Constructing food web of carnivorous fishes using multiple DNA barcoding markers of gut contents: A case from Bohai Bay, China

Constructing food web of carnivorous fishes using multiple DNA barcoding markers of gut contents: A case from Bohai Bay, China | 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 Research Article Constructing food web of carnivorous fishes using multiple DNA barcoding markers of gut contents: A case from Bohai Bay, China Xiaoke Pang, Biao Guo, Kefeng Liu, Chenglong Han, Yifan Zhao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5175724/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Next-generation sequencing (NGS) technology through barcoding of DNA in highly-digested diet samples has become a powerful tool for diet reconstruction in food webs. However, traditional single markers can only detect very few prey species, and the constructed food web cannot reflect all effective feeding information. Here, we used multi-locus NGS with COI-M, COI-m, 18S and 16S markers to analyze the feeding habits of carnivorous fishes in Bohai Bay, China. We compared the prey identification results of single markers and multiple markers on the gut content analysis, and integrated examined the feeding characteristics of carnivorous fishes to reconstruct the food web. Our findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes, which are 1.5 − 6.2 and 1.7 − 9.2 times that of detected by single markers, respectively. Calanoida was detected as one of the primary food sources of carnivorous fishes expect for Gobiiformes and Decapoda at order level, contrasting with the previous researches. Network structure analyses showed significant modularity in the food web of carnivorous fishes and their preys. Species Scomberomorus niphonius , Odontamblyopus rubicundus , Chaeturichthys stigmatias , Platycephalus indicus and Cynoglossus joyneri were found to be divided into three different modules in the food web, indicating that specific and directional proliferation strategies should be advocated to be adopt for their population recoveries. This study reported a detailed trophic network of the carnivorous fishes, providing valuable insights for effective conservation and restoration strategies to enhance fisheries structure and stabilize the marine ecosystem. Food web Carnivorous fishes Gut contents Multiple DNA barcoding markers Food composition Modularity Figures Figure 1 Figure 2 Figure 3 1. Introduction Fishes with the trophic level higher than 3.0 in aquatic ecosystems are defined as carnivorous fishes (Jaureguizar and Milessi 2008 ). In recent years, the rate of large-scale global biodiversity loss has been accelerating, threatening the maintenance of ecosystem functions and services in biological communities and human societies (Butchart et al. 2010 ; Cardinale et al. 2012 ; Ceballos et al. 2015 ). Among the rapidly disappearing biota, carnivorous aquatic fishes are particularly endangered because of high-intensity artificial fishing and their high ecological demands (Jaureguizar and Milessi 2008 ). As aspx and meso-predators of the marine food webs, carnivorous fishes can directly or indirectly regulate other trophic levels through trophic cascades or various behavioral effects (Jaureguizar and Milessi 2008 ; Kume et al. 2021 ). Their presence in marine ecosystems can promote or hinder species coexistence, interspecific competition and species diversity (Jaureguizar and Milessi 2008 ; Kume et al. 2021 ). Therefore, the losses of massive carnivorous fishes led to the change of community structure, such as the trophic downgrading and biodiversity decline, which affected the community stability and ecological function (Kume et al. 2021 ). However, due to years of overfishing, massive emissions of pollutants and large-scale ocean engineering, the total amount of fishery resources has been greatly reduced, especially economic organisms of carnivorous fish, leading that fishery resource’s structure in marine ecosystem tend to be low-trophic (Zhang et al. 2006 ). Therefore, a mechanistic understanding of species interactions and community organization of carnivorous fishes is needed thus to propose informed and effective conservation and restoration decisions to improve fisheries structure and stabilize marine ecosystems (Sergio et al. 2008 ; Ritchie et al. 2012 ). A food web is the basic connection of the nutritional relationship between organisms in the biological community, which reflects the natural attributes of the interdependence, mutual restraint, co-evolution and other interactions between various organisms in nature (Pimm 2002 ). Food webs can directly reflect the functional structure of the ecological community (Rossberg 2013 ), therefore, construction of food webs between species has always been a very vital and active research field in ecology. The topological features of trophic networks (food webs) are of crucial implications for the knowledge of network dynamics and ecosystem stability, and provide a general framework for describing and comparing the interactions between species (Thébault and Fontaine 2010 ; Tylianakis et al. 2010 ; Delmas et al. 2019 ). Food web can be studied at multiple levels varying from the food composition and interspecific trophic niche of individual species to the topological characteristics of community-level networks, such as generality (i.e., the average number of prey per predator) and modularity (i.e., species within the module interacting more frequently than with other species in the community) (Olesen et al. 2007 ; Guimerà et al. 2010 ). Theoretically, these structural features are related to the food web stability and resilience (Tylianakis et al. 2010 ). The network role of species, such as its centrality and connectivity with other species within and across modules, describes the position of species in the network organization and is critical to its functional importance in ecosystem processes (Cirtwill et al. 2018 ; Delmas et al. 2019 ; Hackett et al. 2019 ). Therefore, a comprehensive understanding of the food web structure and the role of related species at the species, interspecific, module and network levels can provide important information for revealing species coexistence occurrence and maintenance mechanisms, and can also predict the impact of species loss on ecosystem dynamics through interactive networks (Thompson et al. 2012 ; Harvey et al. 2017 ; Mata et al. 2021 ). However, owing to the unobtainability of adequate quantity and quality (i.e., prey coverage and species resolution) of community-scale carnivorous fishes’ dietary data, previous food web work involving the organization of ecological networks and the role of associated species has rarely focused on carnivorous fishes. The application of DNA barcoding in diet studies has increased considerably with the advent of next-generation sequencing (NGS) technology (Bowser et al. 2013 ). The DNA fragments of the entire mixed sample are amplified and then subjected to high-throughput sequencing, thus, multiple species in the mixed sample are identified. This can reduce the workload of sampling and maximize the species identification of tissue residues to achieve the feeding analysis (Brandon-Mong et al. 2015 ; Kartzinel et al. 2015 ). It is now possible to identify even the rarest prey from multiple predators to species, genus, or other levels in a single sequencing run while maintaining the ability to trace back each prey to the sample from which it came (Bowser et al. 2013 ). However, prey identification from DNA in diet samples is greatly influenced by technical issues including the uncertainty about the taxonomic diversity expected in the sample, the poor quality of the genomic DNA, the barcoding regions of markers, and the amplification lengths of markers (Hebert et al. 2003a ; Hebert et al. 2003b ; Deagle et al. 2006 ; Bowser et al. 2013 ). This can lead to data loss or distortion of rare or difficult-to-amplify species, and fail to describe the full taxonomic range of the prey consumed (Hebert et al. 2003a ; Hebert et al. 2003b ). Theoretically, the application of multiple markers provides a broader taxonomic resolution of diet as different markers are not suitable barcodes for all taxonomic groups (Bowser et al. 2013 ). Therefore, more barcoding genes with suitable mutation rate and large interspecific differences as well as diversified amplification lengths were advocated to be used synchronously for prey collaborative identification to enhance the prey identification rate (Valentini et al. 2009 ). However, previous studies of feeding analysis were mostly conducted on single markers such as mitochondrial Cytochrome c Oxidase subunit I (COI) genes or 16S rRNA genes (Leray et al. 2013 ; Yang et al. 2019 ; Johnson et al. 2021 ). Few studies have been conducted using multiple markers located in different barcoding regions, or markers with varying lengths within the same barcoding region. The distinctiveness of feeding analysis in different single markers and varying combinations of markers have seldomly been reported, so does the quantification analysis based on the relative read abundance of integrated multiple markers. Furthermore, the proposal of a reasonable marker combination for a more comprehensive feeding analysis of carnivorous fishes will be great significance in this research field. This study focused on the feeding analysis of carnivorous fishes in Bohai Bay, China, where fishing and pollutant emissions have been significant in recent years. Gut content samples from all of the carnivorous fishes were analyzed using Multiple DNA barcoding markers. By examining the dietary datasets obtained through these markers, we aimed to: (1) assess the effectiveness of single marker and different combinations of multiple markers in identifying prey species, (2) elucidate the dietary characteristics and niche relationships of the carnivorous fishes, and (3) construct an interaction network between carnivorous fishes and their preys, thereby characterizing the food web properties of the marine ecosystem. 2. Materials and methods 2.1 Sample collection and DNA extraction Field surveys and sampling were conducted in Bohai Bay, China, in July 2022. The voucher specimens were preserved at -80℃ until DNA extraction at Nankai University (Tianjin, China). Stable isotope ratios nitrogen (δ 15 N) of the tissue for fish samples were estimated using an elemental analyzer/isotope-ratio mass spectrometer (Thermo Fisher Scientific: Flash 2000, ConFloIV, DELTA V Advantage) (Kume et al. 2021 ), and the calculation of the trophic levels of fish followed the method described in Bowes and Thorp ( 2015 ) (Bowes and Thorp 2015 ). The fishes with average trophic levels > 3.0 were selected as the carnivorous fishes for further dietary analysis using gut content (Jaureguizar and Milessi 2008 ). The morphological index and trophic level of each sample were listed in Table S1 . DNA of gut content was extracted in small batches after full grinding. Blank samples were extracted meanwhile to help monitor possible contamination during extractions. The extractions were performed in a laboratory room designated for gut content processing, and its bench tops and equipment were treated with 75% ethanol before and after processing each batch. DNA of gut contents was extracted with a QIAamp DNA Stool Mini Kit (Tiangen, China) according to the manufacturer’s instructions. Concentrations of the DNA extracts were determined using NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, Massachusetts, USA) and agarose gel electrophoresis. 2.2 PCR amplification and sequencing A total of 36 DNA templates were obtained from 36 gut content samples of carnivorous fishes, and each DNA template was amplified for the further feeding analysis. Four paired primers targeting three barcoding regions with small target fragments were used for each DNA template, including two mitochondrial COI genes targeting 400 bp and 313 bp fragments (COI-M and COI-m markers, respectively), the 18S rRNA gene targeting a 380 bp fragment (18S marker), and the 16S rRNA gene targeting a 270 bp fragment (16S marker), to amplify prey DNA. The concrete primer information and thermocycling conditions for primer pairs of this study are listed in Table 1 . Those primers were identified to possess a high species resolution for most prey species of carnivorous fishes in Bohai Bay. The PCRs for generating dietary data were conducted in a total volume of 10 µl, comprising 5 µl KOD FX Neo Buffer, 2 µl dNTP (2 mM), 0.2 KOD FX Neo, 10 µM F/R primers, and 1 µl DNA extract. PCRs were set up in a clean designated pre-PCR chamber. Each 96-well PCR plate contains 4 to 8 PCR blanks (including all PCR reagents except DNA) to check contamination. Each sample was amplified two times to increase the probability of detection of prey taxa. All PCR products were purified firstly, and were then sent to Biomarker Technologies Co., ltd. (Beijing, China) for sequencing by the platform of Illumina novaseq 6000. Table 1 Sequences and reaction conditions of the primers. Markers Primer pairs Primer sequences (5’-3’) Thermocycling conditions COI-M MHemF GCATTYCCACGAATAAATAAYATAAG 94℃ 4 min; [5 cycles: 94℃ 30s; 45℃ 30 s; 72℃ 1 min]; [35 cycles: 94℃ 30s, 51℃ 30s; 72℃ 1min]; 72 ℃ 7min dgHCO-2198 TAAACTTCAGGGTGACCAAARAAYCA COI-m mlCOIintF GGWACWGGWTGAACWGTWTAYCCYCC dgHCO2198 TAAACTTCAGGGTGACCAAARAAYCA 18S TAReuk454FWD1 CCAGCA(G/C)C(C/T)GCGGTAATTCC 95℃ 3 min; [30 cycles: 95℃ 45 s; 55℃ 45 s; 72℃ 1 min]; 72 ℃ 5 min TAReukREV3 ACTTTCGTTCTTGAT(C/T)(A/G)A 16S 16sF AYAAGACGAGAAGACCC 95℃ 5 min; [25 cycles: 95℃ 30 s; 50℃ 30 s, 72℃ 40 s]; 72 ℃ 7 min 16sR GATTGCGCTGTTATTCC 2.3 Bioinformatics sequence processing Raw reads were firstly filtered using Trimmomatic v0.33 (Bolger et al. 2014 ) and then the primer sequences were identified and removed with Cutadapt v1.9.1. Paired-end reads were merged with FLASH v1.2.7 based on overlapping regions in the corresponding reads (Magoč and Salzberg 2011 ). Chimeric sequences were deleted and denoised to form operational taxonomic units (OTUs) in QIIME2 2020.6 (Bolyen et al. 2019 ). If the sequence of PCR product was less than that of the PCR blank control or DNA extraction blank control or less than 1% of the total PCR sequences, it is considered to be derived from cross-contamination and not included in the OTUs of the prey species. In sequence processing, the sequences of less than 98% consistent with the databases were discarded, and the sequences with a difference of less than 2% with the database were merged. In order to conduct an accurate taxonomic annotation for screened OTUs of each marker, we matched three comparatively complete databases for four markers based on the region where the barcoding marker is located correspondingly. Finally, the taxonomic annotation of OTUs of COI, 18S and 16S barcoding regions was processed by BLAST against the Fungene, Sliva and NCBI database, respectively (Fish et al. 2013 ; Quast et al. 2013 ). OTUs appeared form carnivorous fishes body sequences, experimentally contaminated humans’ sequences, and non-Bohai Bay species’ sequences were removed. The average effective prey OTUs after BLAST against three databases of each species were collated and summarized for subsequent feeding analysis. The calculation and analysis of alpha diversity (Chao1, Shannon and Simpson) indexes were completed in QIIME2 software. One-way analysis of variance (ANOVA) and Student 's t test of SPSS 25.0 were used in this study to compare the significant differences in alpha diversity indexes detected by multiple barcoding markers. We used relative abundance (RA) and frequency of occurrence (FO) to measure food composition and occurrence of preys (Deagle et al. 2019 ). Analysis of similarities (ANOSIM) of SPSS 25.0 was conducted to analyze the significances of prey compositions and dietary partitioning among carnivorous fishes. All statistical tests were two-tailed with the significance level set at 0.05. 2.4 Interspecific dietary differences and niche overlaps According to the relative read abundances of prey composition, the Levins index ( B ) was used to describe the food niche breadth, and the Pianka overlap index ( O jk ) was used to measure the degree of food niche overlap (Pianka and R 1973). B and O jk are calculated by: $$\:B=\frac{1}{\sum\:{{P}_{j}}^{2}}$$ 1 where P j represents the proportion of prey j . Species are classified as narrow-niche (0 < B ≤ 3), middle-niche (3 < B < 6) and wide-niche ( B ≥ 6). $$\:{O}_{jk}=\frac{\sum\:({P}_{ij}\cdot\:{P}_{ik})}{\sqrt{\sum\:\left({{P}_{ij}}^{2}\right)\cdot\:\sum\:\left({{P}_{ik}}^{2}\right)}}$$ 2 where P ij and P ik represents the relative abundance of prey i in the species j and k , respectively. The variation range of O jk is 0–1: O jk > 0.3 is considered as an overlap, and O jk > 0.6 is considered as a significant overlap (Krebs 1999 ). 2.5 Food web organization and species’ roles The network-level properties of the food web were estimated using the metrics calculated by the networklevel and grouplevel functions in bipartite. Qualitative measurements were used to characterize the overall network complexity of the food web, including the predator and prey numbers, feeding interactions (Link, L ), link density (LD = L / S , where S is the sum of the numbers of predator species (S A ) and prey species (S B ); i.e., mean number of links per species), and connectance ( C = L / (S A × S B ), the proportion of realized interactions out of all possible bipartite interactions), generality ( G , the average number of prey per predator), and vulnerability ( V , the average number of prey consumed by predators). In order to further understand the organizational structure of the food web, we measured the modularity of the network. The module is defined as the subdivision in the food web, Species belonging to the module have high interactions with each other and low connections with species outside the module (Landi et al. 2018 ; Delmas et al. 2019 ). The higher modularity in the food web is considered to enhance the stability of the network by limiting the propagation of disturbances within the module to other parts of the network (Guimerà et al. 2010 ). The weighted modularity ( Q ) of food web was calculated with the computeModules function in bipartite, and values of Q vary from 0 (no modularity) to 1 (complete modularity) (Beckett 2016 ). The role and importance of individual prey in the food web structure were also assessed by two qualitative and three quantitative indicators calculated by the specieslevel function in bipartite. Qualitative indicators included degree ( D g , the number of predators consuming a given prey) and normalized degree (N D , the proportion of prey consumed by all predators). The quantitative assessments were calculated based on the abundance data of relative read length. Quantitative assessments included two network centrality indicators: (i) betweenness centrality (BC), which measures the importance of species as a connector between different parts in the network; (ii) closeness centrality (CC), which measures the closeness of a species to all other species in the network (Mata et al. 2021 ). Both value range of BC and CC are 0 − 1, and the larger of the value, the higher network centrality of the species. We used the software Gephi v.0.9.2 to construct the food web network, which is convenient for network topology and module visualization. 3. Results 3.1 Species identification of barcoding markers All gut content samples were successfully amplified and sequenced using four barcoding markers (Table 1 ). Based on the sequencing data of four markers, totally 11,510,503 valid sequences were obtained from the 38 samples, with an average of 302, 908 sequences per sample. After removing body sequences of carnivorous fishes, unidentifiable sequences and sequences with abundance < 1%, a total of 190 OTUs were identified by the combination of four markers from the gut contents of eight carnivorous fishes. The identification results of single marker and multiple marker combinations at order and species levels were shown in Table 2 and Fig. 1 . Table 2 Identification results of single marker and multiple marker combinations. Markers Order level Species level Prey items Identification rate Prey items Identification rate Single marker COI-M 18 32% 41 26% COI-m 18 32% 37 24% 18S 37 66% 94 60% 16S 9 16% 17 11% Two-marker combination COI-M & COI-m 24 43% 55 35% COI-M & 18S 50 89% 131 84% COI-M & 16S 21 38% 52 33% COI-m & 18S 49 88% 128 82% COI-m & 16S 23 41% 50 32% 18S & 16S 44 79% 111 71% Three-marker combination COI-M & COI-m & 18S 55 98% 145 93% COI-M & 18S & 16S 51 91% 144 92% COI-M & COI-m & 16S 27 48% 66 42% COI-m & 18S & 16S 52 93% 141 90% Four-marker combination COI-M & COI-m & 18S & 16S 56 100% 156 100% Detailed prey species identified by COI-M, COI-m, 18S and 16S markers was respectively shown in Tables S2-S5. Great significance appeared among four markers due to the differences in barcoding region, amplicon length and database resources. Overall, the prey recognition rates of carnivorous fish group and single species were ranked as 18S > COI-m > COI-M > 16S marker. 18S marker was proved to possess the highest identification level among four markers, and totally 94 preys were successfully detected, accounting for a percentage of 60% in the total prey taxa. Comparative identification result was respectively presented by COI-m and COI-M marker, and 41 and 37 prey species were respectively detected. Least prey species were detected by 16S marker, and only 17 preys were identified. Our findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes, which are 1.5 − 6.2 and 1.7 − 9.2 times that of detected by single markers at order and species levels, respectively (Table 2 ). The identification effects of different combinations of two markers and three markers varied significantly. At order and species levels, 38%−89% and 32%−84% prey items were detected when using two-marker combinations, and the combinations in the presence of 18S marker obtained favorable success rates not less than 79%. 48%−98% and 42%−93% prey items could be effectively detected by three-marker combinations, and the combination with 18S marker always provided a recognition rate not less than 91%. It can be concluded that all of the combinations with 18S marker rather than traditional universal COI marker (COI-m) revealed a satisfied feeding analysis effectiveness of carnivorous fishes. This proposes novel insights in the use of barcoding marker of carnivorous fish feeding analysis. 3.2 Food compositions of carnivorous fishes Preys identified by four-marker combination were most extensive, thus, the identification result of four-marker combination were used for further dietary analysis. According to statistics, a total of 22 classes, 56 orders and 156 species of preys were successfully detected in all of the gut content samples of carnivorous fishes. Prey taxa of each carnivorous fish detected by four barcoding markers at species level was shown in Fig. 1 . The number of prey taxa found in carnivorous fishes at species and order levels were 23 − 69 and 14 − 37, respectively. O. rubicundus was found to own the highest prey taxa, including 37 orders and 69 species of prey. The preys of L. Japonicus were shown to be least, 14 orders and 23 species of prey. Relative abundances of top 20 prey items at species and order levels of the eight carnivorous fishes were detailed shown in Fig. 2 . The foods of carnivorous fishes at phyla level were mainly composed of Chordata and Arthropoda, accounting for 58.87% and 31.96%, respectively. Actinopteri was the most abundant food at class level with the relative abundance of 57.83%. At the order level, Gobiiformes, Decapoda and Calanoida constituted the main foods of carnivorous fishes, accounting for a percentage of 56.39% of all preys. At species level, A. hasta , Thryssa kammalensis , Chaeturichthys stigmatias, Tridentiger brevispinis and Planiliza haematocheilus had the highest relative abundances in the preys, accounting for 5.98%−11.43% of all preys, respectively. The percentage of top 20 prey species in the gut contents of Platycephalus indicus was much lower than other predators, indicating its distinctive feeding category compared with other species. Detailed relative abundance of each carnivorous fish’s food items at species level was shown in Tables S6-S13. ANOSIM analysis showed that significant differences were represented in the prey compositions and dietary partitioning among all predators ( P < 0.05). L. japonicus primarily preyed on the species T. barbatus , Lebbeus Polaris , A. hasta , and all of their abundances in gut contents were shown to be over 20% and frequency of occurrences not less than 60% (Table S6). C. stigmatias and Thryssa kammalensis were presented to be the most abundant preys in the gut contents of S. niphonius (Table S7). The main food components of the four Gobiid predators ( C. stigmatias 、 O. rubicundus 、 A. hasta and T. barbatus ) were found to be species in the orders Gobiiformes as well as Calanoida, for example, A. hasta and C. stigmatias in the order Gobiiformes, and Acartia hudsonica and Pseudodiaptomus marinus in the order Calanoida (Table S5-S8). Particularly, Neomonoceratina microreticulata of Podocopida accounted a considerable abundance of 23.57% in the preys of T. barbatus (Table S9). Comparatively distinctive and complexed prey composition was found in the gut contents of Cynoglossus joyneri . Prey species Erythrops microps , Nereis denhamensis and Thryssa kammalensis in orders of Mysida, Phyllodocida, and Acropomatiformes were found to be largely distributed in its food organisms (Table S12). Species of Calanoida, Veneroida, Mugiliformes and Cypriniformes constituted the main foods of P. indicus (Fig. 2 ), and the abundance of Meropesta nicobarica of Veneroida achieved 22.08% (Table S13). These subtle differences in feeding habits to some extent eased the food competition between carnivorous fishes with similar feeding habits in the biological community, which is conducive to species coexistence and biological community continuation. 3.3 Niche breadth and interspecific dietary niche overlap Niche breadth represents the utilization of resources and the adaptability of organisms to habitats (Bearhop et al. 2004 ). The feeding performance at the order level of species could reflect the food complexity to the greatest extent. The niche breadths of eight carnivorous fishes calculated by the prey relative abundances at the order level were calculated and shown in Table 3 . Based on the niche breadth analysis, L. japonicus was detected as narrow-niche species; A. hasta , C. joyneri and P. indicus were detected as wide-niche species. Other species were determined as middle-niche species. A. hasta had the widest niche breadth ( B = 8.26), followed by C. joyneri (B = 7.65), and L. japonicus had the narrowest food niche breadth ( B = 2.44). The average trophic niche breadth of four Gobiiformes species ( B = 5.41) in this study was much higher than that of L. japonicus in the order Acropomatiformes ( B = 2.44) and S. niphonius in Scombriformes ( B = 3.46), and lower than that of C. joyneri in the order Pleuronectiformes and P. indicus in Perciformes ( B = 7.43 and 6.89, respectively). In general, the trophic niche breadths of the eight fish species differed greatly. Table 3 Niche breadths of eight carnivorous fishes. Species Niche breadth Species Niche breadth L. japonicus 2.44 C. stigmatias 5.76 S. niphonius 3.46 O. rubicundus 3.24 A. hasta 8.26 C. joyneri 7.65 T. barbatus 4.39 P. indicus 6.89 The niche overlap index reflects the utilization degree of domestic resources, and measures the potential competition between species to some extent (Churchfield et al. 1999 ; Vieira and Port 2007 ). High trophic niche overlaps existed in the carnivorous fishes in this area, indicating significant food competition occurred in these species. According to the results of the trophic niche overlap indexes of eight carnivorous fishes in Table 4 , significant niche overlaps existed among most of the carnivorous fishes. As shown, all of the niche indexes among L. japonicus , S. niphonius , A. hasta , T. barbatus , C. stigmatias and O. rubicundus were higher than 0.6. L. japonicus and S. niphonius presented the highest niche overlap index ( O jk = 0.83). The trophic niche of P. indicus was found to have no overlap with L. japonicus and S. niphonius ( O jk 0.3). The trophic niche of C. joyneri only slightly overlapped with C. stigmatias ( O jk = 0.42, Table 4 ). Table 4 Trophic niche overlap indexes of the food items in the carnivorous fishes. L. japonicus S. niphonius A. hasta T. barbatus C. stigmatias O. rubicundus C. joyneri S. niphonius 0.83 A. hasta 0.71 0.53 T. barbatus 0.68 0.70 0.64 C. stigmatias 0.71 0.87 0.62 0.72 O. rubicundus 0.67 0.74 0.66 0.80 0.76 C. joyneri 0.15 0.22 0.29 0.22 0.42 0.20 P. indicus 0.17 0.17 0.45 0.42 0.38 0.44 0.40 3.4 Food web organization and species’ roles The food web organization of carnivorous fishes is shown in Fig. 3 . Each link represents a predator-prey relationship. The thicker the link, the greater the abundance of prey. Nodes of the same color represent that the predator or prey is located in the same module of the food web. The classical network structure indicators of the food web, including the number of links, linkage density, connectance, generality, vulnerability and modularity classes for the food web were evaluated to provide network characteristic data comparable to other food webs and other types of ecological networks (Table 5 ). Meanwhile, we analyzed the network roles of the prey species to understand their functional importances in the food web (Table S14). Table 5 Structure attributes of the carnivorous fishes-prey food web in the study area. Qualitative metrics Quantitative metric S A S B L LD C G V Q 8 156 322 1.99 0.26 19.5 2.09 0.41 Qualitative metrics were based on occurrence data: S A , number of carnivore species; S B , number of prey taxa; L , number of links; LD, link density; C , connectance; G , generality; V , vulnerability. Quantitative metrics were based on prey relative read abundance data: Q , modularity. The network included eight predators (carnivorous fishes) and 156 prey species. Notably, the food web had a significant modular structure (modularity Q = 0.41). Module delineations were shown to be inconsistent with the highly dietary niche overlaps among carnivorous fishes, with three modules totally in the network (Fig. 3 and Table S14). Carnivorous fishes S. niphonius and O. rubicundus were divided into one module, and C. stigmatias and P. indicus formed into another module, and C. joyneri formed a module separately (Table S14). The rest of the species, i.e., L. japonicus, A. hasta and T. barbatus were not included in any modules. The several modules constructed in the food web may be corelated with their food differentiations. It is easy be found that separate modules were constructed with their respective specific and preferential preys. As shown, ten species, A. hasta , C. stigmatias , Planiliza haematocheilus , Acartia ohtsukai , Acartia pacifica , Corbicula fluminea , Oratosquilla oratoria , Pholis fangi , Pseudodiaptomus marinus and Tridentiger brevispinis were typical preys with the highest number of predators (degree D g = 6−7 and normalized degree ND = 0.75 − 0.88). And they had the greatest network centrality, which is reflected in their highest closeness values in the range of 0.80 to 1 in the food web. This indicated that they had frequent and extensive interactions with predatory species both in network and within modules. Predators C. stigmatias and A. hasta , C. joyneri , O. rubicundus and S. niphonius were shown to have betweenness values not less than 0.005. Particularly, C. stigmatias and A. hasta were identified to possess the top two betweenness values of 0.013 and 0.008, respectively, indicating the functional key roles of the two species in the ecosystem. 4. Discussion 4.1 Necessity of multi-marker combination In the current study, the combination of multiple barcoding markers achieves high-resolution food identification and enhances prey diversity detection compared with using any single marker. Applying multiple DNA markers enabled quantitative evaluations of the carnivorous fishes’ dietary compositions at high taxonomic resolution, thus providing hitherto unknown details of species trophic characteristics and feeding strategies. Sequencing results always be affected by the barcoding regions of marker genes, the selected paired primers, amplification preference, and the positive contamination caused by improper operation, and even the perfection of the DNA barcoding reference database, et al. (Jusino et al. 2019 ). In this study, four pairs of primers of three barcoding regions were simultaneously used for amplification, and three databases were selected for taxonomic annotation separately, which avoided the potential incomplete species alignment and unilateral prey identification to the greatest extent. Even though few species may not be effectively detected, our study has minimized this deviation and comparatively precisely illustrated the dietary diversity of carnivorous fishes. We verified that 18S barcoding marker, which was seldomly used in the feeding analysis of carnivorous fishes previously, possessed the maximum success rate in detecting prey information as a single marker. This proposed a huge challenge to the general acknowledgement of the universality of traditional COI-m marker (COI-m) in the prey identification of fishes (Leray et al. 2013 ). The satisfied identification result of 18S marker may be resulted from its superior amplicon diversity and the complete database information (Sliva database). The 18S rRNA V4 region applied in this research is one of the variable regions of 18S rRNA in eukaryotes, which is the optimal choice for 18S rRNA gene analysis and annotation because of its extensive use, the complete database information and the best classification effect (Hadziavdic et al. 2014 ). In this study, the considerable recognition rate of 18S marker was mainly manifested in the following aspects: (1) At class, order and species levels, the prey recognition rates of 18S marker were significantly higher than that of COI-M, COI-m and 16S marker. The prey recognition rate of food items was proved to be higher than 50% at the order and species levels. (2) The ability of 18S marker identifying the dominant prey of each fish was significantly higher than that of other markers according to the distribution of preys’ relative abundance and the frequency of occurrence (Tables S4, S6-S13). It can be speculated that the diversified ecological environment in marine ecosystem provided a variety of feeding source for carnivorous fishes. This offers novel implications for the feeding analysis of the carnivorous fishes that at top trophic levels in the complexed marine ecosystem. In addition, although both COI-M and COI-m markers are targeted COI barcoding genes, the main preys they identified had significant difference ( P < 0.05), which indicated that the primer length also obviously affect the prey identification in the analysis of highly digestible diet samples. The preys detected by 16S marker at class level were relatively single, while it enhanced the resolution of fish preys to a large extent which cannot be achieved by other markers (Table S5). And this offers indispensable evidence for the predatory preference of fish preys. Two-marker and three-marker combinations in the presence of 18S marker always revealed a satisfied feeding analysis effectiveness of carnivorous fishes of less than 79% and 91%, respectively. Considering both the economic cost and the effect of prey identification, two-marker combination of 18S marker and any other markers can be used as the first choice for the feeding analysis of carnivorous fishes, and then the three-marker combinations. This offers novel implications for the feeding analysis of the carnivorous fishes that at top trophic levels in the complexed marine ecosystem. 4.2 Feeding characteristics of carnivorous fishes Novel features of the diet of carnivorous fishes by using multiple marker analysis were found here. 23 − 69 prey species were detected in the diet of carnivorous fishes, with an average of 45 prey species of each predator based on only five samples on average. This value was much higher than the prey taxa of carnivorous fishes based on large number of stomach content samples in the previous studies (Jin et al. 2010 ; Zhang et al. 2018 ; Sui et al. 2021 ). Advantages of using Multiple DNA barcoding markers in the feeding analysis of carnivorous fishes were obviously revealed here. Gobiiformes, Decapoda and Calanoida were firstly found to constitute the main foods of carnivorous fishes. Carnivorous fishes were always found to feed mainly on small fishes and benthic shrimps by analyzing the stomach contents in previous researches (Jin et al. 2010 ; Zhang et al. 2018 ; Sui et al. 2021 ). In contrast, our results particularly stressed the status of Calanoida in the diet of carnivorous fishes. Calanoida in marine ecosystem contains a variety of species, but its crucial role in the diet of carnivorous fishes has never been highlighted before. Our results offer a novel insight and propose a necessity for the concern of its population in the carnivorous fish managements. Furthermore, the rich distributions of Gobiid foods in the gut contents of Gobiid predators ( T. barbatus , C. stigmatias and O. rubicundus ) found in our results (Fig. 2 ) indicated a potential mutual predation among Gobiids. Our methods showed not only the abundant diet diversity of species but also the special nice overlaps among them. Diversified overlaps were found among carnivorous fishes, ranged from 0.15 to 0.83 (Table 5 ). This range was much wider than previous studies (Jin et al. 2010 ; Cicala et al. 2024 ). The minimum overlap of C. joyneri with other species may be caused by its unique feeding strategy compared with other fishes (Fig. 2 and Table 5 ). The highest niche overlap reached up to 0.83 ( L. japonicus and S. niphonius ), reflecting the intense competition for food in the habitat between the two predators. The niche breadths of eight carnivorous fishes were calculated and the species were classified according to calculation result. Particularly, L. japonicus was detected as narrow-niche species for the first time. L. japonicus is one of the highest-trophic predators in this area and always be popular in domestic and foreign markets (Wang et al. 2022 ). The narrowest niche breadth represented by L. japonicus may be explained by the relatively single diet composition and the dominant percentage of Gobiiformes in its food content (RA = 56.79%, Table 3 ). The phenomenon indicated its low ability to utilize food resource, adapt habitats and resist external interference, thus, more attention should be paid to the feeding habit of L. japonicus in the actual fisheries managements. Although several obvious niche overlaps occurred among predators, the decipherment of food composition showed that there were significant differences in the feeding preference (food composition and prey proportion) among carnivorous fishes ( P < 0.05). L. japonicus , S. niphonius , A. hasta , T. barbatus , C. stigmatias and O. rubicundus fed on Gobiid species in large quantities, and the feeding niches of each species basically overlap significantly (Table 5 ). However, obvious differences existed among the feeding organisms they preferentially (Tables S6-S11). For example, L. japonicus fed preferentially on T. barbatus and A. hasta , while S. niphonius and A. hasta fed preferentially on C. stigmatias , and T. barbatus feed preferentially on A. hasta (Tables S6-S8). In addition, both A. hasta and T. barbatus consumed Calanoida in large quantities, but A. hasta mainly fed on Acartia hudsonica , while T. barbatus mainly fed on Pseudodiaptomus marinus (Tables S8 and S9). This can be considered as food differentiation, i.e., the specialization of species relative to the main food types (Churchfield et al. 1999 ). Among the remaining overlapping species, it was found that the pairwise comparison species may feed on the same order of food, but they were separated from each other on their specific food. Therefore, carnivorous fishes in this area may avoid feeding conflicts by adjusting prey compositions and dietary partitioning to reduce the overlap of trophic niches. 4.3 Modularized food web and functional species The organization of food web provides useful information for revealing community assemblages, interaction network structures, and species’ functional roles in ecosystems, but it is rarely applied to the conceptualization of carnivorous fish communities (Thompson et al. 2012 ; Delmas et al. 2019 ). Our results showed that the food web of carnivorous fishes and prey exhibited significant modular organization. Modularization in trophic network is expected to improve community stability by preventing the spread of interference between modules, which has been confirmed by theoretical modeling and empirical research (Thébault and Fontaine 2010 ; Stouffer and Bascompte 2011 ). The exhibition of modular organization of the food web indicated an essential feeding differences among most of the carnivorous fishes. This pattern is consistent with the food differentiations among carnivorous fishes, which can alleviate resource competition among modules and promote the stability of the network. When food species disappear, highly specialized predators that rely on very few prey species are more likely to become extinct than generalists (Cirtwill et al. 2018 ). Significant differences of food composition between carnivorous fishes ( P < 0.05) as well as modular structure in the carnivorous fishes-preys food web ( Q = 0.41, Table 5 ) represented that tedious dietary diversification existed among carnivorous fishes in marine ecosystem, and specific and directional proliferation strategies should be advocated to be adopt for their population recoveries. Understanding the role of prey species in the food web organization s helps to identify functionally important prey, thus providing a reference for conservation practices (Harvey et al. 2017 ). Appropriate management policies can maintain the richness and viability of prey species consumed by more carnivorous fishes and thus have a higher central position in their food webs (Lai et al. 2012 ). C. stigmatias and A. hasta were identified as a key functional role in the network, which is in consistent with the result that being important food organisms of several carnivorous fishes in this study. As for other prey species, Planiliza haematocheilus , Acartia ohtsukai , Acartia pacifica , Corbicula fluminea , Oratosquilla oratoria , Pholis fangi , Pseudodiaptomus marinus and Tridentiger brevispinis were surprisingly identified as regional keystone species in this food web. This indicates an importance to sustain their populations for conserving the biodiversity of the functional carnivorous fishes in the ecosystem. Our results presented the complex trophic interactions and food web organization of carnivorous fishes-prey community in marine ecosystem, as well as emphasizing the different species’ roles in network structures and ecosystem functioning. Although the dietary samples of several carnivorous fishes are limited and may not fully reveal the whole picture of the food items, the main feeding interactions in the community were most likely to be captured under the multi-marker amplification strategy. Thus, the general trophic characteristics of carnivorous fishes and their dietary niche relationships patterns are shown to be sound and robust. Wider seasonal and geographical sampling in marine ecosystem will enhance understanding of predator feeding strategy, interspecific dietary overlap, and temporal and spatial variations in food webs. The modular organization analyzed on the basis of prey abundance and species composition data in the current food web illustrated the importance of adopting specific and directional proliferation strategy for most carnivorous fishes to conduct biodiversity protection and fishery production recoveries. By combining use of DNA multi-barcoding markers and network analysis, we detailly paint a picture of carnivorous fishes’ trophic networks and successfully identify several functionally important species in the ecosystem. The finding can provide effective conservation and restoration decisions for improving fisheries structure and stabilizing marine ecosystem, effectively protect the integrity of ecosystem functions, and enhance the resilience of marine ecosystems in the future. 5. Conclusions In this study, we highlighted the necessity of using multiple DNA barcoding markers for feeding analysis based on gut content of carnivorous fishes. We illustrated the intricate trophic interactions and food web organization within the carnivorous fishes-prey community, and identified functionally important species through NGS technology and network analysis. Our findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes. The importance of order Calanoida in the feeding resource of carnivorous fishes was emphasized, and the peak value up to 0.83 of the niche overlap indicated an intense competition among carnivorous fishes. Significant modularity existed in the carnivorous fishes-prey food web, suggesting that the specific and directional proliferation of certain carnivorous fishes within modules should be promoted in adaptive marine fisheries management. In addition, special attention should be paid to functionally important species identified in the food web to enhance the health of the marine ecosystem. Declarations Acknowledgements This work was supported by the National Key R&D Program of China (2019YFE0122300). Competing Interests The authors declare that there are no competing interests. Authors’ contributions Writing—original draft, Writing—review & editing, Conceptualization, Methodology, Formal analysis, Data curation: Xiaoke Pang; Writing—original draft, Supervision, Resources: Biao Guo and Kefeng Liu; Writing—review & editing, Resources: Chenglong Han, Yifan Zhao, Yufei Liu; Resources: Toshihisa Kinoshita, Osamu Yamashita and Wenhui Wang; Writing—Review & Editing, Supervision: Xueqiang Lu. All authors contributed critically to the drafts and gave final approval for publication. Funding statement This research was supported by the National Key R&D Program of China (2019YFE0122300). Data availability Data will be made available on request. 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Guo","suffix":""},{"id":371597769,"identity":"7c21ad74-09b2-4572-b187-eb431a95399d","order_by":2,"name":"Kefeng Liu","email":"","orcid":"","institution":"Tianjin Fisheries Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kefeng","middleName":"","lastName":"Liu","suffix":""},{"id":371597770,"identity":"5d3c4e78-4c75-40b4-993e-049a418c6e99","order_by":3,"name":"Chenglong Han","email":"","orcid":"","institution":"Nankai University College of Environmental Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Chenglong","middleName":"","lastName":"Han","suffix":""},{"id":371597771,"identity":"b141fd6f-b322-44c5-9cdd-d7e63caee4d6","order_by":4,"name":"Yifan Zhao","email":"","orcid":"","institution":"Hebei Natutal Gas Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zhao","suffix":""},{"id":371597772,"identity":"9f2cd551-398d-49bc-8b59-3303905b4690","order_by":5,"name":"Yufei Liu","email":"","orcid":"","institution":"Nagoya University Graduate School of Environmental Studies: Nagoya Daigaku Daigakuin Kankyogaku Kenkyuka","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Liu","suffix":""},{"id":371597773,"identity":"81a886a9-0153-4984-a803-b98ce401170e","order_by":6,"name":"Toshihisa Kinoshita","email":"","orcid":"","institution":"TBR Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Toshihisa","middleName":"","lastName":"Kinoshita","suffix":""},{"id":371597774,"identity":"09bf3047-049d-4ce4-b20c-16b0b4489382","order_by":7,"name":"Osamu Yamashita","email":"","orcid":"","institution":"TBR Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Osamu","middleName":"","lastName":"Yamashita","suffix":""},{"id":371597775,"identity":"94393554-142f-4dc9-87a8-dfd7af244351","order_by":8,"name":"Wenhui Wang","email":"","orcid":"","institution":"TBR Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Wang","suffix":""},{"id":371597776,"identity":"7f90f0e1-a2c3-482c-b13e-d33b9d7b1434","order_by":9,"name":"Xueqiang Lu","email":"","orcid":"","institution":"Nankai University College of Environmental Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Xueqiang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-09-29 15:23:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5175724/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5175724/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69435299,"identity":"ca578f6d-08f7-49c4-b393-c570a701af6b","added_by":"auto","created_at":"2024-11-20 10:27:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139386,"visible":true,"origin":"","legend":"\u003cp\u003ePrey taxa and prey identification rates of carnivorous fishes detected by four barcoding markers at species and order levels.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5175724/v1/3e859cd2590a0bbded33729c.png"},{"id":69435297,"identity":"73239fbe-70fd-49e8-bcea-3e697b5318f9","added_by":"auto","created_at":"2024-11-20 10:27:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133775,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of top 20 prey items at species (A) and order (B) levels of the eight carnivorous fishes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5175724/v1/e3aa4c0eeac8aec5b72fb7b8.png"},{"id":69435298,"identity":"85b2ec40-bf18-4203-ad2d-4fc6efb44e83","added_by":"auto","created_at":"2024-11-20 10:27:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171626,"visible":true,"origin":"","legend":"\u003cp\u003eFood-web organization of carnivorous fishes in Bohai Bay. The red node represents the predators, and the blue node represents the preys. Each link represents a predator-prey relationship. The larger the node, the greater the abundance of prey.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5175724/v1/e6deca2acb1f5ecff02eaa37.png"},{"id":85932408,"identity":"74726f59-21cb-4343-ba67-aca2a18f2e98","added_by":"auto","created_at":"2025-07-03 09:39:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1817914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5175724/v1/d1adf815-c8f8-453b-a813-d2792bcc93c8.pdf"},{"id":69435300,"identity":"677b3518-1f69-4cd6-9295-949ad1ce0f28","added_by":"auto","created_at":"2024-11-20 10:27:51","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":97937,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5175724/v1/f33bd9c6f3ec33e27af9174e.docx"}],"financialInterests":"","formattedTitle":"Constructing food web of carnivorous fishes using multiple DNA barcoding markers of gut contents: A case from Bohai Bay, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFishes with the trophic level higher than 3.0 in aquatic ecosystems are defined as carnivorous fishes (Jaureguizar and Milessi \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In recent years, the rate of large-scale global biodiversity loss has been accelerating, threatening the maintenance of ecosystem functions and services in biological communities and human societies (Butchart et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cardinale et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ceballos et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among the rapidly disappearing biota, carnivorous aquatic fishes are particularly endangered because of high-intensity artificial fishing and their high ecological demands (Jaureguizar and Milessi \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As aspx and meso-predators of the marine food webs, carnivorous fishes can directly or indirectly regulate other trophic levels through trophic cascades or various behavioral effects (Jaureguizar and Milessi \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kume et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their presence in marine ecosystems can promote or hinder species coexistence, interspecific competition and species diversity (Jaureguizar and Milessi \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kume et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the losses of massive carnivorous fishes led to the change of community structure, such as the trophic downgrading and biodiversity decline, which affected the community stability and ecological function (Kume et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, due to years of overfishing, massive emissions of pollutants and large-scale ocean engineering, the total amount of fishery resources has been greatly reduced, especially economic organisms of carnivorous fish, leading that fishery resource\u0026rsquo;s structure in marine ecosystem tend to be low-trophic (Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, a mechanistic understanding of species interactions and community organization of carnivorous fishes is needed thus to propose informed and effective conservation and restoration decisions to improve fisheries structure and stabilize marine ecosystems (Sergio et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ritchie et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA food web is the basic connection of the nutritional relationship between organisms in the biological community, which reflects the natural attributes of the interdependence, mutual restraint, co-evolution and other interactions between various organisms in nature (Pimm \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Food webs can directly reflect the functional structure of the ecological community (Rossberg \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), therefore, construction of food webs between species has always been a very vital and active research field in ecology. The topological features of trophic networks (food webs) are of crucial implications for the knowledge of network dynamics and ecosystem stability, and provide a general framework for describing and comparing the interactions between species (Th\u0026eacute;bault and Fontaine \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tylianakis et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Delmas et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Food web can be studied at multiple levels varying from the food composition and interspecific trophic niche of individual species to the topological characteristics of community-level networks, such as generality (i.e., the average number of prey per predator) and modularity (i.e., species within the module interacting more frequently than with other species in the community) (Olesen et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Guimer\u0026agrave; et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Theoretically, these structural features are related to the food web stability and resilience (Tylianakis et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The network role of species, such as its centrality and connectivity with other species within and across modules, describes the position of species in the network organization and is critical to its functional importance in ecosystem processes (Cirtwill et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Delmas et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hackett et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, a comprehensive understanding of the food web structure and the role of related species at the species, interspecific, module and network levels can provide important information for revealing species coexistence occurrence and maintenance mechanisms, and can also predict the impact of species loss on ecosystem dynamics through interactive networks (Thompson et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Harvey et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mata et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, owing to the unobtainability of adequate quantity and quality (i.e., prey coverage and species resolution) of community-scale carnivorous fishes\u0026rsquo; dietary data, previous food web work involving the organization of ecological networks and the role of associated species has rarely focused on carnivorous fishes.\u003c/p\u003e \u003cp\u003eThe application of DNA barcoding in diet studies has increased considerably with the advent of next-generation sequencing (NGS) technology (Bowser et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The DNA fragments of the entire mixed sample are amplified and then subjected to high-throughput sequencing, thus, multiple species in the mixed sample are identified. This can reduce the workload of sampling and maximize the species identification of tissue residues to achieve the feeding analysis (Brandon-Mong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kartzinel et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is now possible to identify even the rarest prey from multiple predators to species, genus, or other levels in a single sequencing run while maintaining the ability to trace back each prey to the sample from which it came (Bowser et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, prey identification from DNA in diet samples is greatly influenced by technical issues including the uncertainty about the taxonomic diversity expected in the sample, the poor quality of the genomic DNA, the barcoding regions of markers, and the amplification lengths of markers (Hebert et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e; Hebert et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003b\u003c/span\u003e; Deagle et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bowser et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This can lead to data loss or distortion of rare or difficult-to-amplify species, and fail to describe the full taxonomic range of the prey consumed (Hebert et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003a\u003c/span\u003e; Hebert et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003b\u003c/span\u003e). Theoretically, the application of multiple markers provides a broader taxonomic resolution of diet as different markers are not suitable barcodes for all taxonomic groups (Bowser et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, more barcoding genes with suitable mutation rate and large interspecific differences as well as diversified amplification lengths were advocated to be used synchronously for prey collaborative identification to enhance the prey identification rate (Valentini et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, previous studies of feeding analysis were mostly conducted on single markers such as mitochondrial Cytochrome c Oxidase subunit I (COI) genes or 16S rRNA genes (Leray et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Johnson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Few studies have been conducted using multiple markers located in different barcoding regions, or markers with varying lengths within the same barcoding region. The distinctiveness of feeding analysis in different single markers and varying combinations of markers have seldomly been reported, so does the quantification analysis based on the relative read abundance of integrated multiple markers. Furthermore, the proposal of a reasonable marker combination for a more comprehensive feeding analysis of carnivorous fishes will be great significance in this research field.\u003c/p\u003e \u003cp\u003eThis study focused on the feeding analysis of carnivorous fishes in Bohai Bay, China, where fishing and pollutant emissions have been significant in recent years. Gut content samples from all of the carnivorous fishes were analyzed using Multiple DNA barcoding markers. By examining the dietary datasets obtained through these markers, we aimed to: (1) assess the effectiveness of single marker and different combinations of multiple markers in identifying prey species, (2) elucidate the dietary characteristics and niche relationships of the carnivorous fishes, and (3) construct an interaction network between carnivorous fishes and their preys, thereby characterizing the food web properties of the marine ecosystem.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection and DNA extraction\u003c/h2\u003e \u003cp\u003eField surveys and sampling were conducted in Bohai Bay, China, in July 2022. The voucher specimens were preserved at -80℃ until DNA extraction at Nankai University (Tianjin, China). Stable isotope ratios nitrogen (δ\u003csup\u003e15\u003c/sup\u003eN) of the tissue for fish samples were estimated using an elemental analyzer/isotope-ratio mass spectrometer (Thermo Fisher Scientific: Flash 2000, ConFloIV, DELTA V Advantage) (Kume et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the calculation of the trophic levels of fish followed the method described in Bowes and Thorp (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Bowes and Thorp \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The fishes with average trophic levels\u0026thinsp;\u0026gt;\u0026thinsp;3.0 were selected as the carnivorous fishes for further dietary analysis using gut content (Jaureguizar and Milessi \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The morphological index and trophic level of each sample were listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eDNA of gut content was extracted in small batches after full grinding. Blank samples were extracted meanwhile to help monitor possible contamination during extractions. The extractions were performed in a laboratory room designated for gut content processing, and its bench tops and equipment were treated with 75% ethanol before and after processing each batch. DNA of gut contents was extracted with a QIAamp DNA Stool Mini Kit (Tiangen, China) according to the manufacturer\u0026rsquo;s instructions. Concentrations of the DNA extracts were determined using NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, Massachusetts, USA) and agarose gel electrophoresis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 PCR amplification and sequencing\u003c/h2\u003e \u003cp\u003eA total of 36 DNA templates were obtained from 36 gut content samples of carnivorous fishes, and each DNA template was amplified for the further feeding analysis. Four paired primers targeting three barcoding regions with small target fragments were used for each DNA template, including two mitochondrial COI genes targeting 400 bp and 313 bp fragments (COI-M and COI-m markers, respectively), the 18S rRNA gene targeting a 380 bp fragment (18S marker), and the 16S rRNA gene targeting a 270 bp fragment (16S marker), to amplify prey DNA. The concrete primer information and thermocycling conditions for primer pairs of this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Those primers were identified to possess a high species resolution for most prey species of carnivorous fishes in Bohai Bay. The PCRs for generating dietary data were conducted in a total volume of 10 \u0026micro;l, comprising 5 \u0026micro;l KOD FX Neo Buffer, 2 \u0026micro;l dNTP (2 mM), 0.2 KOD FX Neo, 10 \u0026micro;M F/R primers, and 1 \u0026micro;l DNA extract. PCRs were set up in a clean designated pre-PCR chamber. Each 96-well PCR plate contains 4 to 8 PCR blanks (including all PCR reagents except DNA) to check contamination. Each sample was amplified two times to increase the probability of detection of prey taxa. All PCR products were purified firstly, and were then sent to Biomarker Technologies Co., ltd. (Beijing, China) for sequencing by the platform of Illumina novaseq 6000.\u003c/p\u003e \u003cp\u003e \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 \u003cp\u003eSequences and reaction conditions of the primers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer pairs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequences (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThermocycling conditions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOI-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHemF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCATTYCCACGAATAAATAAYATAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e94℃ 4 min; [5 cycles: 94℃ 30s; 45℃ 30 s; 72℃ 1 min]; [35 cycles: 94℃ 30s, 51℃ 30s; 72℃ 1min]; 72 ℃ 7min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edgHCO-2198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAACTTCAGGGTGACCAAARAAYCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOI-m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emlCOIintF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGWACWGGWTGAACWGTWTAYCCYCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edgHCO2198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAACTTCAGGGTGACCAAARAAYCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAReuk454FWD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCAGCA(G/C)C(C/T)GCGGTAATTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e95℃ 3 min; [30 cycles: 95℃ 45 s; 55℃ 45 s; 72℃ 1 min]; 72 ℃ 5 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAReukREV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTTTCGTTCTTGAT(C/T)(A/G)A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16sF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAYAAGACGAGAAGACCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e95℃ 5 min; [25 cycles: 95℃ 30 s; 50℃ 30 s, 72℃ 40 s]; 72 ℃ 7 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16sR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATTGCGCTGTTATTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Bioinformatics sequence processing\u003c/h2\u003e \u003cp\u003eRaw reads were firstly filtered using Trimmomatic v0.33 (Bolger et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and then the primer sequences were identified and removed with Cutadapt v1.9.1. Paired-end reads were merged with FLASH v1.2.7 based on overlapping regions in the corresponding reads (Magoč and Salzberg \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Chimeric sequences were deleted and denoised to form operational taxonomic units (OTUs) in QIIME2 2020.6 (Bolyen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). If the sequence of PCR product was less than that of the PCR blank control or DNA extraction blank control or less than 1% of the total PCR sequences, it is considered to be derived from cross-contamination and not included in the OTUs of the prey species. In sequence processing, the sequences of less than 98% consistent with the databases were discarded, and the sequences with a difference of less than 2% with the database were merged. In order to conduct an accurate taxonomic annotation for screened OTUs of each marker, we matched three comparatively complete databases for four markers based on the region where the barcoding marker is located correspondingly. Finally, the taxonomic annotation of OTUs of COI, 18S and 16S barcoding regions was processed by BLAST against the Fungene, Sliva and NCBI database, respectively (Fish et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Quast et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). OTUs appeared form carnivorous fishes body sequences, experimentally contaminated humans\u0026rsquo; sequences, and non-Bohai Bay species\u0026rsquo; sequences were removed. The average effective prey OTUs after BLAST against three databases of each species were collated and summarized for subsequent feeding analysis.\u003c/p\u003e \u003cp\u003eThe calculation and analysis of alpha diversity (Chao1, Shannon and Simpson) indexes were completed in QIIME2 software. One-way analysis of variance (ANOVA) and Student 's t test of SPSS 25.0 were used in this study to compare the significant differences in alpha diversity indexes detected by multiple barcoding markers. We used relative abundance (RA) and frequency of occurrence (FO) to measure food composition and occurrence of preys (Deagle et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Analysis of similarities (ANOSIM) of SPSS 25.0 was conducted to analyze the significances of prey compositions and dietary partitioning among carnivorous fishes. All statistical tests were two-tailed with the significance level set at 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Interspecific dietary differences and niche overlaps\u003c/h2\u003e \u003cp\u003eAccording to the relative read abundances of prey composition, the Levins index (\u003cem\u003eB\u003c/em\u003e) was used to describe the food niche breadth, and the Pianka overlap index (\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e) was used to measure the degree of food niche overlap (Pianka and R 1973). \u003cem\u003eB\u003c/em\u003e and \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e are calculated by:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:B=\\frac{1}{\\sum\\:{{P}_{j}}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represents the proportion of prey \u003cem\u003ej\u003c/em\u003e. Species are classified as narrow-niche (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eB\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;3), middle-niche (3\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eB\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;6) and wide-niche (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;6).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{O}_{jk}=\\frac{\\sum\\:({P}_{ij}\\cdot\\:{P}_{ik})}{\\sqrt{\\sum\\:\\left({{P}_{ij}}^{2}\\right)\\cdot\\:\\sum\\:\\left({{P}_{ik}}^{2}\\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eik\u003c/em\u003e\u003c/sub\u003e represents the relative abundance of prey \u003cem\u003ei\u003c/em\u003e in the species \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e, respectively. The variation range of \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e is 0\u0026ndash;1: \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.3 is considered as an overlap, and \u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.6 is considered as a significant overlap (Krebs \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Food web organization and species\u0026rsquo; roles\u003c/h2\u003e \u003cp\u003eThe network-level properties of the food web were estimated using the metrics calculated by the \u003cem\u003enetworklevel\u003c/em\u003e and \u003cem\u003egrouplevel\u003c/em\u003e functions in bipartite. Qualitative measurements were used to characterize the overall network complexity of the food web, including the predator and prey numbers, feeding interactions (Link, \u003cem\u003eL\u003c/em\u003e), link density (LD\u0026thinsp;=\u0026thinsp;\u003cem\u003eL\u003c/em\u003e / \u003cem\u003eS\u003c/em\u003e, where \u003cem\u003eS\u003c/em\u003e is the sum of the numbers of predator species (S\u003csub\u003eA\u003c/sub\u003e) and prey species (S\u003csub\u003eB\u003c/sub\u003e); i.e., mean number of links per species), and connectance (\u003cem\u003eC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;L / (S\u003csub\u003eA\u003c/sub\u003e \u0026times; S\u003csub\u003eB\u003c/sub\u003e), the proportion of realized interactions out of all possible bipartite interactions), generality (\u003cem\u003eG\u003c/em\u003e, the average number of prey per predator), and vulnerability (\u003cem\u003eV\u003c/em\u003e, the average number of prey consumed by predators). In order to further understand the organizational structure of the food web, we measured the modularity of the network. The module is defined as the subdivision in the food web, Species belonging to the module have high interactions with each other and low connections with species outside the module (Landi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Delmas et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The higher modularity in the food web is considered to enhance the stability of the network by limiting the propagation of disturbances within the module to other parts of the network (Guimer\u0026agrave; et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The weighted modularity (\u003cem\u003eQ\u003c/em\u003e) of food web was calculated with the \u003cem\u003ecomputeModules\u003c/em\u003e function in bipartite, and values of \u003cem\u003eQ\u003c/em\u003e vary from 0 (no modularity) to 1 (complete modularity) (Beckett \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe role and importance of individual prey in the food web structure were also assessed by two qualitative and three quantitative indicators calculated by the \u003cem\u003especieslevel\u003c/em\u003e function in bipartite. Qualitative indicators included degree (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e, the number of predators consuming a given prey) and normalized degree (N\u003csub\u003eD\u003c/sub\u003e, the proportion of prey consumed by all predators). The quantitative assessments were calculated based on the abundance data of relative read length. Quantitative assessments included two network centrality indicators: (i) betweenness centrality (BC), which measures the importance of species as a connector between different parts in the network; (ii) closeness centrality (CC), which measures the closeness of a species to all other species in the network (Mata et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Both value range of BC and CC are 0\u0026thinsp;\u0026minus;\u0026thinsp;1, and the larger of the value, the higher network centrality of the species. We used the software Gephi v.0.9.2 to construct the food web network, which is convenient for network topology and module visualization.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Species identification of barcoding markers\u003c/h2\u003e \u003cp\u003eAll gut content samples were successfully amplified and sequenced using four barcoding markers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on the sequencing data of four markers, totally 11,510,503 valid sequences were obtained from the 38 samples, with an average of 302, 908 sequences per sample. After removing body sequences of carnivorous fishes, unidentifiable sequences and sequences with abundance\u0026thinsp;\u0026lt;\u0026thinsp;1%, a total of 190 OTUs were identified by the combination of four markers from the gut contents of eight carnivorous fishes. The identification results of single marker and multiple marker combinations at order and species levels were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification results of single marker and multiple marker combinations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOrder level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSpecies level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrey\u003c/p\u003e \u003cp\u003eitems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentification\u003c/p\u003e \u003cp\u003erate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrey\u003c/p\u003e \u003cp\u003eitems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIdentification\u003c/p\u003e \u003cp\u003erate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSingle marker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTwo-marker combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 COI-m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 18S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-m \u0026 18S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-m \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18S \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThree-marker combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 COI-m \u0026 18S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 18S \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 COI-m \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-m \u0026 18S \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFour-marker combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOI-M \u0026 COI-m \u0026 18S \u0026 16S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed prey species identified by COI-M, COI-m, 18S and 16S markers was respectively shown in Tables S2-S5. Great significance appeared among four markers due to the differences in barcoding region, amplicon length and database resources. Overall, the prey recognition rates of carnivorous fish group and single species were ranked as 18S\u0026thinsp;\u0026gt;\u0026thinsp;COI-m\u0026thinsp;\u0026gt;\u0026thinsp;COI-M\u0026thinsp;\u0026gt;\u0026thinsp;16S marker. 18S marker was proved to possess the highest identification level among four markers, and totally 94 preys were successfully detected, accounting for a percentage of 60% in the total prey taxa. Comparative identification result was respectively presented by COI-m and COI-M marker, and 41 and 37 prey species were respectively detected. Least prey species were detected by 16S marker, and only 17 preys were identified.\u003c/p\u003e \u003cp\u003eOur findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes, which are 1.5\u0026thinsp;\u0026minus;\u0026thinsp;6.2 and 1.7\u0026thinsp;\u0026minus;\u0026thinsp;9.2 times that of detected by single markers at order and species levels, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The identification effects of different combinations of two markers and three markers varied significantly. At order and species levels, 38%\u0026minus;89% and 32%\u0026minus;84% prey items were detected when using two-marker combinations, and the combinations in the presence of 18S marker obtained favorable success rates not less than 79%. 48%\u0026minus;98% and 42%\u0026minus;93% prey items could be effectively detected by three-marker combinations, and the combination with 18S marker always provided a recognition rate not less than 91%. It can be concluded that all of the combinations with 18S marker rather than traditional universal COI marker (COI-m) revealed a satisfied feeding analysis effectiveness of carnivorous fishes. This proposes novel insights in the use of barcoding marker of carnivorous fish feeding analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Food compositions of carnivorous fishes\u003c/h2\u003e \u003cp\u003ePreys identified by four-marker combination were most extensive, thus, the identification result of four-marker combination were used for further dietary analysis. According to statistics, a total of 22 classes, 56 orders and 156 species of preys were successfully detected in all of the gut content samples of carnivorous fishes. Prey taxa of each carnivorous fish detected by four barcoding markers at species level was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The number of prey taxa found in carnivorous fishes at species and order levels were 23\u0026thinsp;\u0026minus;\u0026thinsp;69 and 14\u0026thinsp;\u0026minus;\u0026thinsp;37, respectively. \u003cem\u003eO. rubicundus\u003c/em\u003e was found to own the highest prey taxa, including 37 orders and 69 species of prey. The preys of \u003cem\u003eL. Japonicus\u003c/em\u003e were shown to be least, 14 orders and 23 species of prey. Relative abundances of top 20 prey items at species and order levels of the eight carnivorous fishes were detailed shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The foods of carnivorous fishes at phyla level were mainly composed of Chordata and Arthropoda, accounting for 58.87% and 31.96%, respectively. Actinopteri was the most abundant food at class level with the relative abundance of 57.83%. At the order level, Gobiiformes, Decapoda and Calanoida constituted the main foods of carnivorous fishes, accounting for a percentage of 56.39% of all preys. At species level, \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eThryssa kammalensis\u003c/em\u003e, \u003cem\u003eChaeturichthys stigmatias, Tridentiger brevispinis\u003c/em\u003e and \u003cem\u003ePlaniliza haematocheilus\u003c/em\u003e had the highest relative abundances in the preys, accounting for 5.98%\u0026minus;11.43% of all preys, respectively. The percentage of top 20 prey species in the gut contents of \u003cem\u003ePlatycephalus indicus\u003c/em\u003e was much lower than other predators, indicating its distinctive feeding category compared with other species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed relative abundance of each carnivorous fish\u0026rsquo;s food items at species level was shown in Tables S6-S13. ANOSIM analysis showed that significant differences were represented in the prey compositions and dietary partitioning among all predators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eL. japonicus\u003c/em\u003e primarily preyed on the species \u003cem\u003eT. barbatus\u003c/em\u003e, \u003cem\u003eLebbeus Polaris\u003c/em\u003e, \u003cem\u003eA. hasta\u003c/em\u003e, and all of their abundances in gut contents were shown to be over 20% and frequency of occurrences not less than 60% (Table S6). \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eThryssa kammalensis\u003c/em\u003e were presented to be the most abundant preys in the gut contents of \u003cem\u003eS. niphonius\u003c/em\u003e (Table S7). The main food components of the four Gobiid predators (\u003cem\u003eC. stigmatias\u003c/em\u003e、\u003cem\u003eO. rubicundus\u003c/em\u003e、\u003cem\u003eA. hasta\u003c/em\u003e and \u003cem\u003eT. barbatus\u003c/em\u003e) were found to be species in the orders Gobiiformes as well as Calanoida, for example, \u003cem\u003eA. hasta\u003c/em\u003e and \u003cem\u003eC. stigmatias\u003c/em\u003e in the order Gobiiformes, and \u003cem\u003eAcartia hudsonica\u003c/em\u003e and \u003cem\u003ePseudodiaptomus marinus\u003c/em\u003e in the order Calanoida (Table S5-S8). Particularly, \u003cem\u003eNeomonoceratina microreticulata\u003c/em\u003e of Podocopida accounted a considerable abundance of 23.57% in the preys of \u003cem\u003eT. barbatus\u003c/em\u003e (Table S9). Comparatively distinctive and complexed prey composition was found in the gut contents of \u003cem\u003eCynoglossus joyneri\u003c/em\u003e. Prey species \u003cem\u003eErythrops microps\u003c/em\u003e, \u003cem\u003eNereis denhamensis\u003c/em\u003e and \u003cem\u003eThryssa kammalensis\u003c/em\u003e in orders of Mysida, Phyllodocida, and Acropomatiformes were found to be largely distributed in its food organisms (Table S12). Species of Calanoida, Veneroida, Mugiliformes and Cypriniformes constituted the main foods of \u003cem\u003eP. indicus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the abundance of \u003cem\u003eMeropesta nicobarica\u003c/em\u003e of Veneroida achieved 22.08% (Table S13). These subtle differences in feeding habits to some extent eased the food competition between carnivorous fishes with similar feeding habits in the biological community, which is conducive to species coexistence and biological community continuation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Niche breadth and interspecific dietary niche overlap\u003c/h2\u003e \u003cp\u003eNiche breadth represents the utilization of resources and the adaptability of organisms to habitats (Bearhop et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The feeding performance at the order level of species could reflect the food complexity to the greatest extent. The niche breadths of eight carnivorous fishes calculated by the prey relative abundances at the order level were calculated and shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on the niche breadth analysis, \u003cem\u003eL. japonicus\u003c/em\u003e was detected as narrow-niche species; \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eC. joyneri\u003c/em\u003e and \u003cem\u003eP. indicus\u003c/em\u003e were detected as wide-niche species. Other species were determined as middle-niche species. \u003cem\u003eA. hasta\u003c/em\u003e had the widest niche breadth (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.26), followed by \u003cem\u003eC. joyneri\u003c/em\u003e (B\u0026thinsp;=\u0026thinsp;7.65), and \u003cem\u003eL. japonicus\u003c/em\u003e had the narrowest food niche breadth (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.44). The average trophic niche breadth of four Gobiiformes species (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.41) in this study was much higher than that of \u003cem\u003eL. japonicus\u003c/em\u003e in the order Acropomatiformes (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.44) and \u003cem\u003eS. niphonius\u003c/em\u003e in Scombriformes (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.46), and lower than that of \u003cem\u003eC. joyneri\u003c/em\u003e in the order Pleuronectiformes and \u003cem\u003eP. indicus\u003c/em\u003e in Perciformes (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.43 and 6.89, respectively). In general, the trophic niche breadths of the eight fish species differed greatly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNiche breadths of eight carnivorous fishes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiche breadth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNiche breadth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL. japonicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eC. stigmatias\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. niphonius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eO. rubicundus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. hasta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eC. joyneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. barbatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP. indicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe niche overlap index reflects the utilization degree of domestic resources, and measures the potential competition between species to some extent (Churchfield et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Vieira and Port \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). High trophic niche overlaps existed in the carnivorous fishes in this area, indicating significant food competition occurred in these species. According to the results of the trophic niche overlap indexes of eight carnivorous fishes in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, significant niche overlaps existed among most of the carnivorous fishes. As shown, all of the niche indexes among \u003cem\u003eL. japonicus\u003c/em\u003e, \u003cem\u003eS. niphonius\u003c/em\u003e, \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eT. barbatus\u003c/em\u003e, \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eO. rubicundus\u003c/em\u003e were higher than 0.6. \u003cem\u003eL. japonicus\u003c/em\u003e and \u003cem\u003eS. niphonius\u003c/em\u003e presented the highest niche overlap index (\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e = 0.83). The trophic niche of \u003cem\u003eP. indicus\u003c/em\u003e was found to have no overlap with \u003cem\u003eL. japonicus\u003c/em\u003e and \u003cem\u003eS. niphonius\u003c/em\u003e (\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.3), but overlapped with other five fishes (\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.3). The trophic niche of \u003cem\u003eC. joyneri\u003c/em\u003e only slightly overlapped with \u003cem\u003eC. stigmatias\u003c/em\u003e (\u003cem\u003eO\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e = 0.42, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrophic niche overlap indexes of the food items in the carnivorous fishes.\u003c/p\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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eL. japonicus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eS. niphonius\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eA. hasta\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eT. barbatus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eC. stigmatias\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eO. rubicundus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eC. joyneri\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. niphonius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. hasta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. barbatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC. stigmatias\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eO. rubicundus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC. joyneri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. indicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Food web organization and species\u0026rsquo; roles\u003c/h2\u003e \u003cp\u003eThe food web organization of carnivorous fishes is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Each link represents a predator-prey relationship. The thicker the link, the greater the abundance of prey. Nodes of the same color represent that the predator or prey is located in the same module of the food web. The classical network structure indicators of the food web, including the number of links, linkage density, connectance, generality, vulnerability and modularity classes for the food web were evaluated to provide network characteristic data comparable to other food webs and other types of ecological networks (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Meanwhile, we analyzed the network roles of the prey species to understand their functional importances in the food web (Table S14).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructure attributes of the carnivorous fishes-prey food web in the study area.\u003c/p\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eQualitative metrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuantitative metric\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eA\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eQualitative metrics were based on occurrence data: \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eA\u003c/em\u003e\u003c/sub\u003e, number of carnivore species; \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e, number of prey taxa; \u003cem\u003eL\u003c/em\u003e, number of links; LD, link density; \u003cem\u003eC\u003c/em\u003e, connectance; \u003cem\u003eG\u003c/em\u003e, generality; \u003cem\u003eV\u003c/em\u003e, vulnerability. Quantitative metrics were based on prey relative read abundance data: \u003cem\u003eQ\u003c/em\u003e, modularity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe network included eight predators (carnivorous fishes) and 156 prey species. Notably, the food web had a significant modular structure (modularity \u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41). Module delineations were shown to be inconsistent with the highly dietary niche overlaps among carnivorous fishes, with three modules totally in the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S14). Carnivorous fishes \u003cem\u003eS. niphonius\u003c/em\u003e and \u003cem\u003eO. rubicundus\u003c/em\u003e were divided into one module, and \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eP. indicus\u003c/em\u003e formed into another module, and \u003cem\u003eC. joyneri\u003c/em\u003e formed a module separately (Table S14). The rest of the species, i.e., \u003cem\u003eL. japonicus, A. hasta\u003c/em\u003e and \u003cem\u003eT. barbatus\u003c/em\u003e were not included in any modules. The several modules constructed in the food web may be corelated with their food differentiations. It is easy be found that separate modules were constructed with their respective specific and preferential preys. As shown, ten species, \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eC. stigmatias\u003c/em\u003e, \u003cem\u003ePlaniliza haematocheilus\u003c/em\u003e, \u003cem\u003eAcartia ohtsukai\u003c/em\u003e, \u003cem\u003eAcartia pacifica\u003c/em\u003e, \u003cem\u003eCorbicula fluminea\u003c/em\u003e, \u003cem\u003eOratosquilla oratoria\u003c/em\u003e, \u003cem\u003ePholis fangi\u003c/em\u003e, \u003cem\u003ePseudodiaptomus marinus\u003c/em\u003e and \u003cem\u003eTridentiger brevispinis\u003c/em\u003e were typical preys with the highest number of predators (degree D\u003csub\u003eg\u003c/sub\u003e = 6\u0026minus;7 and normalized degree ND\u0026thinsp;=\u0026thinsp;0.75\u0026thinsp;\u0026minus;\u0026thinsp;0.88). And they had the greatest network centrality, which is reflected in their highest closeness values in the range of 0.80 to 1 in the food web. This indicated that they had frequent and extensive interactions with predatory species both in network and within modules. Predators \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eC. joyneri\u003c/em\u003e, \u003cem\u003eO. rubicundus\u003c/em\u003e and \u003cem\u003eS. niphonius\u003c/em\u003e were shown to have betweenness values not less than 0.005. Particularly, \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eA. hasta\u003c/em\u003e were identified to possess the top two betweenness values of 0.013 and 0.008, respectively, indicating the functional key roles of the two species in the ecosystem.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Necessity of multi-marker combination\u003c/h2\u003e \u003cp\u003eIn the current study, the combination of multiple barcoding markers achieves high-resolution food identification and enhances prey diversity detection compared with using any single marker. Applying multiple DNA markers enabled quantitative evaluations of the carnivorous fishes\u0026rsquo; dietary compositions at high taxonomic resolution, thus providing hitherto unknown details of species trophic characteristics and feeding strategies. Sequencing results always be affected by the barcoding regions of marker genes, the selected paired primers, amplification preference, and the positive contamination caused by improper operation, and even the perfection of the DNA barcoding reference database, et al. (Jusino et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, four pairs of primers of three barcoding regions were simultaneously used for amplification, and three databases were selected for taxonomic annotation separately, which avoided the potential incomplete species alignment and unilateral prey identification to the greatest extent. Even though few species may not be effectively detected, our study has minimized this deviation and comparatively precisely illustrated the dietary diversity of carnivorous fishes.\u003c/p\u003e \u003cp\u003eWe verified that 18S barcoding marker, which was seldomly used in the feeding analysis of carnivorous fishes previously, possessed the maximum success rate in detecting prey information as a single marker. This proposed a huge challenge to the general acknowledgement of the universality of traditional COI-m marker (COI-m) in the prey identification of fishes (Leray et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The satisfied identification result of 18S marker may be resulted from its superior amplicon diversity and the complete database information (Sliva database). The 18S rRNA V4 region applied in this research is one of the variable regions of 18S rRNA in eukaryotes, which is the optimal choice for 18S rRNA gene analysis and annotation because of its extensive use, the complete database information and the best classification effect (Hadziavdic et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, the considerable recognition rate of 18S marker was mainly manifested in the following aspects: (1) At class, order and species levels, the prey recognition rates of 18S marker were significantly higher than that of COI-M, COI-m and 16S marker. The prey recognition rate of food items was proved to be higher than 50% at the order and species levels. (2) The ability of 18S marker identifying the dominant prey of each fish was significantly higher than that of other markers according to the distribution of preys\u0026rsquo; relative abundance and the frequency of occurrence (Tables S4, S6-S13). It can be speculated that the diversified ecological environment in marine ecosystem provided a variety of feeding source for carnivorous fishes. This offers novel implications for the feeding analysis of the carnivorous fishes that at top trophic levels in the complexed marine ecosystem. In addition, although both COI-M and COI-m markers are targeted COI barcoding genes, the main preys they identified had significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which indicated that the primer length also obviously affect the prey identification in the analysis of highly digestible diet samples. The preys detected by 16S marker at class level were relatively single, while it enhanced the resolution of fish preys to a large extent which cannot be achieved by other markers (Table S5). And this offers indispensable evidence for the predatory preference of fish preys. Two-marker and three-marker combinations in the presence of 18S marker always revealed a satisfied feeding analysis effectiveness of carnivorous fishes of less than 79% and 91%, respectively. Considering both the economic cost and the effect of prey identification, two-marker combination of 18S marker and any other markers can be used as the first choice for the feeding analysis of carnivorous fishes, and then the three-marker combinations. This offers novel implications for the feeding analysis of the carnivorous fishes that at top trophic levels in the complexed marine ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Feeding characteristics of carnivorous fishes\u003c/h2\u003e \u003cp\u003eNovel features of the diet of carnivorous fishes by using multiple marker analysis were found here. 23\u0026thinsp;\u0026minus;\u0026thinsp;69 prey species were detected in the diet of carnivorous fishes, with an average of 45 prey species of each predator based on only five samples on average. This value was much higher than the prey taxa of carnivorous fishes based on large number of stomach content samples in the previous studies (Jin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sui et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Advantages of using Multiple DNA barcoding markers in the feeding analysis of carnivorous fishes were obviously revealed here. Gobiiformes, Decapoda and Calanoida were firstly found to constitute the main foods of carnivorous fishes. Carnivorous fishes were always found to feed mainly on small fishes and benthic shrimps by analyzing the stomach contents in previous researches (Jin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sui et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, our results particularly stressed the status of Calanoida in the diet of carnivorous fishes. Calanoida in marine ecosystem contains a variety of species, but its crucial role in the diet of carnivorous fishes has never been highlighted before. Our results offer a novel insight and propose a necessity for the concern of its population in the carnivorous fish managements. Furthermore, the rich distributions of Gobiid foods in the gut contents of Gobiid predators (\u003cem\u003eT. barbatus\u003c/em\u003e, \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eO. rubicundus\u003c/em\u003e) found in our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated a potential mutual predation among Gobiids.\u003c/p\u003e \u003cp\u003eOur methods showed not only the abundant diet diversity of species but also the special nice overlaps among them. Diversified overlaps were found among carnivorous fishes, ranged from 0.15 to 0.83 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This range was much wider than previous studies (Jin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cicala et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The minimum overlap of \u003cem\u003eC. joyneri\u003c/em\u003e with other species may be caused by its unique feeding strategy compared with other fishes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The highest niche overlap reached up to 0.83 (\u003cem\u003eL. japonicus\u003c/em\u003e and \u003cem\u003eS. niphonius\u003c/em\u003e), reflecting the intense competition for food in the habitat between the two predators. The niche breadths of eight carnivorous fishes were calculated and the species were classified according to calculation result. Particularly, \u003cem\u003eL. japonicus\u003c/em\u003e was detected as narrow-niche species for the first time. \u003cem\u003eL. japonicus\u003c/em\u003e is one of the highest-trophic predators in this area and always be popular in domestic and foreign markets (Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The narrowest niche breadth represented by \u003cem\u003eL. japonicus\u003c/em\u003e may be explained by the relatively single diet composition and the dominant percentage of Gobiiformes in its food content (RA\u0026thinsp;=\u0026thinsp;56.79%, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The phenomenon indicated its low ability to utilize food resource, adapt habitats and resist external interference, thus, more attention should be paid to the feeding habit of \u003cem\u003eL. japonicus\u003c/em\u003e in the actual fisheries managements.\u003c/p\u003e \u003cp\u003eAlthough several obvious niche overlaps occurred among predators, the decipherment of food composition showed that there were significant differences in the feeding preference (food composition and prey proportion) among carnivorous fishes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eL. japonicus\u003c/em\u003e, \u003cem\u003eS. niphonius\u003c/em\u003e, \u003cem\u003eA. hasta\u003c/em\u003e, \u003cem\u003eT. barbatus\u003c/em\u003e, \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eO. rubicundus\u003c/em\u003e fed on Gobiid species in large quantities, and the feeding niches of each species basically overlap significantly (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, obvious differences existed among the feeding organisms they preferentially (Tables S6-S11). For example, \u003cem\u003eL. japonicus\u003c/em\u003e fed preferentially on \u003cem\u003eT. barbatus\u003c/em\u003e and \u003cem\u003eA. hasta\u003c/em\u003e, while \u003cem\u003eS. niphonius\u003c/em\u003e and \u003cem\u003eA. hasta\u003c/em\u003e fed preferentially on \u003cem\u003eC. stigmatias\u003c/em\u003e, and \u003cem\u003eT. barbatus\u003c/em\u003e feed preferentially on \u003cem\u003eA. hasta\u003c/em\u003e (Tables S6-S8). In addition, both \u003cem\u003eA. hasta\u003c/em\u003e and \u003cem\u003eT. barbatus\u003c/em\u003e consumed Calanoida in large quantities, but \u003cem\u003eA. hasta\u003c/em\u003e mainly fed on \u003cem\u003eAcartia hudsonica\u003c/em\u003e, while \u003cem\u003eT. barbatus\u003c/em\u003e mainly fed on \u003cem\u003ePseudodiaptomus marinus\u003c/em\u003e (Tables S8 and S9). This can be considered as food differentiation, i.e., the specialization of species relative to the main food types (Churchfield et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Among the remaining overlapping species, it was found that the pairwise comparison species may feed on the same order of food, but they were separated from each other on their specific food. Therefore, carnivorous fishes in this area may avoid feeding conflicts by adjusting prey compositions and dietary partitioning to reduce the overlap of trophic niches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Modularized food web and functional species\u003c/h2\u003e \u003cp\u003eThe organization of food web provides useful information for revealing community assemblages, interaction network structures, and species\u0026rsquo; functional roles in ecosystems, but it is rarely applied to the conceptualization of carnivorous fish communities (Thompson et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Delmas et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results showed that the food web of carnivorous fishes and prey exhibited significant modular organization. Modularization in trophic network is expected to improve community stability by preventing the spread of interference between modules, which has been confirmed by theoretical modeling and empirical research (Th\u0026eacute;bault and Fontaine \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stouffer and Bascompte \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The exhibition of modular organization of the food web indicated an essential feeding differences among most of the carnivorous fishes. This pattern is consistent with the food differentiations among carnivorous fishes, which can alleviate resource competition among modules and promote the stability of the network. When food species disappear, highly specialized predators that rely on very few prey species are more likely to become extinct than generalists (Cirtwill et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Significant differences of food composition between carnivorous fishes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as well as modular structure in the carnivorous fishes-preys food web (\u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) represented that tedious dietary diversification existed among carnivorous fishes in marine ecosystem, and specific and directional proliferation strategies should be advocated to be adopt for their population recoveries.\u003c/p\u003e \u003cp\u003eUnderstanding the role of prey species in the food web organization s helps to identify functionally important prey, thus providing a reference for conservation practices (Harvey et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Appropriate management policies can maintain the richness and viability of prey species consumed by more carnivorous fishes and thus have a higher central position in their food webs (Lai et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eC. stigmatias\u003c/em\u003e and \u003cem\u003eA. hasta\u003c/em\u003e were identified as a key functional role in the network, which is in consistent with the result that being important food organisms of several carnivorous fishes in this study. As for other prey species, \u003cem\u003ePlaniliza haematocheilus\u003c/em\u003e, \u003cem\u003eAcartia ohtsukai\u003c/em\u003e, \u003cem\u003eAcartia pacifica\u003c/em\u003e, \u003cem\u003eCorbicula fluminea\u003c/em\u003e, \u003cem\u003eOratosquilla oratoria\u003c/em\u003e, \u003cem\u003ePholis fangi\u003c/em\u003e, \u003cem\u003ePseudodiaptomus marinus\u003c/em\u003e and \u003cem\u003eTridentiger brevispinis\u003c/em\u003e were surprisingly identified as regional keystone species in this food web. This indicates an importance to sustain their populations for conserving the biodiversity of the functional carnivorous fishes in the ecosystem.\u003c/p\u003e \u003cp\u003eOur results presented the complex trophic interactions and food web organization of carnivorous fishes-prey community in marine ecosystem, as well as emphasizing the different species\u0026rsquo; roles in network structures and ecosystem functioning. Although the dietary samples of several carnivorous fishes are limited and may not fully reveal the whole picture of the food items, the main feeding interactions in the community were most likely to be captured under the multi-marker amplification strategy. Thus, the general trophic characteristics of carnivorous fishes and their dietary niche relationships patterns are shown to be sound and robust. Wider seasonal and geographical sampling in marine ecosystem will enhance understanding of predator feeding strategy, interspecific dietary overlap, and temporal and spatial variations in food webs. The modular organization analyzed on the basis of prey abundance and species composition data in the current food web illustrated the importance of adopting specific and directional proliferation strategy for most carnivorous fishes to conduct biodiversity protection and fishery production recoveries. By combining use of DNA multi-barcoding markers and network analysis, we detailly paint a picture of carnivorous fishes\u0026rsquo; trophic networks and successfully identify several functionally important species in the ecosystem. The finding can provide effective conservation and restoration decisions for improving fisheries structure and stabilizing marine ecosystem, effectively protect the integrity of ecosystem functions, and enhance the resilience of marine ecosystems in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, we highlighted the necessity of using multiple DNA barcoding markers for feeding analysis based on gut content of carnivorous fishes. We illustrated the intricate trophic interactions and food web organization within the carnivorous fishes-prey community, and identified functionally important species through NGS technology and network analysis. Our findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes. The importance of order Calanoida in the feeding resource of carnivorous fishes was emphasized, and the peak value up to 0.83 of the niche overlap indicated an intense competition among carnivorous fishes. Significant modularity existed in the carnivorous fishes-prey food web, suggesting that the specific and directional proliferation of certain carnivorous fishes within modules should be promoted in adaptive marine fisheries management. In addition, special attention should be paid to functionally important species identified in the food web to enhance the health of the marine ecosystem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2019YFE0122300).\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that\u0026nbsp;there are no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eWriting\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing, Conceptualization, Methodology, Formal analysis, Data curation: Xiaoke Pang; Writing\u0026mdash;original draft, Supervision, Resources: Biao Guo and Kefeng Liu; Writing\u0026mdash;review \u0026amp; editing, Resources: Chenglong Han, Yifan Zhao, Yufei Liu; Resources: Toshihisa Kinoshita, Osamu Yamashita and Wenhui Wang; Writing\u0026mdash;Review \u0026amp; Editing, Supervision: Xueqiang Lu. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the National Key R\u0026amp;D Program of China (2019YFE0122300).\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBearhop S, Adams CE, Waldron S, Fuller RA, Macleod H (2004) Determining trophic niche width: a novel approach using stable isotope analysis. 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Ocean Coastal Manage 49:706\u0026ndash;716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ocecoaman.2006.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ocecoaman.2006.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food web, Carnivorous fishes, Gut contents, Multiple DNA barcoding markers, Food composition, Modularity","lastPublishedDoi":"10.21203/rs.3.rs-5175724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5175724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNext-generation sequencing (NGS) technology through barcoding of DNA in highly-digested diet samples has become a powerful tool for diet reconstruction in food webs. However, traditional single markers can only detect very few prey species, and the constructed food web cannot reflect all effective feeding information. Here, we used multi-locus NGS with COI-M, COI-m, 18S and 16S markers to analyze the feeding habits of carnivorous fishes in Bohai Bay, China. We compared the prey identification results of single markers and multiple markers on the gut content analysis, and integrated examined the feeding characteristics of carnivorous fishes to reconstruct the food web. Our findings revealed that a four-marker combination could detect up to 56 orders and 156 species of preys in the diets of these fishes, which are 1.5\u0026thinsp;\u0026minus;\u0026thinsp;6.2 and 1.7\u0026thinsp;\u0026minus;\u0026thinsp;9.2 times that of detected by single markers, respectively. Calanoida was detected as one of the primary food sources of carnivorous fishes expect for Gobiiformes and Decapoda at order level, contrasting with the previous researches. Network structure analyses showed significant modularity in the food web of carnivorous fishes and their preys. Species \u003cem\u003eScomberomorus niphonius\u003c/em\u003e, \u003cem\u003eOdontamblyopus rubicundus\u003c/em\u003e, \u003cem\u003eChaeturichthys stigmatias\u003c/em\u003e, \u003cem\u003ePlatycephalus indicus\u003c/em\u003e and \u003cem\u003eCynoglossus joyneri\u003c/em\u003e were found to be divided into three different modules in the food web, indicating that specific and directional proliferation strategies should be advocated to be adopt for their population recoveries. This study reported a detailed trophic network of the carnivorous fishes, providing valuable insights for effective conservation and restoration strategies to enhance fisheries structure and stabilize the marine ecosystem.\u003c/p\u003e","manuscriptTitle":"Constructing food web of carnivorous fishes using multiple DNA barcoding markers of gut contents: A case from Bohai Bay, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:27:47","doi":"10.21203/rs.3.rs-5175724/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11f0d24e-2648-4aa5-9200-a1250b0b3c3c","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-03T09:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 10:27:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5175724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5175724","identity":"rs-5175724","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}