em\u003e, and \u003cem\u003eNitzschia closterium\u003c/em\u003e via pseudofaeces, and Jiang et al.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e discovered that \u003cem\u003eArgopecten irradians\u003c/em\u003e favored ingesting micro- and nano- phytoplankton with larger particle sizes. However, it has been proposed that selective feeding characteristics of filter-feeding shellfish may be affected by the geographical environment. Laboratory experiments often fail to accurately capture the way in which shellfish consume aquatic phytoplankton in their natural environment\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In this study, we selected in situ \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e from Geligang for high-throughput sequencing to investigate their feeding ecological characteristics. This approach aims to provide a more accurate understanding of how these shellfish species interact with their natural environment and contribute to marine ecosystem dynamics.\u003c/p\u003e\u003cp\u003eMicroscopic examination is a widely used technique to determine the diet of organisms; however, it faces challenges in identifying smaller individuals such as pico- and nano-phytoplankton, and in distinguishing digested and semi-digested food in stomach contents\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. While fatty acid and stable carbon and nitrogen isotopic analyses can characterize the extent of the food source, they often fall short in determining the precise composition of the food\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. High-throughput sequencing provides a solution to these problems by identifying not only the major food components but also tiny, highly digestible bait organisms\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. With advancements in high-throughput sequencing technology, its application has expanded widely in feeding ecology, food web structure, species identification, and classification of aquatic organisms\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47 CR48\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For example, Qiao et al.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e used the 23S rRNA gene as a molecular marker to explore differences in the feeding selectivity of \u003cem\u003eMercenaria mercenaria\u003c/em\u003e, \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eRuditapes philippinarum\u003c/em\u003e in natural waters. Their results showed that these three filter-feeding shellfish exhibite different preferences for phytoplankton genera, with some picophytoplankton dominating the hepatopancreas samples. Similarly, Chen Xiaolei et al.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e utilized the mitochondrial cytochrome c oxidase subunit I (CO Ⅰ) as a molecular marker to identify the food composition of \u003cem\u003eDiaphus splendidus\u003c/em\u003e in the South China Sea. They identified Ostracoda, Copepoda, and Amphipoda as the dominant groups in its food composition, and also discovered jellyfish that were not identified through traditional morphological identification. Li Yulong et al.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e employed the 18S rDNA high-throughput sequencing method to study the food composition and feeding selection of \u003cem\u003eHyporhamphus sajori\u003c/em\u003e juveniles at different developmental stages under pond culture in the northern Yellow Sea. Their study revealed differences in the food composition of \u003cem\u003eH.sajori\u003c/em\u003e juveniles at different developmental stages and detected Fungi, Intramacronucleata, and Streptophyta, which were not identified by traditional morphological identification. In this study, we used 23S rDNA as a molecular marker to investigate the diet of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e. Our findings showed that the primary taxa in the stomach contents of \u003cem\u003eM. meretrix\u003c/em\u003e were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta. For \u003cem\u003eM. veneriformis\u003c/em\u003e, the primary taxa were Cryptophyta, Chlorophyta, Methanophyta, and Chrysophyta.\u003c/p\u003e\u003cp\u003eLiu et al.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e employed the 18S rDNA high-throughput sequencing method to study the feeding selectivity of \u003cem\u003eR.philippinarum\u003c/em\u003e and found that \u003cem\u003eR.philippinarum\u003c/em\u003e actively avoided Bacillariophyta, Chrysophyta, and Cryptophyta, while it preferentially selected Pyrrophyta and Chlorophyta. Similarly, Zhang et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e also employed the 18S rDNA high-throughput sequencing method and noted that \u003cem\u003eBellamya aeruginosa\u003c/em\u003e preferred feeding on Chlorella and Euglena, showing lower selectivity for Cryptophyta and Oscillatoriales. In this study, there was a significant difference in food composition between \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e. \u003cem\u003eM. meretrix\u003c/em\u003e preferred Bacillariophyta, Chlorophyta and Cryptophyta, whereas \u003cem\u003eM. veneriformis\u003c/em\u003e favored Chrysophyta. The filter-feeding shellfish employ two primary feeding mechanisms: one relies on the coordinated movement of gill filaments and cilia to capture food particles\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, hydrodynamic transport of water with different food concentrations for bivalve filtration. While the other utilizes water flow to transport food particles through the gill filaments to the back of the gills and along the dorsal grooves into the labellum\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, selective feeding by bivalves. Both species are passive filter feeders, The ecological niches occupied by \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e in this study are basically the same, so their feeding selectivity may be related to differences in their gill structures. Filter-feeding shellfish with large anterolateral cilia, such as \u003cem\u003eM.mercenaria\u003c/em\u003e, retained microalgae larger than 4 µm with 100% efficiency. In contrast, species with small or no anterolateral cilia, like \u003cem\u003eCrassostrea virginica\u003c/em\u003e and \u003cem\u003eA. irradians\u003c/em\u003e, have retention efficiencies of only 75%-85%\u003csup\u003e57\u003c/sup\u003e. The gill structure of the \u003cem\u003eM. meretrix\u003c/em\u003e belongs to the heterorhabdic gill type. The gill filaments are divided into main gill filaments and common gill filaments. The common gill filaments have dense anterior and lateral cilia on the surfaces, which can effectively retain particles larger than 8 µm in size\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. On the other hand, the gills of \u003cem\u003eM. veneriformis\u003c/em\u003e are divided into inner and outer gills, which are petal-shaped. The inner gills are larger than the outer gills and have dense cilia, allowing them to feed on small nutrients flowing into the outer coat cavity. Moreover, the concentration of phytoplankton and their nutritional content can also impact the filter-feeding shellfish's ability to selectively feed\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHigh-throughput sequencing can detect DNA sequences present in low quantities at a reduced cost. The number of DNA sequences can reflect the relative amount of bait ingestion\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. However, it cannot provide absolute quantification due to the overestimation of OTUs for species\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Additionally, a portion of the OTUs in this study were not annotated to the species level. There is still much taxonomic information need to be added to the present incomplete 23S rDNA database\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. In addition to phytoplankton, filter-feeding shellfish also consume zooplankton and organic detritus. However, there are few studies on the potential diets of these filter-feeding shellfish\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In high-density aquaculture, the selective feeding of filter-feeding shellfish on different groups of phytoplankton can lead to changes in the structure of the phytoplankton community\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In future study, it is also necessary to improve the coverage of taxonomic information, enhance the 23S rDNA database, and increase the identification of potential food sources for zooplankton and other filter-feeding shellfish. Additionally, establishing a long-term monitoring mechanism to observe the succession mechanism of phytoplankton communities is crucial. Furthermore, improving the analysis of the feeding habits of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e is essential to provide valuable data for the study of their feeding ecology and aid in their scientific management and conservation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we employed high-throughput sequencing of 23S rDNA as a molecular marker to analyze the diet composition and feeding habits of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e in Geligang. Our findings revealed that the main taxa in the stomach contents of \u003cem\u003eM. meretrix\u003c/em\u003e were Chlorophyta, Cryptophyta, Pyrrophyta, and Bacillariophyta. For \u003cem\u003eM. veneriformis\u003c/em\u003e, the predonminant taxa were Cryptophyta, Chlorophyta, Pyrrophyta, and Chryptophyta. The adonis test results indicated significant differences in the composition of the stomach contents between the two species. \u003cem\u003eM. meretrix\u003c/em\u003e showed a preference for Bacillariophyta, Chlorophyta, and Cryptophyta, while \u003cem\u003eM. veneriformis\u003c/em\u003e favored Chrysophyta. These feeding preferences may be related to different gill structures. This study provides fundamental insights for research on the feeding ecology and resource conservation of \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e in Geligang.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eSample collection.\u003c/b\u003e As seen in Fig.\u0026nbsp;8, the \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e were randomly collected from the Geligang of Liaodong Bay in June 2023. A total of 10 \u003cem\u003eM. meretrix\u003c/em\u003e and 10 \u003cem\u003eM. veneriformis\u003c/em\u003e were chosen for predation investigation. Following the collection of the shellfish samples, they were immediately cleaned with water and subjected to standard biological measurements (shell length, shell width, shell height and weight). The stomach contents were sampled, immediately preserved in liquid nitrogen and returned to the laboratory for DNA extraction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDNA extraction.\u003c/b\u003e The stomach contents were homogenized after being washed with phosphate buffer (pH 7.2–7.6). The whole DNA was extracted according to the instructions using the Marine DNA Extraction Kit (Tiangen, Beijing, China). The acquired total DNA was measured using a UV spectrophotometer, identified by electrophoresis using 0.8% agarose gel, and the qualified DNA was kept at -20°C.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-Throughput Sequencing.\u003c/b\u003e The PCR reaction was conducted in a 20 µL system and sample DNA was amplified by PCR using the 23S rDNA primers p23SrV_f1 (5′-GGA CAG AAA GAC CCT ATG AA-3′) and p23SrV_r1 (5′-TCA GCC TGT TAT CCC TAG AG-3′)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. 10 ng of DNA template, 0.5 µL of Q5 high-fidelity Taq enzyme (NEB, Beijing, China), 1 µL of each of the positive and negative primers (5 µmol/L), 4 µL of 5× buffer, and 2 µL of dNTPs (2.5 mmol/L) were used. With a reaction cycle of 29, the PCR process was as follows: pre-denaturation at 95°C for 7 min, denaturation at 95°C for 45 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, and final extension at 72°C for 10 min. Then the PCR products were identified using 2% agarose gel electrophoresis, and purified by, and purified by AxyPrep DNA Gel Extraction Kit (Axygen, Silicon Valley, USA) before being submitted to bipartite sequencing on the Illumina Miseq sequencing platform (Illumina, San Diego, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData analysis.\u003c/b\u003e Firstly, the primer fragments of the sequence were removed using cutadapt (v2.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cutadapt.readthedocs.io/en/stable/\u003c/span\u003e\u003cspan address=\"http://cutadapt.readthedocs.io/en/stable/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e68\u003c/sup\u003e, and then the raw sequencing data were analyzed by Vsearch (v2.13.4, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/torognes/vsearch\u003c/span\u003e\u003cspan address=\"https://github.com/torognes/vsearch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e69\u003c/sup\u003e for quality control procedures such as quality filtering, noise reduction, splicing and chimera removal to obtain high-quality sequences. QIIME2 (v2019.4) was used to combine the sequences and classify them into operational taxonomic units (OTUs) based on 97% sequence similarity. Representative sequences within the OTUs were chosen for species annotation analysis using the NCBI locally-accounted sequence database (v20230626, ncbi.nlm.nih.gov/). OTU analyses can provide information on the diversity of bait organisms and the relative abundance of different species.\u003c/p\u003e\u003cp\u003eThe alpha diversity index (Chao1 index, Good's coverage, Simpson index, Pielou's evenness, Shannon index, Observed species index) was calculated using QIIME2, and the Wilcoxon test was used to determine whether group differences were statistically significant. Non-metric multidimensional scaling (NMDS) and hierarchical clustering plots based on Bray-Curtis similarity coefficients were plotted using the vegen package of R 3.6.2 and combined with permutational multivariate analysis of variance (Adonis) to analyze the significance of the differences between the stomach content composition of the \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e. Linear discriminant analysis Effect Size (LEfSe) was performed by the microeco package of R 3.6.2 to identify differential bait organisms in \u003cem\u003eM. meretrix\u003c/em\u003e and \u003cem\u003eM. veneriformis\u003c/em\u003e, with the LDA threshold set at 2. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01 indicates highly significant differences, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 indicates a significant difference.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2023YFD2400800), Central Public-interest Scientific Institution Basal Research Fund, YSFRI, CAFS (NO.2020TD50, NO.20603022023007), the Marine Economy Development Project of Liaoning Province (20224722).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.L.:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Software analysis, Writing. \u003cstrong\u003eY.B.\u003c/strong\u003e: Conceptualization, Methodology. \u003cstrong\u003eL.Z.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Methodology, Writing- review \u0026amp; editing. \u003cstrong\u003eS.X.:\u003c/strong\u003e Investigation, Data curation, Writing- review \u0026amp; editing. \u003cstrong\u003eJ.L.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Methodology, Writing- review \u0026amp; editing. \u003cstrong\u003eX.L.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Investigation. \u003cstrong\u003eL.L.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Data curation, Supervision. \u003cstrong\u003eL.L.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Investigation. \u003cstrong\u003eY.M.\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization, Methodology, Writing- review \u0026amp; editing,\u0026nbsp;Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Y.M.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang, S., Edie, S.M., Collins, K.S., Crouch, N.M.A., Roy, K. \u0026amp; Jablonski, D\u003cem\u003e.\u003c/em\u003e Diversity, distribution and intrinsic extinction vulnerability of exploited marine bivalves. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 4639 (2023). https://doi.org/10.1038/s41467-023-40053-y\u003c/li\u003e\n\u003cli\u003eWan Mahari, W. 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Nutrient cycling and phytoplankton dynamics in relation to mussel grazing in a mesocosm experiment. \u003cem\u003eOphelia\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 289-315 (1995). https://doi.org/10.1080/00785236.1995.10422049\u003c/li\u003e\n\u003cli\u003eSherwood, A.R. \u0026amp; Presting, G.G. Universal primers amplify a 23S rDNA plastid marker in eukaryotic algae and cyanobacteria. \u003cem\u003eJournal of Phycology\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 605-608 (2007). https://doi.org/10.1111/j.1529-8817.2007.00341.x\u003c/li\u003e\n\u003cli\u003eA\u0026szlig;hauer, K.P., Wemheuer, B., Daniel, R. \u0026amp; Meinicke, P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 2882-2884 (2015). https://doi.org/10.1093/bioinformatics/btv287\u003c/li\u003e\n\u003cli\u003eRognes, T., Flouri, T., Nichols, B., Quince, C. \u0026amp; Mah\u0026eacute;, F. VSEARCH: a versatile open source tool for metagenomics. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e2584 (2016). https://doi.org/10.7717/peerj.2584\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eStatistics of operational taxonomic units (OTUs) identification and classification status\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSpecies\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSample number\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePhylum\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eClass\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eOrder\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eFamily\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eGenus\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eSpecies\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM. meretrix\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e40\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e54\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e82\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e125\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e35\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e48\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e67\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e102\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e41\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e83\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e51\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e69\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e102\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e42\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e57\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e83\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e126\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e35\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e64\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e22\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e51\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e70\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e109\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e78\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e111\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e22\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e37\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e79\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e112\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eW_10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e35\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e47\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e67\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e99\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eM. veneriformis\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eS_1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e64\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e96\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eS_2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e36\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e46\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e68\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eS_3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e28\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e51\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e71\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4903946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4903946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding the diet composition and feeding habits of bivalve shellfish is crucial for developing conservation measures to enhance their resources. This is particularly important for the main economic species in shellfish-producing regions. In this study, we analyzed the stomach contents composition of the two main economic shellfish in Geligang, specifically Meretrix meretrix and Mactra veneriformis, using high-throughput sequencing. The results revealed that 956 operational taxonomic units (OTUs) were common to both M. meretrix and M. veneriformis, with 1117 OTUs unique to M. meretrix and 412 OTUs unique to M. veneriformis. We identified a total of 50 bait organisms from 11 phyla. The main taxa in the stomach contents of M. meretrix were Chlorophyta, Cryptophyta, Pyrrophyta and Bacillariophyta, while Cryptophyta, Chlorophyta, Pyrrophyta and Chrysophyta dominated the stomach contents of M. veneriformis. Non-metric multidimensional scaling (NMDS) analysis indicated less compositional variety in the stomach contents of M. meretrix compared to M. veneriformis. Additionally, the Linear Discriminant Analysis Effect Size (LEfSe) results showed a significant difference in food composition between the two species. Specifically, M. meretrix and M. veneriformis preferred feeding on Bacillariophyta, Chlorophyta, and Cryptophyta, while M. veneriformis favored Chrysophyta. Overall, our study provides fundamental insights for ecological research on feeding habits and resource conservation of M. meretrix and M. veneriformis in Geligang, which can inform the development of effective conservation measures for the shellfish resources.","manuscriptTitle":"Diet composition and feeding habits of Meretrix meretrix and Mactra veneriformis in the Northern Bohai Sea based on high- throughput sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-23 10:38:06","doi":"10.21203/rs.3.rs-4903946/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T08:32:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-01T23:07:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199467927533377631201823986194472230181","date":"2025-02-21T20:12:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40300055920438728954566320161618608419","date":"2025-02-21T11:51:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-06T01:27:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4176274755020107735597042691290251098","date":"2025-01-03T11:34:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287902546722343350445721947751906492489","date":"2024-12-26T07:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-17T14:23:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-18T07:28:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-23T10:30:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-23T01:52:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-13T04:11:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31e62e28-cab2-40cb-9d1c-001b25e74d38","owner":[],"postedDate":"September 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37819622,"name":"Biological sciences/Molecular biology"},{"id":37819623,"name":"Biological sciences/Zoology/Animal behaviour"}],"tags":[],"updatedAt":"2025-05-12T16:06:22+00:00","versionOfRecord":{"articleIdentity":"rs-4903946","link":"https://doi.org/10.1038/s41598-025-01269-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-09 15:57:46","publishedOnDateReadable":"May 9th, 2025"},"versionCreatedAt":"2024-09-23 10:38:06","video":"","vorDoi":"10.1038/s41598-025-01269-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01269-8","workflowStages":[]},"version":"v1","identity":"rs-4903946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4903946","identity":"rs-4903946","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}