Abstract
DNA metabarcoding is increasingly used in dietary studies, but this molecular technique has limitations including DNA extraction, primer choice suitability and completeness of DNA reference databases. Together, these limitations can create successive biases; some of which can be identified by careful data interrogation. Here, we examine some of the influential factors impacting DNA metabarcoding by focusing on the diet of the grazing coral reef parrotfish Scarus rivulatus (Labridae). The 16S V3-4 and 18S V4 gene regions were targeted to detect a wide range of taxa in the pharyngeal content, but genus-level amplicon sequence variant (ASV) assignments were ≤ 40%. Multiple universal 18S V4 primer sets, which amplified the same portion of the gene region, highlighted disparities that impacted resulting datasets and dietary data interpretation. Approximately 50% of ASVs overlapped among these 18S V4 datasets. Overall, our results show that protein-rich epilithic and endolithic microscopic photoautotrophs were consistently present and that S. rivulatus appears to rely on protein-rich taxa, especially dinoflagellates and cyanobacteria. Even if all qualitative limitations could be addressed, our data emphasise the difficulty of making straightforward quantitative conclusions about the diet composition of grazing fishes using DNA metabarcoding alone. Thus, until DNA metabarcoding reaches a state of maturity where quantitative data can be gained, additional support is required from other dietary analysis methods to provide robust interpretations and conclusions.
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Introduction
DNA metabarcoding is being increasingly integrated into dietary studies (e.g. van der Reis et al. 2020; Homma et al. 2022), as it can identify multiple species from mixed complex samples, irrespective of the morphological state of ingesta. Although this molecular technique has enhanced our capability to refine the taxonomic resolution of previously indistinguishable organic material, it is not without its limitations. DNA metabarcoding typically targets a conserved gene region in the DNA that also has adequate sequence variation to allow discrimination between closely related taxa, e.g. COI (Leray et al. 2013; Lobo et al. 2013), 16S rRNA (Herlemann et al. 2011; Klindworth et al. 2013) and 18S rRNA (Stoeck et al. 2010; Zhan et al. 2013). Arguably, these data provide qualitative diet analysis (i.e. which dietary taxa are eaten) but not quantitative diet analysis (i.e. the relative mass or volumetric proportions of different foods; e.g. Deagle et al. 2019; Lamb et al. 2019; Piñol et al. 2019). Other important limitations of this molecular technique include DNA extraction, primer choice suitability and completeness of DNA reference databases. Commercially available DNA extraction kits have typically been optimised for the taxa of interest and/or origin of collection for optimal DNA yield and biodiversity richness detection (Carrigg et al. 2007; Deiner et al. 2015; Pearman et al. 2020). More obvious limitations are associated with primers, as no one primer set is truly universal. In silico analyses of primers can indicate which groups of sample taxa may fail amplification (Parada et al. 2016; Tournayre et al. 2020), although these rely on potential prey taxa being represented in sequence databases. Database completeness also affects taxonomic assignment of sequences and can be a hindrance during data interrogation. Thus, species-level identification will not always be possible (Ficetola et al. 2010; Hestetun et al. 2020), but bioinformatics tools can be used to assign at a higher level (e.g. genus or family; e.g. RDP classifier–Wang et al. 2007).
The most pressing issue for DNA metabarcoding is likely to be completeness of reference databases, as reviewed in Keck et al. (2023). Database completeness influences primer design and choice, and subsequent taxonomic assignment of amplified sequences. Historically, the development for DNA reference databases was driven by specific informative gene regions (e.g. COI) that effectively represented a barcode for species identification, and the need to develop primers for those gene regions to amplify a range of taxa (Folmer et al. 1994; Hebert et al. 2003; Meyer 2003; Ratnasingham and Hebert 2007). Given the previous financial constraints of molecular work (Wetterstrand 2023), it made sense for the science community to target these gene regions in subsequent studies to build suitable reference databases. However, methodological issues can arise where other gene regions are more reliable for the amplification of certain taxa (e.g. animals versus plants; Newmaster et al. 2006). Thus, taxa reference sequences, compared across gene regions, are disproportionate within databases (e.g. COI versus Cytb; Ficetola et al. 2010). It is, therefore, critical to target more than one gene region for any DNA metabarcoding study to broaden taxonomic identification. In addition to this, reference databases are still largely incomplete, lacking critical metadata and species representativeness (McGee et al. 2019; Keck et al. 2023). Curated reference databases encourage higher quality data input, but infrequent updates impede data analyses and create new issues (e.g. outdated taxonomic assignments). Current sequencing technologies have helped to facilitate species representation within reference databases by decreasing sequencing costs (van der Reis et al. 2023a; Wetterstrand 2023), enabling more gene regions and/or allowing for longer sections of the DNA (or genomes) to be sequenced, permitting more gene regions to be incorporated in taxonomic identification. However, in studies where samples are likely to be degraded (e.g. dietary studies), it is still optimal to amplify shorter gene regions for taxonomic identification.
Together, these limitations create successive biases that not only influence identification but also quantification of prey taxa, as more intricate complexities associated with the DNA itself exist, such as starting biomass, primer bias, the DNA degradation often associated with dietary work, and variations among taxa in gene copy number (Braukmann et al. 2019; Gong and Marchetti 2019; Martin et al. 2022). DNA metabarcoding is considered to produce relatively robust qualitative results (i.e. frequency of occurrence—FOO) but is generally considered to produce less reliable results for quantification, generating semi-quantitative results (i.e. relative read abundance—RRA; e.g. Deagle et al. 2019; Lamb et al. 2019; Piñol et al. 2019). These limitations are more exacerbated in dietary studies where the biomass, and thus, quantity of DNA, of non-target items may be higher than the actual target items (i.e. peanut butter versus crackers concept; Smoot and Findlay 2010; Clements et al. 2017), as in many grazing fishes. Rates of digestion also differ among taxa (e.g. gelatinous versus invertebrate species) and will influence DNA quality and therefore subsequent DNA amplification.
Here, we examine some of the influential factors impacting DNA metabarcoding (including primer choice–in silico and in vitro, and database deficiencies) with a focus on the diet of the coral reef parrotfish Scarus rivulatus (Labridae). Parrotfishes are key agents of bioerosion and sediment dynamics in coral reef ecosystems (Bonaldo et al. 2014). They grind ingested material with their pharyngeal mill, which disrupts the cells of dietary items making cell contents available for digestion (Lobel 1981; Carr et al. 2006; Clements and Choat 2018). However, the exact predator–prey dynamics between these herbivorous fishes and their dietary targets remain unresolved. Parrotfishes have been categorised as generalist herbivores, consuming the epilithic algal matrix (EAM), by grazing on or excavating EAM-covered substrata (Bellwood and Choat 1990; Lefèvre and Bellwood 2011; Bonaldo et al. 2014; Steneck et al. 2017; Bellwood et al. 2018; Siqueira et al. 2019). EAM is defined as ‘a conglomeration of short, turf-forming filamentous algae (< 1 cm high), macroalgal spores, microalgae, sediment, detritus and associated fauna' (Wilson 1997; Wilson et al. 2003). This generalist dietary characterisation was mainly based on behavioural observations and gut content analysis, both of which have limitations in resolving parrotfish diets. For example, in behavioural observations, the visible and abundant taxa that are ingested incidentally can mask taxa (especially microscopic taxa) targeted for ingestion, and visual identification of dietary taxa in conventional gut content analyses is limited by pharyngeal trituration of ingested material. Other lines of evidence for parrotfish diets including stable isotope and fatty acid biomarkers, trophic morphology and macronutrient analysis indicate a dietary focus on protein-rich microscopic photoautotrophs, especially diazotrophic cyanobacteria (Clements et al. 2017; Clements and Choat 2018). More recent work on feeding substrata supports this hypothesis (Nicholson and Clements 2020, 2022, 2023a; Manning and McCoy 2023), which the present study seeks to test further using DNA diet metabarcoding.
Scarus rivulatus has been classified as a scraping species (Bellwood and Choat 1990) that grazes bioeroded substrata with an epilithic biota dominated by cyanobacterial tufts and diatoms with lower proportions of filamentous red algae (Gigartinales and Ceramiales; Nicholson and Clements 2020, 2021, 2023a). This study aims to characterise the biotic composition of samples of pharyngeal content from S. rivulatus collected on the Great Barrier Reef, Australia (1) by developing a bioinformatically- and laboratory-informed methodology to target the 18S V4 rRNA gene region to ensure maximal representation of phylogenetic diversity and (2) using 16S V3-4 and 18S V4 rRNA gene regions to assess the utility of diet metabarcoding in characterising the diet of this grazing fish species. We hypothesise that (1) the pharyngeal content of S. rivulatus specimens will be dominated by protein-rich microscopic photoautotrophs (Clements et al. 2017) and (2) the cyanobacteria taxa detected will reflect the quantitative results of biotic cover on bite cores extracted from S. rivulatus feeding substrata (Nicholson and Clements 2023a).
Materials and methods
Specimen collection and sample preparation
Scarus rivulatus prefers sheltered reefs, and on the Great Barrier Reef (GBR; Australia), they are most abundant on inner-shelf and mid-shelf reefs in lagoonal, reef flat and back-reef habitats that typically have high levels of sediment (Russ 1984; Gust et al. 2001; Hoey and Bellwood 2008; Gordon et al. 2016; Tebbett et al. 2017). Scarus rivulatus specimens were collected by spear on snorkel from the Lizard Island complex, GBR in December 2016 and December 2017. A total of eight specimens were collected from various locations: two between South and Palfrey Islands (specimens SP1 and SP2; 2016), three at Corner Reef (CR1, CR2, CR3; 2017), two at Martin Reef (MR1 and MR2; 2017), and one at North Direction Island (ND1; 2017; Fig. 1; Online Resource Table S1). The scale these fish feed over is large and the habitats where collection took place were a mixture of patch and fringing reefs. It should be noted that feeding substrata are selected at a much finer scale than that of habitat (e.g. Nicholson and Clements 2022, 2023a). Fish collection was covered by James Cook University Animal Ethics Committee approval A2237.
Fish were placed on ice immediately following capture. Upon return to the laboratory at Lizard Island Research Station, the pharyngeal baskets were removed by dissection and pharyngeal content was preserved in 70% ethanol. Some of the pharyngeal content was partially triturated.
DNA extraction
DNeasy Powersoil Kit (Qiagen; discontinued and replaced by DNeasy Powersoil Pro Kit) was used for the extractions, following the manufacturer’s protocol, with the only change to the protocol reducing the vortex duration to 2.5 min for the bead-beading with lysis solution step. Preliminary testing of 10 (protocol standard), 5 and 2 min on additional parrotfish species pharyngeal content subsets indicated that continuous vortexing (using a horizontal vortex adapter) longer than 5 min affected the DNA extraction substantially (no amplification seen for any subsets vortexed for 10 min). Thus, further extractions had a vortex step of 2.5 min. This physical cell-lysis step has shown to be more efficient for DNA extraction from cyanobacteria (which have a protective matrix) than methods without (e.g. Gaget et al. 2017) and greatly assists the disruption of algal cell walls (e.g. Fawley and Fawley 2004; Jagielski et al. 2017), which was essential to this study. A DNA extraction was also done for the same pharyngeal samples (subsets) without a physical cell-lysis step (Puregene Tissue Kit, Qiagen), and results were consistent with other studies that suggest a physical cell-lysis step is beneficial for extraction and subsequent amplification.
Primer selection and amplification
To ensure that various cyanobacteria species were represented in the data, as well as a greater spectrum of other bacterial species, 16S rRNA primers targeting the V3-4 gene region were selected for amplification (Herlemann et al. 2011; Klindworth et al. 2013). To target eukaryotes, the 18S V4 gene region was selected. Two common universal primer pairs were chosen (Stoeck et al. 2010; Zhan et al. 2013), with a third set (Bradley et al. 2016; Fiore-Donno et al. 2018) selected by using the PR2 primer database (Vaulot et al. 2021; Online Resource Fig. S1). To compare diatom detection when using the 18S universal primers, a primer set designed specifically for diatoms was chosen (Zimmermann et al. 2011). It is well known that diatoms are difficult to extract DNA from (e.g. Manoylov et al. 2016), and thus, this primer set was selected to act as a reference as to what extent the other 18S rRNA V4 universal primer sets would potentially miss these rarer DNA templates. Hereafter, 18S primer sets are referred to by first author (or an abbreviation thereof) from their respective published papers to distinguish them (Table 1).
While the Stoeck primer set is often used, the forward and reverse primers have substantially different optimal polymerase chain reactions (PCRs) annealing temperatures which hinder their compatibility, and thus potentially limiting strong amplification (Stoeck et al. 2010). Although the original PCR profile called for a two-step PCR, recent research suggests that amplification on herbivorous fish dietary content is possible at a single annealing temperature of 55 °C (Lin et al. 2021). On the other hand, the Zhan primers amplify well at a single annealing temperature, but research has identified that it fails to amplify Ochrophyta which is essential to this study (Briand et al. 2018). Thus, a variety of 18S V4 rRNA primers (Vaulot et al. 2021) was selected from the PR2 primer database and tested in silico to mitigate these primer sets’ caveats, focusing on Ochrophyta sequence matches for possible future use in conjunction with Zhan if needed (Online Resource Table S2). The forward and reverse primers were independently tested using cutadapt v2.10 (Martin 2011), with the curated DNA reference databases SILVA v138.1 (Quast et al. 2013) and PR2 v4.13.0 (Guillou et al. 2013). Cutadapt, although specifically designed for adapter/primer removal from next-generation sequences, can be used for a very quick and easy way to not only test primers, but also to customise databases.
PCRs were done using MyTaq Red Mix (Bioline; Meridian Bioscience) master mix; 6.25 μl MyTaq Red Mix, 0.5 μl of each primer (10 μM), 4.25 μl UltraPure DNase/RNase-Free Distilled Water (Invitrogen—Thermo Fisher Scientific, Massachusetts, USA), 1 μl DNA and 1 μl BSA (1%; when necessary for optimal DNA amplification). PCR profile for Herlemann primers followed Klindworth et al. (2013) (Table 1); however, for 18S, the PCR profiles were developed that assessed the original PCR profiles to then create a single step PCR profile that tested a range of annealing temperatures. Previous PCR profiles had annealing temperatures ranging from 50 °C to 57 °C; thus, 51 °C, 53 °C, 55 °C and 57 °C were tested. The optimal annealing temperature was found to be 53 °C for all primers (Table 1). If amplification did not occur due to low DNA concentration, the DNA volume was increased (e.g. 2 μl DNA) and the water volume was decreased (e.g. 3.25 μl water), proportionally. Negative controls were included in every set of DNA extractions (extraction blank—no tissue added) and every PCR run (PCR blank—no DNA added) to check for possible cross-contamination. Primer pairs had Illumina Nextera library adapters added (Illumina 2013). The PCR products were run on a 1.6% agarose gel and visualised using Gel Red (Biotium), in a Gel Doc XR + (Bio-Rad).
PCR clean-up and pooling
PCRs were performed in triplicate and were pooled together, by sample per primer. Agencourt AMPure XP (Beckman Coulter) was used following the Illumina protocol for PCR clean-up (Illumina 2013). The concentration of the purified PCR products was determined using Qubit dsDNA HS Assay Kit (Invitrogen, Thermo Fisher Scientific) following the manufacturer’s instructions. The PCR products were brought to equal molarity, 2 ng μl− 1 where possible, and primers were then pooled by sample in two groups due to overlapping primers (group 1: FD-Bra and Zim; group 2: Stoeck and Zhan). Sequencing was done through Auckland Genomics (Auckland, New Zealand) where indexing, using the Nextera DNA library Prep Kit and the second round of PCR clean-up occurred before sequencing on an Illumina MiSeq System. The 16S (2 × 300 bp pair-end run) and 18S (2 × 250 bp paired-end run) sequencing was done separately, as the 16S data were utilised from a separate study.
DNA databases and bioinformatics
Raw demultiplexed sequencing data underwent primer removal (cutadapt) and quality filtering (Qiime 2 v2021.2; Bolyen et al. 2019). DADA2 (within Qiime 2; Callahan et al. 2016) was used for sequence truncation (based on visualisation of raw read quality scores), filtering to retain only high quality sequences that passed denoising and merging and did not form chimeras. This produced sequences that were then clustered at 100% identity (known as amplicon sequence variants – ASVs; Callahan et al. 2017). The Naïve Bayes Classifier (within Qiime 2; Wang et al. 2007) was used for assigning taxonomy at minimum confidence threshold of 97% confidence from the curated database SILVA v138.1 (16S and 18S), PR2 v4.13.0 (16S—chloroplast source identification) and CyanoSeq v1.1.2 (16S—most up-to-date database for Cyanobacteriota assignment; Lefler et al. 2023).
The plastid 23S V5 gene region was also targeted (Sherwood and Presting 2007) and followed a similar workflow to 16S and 18S. However, taxonomic assignments with SILVA for algae were problematic (1016/1800 ASVs were assigned as ‘Chloroplast’) and assigned cyanobacteria genera were comparable to 16S (Online Resource Table S3 and Fig. S2). Thus, it was decided to focus this study on 16S and 18S only.
Data analyses
R Studio (v1.4.1106; R base v4.1.0) was used for further data filtering and analyses. The data were filtered to remove any cross-contamination using proportional subtraction of DNA and PCR negatives. To further increase confidence in ASVs detected within samples, ASVs were retained only if they had > 5 reads. Any fish ASVs detected were removed as they likely originated from the host (S. rivulatus). A phylogenetic tree was constructed for Cyanobacteriota assigned ASVs (within Qiime 2) using MAFFT (Katoh et al. 2002) and FastTree (Price et al. 2009). The tree was then annotated in ggtree (v3.9.1; Yu et al. 2017). The 18S primer similarities and differences were investigated. The exact sequence matches among primer datasets were explored using global regular expression print (GNU grep; v2.20; command-line utility). The R package ggplot2 (v3.4.3; Wickham 2016) was primarily used for visualisations. Bacillariophyceae (diatoms) and Phaeophyceae (brown algae) were separated from other Ochrophyta taxa for the purpose of this study in visualisations (Azuma et al. 2022).
Results
Primer performance
In silico analyses identified FD-Bra as a primer set that matched a high number of Ochrophyta sequences within SILVA and PR2, while also having relatively comparable matches to the overall number of sequences in the databases to the other primers (Online Resource Table S2). This pair also had a melting temperature difference < 5 °C, which is within the recommended limit (Ye et al. 2012; see tmcalculator.neb.com) and allows for efficient annealing during a PCR. This difference was better than either of the other universal primers which were both outside the recommended limit. In vitro testing also provided further confidence in this primer set as it had stronger amplification across samples in comparison with the Stoeck primers which typically resulted in visibly lower amplification, across all annealing temperatures tested.
Data filtering
Post DADA2 (Online Resource Table S4), the 16S dataset had 116,778 reads and 2,089 ASVs (Online Resource Table S5), FD-Bra had 113,341 reads and 426 ASVs, Stoeck had 48,564 reads and 277 ASVs, Zhan had 30,547 reads and 293 ASVs and Zim had 50,706 and 575 ASVs (Online Resource Table S6). Only one ASV was identified in the 16S negative control (13 reads), and none were identified for 18S. Thus, after proportional subtraction took place, the 16S dataset had 116,674 reads. The datasets were filtered to retain only those ASVs within samples that had > 5 reads, which were taxonomically assigned with ≥ 97% confidence and were not assigned to fish (18S datasets). The 16S dataset had 115,198 reads and 1,686 ASVs after filtering, FD-Bra had 12,863 reads and 283 ASVs, Stoeck had 6,979 reads and 166 ASVs, Zhan had 5,358 reads and 169 ASVs and Zim had 43,754 reads and 438 ASVs.
18S V4 primers and taxonomic assignments
The overlap varied between FD-Bra and the other 18S V4 primers. There were 85 FD-Bra ASVs that overlapped with Stoeck (51%), 79 with Zhan (47%) and 92 with Zim (21%). The majority of these ASVs were given the same taxonomic assignment by the RDP classifier. Only four, five and three FD-Bra ASVs had different taxonomic assignments compared to Stoeck, Zhan and Zim, respectively (Table 2). Further investigation into these differences indicated that some of these differences were due to the taxonomic level of assignment and some miscellaneous labelling within the database (e.g. ‘uncultured eukaryote’ entered as species– FD-Bra ID ‘1’; Table 2).
The general trend within individuals among the different universal primers (FD-Bra, Stoeck and Zhan) was similar when comparing the phyla captured (Fig. 2; Online Resource Table S7). CR2 had the highest average number of phyla (14), and MR1 had the lowest (2). Noticeable differences based on the RRA values were the smaller proportion of Rhodophyta in FD-Bra; however, the phylum was found to be categorised as ‘Other’ (i.e. RRA < 5%; Fig. 2). Rhodophyta was not present in MR1 (present with Stoeck) and MR2 and CR1 (present with Zhan). Zhan did not capture any ochrophyte taxa (e.g. Bacillariophyceae and Phaeophyceae). Zim indicated that Bacillariophyceae, Chlorophyta and Phaeophyceae were presented in all individual samples, but this was not seen with the other universal primers.
Overall, individual S. rivulatus specimens had ingested a variety of taxa (Fig. 2 and 3; Online Resource Fig. S3). Based on the universal primers, Dinoflagellata were presented in all individuals and detected by all primers. Dinoflagellata were also dominant based on RRA values, with 75% of individuals having a RRA value of > 15% for each universal primer (FD-Bra, Stoeck and Zhan; Fig. 2; Online Resource Table S8). Specifically, Prorocentrum spp. were detected in all individuals (Fig. 3). Annelida, Arthropoda and Rhodophyta were detected in > 50% of specimens (Rhodophyta were detected in Zim, but < 50%). The number of individuals detected within some phyla was dependant on the primer, such as Porifera (FD-Bra: 75%; Stoeck: 37.5%; Zhan: 12.5%; Zim: 75%), Platyhelminthes (FD-Bra: 50%; Stoeck: 25%; Zhan: 0%; Zim: 0%) and Ciliophora (FD-Bra: 25%; Stoeck: 25%; Zhan: 12.5%; Zim: 62.5%).
16S V3-V4 taxonomic assignments
The majority of taxa identified from the 16S dataset were bacteria (Fig. 4). The phyla with the greatest relative read abundance across the eight pharyngeal samples were Proteobacteria (> 50% RRA in 75% of samples) and Bacteroidota, respectively.
There were 156 ASVs (total 1,686 ASVs) in the 16S dataset that was assigned to Cyanobacteriota and ‘chloroplast’ when using the SILVA database. For these ASVs, the lineage was identified as chloroplast throughout the taxonomic ranks (order to genus); however, at species level (if this resolution was possible), algal species were assigned. There were 16 species assigned to algae. Using the PR2 database, these ‘chloroplast’ ASVs were able to be assigned using the full algal lineage, and thus, higher level assignments made (e.g. to family). Phaeophyceae was detected in all eight samples, with three samples having a Phaeophyceae RRA value < 1%. Bacillariophyta (not detected in SP1) and Rhodophyta (not detected in MR1) were detected in seven samples. Chlorophyta was only detected in two samples, and both had ~ 7% RRA. From the SILVA dataset, 16 chloroplast ASVs could be assigned to species. There were six overlaps at genus or species level with the PR2 database (matching by ASVs), namely Cylindrotheca closterium, Prasinoderma colonial, Ostreobium sp. (Ostreobium quekettii in SILVA), Erythrotrichia carnea, Porphyridium purpureum and Caulerpella ambigua. Of these six, only three overlapped with the 18S dataset. The remaining 10 typically matched PR2 at various taxonomic levels. For example, Dictyopteris undulata (SILVA; 99.9% confidence) matched Pylaiella littoralis (PR2; 99% confidence), both of which belong to the class Phaeophyceae. PR2 assigned 22 ASVs to species in total; however, some were ambiguous and lacked species-specific details (e.g. Pinguiochrysidaceae sp. and Dictyochophyceae sp.; (Fig. 5), and when these were removed, only samples ND1, CR1 and CR2 had ASVs assigned to genus or species level.
A variety of Cyanobacteriota taxa was detected, including a mixture of filamentous and unicellular taxa (Fig. 6). Capilliphycus was the most frequently detected filamentous cyanobacteria genus detected among samples (> 50% of samples). Aegeococcus, Parasynechoccus and Xenococcus were the most frequent unicellular genera detected (≥ 50% of samples). Samples MR2 (n = 15) and ND1 (16) had the greatest diversity of cyanobacteria genera. No cyanobacteria were detected in sample CR3.
Discussion
In any dietary study, there are biases that can impact the interpretation of the results. It is important to identify and address these problems to ensure essential dietary data are accurately represented. Not all taxa identified are nutritionally valuable primary targets, and those that are need to be identified. This process generally should involve several different methodological approaches, i.e. a polyphasic approach. In this study, we aimed to highlight the complexity of using DNA metabarcoding to reveal primary dietary targets and why it is necessary to have a good understanding of the consumer and dietary species before drawing conclusions. We highlight several areas where biases could unknowingly occur and how these could affect the dietary assessment. What was apparent in this study was the difficulty of making straightforward conclusions about the dietary targets of S. rivulatus when considering the variation detected among replicate specimens, and about which dietary targets are likely nutritional targets. We discuss the results from both methodological and dietary viewpoints.
Methodological considerations needed to decipher diet
Using the Zim primer set (diatom-specific) showed that the mechanical lysis in the DNA extraction method was sufficient for penetrating species with fortified cell walls or resilient membranes (e.g. cyanobacteria—polysaccharide sheath and capsule, De Philippis and Vincenzini 1998; dinoflagellates—cellulosic theca, Lau et al. 2007; diatoms—silica frustules, Hamm et al. 2003). Mechanical lysis, such as bead beating, is time-efficient and convenient in DNA extractions, but as identified in this study ‘excessive’ treatment tended to fragment the DNA, thus hindering or completely preventing amplification. Given the wide range of taxa detected in the pharyngeal content, the DNA extractions may be improved by using a two-step lysis approach where an overnight lysis buffer is first applied and thereafter mechanical lysis (see Yuan et al. 2015), but this may not necessarily increase the biodiversity detected when taking into account the DNA extraction and PCR stochasticity (i.e. abundant taxa versus rarer; Alberdi et al. 2018, 2019; Ramírez et al. 2018; Liu et al. 2019).
The PCR results indicated that the Stoeck primers did not achieve optimal amplification likely due to the difference in the annealing temperatures between forward and reverse primers. While the lower amplification results are not ideal, it did not appear to create a bias in taxa detection. Having specimens amplified with the respective primers diluted to equal molarity is key to a balanced sequencing run and ultimately provides an equal opportunity for taxa detection. Realistically, this is difficult to achieve, but the effort should be made to reduce sequencing biases (i.e. shorter amplicons sequencing preferentially to longer ones). The read numbers post-DADA2 revealed that equal molarity was not achieved. FD-Bra generated more than double the number of reads achieved with the other 18S primer sets. This difference in initial molarity allowed FD-Bra to have a greater read depth, and thus, the potential discovery of low template concentration/rarer taxa in theory favoured the FD-Bra dataset. However, an increase in FD-Bra ASVs that reflected the inclusion of these ‘additional’ taxa was only apparent in specimen CR2 for non-algal taxa (Online Resource Fig. S3). Further investigation validated these findings as this specimen had the highest number of reads in the FD-Bra dataset. Irrespective of this, the results suggest that FD-Bra performed comparably to the other universal primers. Furthermore, while identifying low template concentration/rarer taxa is beneficial in community studies, their presence in dietary studies is typically considered insignificant because of the low FOO and RRA values among replicate samples. These taxa are not necessarily targeted dietary items and are more commonly categorised as having been consumed incidentally or as environmental DNA (free-floating DNA; Sheppard and Harwood 2005; Bowser et al. 2013; Oehm et al. 2017). Rates of digestion and how they may affect results also need to be considered in dietary studies (Berry et al. 2015; Devloo-Delva et al. 2018; de Sousa et al. 2019). Targeting the most proximal source for consumed matter, such as the pharyngeal content used in this study, is optimal for DNA metabarcoding for taxa detection (i.e. avoiding the digestive process). It is imperative that patterns in the FOO among replicate specimens must be considered alongside summary RRA values (i.e. mean and median) of taxa when determining possible importance of dietary items. For example, reporting only a mean can lead to inaccurate conclusions (e.g. see Chlorophyta in Fig. 2 and Online Resource Fig. S4). In addition, there was a noticeable difference in Rhodophyta for specimens SP2, ND1 and CR2 which had greater RRAs for both Stoeck and Zhan when compared to FD-Bra (Fig. 2). This highlights the issue of how to interpret RRA values quantitatively for complex mixtures when the primer used influences both the taxa detected and the variation in RRA (see Deagle and Tollit 2007; Pompanon et al. 2012; Nielsen et al. 2018; Lamb et al. 2019).
The dissimilarities in the universal primers are more noticeable when comparing ASV overlap for the 18S universal primer sets; ~ 50% of Stoeck and Zhan ASVs overlapped with FD-Bra (and with each other). This indicates the substantial variation among primers in ASV composition despite these primers being designed to be ‘universal’. This type of issue is likely to be resolved in the future as sequencing costs decrease and sequencing technologies increase in capabilities (e.g. shotgun metagenomic sequencing using Oxford Nanopore Technologies; Duncan et al. 2022; National Human Genome Research Institute 2023; van der Reis et al. 2023a). However, database completeness will still be essential for taxonomic assignment (Keck et al. 2023). Our results suggest that using more than one primer per gene region targeted is beneficial for PCR-based methods in capturing a wider spectrum of ASVs and thus taxa. This is crucial when the diet of the species in question is not well understood. In silico testing is useful to test primer sets and estimate their ability to capture a broad (or specific) range of dietary taxa, but incomplete databases are still an issue for testing primer selection.
The ability to assign taxonomy to ASVs at a low level (e.g. genus or species) is directly influenced by database completeness and can limit studies (Devloo-Delva et al. 2018; Keck et al. 2023; van der Reis et al. 2023a). At a 97% confidence assignment parameter, ASV assignments at genus-level ranged from 30 to 40% for the 18S primer sets and 40% for the 16S primer set. This limits identifying dietary items at a resolution that aids further investigation, as typically at a higher level (e.g. family), there is not enough information to draw links to the ‘why’ of an item being targeted for consumption (i.e. data interpretation). A solution is to use another gene region to broaden the taxonomic scope by accessing non-overlapping entries for different taxa (e.g. van der Reis et al. 2023b). For example, the COI barcode marker is often utilised in studies for species-level taxonomic assignments; however, algae have proven difficult to amplify (Kezlya et al. 2023). Other options that suit sequencing regarding amplicon size are the ITS (White et al. 1990) and 23S (Sherwood and Presting 2007) gene regions. As briefly mentioned, 23S was also targeted and initially investigated in this study, but the incompleteness of curated DNA reference databases limited comparing the resulting data across gene regions (e.g. cyanobacteria; Online Resource Fig. S2). This was somewhat mitigated in our 16S dataset by using the most up-to-date databases, e.g. CyanoSeq for cyanobacteria and PR2 for algae. Incorporating multiple databases is not ideal, but it allowed us to identify algae using chloroplast sequences originally assigned to cyanobacteria (cyanobacteria do not contain chloroplasts; Raven and Allen 2003; Hanshew et al. 2013; Sato 2021). There were few algal species assignments and little overlap between databases for the 16S ASVs (i.e. SILVA versus PR2), and of those that did overlap only half were identified in the 18S datasets. Not only does this highlight the disparity among databases and their respective gene region entries, but it indicates the need for caution when investigating results at a species, and even genus, level.
An indication that protein-rich resources are nutritionally important for parrotfish
Scarus rivulatus is one of the most abundant parrotfishes on the GBR, Australia (Russ 1984; Fox and Bellwood 2007; Hoey and Bellwood 2008; Choat et al. 2012) and has a preference for fine-grained reefal sediments with high organic loads (Gordon et al. 2016), with minimal grazing on live coral (Bellwood and Choat 1990). We hypothesised that (1) the pharyngeal content of S. rivulatus specimens would be dominated by protein-rich microscopic photoautotrophs (Clements et al. 2017) and (2) that the cyanobacteria detected would reflect the general quantitative results from bite cores extracted from S. rivulatus feeding substrata (Nicholson and Clements 2023a). Overall, our results show that protein-rich epilithic and endolithic microscopic photoautotrophs were consistently presented in pharyngeal contents. Dinoflagellates were readily detected in the pharyngeal content, among and within samples (FOO and RRA; Fig. 2), but the diets detected are complex mixtures that include a variety of food sources (e.g. Annelida and Arthropods). The low FOO and RRA of cyanobacteria detected (Fig. 4) in the pharyngeal content among samples somewhat reflects the quantitative pattern found on the bite cores of S. rivulatus, which displayed relatively low cyanobacterial filament density compared to other syntopic parrotfish species at Lizard Island (Nicholson and Clements 2023a).
The dinoflagellates Prorocentrum spp. (18S data; microscopic algae) were presented in all samples (FOO) and generally had a high relative abundance within samples (RRA). Prevalence of Prorocentrum 18S sequences was also detected in recent research undertaken on the gut content of Scarus globiceps and Scarus schlegeli (Lin et al. 2023). In contrast, macroalgae is typically absent or in low abundance on parrotfish bite cores (e.g. Padina spp.; Nicholson and Clements 2020), which is likely the reason that no single genus or species of macroalgae were consistently detected in our 18S data at high relative abundance. Microalgae typically have higher protein content than macroalgae (Bleakley and Hayes 2017; Clements and Choat 2018; Lim et al. 2018; Geada et al. 2021; Sheppard et al. 2023), likely to support the protein demand required for the high growth rates seen in parrotfish (Bowen et al. 1995; Choat et al. 2002; Taylor and Choat 2014; Lin et al. 2023).
In the diatom-focused Zim primer dataset, RRA and FOO were in general higher for the epilithic and epiphytic filamentous phaeophyte Sphacelaria sp. than diatoms (Fig. 2 and 3). The inconsistent prevalence of diatoms has also been noted in other parrotfish dietary studies (Cnudde et al. 2015; Clements and Choat 2018; Lin et al. 2023). Sphacelaria sp. was found in all our samples, although in six specimens, it was only detected when using the universal primers. This association between S. rivulatus and Sphacelaria, which is relatively rich in protein, was also found in other parrotfish studies (Lin et al. 2023; Nicholson and Clements 2023a,b). Tropical filamentous Sphacelaria species grow on rocks and dead coral and are also epiphytic on algae (e.g. Padina and Sargassum) and on seagrasses (Van Elven et al. 2004; Titlyanov et al. 2017). Our 18S sequence data closely matched Sphacelaria sp. UTEX LB 800, a cultured sequence most closely affiliated with Sphacelaria rigidula (Tsiamis et al. 2017). This filamentous alga is tiny, with microscopic filaments (Keum et al. 2005). Dinoflagellates, especially Prorocentrum species, occur in high numbers on phaeophytes and filamentous algae (Kohler and Kohler 1992; Delgado et al. 2006). Perhaps, Sphacelaria helps create an ideal habitat (likely spatially and temporally dependent) for Prorocentrum and other protein-rich microscopic photoautotrophs, and thus is colonised more heavily making this tiny alga even more attractive to S. rivulatus and other herbivores (Fricke et al. 2011; Hensley et al. 2013; Stanca and Parsons 2021; Nieder et al. 2022; Nicholson and Clements 2023a,b). We note that Kohler and Kohler (1992) pointed out that filamentous algae and associated epiphytic dinoflagellates were targeted by coral reef fish (including parrotfish).
Cyanobacteria often co-occur with dinoflagellates on benthic mats (Biessy et al. 2021) and are considered to be one of the main protein-rich dietary targets of parrotfish in recent studies (Clements et al. 2017; Nicholson and Clements 2023a,b). However, the relatively low FOO and RRA for cyanobacteria (genus-level) among our S. rivulatus pharyngeal samples indicate this sediment-tolerant parrotfish species (i.e. not deterred by fine-grained sediments; Gordon et al. 2016) may rely more on other microscopic protein-rich taxa, such as dinoflagellates. Scarus rivulatus bite cores (sampled from the Lizard Island complex) had lower densities of filamentous cyanobacteria compared to most other parrotfish species, although not significantly lower, and some genera were detected on more than 90% of the respective bite cores sampled (i.e. Lyngbya-morphotypes and Calothrix/Rivularia; Nicholson and Clements 2023a). Lyngbya-morphotypes, Capilliphycus and Okeania (Nuryadi et al. 2020) were detected in six of the eight pharyngeal content samples (FOO 75%). Calothrix and Rivularia were not detected in the pharyngeal content, but Calothrix is not monophyletic and includes Nunduva and Kyrtuthrix which were detected in 50% of our samples (Gonzalez et al. 2018; Johansen et al. 2021). The continuous taxonomic revision of cyanobacteria, due to widespread polyphyly, is problematic for directly comparing taxa detected among studies. This is an advantage of molecular work; data can be compiled across studies and run together through the most recent database release to compare ASVs. Regardless, resolving the relative abundance of cyanobacteria in comparison with dinoflagellates, diatoms and other microscopic algae indicates that microscopic work should be incorporated in future studies, especially since cyanobacterial 16S sequences cannot be quantitatively compared to 18S sequences from eukaryotic microalgae and macroalgae.
The complex dietary mixture identified from S. rivulatus pharyngeal content emphasises the difficulty of deriving the primary dietary targets of grazing fish using DNA metabarcoding alone. Ultimately, the feeding substrata for this parrotfish species are spatially complex microhabitats with early successional flora associated with invertebrates such as Annelida and Arthropods (Fig. 2). The relative stochasticity of biotic composition at the small spatial scales at which feeding substrata are selected by grazing fish such as S. rivulatus would also influence the variation in DNA metabarcoding data. Invertebrates have also been identified in other parrotfish DNA metabarcoding diet studies (Lin et al. 2023). The presence of these taxa contributes to protein intake, but it is unlikely they are a primary nutritional target given that their relative contribution towards overall protein intake is likely negligible (Kramer et al. 2013). The nutritional contribution made by these minor dietary components requires further investigation using other methods such as fatty acid analysis and compound-specific stable isotope analysis. In addition, the ingested bacterial community characterised using 16S can provide insight into potential dietary variation, where it is more valuable for inter-specific diet comparisons than within a single species.
Fatty acids allow the predator–prey relationships to be traced due to the majority of fatty acids being acquired through the diet (Bergé and Barnathan 2005; Kelly and Scheibling 2012). Specific fatty acids are known as biomarkers as they are unique to their respective origin and thus can be traced through the successive consumers, helping identify trophic links in a food web (Galloway and Budge 2020). For example, C22:6n-3 and C20:5n-3 are polyunsaturated fatty acids (PUFAs) that are often used as biomarkers for dinoflagellates and diatoms, respectively (Pond et al. 2005; Kelly and Scheibling 2012). C22:6n-3 and C20:5n-3 PUFAs are present in substantially higher levels in dinoflagellates than cyanobacteria, whereas cyanobacteria are higher in C18:3n-3 (Strandberg et al. 2015; Zea-Obando et al. 2017; Jónasdóttir 2019; Taipale et al. 2020). Furthermore, some biomarkers (e.g. C20:5n-3) need to be taken in consideration with others for a holistic understanding of the various dietary signals (Bergé and Barnathan 2005; Kelly and Scheibling 2012; Cnudde et al. 2015). A study on five coral reef fishes (Thalassoma lunare, Lutjanus lutjanus, Abudefduf bengalensis, S. rivulatus and Scolopsis affinis) indicated elevated levels of PUFAs (C18:3n-3, C18:3n-6, C20:3n-3, C20:5n-3 and C22:6n-3) in S. rivulatus (Arai et al. 2015a), and these values were also relatively consistent among other parrotfish species (Arai et al. 2015b). While a larger array of fatty acids would need to be tested (alongside associated feeding substrata) to allow more robust interpretations of the complex diet, the avoidance of macroalgae (Bonaldo and Bellwood 2008) suggests that more protein-rich cyanobacteria and dinoflagellates (and other microalgae) are probable sources for some of these biomarker fatty acids (Clements et al. 2017; Clements and Choat 2018; Sheppard et al. 2023). Biochemical analyses (i.e. fatty acid and stable isotope analyses) and morphological content analyses remain a fundamental component in dietary analyses that should be paired with DNA metabarcoding to quantify diet composition in grazing fish species.
Conclusion
EAM is a term used in the coral reef herbivory literature that fails to capture the level of detail needed to resolve the diet of grazing species, especially when evidence points to fine-scale resource partitioning among grazing fish species (e.g. Choat et al. 2002; Crossman et al. 2005; Clements et al. 2017; Nicholson and Clements 2021, 2023a, 2023b; Tebbett et al. 2022; Lin et al. 2023). In this dietary study on S. rivulatus, other lines of evidence indicate a protein- and lipid-rich diet with dinoflagellates appearing to be an important source of nutrients, especially PUFA. The present study supports previous work on this species (Nicholson and Clements 2022, 2023a) suggesting that microscopic photoautotrophs are a consistently important dietary component. However, filamentous cyanobacteria were less well represented in the sequence data than indicated by microhistology of feeding substrata, as reported in previous studies (Nicholson and Clements 2023a). The likely cause of this may be the thick exopolysaccharide sheaths of dietary Nostocales taxa interfering with DNA extraction, as found in previous studies (Sihvonen et al. 2007; Urrejola et al. 2019). Gene copy number is one of the prevalent factors in DNA metabarcoding that hinders quantitative interpretation of RRA if correction factors are not considered (Martin et al. 2022). However, unless the exact (relatively simple) diet is known a priori, it would be an immense undertaking to investigate gene copy number for multiple unknown dietary taxa, and even then the nutritional value to the consumer remains ‘unknown’. Thus, until DNA metabarcoding reaches a state of maturity where there is complete quantitative taxonomic coverage, additional support is required from other dietary analysis methods to provide robust interpretations and conclusions. At present, the use of diet metabarcoding for grazing fishes is probably of most value qualitatively for determining potential trophic partitioning among species assemblages, provided appropriate care is taken with taxonomic assignments.
Data availability
The DNA sequencing fastq files are available on GenBank (https://www.ncbi.nlm.nih.gov/sra/PRJNA1124164) and the R/bioinformatics code on Github (https://github.com/AvdReis/Scarus_rivulatus).
References
Alberdi A, Aizpurua O, Bohmann K, Gopalakrishnan S, Lynggaard C, Nielsen M, Gilbert MTP (2019) Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol Ecol Resour 19:327–348. https://doi.org/10.1111/1755-0998.12960
Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K (2018) Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol 9:134–147. https://doi.org/10.1111/2041-210X.12849
Arai T, Amalina R, Bachok Z (2015a) Fatty acid composition indicating diverse habitat use in coral reef fishes in the Malaysian South China Sea. Biol Res 48:13. https://doi.org/10.1186/s40659-015-0004-0
Arai T, Amalina R, Bachok Z (2015b) Similarity in the feeding ecology of parrotfish (Scaridae) in coral reef habitats of the Malaysian South China Sea, as revealed by fatty acid signatures. Biochem Syst Ecol 59:85–90. https://doi.org/10.1016/j.bse.2015.01.011
Azuma T, Pánek T, Tice AK, Kayama M, Kobayashi M, Miyashita H, Suzaki T, Yabuki A, Brown MW, Kamikawa R (2022) An enigmatic stramenopile sheds light on early evolution in Ochrophyta plastid organellogenesis. Mol Biol Evol 39:msac065. https://doi.org/10.1093/molbev/msac065
Bellwood DR, Choat JH (1990) A functional analysis of grazing in parrotfishes (family Scaridae): The ecological implications. Environ Biol Fish 28:189–214. https://doi.org/10.1007/BF00751035
Bellwood DR, Tebbett SB, Bellwood O, Mihalitsis M, Morais RA, Streit RP, Fulton CJ (2018) The role of the reef flat in coral reef trophodynamics: Past, present, and future. Ecol Evol 8:4108–4119. https://doi.org/10.1002/ece3.3967
Bergé J-P, Barnathan G (2005) Fatty acids from lipids of marine organisms: Molecular biodiversity, roles as biomarkers, biologically active compounds, and economical aspects. Adv Biochem Eng Biot 96:49–125. https://doi.org/10.1007/b135782
Berry O, Bulman C, Bunce M, Coghlan M, Murray DC, Ward RD (2015) Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar Ecol Prog Ser 540:167–181. https://doi.org/10.3354/meps11524
Biessy L, Wood SA, Chinain M, Roué M, Smith KF (2021) Exploring benthic cyanobacterial diversity and co-occurring potentially harmful dinoflagellates in six islands of the South Pacific. Hydrobiologia 848:2815–2829. https://doi.org/10.1007/s10750-021-04599-6
Bleakley S, Hayes M (2017) Algal proteins: Extraction, application, and challenges concerning production. Foods 6:33. https://doi.org/10.3390/foods6050033
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/s41587-019-0209-9
Bonaldo R, Hoey A, Bellwood D (2014) The ecosystem roles of parrotfishes on tropical reefs. Oceanog Mar Biol 52:81–132. https://doi.org/10.1201/b17143-3
Bonaldo RM, Bellwood DR (2008) Size-dependent variation in the functional role of the parrotfish Scarus rivulatus on the Great Barrier Reef, Australia. Mar Ecol Prog Ser 360:237–244
Bowen SH, Lutz EV, Ahlgren MO (1995) Dietary protein and energy as determinants of food quality: Trophic strategies compared. Ecology 76:899–907. https://doi.org/10.2307/1939355
Bowser AK, Diamond AW, Addison JA (2013) From puffins to plankton: A DNA-based analysis of a seabird food chain in the northern Gulf of Maine. PLoS ONE 8:e83152. https://doi.org/10.1371/journal.pone.0083152
Bradley IM, Pinto AJ, Guest JS, Voordouw G (2016) Design and evaluation of Illumina MiSeq-compatible, 18S rRNA gene-specific primers for improved characterization of mixed phototrophic communities. Appl Environ Microb 82:5878–5891. https://doi.org/10.1128/AEM.01630-16
Braukmann TWA, Ivanova NV, Prosser SWJ, Elbrecht V, Steinke D, Ratnasingham S, de Waard JR, Sones JE, Zakharov EV, Hebert PDN (2019) Metabarcoding a diverse arthropod mock community. Mol Ecol Resour 19:711–727. https://doi.org/10.1111/1755-0998.13008
Briand J-F, Pochon X, Wood SA, Bressy C, Garnier C, Réhel K, Urvois F, Culioli G, Zaiko A (2018) Metabarcoding and metabolomics offer complementarity in deciphering marine eukaryotic biofouling community shifts. Biofouling 34:657–672. https://doi.org/10.1080/08927014.2018.1480757
Callahan BJ, McMurdie PJ, Holmes SP (2017) Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 11:2639. https://doi.org/10.1038/ismej.2017.119
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869
Carr A, Tibbetts IR, Kemp A, Truss R, Drennan J (2006) Inferring parrotfish (Teleostei: Scaridae) pharyngeal mill function from dental morphology, wear, and microstructure. J Morphol 267:1147–1156. https://doi.org/10.1002/jmor.10457
Carrigg C, Rice O, Kavanagh S, Collins G, O’Flaherty V (2007) DNA extraction method affects microbial community profiles from soils and sediment. Appl Microbiol Biot 77:955–964. https://doi.org/10.1007/s00253-007-1219-y
Choat J, Clements K, Robbins W (2002) The trophic status of herbivorous fishes on coral reefs. Mar Biol 140:613–623. https://doi.org/10.1007/s00227-001-0715-3
Choat JH, Klanten OS, Van Herwerden L, Robertson DR, Clements KD (2012) Patterns and processes in the evolutionary history of parrotfishes (Family Labridae). Biol J Linn Soc 107:529–557. https://doi.org/10.1111/j.1095-8312.2012.01959.x
Clements K, Choat J (2018) Nutritional ecology of parrotfishes (Scarinae, Labridae). In: Hoey A, Bonaldo R (eds) Biology of Parrotfishes. CRC Press, Boca Raton, pp 42–68
Clements KD, German DP, Piché J, Tribollet A, Choat JH (2017) Integrating ecological roles and trophic diversification on coral reefs: Multiple lines of evidence identify parrotfishes as microphages. Biol J Linn Soc 120:729–751. https://doi.org/10.1111/bij.12914
Cnudde C, Moens T, Werbrouck E, Lepoint G, Van Gansbeke D, De Troch M (2015) Trophodynamics of estuarine intertidal harpacticoid copepods based on stable isotope composition and fatty acid profiles. Mar Ecol Prog Ser 524:225–239. https://doi.org/10.3354/meps11161
Crossman DJ, Choat JH, Clements KD (2005) Nutritional ecology of nominally herbivorous fishes on coral reefs. Mar Ecol Prog Ser 296:129–142
De Philippis R, Vincenzini M (1998) Exocellular polysaccharides from cyanobacteria and their possible applications. FEMS Microbiol Rev 22:151–175. https://doi.org/10.1111/j.1574-6976.1998.tb00365.x
de Sousa LL, Silva SM, Xavier R (2019) DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environ DNA 1:199–214. https://doi.org/10.1002/edn3.27
Deagle BE, Thomas AC, McInnes JC, Clarke LJ, Vesterinen EJ, Clare EL, Kartzinel TR, Eveson JP (2019) Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol Ecol 28:391–406. https://doi.org/10.1111/mec.14734
Deagle BE, Tollit DJ (2007) Quantitative analysis of prey DNA in pinniped faeces: Potential to estimate diet composition? Conserv Genet 8:743–747. https://doi.org/10.1007/s10592-006-9197-7
Deiner K, Walser J-C, Mächler E, Altermatt F (2015) Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol Conserv 183:53–63. https://doi.org/10.1016/j.biocon.2014.11.018
Delgado G, Lechuga-Devéze C, Popowski G, Troccoli L, Salinas C (2006) Epiphytic dinoflagellates associated with ciguatera in the northwestern coast of Cuba. Rev Biol Trop 54:299–310. https://doi.org/10.15517/rbt.v54i2.13870
Devloo-Delva F, Huerlimann R, Chua G, Matley J, Heupel M, Simpfendorfer C, Maes G (2018) How does marker choice affect your diet analysis: Comparing genetic markers and digestion levels for diet metabarcoding of tropical-reef piscivores. Mar Freshwater Res 70:8–18. https://doi.org/10.1071/MF17209
Duncan A, Barry K, Daum C, Eloe-Fadrosh E, Roux S, Schmidt K, Tringe SG, Valentin KU, Varghese N, Salamov A et al (2022) Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans. Microbiome 10:67. https://doi.org/10.1186/s40168-022-01254-7
Fawley MW, Fawley KP (2004) A simple and rapid technique for the isolation of DNA from microalgae. J Phycol 40:223–225. https://doi.org/10.1111/j.0022-3646.2004.03-081.x
Ficetola GF, Coissac E, Zundel S, Riaz T, Shehzad W, Bessière J, Taberlet P, Pompanon F (2010) An In silico approach for the evaluation of DNA barcodes. BMC Genomics 11:434. https://doi.org/10.1186/1471-2164-11-434
Fiore-Donno AM, Rixen C, Rippin M, Glaser K, Samolov E, Karsten U, Becker B, Bonkowski M (2018) New barcoded primers for efficient retrieval of cercozoan sequences in high-throughput environmental diversity surveys, with emphasis on worldwide biological soil crusts. Mol Ecol Resour 18:229–239. https://doi.org/10.1111/1755-0998.12729
Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R (1994) DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotech 3:294–299
Fox RJ, Bellwood DR (2007) Quantifying herbivory across a coral reef depth gradient. Mar Ecol Prog Ser 339:49–59. https://doi.org/10.3354/meps339049
Fricke A, Titlyanova TV, Nugues MM, Bischof K (2011) Depth-related variation in epiphytic communities growing on the brown alga Lobophora variegata in a Caribbean coral reef. Coral Reefs 30:967–973. https://doi.org/10.1007/s00338-011-0772-0
Gaget V, Keulen A, Lau M, Monis P, Brookes JD (2017) DNA extraction from benthic Cyanobacteria: Comparative assessment and optimization. J Appl Microbiol 122:294–304. https://doi.org/10.1111/jam.13332
Galloway AWE, Budge SM (2020) The critical importance of experimentation in biomarker-based trophic ecology. Philos T R Soc B 375:20190638. https://doi.org/10.1098/rstb.2019.0638
Geada P, Moreira C, Silva M, Nunes R, Madureira L, Rocha CMR, Pereira RN, Vicente AA, Teixeira JA (2021) Algal proteins: Production strategies and nutritional and functional properties. Bioresour Technol 332:125125. https://doi.org/10.1016/j.biortech.2021.125125
Gong W, Marchetti A (2019) Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci 6:219. https://doi.org/10.3389/fmars.2019.00219
Gonzalez L, Johansen J, Alba-Lois L, Segal-Kischinevzky C, Escobar V, Garcia L, Hauer T, Leon Tejera H (2018) Nunduva, a new marine genus of Rivulariaceae (Nostocales, Cyanobacteria) from marine rocky shores. Fottea 18:86–105. https://doi.org/10.5507/fot.2017.018
Gordon SE, Goatley CHR, Bellwood DR (2016) Low-quality sediments deter grazing by the parrotfish Scarus rivulatus on inner-shelf reefs. Coral Reefs 35:285–291. https://doi.org/10.1007/s00338-015-1374-z
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, Boutte C, Burgaud G, de Vargas C, Decelle J et al (2013) The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res 41:D597–D604. https://doi.org/10.1093/nar/gks1160
Gust N, Choat JH, McCormick MI (2001) Spatial variability in reef fish distribution, abundance, size and biomass: a multi scale analysis. Mar Ecol Prog Ser 214:237–251
Hamm CE, Merkel R, Springer O, Jurkojc P, Maier C, Prechtel K, Smetacek V (2003) Architecture and material properties of diatom shells provide effective mechanical protection. Nature 421:841–843. https://doi.org/10.1038/nature01416
Hanshew AS, Mason CJ, Raffa KF, Currie CR (2013) Minimization of chloroplast contamination in 16S rRNA gene pyrosequencing of insect herbivore bacterial communities. J Microbiol Methods 95:149–155. https://doi.org/10.1016/j.mimet.2013.08.007
Hebert PDN, Ratnasingham S, deWaard JR (2003) Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc Royal Soc B 270:S96–S99. https://doi.org/10.1098/rsbl.2003.0025
Hensley NM, Elmasri OL, Slaughter EI, Kappus S, Fong P (2013) Two species of Halimeda, a calcifying genus of tropical macroalgae, are robust to epiphytism by cyanobacteria. Aquat Ecol 47:433–440. https://doi.org/10.1007/s10452-013-9456-x
Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579. https://doi.org/10.1038/ismej.2011.41
Hestetun JT, Bye-Ingebrigtsen E, Nilsson RH, Glover AG, Johansen P-O, Dahlgren TG (2020) Significant taxon sampling gaps in DNA databases limit the operational use of marine macrofauna metabarcoding. Mar Biodivers 50:70. https://doi.org/10.1007/s12526-020-01093-5
Hoey AS, Bellwood DR (2008) Cross-shelf variation in the role of parrotfishes on the Great Barrier Reef. Coral Reefs 27:37–47. https://doi.org/10.1007/s00338-007-0287-x
Homma C, Inokuchi D, Nakamura Y, Ohnishi K, Funaki H, Yamaguchi H, Adachi M (2022) Comparison of the diets of the parrotfishes Scarus ovifrons and Calotomus japonicus using rDNA metabarcoding. Fisheries Sci 88:539–553. https://doi.org/10.1007/s12562-022-01623-z
Illumina (2013) 16S metagenomic sequencing library preparation: Preparing 16S ribosomal RNA gene amplicons for the Illumina MiSeq system 15044223 Rev. B 1–28. https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf. Accessed 2 October 2018
Jagielski T, Gawor J, Bakuła Z, Zuchniewicz K, Żak I, Gromadka R (2017) An optimized method for high quality DNA extraction from microalga Prototheca wickerhamii for genome sequencing. Plant Methods 13:77. https://doi.org/10.1186/s13007-017-0228-9
Johansen JR, González-Resendiz L, Escobar-Sánchez V, Segal-Kischinevzky C, Martínez-Yerena J, Hernández-Sánchez J, Hernández-Pérez G, León-Tejera H (2021) When will taxonomic saturation be achieved? A case study in Nunduva and Kyrtuthrix (Rivulariaceae, Cyanobacteria). J Phycol 57:1699–1720. https://doi.org/10.1111/jpy.13201
Jónasdóttir SH (2019) Fatty acid profiles and production in marine phytoplankton. Mar Drugs 17:151. https://doi.org/10.3390/md17030151
Katoh K, Misawa K, Ki K, Miyata T (2002) MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059–3066. https://doi.org/10.1093/nar/gkf436
Keck F, Couton M, Altermatt F (2023) Navigating the seven challenges of taxonomic reference databases in metabarcoding analyses. Mol Ecol Resour 23:742–755. https://doi.org/10.1111/1755-0998.13746
Kelly JR, Scheibling RE (2012) Fatty acids as dietary tracers in benthic food webs. Mar Ecol Prog Ser 446:1–22. https://doi.org/10.3354/meps09559
Keum YS, Oak JH, Draisma SG, van Reine WF, Lee IK (2005) Taxonomic reappraisal of Sphacelaria rigidula and S. fusca (Sphacelariales, Phaeophyceae) based on morphology and molecular data with special reference to S. didichotoma. Algae 20:1–13. https://doi.org/10.4490/algae.2005.20.1.001
Kezlya E, Tseplik N, Kulikovskiy M (2023) Genetic markers for metabarcoding of freshwater microalgae: Review. Biology 12:1038. https://doi.org/10.3390/biology12071038
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glöckner FO (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41:e1. https://doi.org/10.1093/nar/gks808
Kohler ST, Kohler CC (1992) Dead bleached coral provides new surfaces for dinoflagellates implicated in ciguatera fish poisonings. Environ Biol Fish 35:413–416. https://doi.org/10.1007/BF00004993
Kramer MJ, Bellwood O, Bellwood DR (2013) The trophic importance of algal turfs for coral reef fishes: The crustacean link. Coral Reefs 32:575–583. https://doi.org/10.1007/s00338-013-1009-1
Lamb PD, Hunter E, Pinnegar JK, Creer S, Davies RG, Taylor MI (2019) How quantitative is metabarcoding: A meta-analytical approach. Mol Ecol 28:420–430. https://doi.org/10.1111/mec.14920
Lau RK, Kwok AC, Chan WK, Zhang TY, Wong JT (2007) Mechanical characterization of cellulosic thecal plates in dinoflagellates by nanoindentation. J Nanosci Nanotechnol 7:452–457. https://doi.org/10.1166/jnn.2007.18041
Lefèvre CD, Bellwood DR (2011) Temporal variation in coral reef ecosystem processes: Herbivory of macroalgae by fishes. Mar Ecol Prog Ser 422:239–251. https://doi.org/10.3354/meps08916
Lefler FW, Berthold DE, Laughinghouse HD IV (2023) Cyanoseq: A database of cyanobacterial 16S rRNA gene sequences with curated taxonomy. J Phycol 59:470–480. https://doi.org/10.1111/jpy.13335
Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, Boehm JT, Machida RJ (2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front Zool 10:34. https://doi.org/10.1186/1742-9994-10-34
Lim AS, Jeong HJ, Kim SJ, Ok JH (2018) Amino acids profiles of six dinoflagellate species belonging to diverse families: Possible use as animal feeds in aquaculture. Algae 33:279–290. https://doi.org/10.4490/algae.2018.33.9.10
Lin X, Hu S, Liu Y, Zhang L, Huang H, Liu S (2021) Disturbance-mediated changes in coral reef habitat provoke a positive feeding response in a major coral reef detritivore. Ctenochaetus Striatus Front Mar Sci 8:682697. https://doi.org/10.3389/fmars.2021.682697
Lin X, Hu S, Zhou Y, Huang H, Zhang L, Liu S (2023) A multiple-methods approach to investigate dietary differences among nominally herbivorous fishes. Mar Biol 170:134. https://doi.org/10.1007/s00227-023-04269-2
Liu M, Xue Y, Yang J (2019) Rare plankton subcommunities are far more affected by DNA extraction kits than abundant plankton. Front Microbiol 10:454. https://doi.org/10.3389/fmicb.2019.00454
Lobel PS (1981) Trophic biology of herbivorous reef fishes: Alimentary pH and digestive capabilities. J Fish Biol 19:365–397. https://doi.org/10.1111/j.1095-8649.1981.tb05842.x
Lobo J, Costa PM, Teixeira MAL, Ferreira MSG, Costa MH, Costa FO (2013) Enhanced primers for amplification of DNA barcodes from a broad range of marine metazoans. BMC Ecol 13:34. https://doi.org/10.1186/1472-6785-13-34
Manning JC, McCoy SJ (2023) Preferential consumption of benthic cyanobacterial mats by Caribbean parrotfishes. Coral Reefs 42:967–975. https://doi.org/10.1007/s00338-023-02404-5
Manoylov KM, France YE, Geletu A, Dominy JN (2016) Algal community membership of estuarine mudflats from the Savannah River. United States J Mar Sci Eng 4:11. https://doi.org/10.3390/jmse4010011
Martin JL, Santi I, Pitta P, John U, Gypens N (2022) Towards quantitative metabarcoding of eukaryotic plankton: An approach to improve 18S rRNA gene copy number bias. Metabarcoding Metagenom 6:e85794. https://doi.org/10.3897/mbmg.6.85794
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J 17:3. https://doi.org/10.14806/ej.17.1.200
McGee KM, Robinson CV, Hajibabaei M (2019) Gaps in DNA-based biomonitoring across the globe. Front Ecol Evol 7:337. https://doi.org/10.3389/fevo.2019.00337
Meyer C (2003) Molecular systematics of cowries (Gastropoda: Cypraeidae) and diversification patterns in the tropics. Biol J Linn Soc 79:401–459. https://doi.org/10.1046/j.1095-8312.2003.00197.x
National Human Genome Research Institute (2023) DNA sequencing costs: Data. National Human Genome Research Institute. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data. Accessed 3 November 2023
Newmaster SG, Fazekas AJ, Ragupathy S (2006) DNA barcoding in land plants: Evaluation of rbcL in a multigene tiered approach. Can J Bot 84:335–341. https://doi.org/10.1139/b06-047
Nicholson GM, Clements KD (2020) Resolving resource partitioning in parrotfishes (Scarini) using microhistology of feeding substrata. Coral Reefs 39:1313–1327. https://doi.org/10.1007/s00338-020-01964-0
Nicholson GM, Clements KD (2021) Ecomorphological divergence and trophic resource partitioning in 15 syntopic Indo-Pacific parrotfishes (Labridae: Scarini). Biol J Linn Soc 132:590–611. https://doi.org/10.1093/biolinnean/blaa210
Nicholson GM, Clements KD (2022) Scarus spinus, crustose coralline algae and cyanobacteria: An example of dietary specialization in the parrotfishes. Coral Reefs 41:1465–1479. https://doi.org/10.1007/s00338-022-02295-y
Nicholson GM, Clements KD (2023a) Micro-photoautotroph predation as a driver for trophic niche specialization in 12 syntopic Indo-Pacific parrotfish species. Biol J Linn Soc 139:91–114. https://doi.org/10.1093/biolinnean/blad005
Nicholson GM, Clements KD (2023b) Fine-scale analysis of substrata grazed by parrotfishes (Labridae:Scarini) on the outer-shelf of the Great Barrier Reef. Australia Mar Biol 170:121. https://doi.org/10.1007/s00227-023-04277-2
Nieder C, Liao C-P, Lee C-L, Clements KD, Liu S-L (2022) Novel field observations of coral reef fishes feeding on epiphytic and epizoic organisms associated with the allelopathic seaweed Galaxaura divaricata. Ecol Evol 12:e9529. https://doi.org/10.1002/ece3.9529
Nielsen JM, Clare EL, Hayden B, Brett MT, Kratina P (2018) Diet tracing in ecology: Method comparison and selection. Methods Ecol Evol 9:278–291. https://doi.org/10.1111/2041-210X.12869
Nuryadi H, Sumimoto S, Teruya T, Suenaga K, Suda S (2020) Characterization of macroscopic colony-forming filamentous cyanobacteria from Okinawan coasts as potential sources of bioactive compounds. Mar Biotechnol 22:824–835. https://doi.org/10.1007/s10126-020-10010-7
Oehm J, Thalinger B, Eisenkölbl S, Traugott M (2017) Diet analysis in piscivorous birds: What can the addition of molecular tools offer? Ecol Evol 7:1984–1995. https://doi.org/10.1002/ece3.2790
Parada AE, Needham DM, Fuhrman JA (2016) Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18:1403–1414. https://doi.org/10.1111/1462-2920.13023
Pearman JK, Keeley NB, Wood SA, Laroche O, Zaiko A, Thomson-Laing G, Biessy L, Atalah J, Pochon X (2020) Comparing sediment DNA extraction methods for assessing organic enrichment associated with marine aquaculture. PeerJ 8:e10231. https://doi.org/10.7717/peerj.10231
Piñol J, Senar MA, Symondson WOC (2019) The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol Ecol 28:407–419. https://doi.org/10.1111/mec.14776
Pompanon F, Deagle BE, Symondson WOC, Brown DS, Jarman SN, Taberlet P (2012) Who is eating what: Diet assessment using next generation sequencing. Mol Ecol 21:1931–1950. https://doi.org/10.1111/j.1365-294X.2011.05403.x
Pond DW, Atkinson A, Shreeve RS, Tarling G, Ward P (2005) Diatom fatty acid biomarkers indicate recent growth rates in Antarctic krill. Limnol Oceanogr 50:732–736. https://doi.org/10.4319/lo.2005.50.2.0732
Price MN, Dehal PS, Arkin AP (2009) FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26:1641–1650. https://doi.org/10.1093/molbev/msp077
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https://doi.org/10.1093/nar/gks1219
Ramírez GA, Graham D, D’Hondt S (2018) Influence of commercial DNA extraction kit choice on prokaryotic community metrics in marine sediment. Limnol Oceanogr-Meth 16:525–536. https://doi.org/10.1002/lom3.10264
Ratnasingham S, Hebert PDN (2007) BOLD: THE Barcode of Life Data System. Mol Ecol Notes 7:355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x
Raven JA, Allen JF (2003) Genomics and chloroplast evolution: what did cyanobacteria do for plants? Genome Biol 4:209. https://doi.org/10.1186/gb-2003-4-3-209
Russ G (1984) Distribution and abundance of herbivorous grazing fishes in the central great barrier reef. I. levels of variability across the entire continental shelf. Mar Ecol Prog Ser 20:23–34
Sato N (2021) Are cyanobacteria an ancestor of chloroplasts or just one of the gene donors for plants and algae? Genes 12:823. https://doi.org/10.3390/genes12060823
Sheppard EJ, Hurd CL, Britton DD, Reed DC, Bach LT (2023) Seaweed biogeochemistry: global assessment of C: N and C: P ratios and implications for ocean afforestation. J Phycol 59:879–892. https://doi.org/10.1111/jpy.13381
Sheppard SK, Harwood JD (2005) Advances in molecular ecology: tracking trophic links through predator–prey food-webs. Funct Ecol 19:751–762. https://doi.org/10.1111/j.1365-2435.2005.01041.x
Sherwood AR, Presting GG (2007) Universal primers amplify a 23S rDNA plastid marker in eukaryotic algae and cyanobacteria. J Phycol 43:605–608. https://doi.org/10.1111/j.1529-8817.2007.00341.x
Sihvonen LM, Lyra C, Fewer DP, Rajaniemi-Wacklin P, Lehtimäki JM, Wahlsten M, Sivonen K (2007) Strains of the cyanobacterial genera Calothrix and Rivularia isolated from the Baltic Sea display cryptic diversity and are distantly related to Gloeotrichia and Tolypothrix. FEMS Microbiol Ecol 61:74–84. https://doi.org/10.1111/j.1574-6941.2007.00321.x
Siqueira AC, Bellwood DR, Cowman PF (2019) The evolution of traits and functions in herbivorous coral reef fishes through space and time. Proc Royal Soc B 286:20182672. https://doi.org/10.1098/rspb.2018.2672
Smoot JC, Findlay RH (2010) Microbes as food for sediment-ingesting detritivores: low-density particles confer a nutritional advantage. Aquat Microb Ecol 59:103–109. https://doi.org/10.3354/ame01383
Stanca E, Parsons ML (2021) Examining the dynamic nature of epiphytic microalgae in the Florida Keys: what factors influence community composition? J Exp Mar Biol Ecol 538:151538. https://doi.org/10.1016/j.jembe.2021.151538
Steneck RS, Bellwood DR, Hay ME (2017) Herbivory in the marine realm. Curr Biol 27:R484–R489. https://doi.org/10.1016/j.cub.2017.04.021
Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, Richards TA (2010) Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol 19:21–31. https://doi.org/10.1111/j.1365-294X.2009.04480.x
Strandberg U, Taipale SJ, Hiltunen M, Galloway AWE, Brett MT, Kankaala P (2015) Inferring phytoplankton community composition with a fatty acid mixing model. Ecosphere 6:art16. https://doi.org/10.1890/ES14-00382.1
Taipale S, Peltomaa E, Salmi P (2020) Variation in ω-3 and ω-6 polyunsaturated fatty acids produced by different phytoplankton taxa at early and late growth phase. Biomolecules 10:559. https://doi.org/10.3390/biom10040559
Taylor BM, Choat JH (2014) Comparative demography of commercially important parrotfish species from Micronesia. J Fish Biol 84:383–402. https://doi.org/10.1111/jfb.12294
Tebbett SB, Goatley CHR, Bellwood DR (2017) The effects of algal turf sediments and organic loads on feeding by coral reef surgeonfishes. PLoS ONE 12:e0169479. https://doi.org/10.1371/journal.pone.0169479
Tebbett SB, Siqueira AC, Bellwood DR (2022) The functional roles of surgeonfishes on coral reefs: Past, present and future. Rev Fish Biol Fisher 32:387–439. https://doi.org/10.1007/s11160-021-09692-6
Titlyanov EA, Titlyanova TV, Li X, Huang H (2017) Chapter 4 - Common marine algae of Hainan Island (guidebook). In: Titlyanov EA, Titlyanova TV, Li X, Huang H (eds) Coral Reef Marine Plants of Hainan Island. Academic Press, pp 75–228
Tournayre O, Leuchtmann M, Filippi-Codaccioni O, Trillat M, Piry S, Pontier D, Charbonnel N, Galan M (2020) In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses. Ecol Evol 10:6310–6332. https://doi.org/10.1002/ece3.6362
Tsiamis K, Peters AF, Shewring DM, Asensi AO, Van West P, Küpper FC (2017) Marine benthic algal flora of Ascension Island, South Atlantic. J Mar Biol Assoc UK 97:681–688. https://doi.org/10.1017/S0025315414000952
Urrejola C, Alcorta J, Salas L, Vásquez M, Polz MF, Vicuña R, Díez B (2019) Genomic features for desiccation tolerance and sugar biosynthesis in the extremophile Gloeocapsopsis sp. UTEX B3054. Front Microbiol 10:950. https://doi.org/10.3389/fmicb.2019.00950
van der Reis A, Beckley L, Olivar M, Jeffs A (2023a) Nanopore short-read sequencing: A quick, cost-effective and accurate method for DNA metabarcoding. Environ DNA 5:282–296. https://doi.org/10.1002/edn3.374
van der Reis A, Sewell M, Nelson W (2023b) Investigating seed bank potential of crustose coralline algae using DNA metabarcoding. J Phycol 60:195–202. https://doi.org/10.1111/jpy.13403
van der Reis AL, Jeffs AG, Lavery SD (2020) From feeding habits to food webs: EXPLORING the diet of an opportunistic benthic generalist. Mar Ecol Prog Ser 655:107–121. https://doi.org/10.3354/meps13511
Van Elven BR, Lavery PS, Kendrick GA (2004) Reefs as contributors to diversity of epiphytic macroalgae assemblages in seagrass meadows. Mar Ecol Prog Ser 276:71–83
Vaulot D, Geisen S, Mahé F, Bass D (2021) pr2-primers: An 18S rRNA primer database for protists. Mol Ecol Resour 22:168–179. https://doi.org/10.1111/1755-0998.13465
Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267. https://doi.org/10.1128/aem.00062-07
Wetterstrand K (2023) DNA sequencing costs: Data from the NHGRI genome sequencing program (GSP). National Human Genome Research Institute. www.genome.gov/sequencingcostsdata. Accessed 28 July 2023
White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ (eds) PCR Protocols. Academic Press, San Diego, pp 315–322
Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York
Wilson S (1997) Cryptic dietary components of territorial damselfishes (Pomacentridae, Labroidei). Mar Ecol Prog Ser 153:299–310. https://doi.org/10.3354/meps153299
Wilson SK, Bellwood DR, Choat JH, Furnas MJ (2003) Detritus in the epilithic algal matrix and its use by coral reef fishes. Oceanog Mar Biol 41:279–310
Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL (2012) Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinform 13:134. https://doi.org/10.1186/1471-2105-13-134
Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Y (2017) ggtree: An R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8:28–36. https://doi.org/10.1111/2041-210X.12628
Yuan J, Li M, Lin S (2015) An improved DNA extraction method for efficient and quantitative recovery of phytoplankton diversity in natural assemblages. PLoS ONE 10:e0133060. https://doi.org/10.1371/journal.pone.0133060
Zea-Obando C, Linossier I, Dufossé L, Zubia M, Tunin-Ley A, Turquet J, Grondin I, R T, Fay F, Rehel K, (2017) Fatty acid profiling of tropical microalgae and cyanobacteria strains isolated from southwest Indian Ocean islands. J Aquac Mar Biol 3(1):14
Zhan A, Hulák M, Sylvester F, Huang X, Adebayo AA, Abbott CL, Adamowicz SJ, Heath DD, Cristescu ME, MacIsaac HJ (2013) High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol Evol 4:558–565. https://doi.org/10.1111/2041-210X.12037
Zimmermann J, Jahn R, Gemeinholzer B (2011) Barcoding diatoms: Evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org Divers Evol 11:173. https://doi.org/10.1007/s13127-011-0050-6
Acknowledgements
The authors wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high-performance computing facilities, consulting support and/or training services as part of this research. New Zealand's national facilities are provided by NeSI and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation and Employment’s Research Infrastructure programme. URL https://www.nesi.org.nz. We thank Howard Choat for helpful comments on the manuscript.
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Open Access funding enabled and organized by CAUL and its Member Institutions. Funding for this work was received from the Departmental Research Development Funding, School of Biological Sciences, University of Auckland (2022; 2020), the Computational Biology Theme Seeds Funds, School of Biological Sciences, University of Auckland and Cellular, Molecular and Physiological Academic Group Funds, School of Biological Sciences, University of Auckland (2021).
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Aimee L. van der Reis contributed to conceptualization (supporting); data curation (lead); funding acquisition (supporting); formal analysis (lead); investigation (equal); methodology (lead); project administration (supporting); software (lead); validation (lead); visualisation (lead); writing—original draft (lead) and writing—review and editing (equal). Kendall D. Clements was involved in conceptualization (lead); data curation (supporting); funding acquisition (lead); investigation (equal); methodology (supporting); project administration (lead); resources (lead); supervision (lead); validation (supporting); visualisation (supporting); writing—original draft (supporting) and writing—review and editing (equal).
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van der Reis, A.L., Clements, K.D. DNA, databases and diet: a case study on the parrotfish Scarus rivulatus. Coral Reefs 43, 1189–1206 (2024). https://doi.org/10.1007/s00338-024-02527-3
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DOI: https://doi.org/10.1007/s00338-024-02527-3