# Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs

Jordan Casey, Christopher Meyer, Fabien Morat, Simon Brandl, Serge Planes, Valeriano Parravicini

► To cite this version:

Jordan Casey, Christopher Meyer, Fabien Morat, Simon Brandl, Serge Planes, et al. Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. Methods in Ecology and Evolution, 2019, 10 (8), pp.1157-1170. 10.1111/2041-210X.13206. hal-02308683

HAL Id: hal-02308683

https://univ-perp.hal.science/hal-02308683v1

Submitted on 8 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs

Jordan M. Casey<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID
Serge Planes<fnref n="1" />,<fnref n="2" />

Christopher P. Meyer<fnref n="3" />

Fabien Morat<fnref n="1" />,<fnref n="2" />

Simon J. Brandl<fnref n="4" /> ID

Valeriano Parravicini<fnref n="1" />,<fnref n="2" />

**Correspondence**

Jordan M. Casey
Email: jcasey508@gmail.com

**Funding information**

Fondation BNP Paribas, Grant/Award
Number: Reef Services Project; National
Science Foundation, Grant/Award
Number: 1243541; Make Our Planet Great
Again, Grant/Award Number: mopga-
pdf-0000000144; Labex CORAIL, Grant/
Award Number: NECTAR Project; Agence
Nationale de la Recherche, Grant/Award
Number: ANR-17-CE32-0006

Handling Editor: Andrew Mahon

**Abstract**

1. Anthropogenic stressors have strong impacts on ecosystems. To understand their influence, detailed knowledge about trophic relationships among species is critical. However, this requires both exceptional resolution in dietary assessments and sampling breadth within communities, especially for highly diverse, tropical ecosystems.

2. We used gut content metabarcoding across a broad range of coral reef fishes (8 families, 22 species) in Mo'orea, French Polynesia, to test whether this technique has the potential to capture the structure of a hyperdiverse marine food web. Moreover, we explored whether taxonomic groups (families) and traditional, broad-scale trophic assignments explained fish diet across four different metrics of quantifying predator-prey interactions.

3. Metabarcoding yielded a large number (4,341) of unique operational taxonomic units (i.e. prey) with high-resolution taxonomic assignments (i.e. often to the level of genus or species). We demonstrate that across multiple metrics, taxonomic group at the family level is a consistently better, albeit still weak, predictor of empirical trophic relationships than frequently used, broad-scale functional assignments. Our method also reveals a complex trophic network with fine-scale partitioning among species, further emphasizing the importance of examining fish diets beyond broad trophic categories.

4. We demonstrate the capacity of metabarcoding to reconstruct diverse and complex food webs with exceptional resolution, a significant advancement from traditional food web reconstruction. Furthermore, this method allows us to pinpoint the trophic niche of species with niche-based modelling, even across hyperdiverse species assemblages such as coral reefs. In conjunction with complementary techniques such as stable isotope analysis, applying metabarcoding to whole communities will provide unparalleled information about energy and nutrient fluxes and inform their susceptibility to disturbances even in the world's most diverse ecosystems.

**KEYWORDS**

coral reef fish, cytochrome c oxidase subunit I, DNA metabarcoding, fish gut contents, functional traits, high-throughput sequencing, trophic groups, trophic interactions

<footnote n="1">¹PSL Université Paris: EPHE-UPVD-CNRS.
USR 3278 CRIOBE, Université de Perpignan,
Perpignan, France</footnote>

<footnote n="2">²Laboratoire d'Excellence "CORAIL",
Perpignan, France</footnote>

<footnote n="3">³Department of Invertebrate
Zoology, National Museum of Natural
History, Smithsonian Institution,
Washington, District of Columbia, USA</footnote>

<footnote n="4">⁴Department of Biological Sciences, Simon
Fraser University, Burnaby, BC, Canada</footnote>
# 1 | INTRODUCTION

As anthropogenic stressors alter natural communities at ever-increasing rates, there is an urgent need to understand and protect the functioning of ecosystems (Cardinale et al., 2012). Yet, we still do not comprehend many of the complex relationships that underpin energy flow in ecological communities (Blois, Zarnetske, Fitzpatrick, & Finnegan, 2013). Nowhere is this chasm more precarious than in the world's tropics, where the scope of biotic interactions in hyperdiverse communities is high, and a plethora of human-induced disturbances threaten the persistence of ecosystems (Barlow et al., 2018). This poses a monumental challenge for ecologists (du Toit, Walker, & Campbell, 2004), especially because biodiversity loss often results in the breakdown of biotic interactions that underpin ecosystem functioning (Valiente-Banuet et al., 2015).

Coral reefs harbour one-third of marine species, but increasingly frequent and intense anthropogenically driven disturbances such as coral bleaching, acidification, storms and overfishing are now fundamentally changing coral reef communities world-wide (Hughes et al., 2018). The high diversity of this system makes it challenging to depict all trophic interactions, and the paucity of detailed trophic information for many species impairs our ability to understand the potential propagation of disturbances through a food web (Loreau et al., 2001). To date, we do not understand the effects of those changes on biotic interactions, including trophic relationships that may sustain critical ecological processes on reefs. This is particularly true for reef assemblages in the highly diverse Indo-Pacific, where food webs have been altered by human impacts and over-simplified in the literature (Braga, Bornatowski, & Simões Vitule, 2012).

Visually examining reef fish gut contents to assess fish ingestion (e.g. Choat, Clements, & Robbins, 2002; Kulbicki et al., 2005; Randall, 1967) has provided a baseline for assigning species to broad trophic categories. For example, reef fish species are often assigned to trophic categories such as ‘mesopredators’ and ‘mobile herbivores’ when assessing trophic interactions (e.g. Casey et al., 2016; Mouillot et al., 2014). However, broad trophic assignments may not capture the innate complexity of an animal’s foraging behaviour or the high richness of prey embedded in coral reef food webs (Eurich, Matley, Baker, McCormick, & Jones, 2019; Polis & Strong, 1996). For instance, species within the family Acanthuridae are typically classified as grazing, mobile herbivores, but they exhibit a wide breadth of feeding behaviours and diets, with evidence of fine-scale morphological, dietary and behavioural specialization (Brandl, Robbins, & Bellwood, 2015).

To better understand complex trophic interactions, some researchers have attempted to assemble holistic food webs (Arias-González, Delsalle, Salvat, & Galzin, 1997; Bascompte, Melián, & Sala, 2005; Morales-Castilla, Matias, Gravel, & Araújo, 2015; Roopnarine & Hertog, 2013). However, these networks were compiled based on visual examinations of gut contents, stable isotope analyses and behavioural observations. While techniques such as stable isotope analysis provide valuable information about a

species' approximate trophic position, the techniques embedded in these food web analyses do not provide adequate taxonomic resolution to assign species-specific trophic pathways, especially in complex food webs such as coral reefs (Longenecker, ). Thus, incorporating complementary techniques that improve the taxonomic resolution of trophic linkages would more fully capture food web dynamics.

Recent advances in molecular techniques have made DNA metabarcoding, the mass amplification of DNA barcodes from samples containing a multitude of prokaryotes and eukaryotes, an accessible and invaluable tool to assess marine biodiversity (Leray & Knowlton, 2015, 2016). Over the past decade or so, DNA metabarcoding has been employed to analyse fish diet composition in both freshwater and marine environments (Corse et al., 2010; Leray, Meyer, & Mills, 2015; Leray, Yang, et al., 2013; Matley et al., 2018). However, this has only been conducted on a small scale with a limited scope, typically across a few species in the same family or for fishes at the same approximate trophic level (Leray et al., 2019, 2015). The existence of universal primers, such as the mitochondrial cytochrome c oxidase subunit I (COI) and 18S rRNA genes, that amplify well across a broad array of taxonomic biodiversity (Leray, Agudelo, Mills, & Meyer, 2013) makes gut content metabarcoding feasible across a large range of eukaryotic and prokaryotic organisms (Blankenship & Yayanos, 2005).

However, the efficacy of metazoan metabarcoding is heavily reliant on the existence of local genetic inventories that include cryptic species like meiofauna (Leray, Yang, et al., 2013; Ransome et al., 2017). Mo'orea, French Polynesia, provides a unique opportunity for fish dietary analysis due to the existence of an extensive DNA-barcode library created through the Mo'orea BIOCODE Project, a comprehensive inventory that contains barcodes of the majority of terrestrial and marine metazoans on the island (Leray, Boehm, Mills, & Meyer, 2012; Meyer, 2016). This barcode database enables the identification of prey items with a high degree of resolution, making Mo'orea an ideal case study to execute coral reef fish gut content metabarcoding with unparalleled taxonomic detail (Leray, Agudelo, et al., 2013; Leray et al., 2012, 2015; Leray, Yang, et al., 2013).

Here, we employed COI fish gut content metabarcoding and next-generation sequencing on coral reef fishes collected around Mo'orea across a wide array of trophic groups and taxonomic families. Our aim was to assess the utility of a DNA metabarcoding to discern cryptic trophic linkages across a highly diverse food web and quantify the occupied niche space of consumer species at high resolution. Specifically, we explored the potential of metabarcoding to describe highly complex food webs and tested whether different methods of quantifying predator-prey interactions (Deagle et al., 2018) alter the interpretation of trophic linkages in food webs. Moreover, we examined whether broad taxonomic and traditionally employed trophic categories serve as adequate predictors of fish diet, and whether fine-scale niche partitioning is apparent across a broad range of primarily invertebrate-feeding coral reef fishes.
2 | MATERIALS AND METHODS

2.1 | Fish collection and dissection

We conducted all fish collections in March of 2016 on shallow
coral reefs (2–12 m depth) in Mo'orea, French Polynesia (Figure 1).
Collections took place in the lagoon close to the pass of Opunohu
Bay. Study species included 22 species across eight families

(Apogonidae, Balistidae, Chaetodontidae, Gobiidae, Holocentridae, Labridae, Pempheridae and Pomacentridae). We primarily selected species whose diet could be effectively described with a single universal marker in line with the existence of the Mo'orea BIOCODE project across broad trophic categories (i.e. carnivore, invertivore, omnivore, planktivore, facultative corallivore and spongivore) and feeding behaviours (i.e. diurnal and nocturnal feeders). Table 1

<figure><img src="image_2.png" /><figcaption>FIGURE 1 Map of French Polynesia and the study site (marked with a star) around Mo'orea, French Polynesia</figcaption></figure>
<table><caption>TABLE 1 List of fish species included in this study, categorized by taxonomic family, trophic group and circadian activity pattern</caption><thead><tr><th>Family</th><th>Species</th><th>Trophic</th><th>Activity</th><th>Sample size</th></tr></thead><tbody><tr><td>Apogonidae</td><td>Ostorhinchus angustatus</td><td>invertivore</td><td>nocturnal</td><td>5</td></tr><tr><td rowspan="4">Balistidae</td><td>Balistapus undulatus</td><td>omnivore</td><td>diurnal</td><td>5</td></tr><tr><td>Melichthys vidua</td><td>omnivore</td><td>diurnal</td><td>3</td></tr><tr><td>Odonus niger</td><td>planktivore</td><td>diurnal</td><td>3</td></tr><tr><td>Rhinecanthus aculeatus</td><td>spongivore</td><td>diurnal</td><td>4</td></tr><tr><td rowspan="4">Chaetodontidae</td><td>Chaetodon auriga</td><td>facultative corallivore</td><td>diurnal</td><td>1</td></tr><tr><td>Chaetodon citrinellus</td><td>facultative corallivore</td><td>diurnal</td><td>5</td></tr><tr><td>Chaetodon pelewensis</td><td>facultative corallivore</td><td>diurnal</td><td>2</td></tr><tr><td>Chaetodon trichrous</td><td>invertivore</td><td>diurnal</td><td>2</td></tr><tr><td>Gobiidae</td><td>Valenciennea strigata</td><td>invertivore</td><td>diurnal</td><td>3</td></tr><tr><td rowspan="2">Holocentridae</td><td>Myripristis berndti</td><td>planktivore</td><td>nocturnal</td><td>5</td></tr><tr><td>Sargocentron microstoma</td><td>carnivore</td><td>nocturnal</td><td>4</td></tr><tr><td rowspan="5">Labridae</td><td>Cheilinus chlorourus</td><td>invertivore</td><td>diurnal</td><td>5</td></tr><tr><td>Cheilinus trilobatus</td><td>carnivore</td><td>diurnal</td><td>3</td></tr><tr><td>Epibulus insidiator</td><td>carnivore</td><td>diurnal</td><td>6</td></tr><tr><td>Halichoeres trimaculatus</td><td>carnivore</td><td>diurnal</td><td>3</td></tr><tr><td>Thalassoma hardwicke</td><td>carnivore</td><td>diurnal</td><td>2</td></tr><tr><td>Pempheridae</td><td>Pempheris oualensis</td><td>carnivore</td><td>nocturnal</td><td>5</td></tr><tr><td rowspan="4">Pomacentridae</td><td>Abudedefduf sexfasciatus</td><td>planktivore</td><td>diurnal</td><td>4</td></tr><tr><td>Chromis iomelas</td><td>planktivore</td><td>diurnal</td><td>3</td></tr><tr><td>Dascyllus aruanus</td><td>planktivore</td><td>diurnal</td><td>5</td></tr><tr><td>Stegastes nigricans</td><td>omnivore</td><td>diurnal</td><td>6</td></tr></tbody></table>

Note: Sample size indicates the number of individuals included in this study. We retrieved information on trophic group and activity pattern from FishBase (Froese & Pauly, 2000).

provides a list of all species, trophic categories and sample sizes. We collected all fishes by spearfishing, and fishes were kept on ice until dissection, which was conducted under sterile conditions in the Centre de Recherches Insulaires et Observatoire de l'Environnement (CRIOBE) in Mo'orea. We removed the gastrointestinal tract of the fish and scraped all contents from the gut, minimizing tissue from the host fish so that the samples were not overwhelmed by host DNA. We immediately preserved gut contents in 95% ethanol and stored samples at -80°C.

## 2.2 | Fish gut content metabarcoding

We conducted sample homogenization and DNA extraction at CRIOBE, Université de Perpignan in Perpignan, France. Before DNA extraction, fish gut contents were thoroughly homogenized with a mortar and pestle using sterile procedures. We drained all ethanol from the samples prior to subsampling the homogenized gut contents for DNA extraction. For each DNA extract, we used approximately 0.3-0.4 g of homogenized gut contents, and DNA extractions were conducted with a PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany).

DNA clean-ups, PCR, library preparation and sequencing took place at the National Museum of Natural History (NMNH) in

Washington, DC, USA. We used a DNEasy PowerClean Cleanup Kit (Qiagen, Hilden, Germany) to clean DNA from PCR inhibitors before library preparation. We targeted the 313 bp mitochondrial cytochrome c oxidase subunit I (COI) region with seven tailed primer pairs of m1COlintF and jgHCO2198 (Geller, Meyer, Parker, & Hawk, 2013; Leray, Yang, et al., 2013) due to their versatility across a wide range of metazoan prey items. Each reaction for PCR amplification was run at a volume of 20 µl: 1 µl of 10 µM forward primer, 1 µl of 10 µM reverse primer, 1.4 µl dNTP, 2 µl of Advantage 2 DNA Buffer (Takara Bio USA, Mountain View, CA, USA), 0.4 µl of Advantage 2 Polymerase (Takara Bio USA, Mountain View, CA, USA), 13.2 µl of double-distilled water and 1 µl of genomic DNA. We used a touchdown PCR protocol with two sets of cycles. After an initial denaturation at 95°C for 10 min, the first set included 16 cycles: denaturation at 95°C for 10 s, annealing at 62°C (-1°C per cycle) for 30 s and extension at 72°C for 60 s. The second set included 20 cycles with the same denaturation and extension steps, but with annealing at 46°C for 30 s as well as a final extension at 72°C for 7 min. PCR was run in triplicate, and amplification success was verified on 1.2% agarose gels. We pooled all successful triplicate PCR reactions into a single product.

All PCR products were measured using a Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA) using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), then we pooled
tailed primer pairs in equimolar concentrations. To purify the PCR product, we bead-cleaned pooled samples with Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) at a concentration of 0.8× vol/vol prior to proceeding with library preparation.

A TruSeq DNA PCR-Free LT Library Prep Kit (Illumina, San Diego, CA, USA) was used for library preparation, including steps to repair ends, select library size, adenylate 3' ends and ligate adaptors. We quantified indexed samples using a Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA), and we pooled 50 ng of each library. We ultimately normalized the sample to 4 nM and sequenced on an Illumina MiSeq with a MiSeq Reagent Kit v3, 600-cycle (Illumina, San Diego, CA, USA) with a 1% PhiX spike.

## 2.3 | Analysis of sequencing data

We used a combination of USEARCH, a sequence analysis tool, and Mothur, a bioinformatics software, to process our dataset. BFC was employed for denoising in order to correct Illumina sequencing errors and salvage short reads (Li, 2015). We used USEARCH (Edgar, 2010) to assemble pair-end reads and for initial quality filtering. We then employed Mothur (Schloss et al., 2009) for the remainder of sequence processing: filtering, dereplicating sequences, sequence alignment, trimming ends, denoising, chimera removal, clustering and the generation of an operational taxonomic unit (OTU) table. For quality filtering, reads were removed with ambiguous base calls, mismatches in primer sequences and homopolymer regions longer than 8bp, as well as reads that were shorter than 300bp. After dereplication, we aligned sequences to a reference dataset curated from the Mo'orea BIOCODE barcode library. We trimmed sequence ends and conducted further denoising by merging sequences within two nucleotides of each other. Chimeras were identified and removed using VSEARCH, a bioinformatics tool that processes nucleotide sequence data (Rognes, Flouri, Nichols, Quince, & Mahé, 2016).

We clustered sequences into OTUs at 97% and assigned taxonomy with the basic local alignment search tool (BLASTn; Altschul, Gish, Miller, Myers, & Lipman, 1990) through a local BIOCODE database (FASTA data available online; Meyer, 2016) and GenBank. The vast majority of sequences were assigned taxonomy through the BIOCODE database. Genus-level taxonomy was assigned when sequence similarity was 97%, and phylum-level taxonomy was assigned when sequence similarity was ≥80%. We removed 'self-hits' of fish species since these were most likely a result of sequencing of the host tissue rather than an act of cannibalism. Overall, self-hits were low in this dataset, with only four species with high rates of self-hits across individuals (*Balistapus undulatus*, *Sargocentron microstoma*, *Abudefduf sexfasciatus* and *Chromis iomelas*). High rates of self-hits were defined as when self-hits were the highest taxonomic assignment across the majority of individuals of that species. Furthermore, all singleton OTUs were discarded to guard against sequencing error. We then converted the raw sequence read data into read abundance and occurrence data in order to examine the utility of both data types to analyse hyperdiverse dietary metabarcoding data. In accordance with Deagle et al. (2018), we analysed sequence

reads based on four metrics: relative read abundance (RRA), relative frequency of occurrence (FOO), percent of occurrence (POO) and weighted percent of occurrence (wPOO). RRA was calculated relative to each fish individual.

Prior to data analysis, we assigned three categorical variables to each fish species: family, trophic group and circadian activity (diurnal vs. nocturnal). We assigned trophic groups and activity patterns based on dietary and behavioural information retrieved from FishBase (Froese & Pauly, 2000; Table 1). Using the OTU tables with sequence reads converted with the four metrics (RRA, FOO, POO, and wPOO), we performed a non-metric multidimensional scaling (nMDS) ordinations based on a Bray-Curtis dissimilarity matrix to examine the patterns of niche space across different species.

We then sought to quantify the explanatory power of our pre-defined groups (fish family, trophic group and activity) on trophic relationships based on the four metrics. To do so, we employed permutational multivariate analysis of variance (PERMANOVA) and the variance partitioning method (Peres-Neto, Legendre, Dray, & Borcard, ). Specifically, for each metric, we used the species × OTUs matrix as a response variable, and we used the species' family (n = 8), trophic group (n = 6) and activity pattern (n = 2) as explanatory variables. For each of the four metrics, we ran a PERMANOVA with 999 permutations to test the significance of family, trophic group and activity patterns in explaining species' dietary regimes; then, we calculated the explained percent of variation metrics with the R² values (Peres-Neto et al., ). In addition to this analysis, we assessed the relationship between taxonomic family and niche space across fish species. Given the similar trends across the four metrics, we proceeded exclusively with the RRA data. We utilized individual-based convex hulls to calculate two-dimensional niche space occupation for each species (when n ≥ 3; Brandl & Bellwood, 2014; Cornwell, Schwilk, & Ackerly, ). To account for different samples sizes among fish species, we calculated functional niche area by running 999 randomized permutations on subsamples of three individuals for each species. Niche area of each species was then quantified as the proportion of total niche area occupied by all species (i.e. the convex hull area delineated by the outermost vertices of the community).

Finally, we examined the taxonomic assignments of the most common OTUs using the RRA data by excluding the rarest OTUs (OTUs ≤ 1% RRA). For these common OTUs, we further curated taxonomic assignments with BLAST and the local BIOCODE database to permit a thorough consideration of pairwise identity (sequence similarity) and query coverage in taxonomic assignments. We constructed a bipartite plot to visualize all predator-prey interactions, with a focus on Arthropoda due to the high relative abundance and diversity of OTUs in this phylum. Next, we examined whether the most abundant invertebrate OTUs (i.e. prey) were consumed by a wider range of fish predators as compared to the lesser abundant OTUs. We generated a scatterplot to analyse the correlation between OTU relative abundance and frequency across species (i.e. the number of fish species for which an OTU occurs), scaling each point to represent OTU frequency across fish families (i.e. the number of fish families for which an OTU occurs).
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** Non-metric multidimensional scaling (nMDS) ordinations based on relative read abundance (RRA; a-c), relative frequency of occurrence (FOO; d-f), percent of occurrence (POO; g-i) and weighted percent of occurrence (wPOO; j-l) of OTUs ingested by 22 fish species. Each polygon represents the niche space of a single fish species, with each point corresponding to a single individual. Colours represent categorical groups, including (a,d,g,j) taxonomic family, (b,e,h,k) trophic group and (c,f,i,l) circadian activity (diurnal vs. nocturnal).</figcaption></figure>
**FIGURE 3** Percent variation in fish diet explained by taxonomic family, trophic group and both categorical groups. Values were obtained via variance partitioning from PERMANOVAs based on four metrics: (a) relative read abundance (RRA); (b) relative frequency of occurrence (FOO); (c) percent of occurrence (POO); and (d) weighted percent of occurrence (wPOO). Calculations were performed according to the variance partitioning method

<figure><img src="image_4.png" /><figcaption>(b) Relative frequency of occurrence (FOO)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c) Percent of occurrence (POO)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d) Weighted percent of occurrence (wPOO)</figcaption></figure>

<figure><img src="image_7.png" /></figure>

Finally, we performed a Spearman's rank-order correlation test to determine whether there was a correlation between OTUs with a higher relative abundance across fish species and a more frequent occurrence across fish species.

We conducted all analyses with the statistical software R (R Core Team, 2018) with the packages *GGMAP* (Kahle & Wickham, 2013), *VEGAN* (Oksanen et al., 2018), *EULERR* (Larsson, 2019) and *bipartite* (Dormann, Fruend, Bluetgen, & Gruber, 2009).

## 3 | RESULTS

We obtained 764,329 total sequences, including 64,147 unique sequences. After quality filtering and removal of self-hits and singletons, the final OTU table included 4,341 high-quality OTUs (available on Dryad; Casey et al., 2019). Of the 4,341 OTUs, we detected between 57 (*Halichoeres trimaculatus*) and 947 (*Abudefduf sexfasciatus*) OTUs across species, with an average of 331 OTUs per species. Considering only the most common OTUs across the community (based on RRA data; >1% occurrence across the original 4,341 OTUs) resulted in 233 OTUs. We considered genus-level taxonomy reliable when sequence similarity was ≥97%, which resulted in 148 OTUs (64%) with high-resolution taxonomic assignments. We assigned phylum-level taxonomy when sequence similarity was ≥80%, which resulted in an additional 78 OTUs (33%) with broad taxonomic assignments. The remaining seven OTUs (3%) could not be reliably identified, and we designated their taxonomic assignment as 'unidentified'. Three quarters (3,246 of 4,341) of all OTUs were ingested by a single fish individual (i.e. there was no overlap in diet for three quarters of OTUs). Furthermore, of the 233 OTUs with the highest relative abundance across fish species, 46 (20%) were unique to only one

fish species (i.e. there was no overlap in diet for 20% of the most common OTUs).

To analyse the niche space of the fish species, we considered all 4,341 OTUs. The nMDS ordination analyses revealed limited clustering by the categories family, trophic group and activity pattern (Figure 2). Overall, the RRA (Figure 2a-c), FOO (Figure 2d-f), POO (Figure 2g-i) and wPOO (Figure 2j-l) metrics exhibited similar clustering patterns across all categories. We detected some family-based clustering, especially in the Labridae and Holocentridae (Figure 2a,d,g,j). Furthermore, there appeared to be a separation between diurnal and nocturnal feeders (Figure 2c,f,i,l). Yet, the purportedly nocturnal species, *Ostorhinchus angustatus*, clustered with the diurnal species. When employing the variance partitioning method, no additional variation was explained by activity pattern, so it was removed from the analysis because of collinearity with

<table><caption>**TABLE 2** The explanatory power of categorical groups (taxonomic family and trophic group) on fish diet</caption><thead><tr><th>Metric</th><th>Categorical group</th><th>df</th><th>F-value</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="2">(a) RRA</td><td>Family</td><td>7</td><td>2.379</td><td>0.172</td></tr><tr><td>Trophic</td><td>5</td><td>1.883</td><td>0.097</td></tr><tr><td rowspan="2">(b) FOO</td><td>Family</td><td>7</td><td>5.900</td><td>0.317</td></tr><tr><td>Trophic</td><td>5</td><td>3.616</td><td>0.139</td></tr><tr><td rowspan="2">(c) POO</td><td>Family</td><td>7</td><td>4.901</td><td>0.285</td></tr><tr><td>Trophic</td><td>5</td><td>3.025</td><td>0.126</td></tr><tr><td rowspan="2">(d) wPOO</td><td>Family</td><td>7</td><td>3.486</td><td>0.228</td></tr><tr><td>Trophic</td><td>5</td><td>2.326</td><td>0.109</td></tr></tbody></table>

Note: PERMANOVAs were performed based on four metrics: (a) relative read abundance (RRA), (b) relative frequency of occurrence (FOO), (c) percent of occurrence (POO) and (d) weighted percent of occurrence (wPOO). F-values and R² are obtained from 999 permutations.
<figure><img src="image_8.png" /><figcaption>**FIGURE 4** Ranked representation of OTUs in two different metrics (on the log scale): relative read abundance (RRA) and weighted percent of occurrence (wPOO). OTUs were ranked independently for each of the two metrics</figcaption></figure>

taxonomic family. Both taxonomic family and trophic group were significant (*p* < 0.01) across all metrics; however, as revealed by the explanatory power of the categorical groups, both were poor predictors of fish diet, and taxonomic family was consistently a better predictor of species' diet than trophic group across the four metrics (PERMANOVAs; Figure 3; Table 2). Given the consistent results

among the four metrics and the over-inflation of rare OTUs with the occurrence metrics (Figure 4), we proceeded with only the RRA data for all further analyses.

In terms of niche space occupation, we were unable to detect a relationship between fish family and relative niche area of the species. By computing the two-dimensional niche space (convex hull areas) of each fish species, we found no family-driven trend interlinking percent niche area relative to total niche space (Figure 5). *Epibulus insidiator* (family: Labridae) and *Myripristis berndti* (family: Holocentridae) had the largest proportional niche space occupation area while *Chaetodon citrinellus* (family: Chaetodontidae) and *Valenciennea strigata* (family: Gobiidae) had the smallest percent niche areas.

To analyse the taxonomic composition of prey items, we considered the most common 233 OTUs with greater than 1% occurrence across fish species. The bipartite plot demonstrates the broad and diverse associations between fish species and OTUs (Figure 6). A large proportion of OTUs were in the phylum Arthropoda. Brachyura (crabs) and Caridea (shrimps), two infraorders within the order Decapoda, were by far the most dominant subgroups within Arthropoda. Other phyla with a significant proportion of OTUs were Algae, Annelida, Chordata, Cnidaria, Echinodermata, Mollusca and Porifera. Among these groups, the most dominant subgroup was the order Perciformes (perch-like fishes), within Chordata.

Examining the relationship between OTU relative abundance across fish species and OTU frequency across fish species uncovered

<figure><img src="image_9.png" /><figcaption>**FIGURE 5** Relative niche area of 18 fish species calculated as a proportion of total niche area. Individual-based convex hulls in ordination space were utilized to compute the extent of two-dimensional niche space occupation of each species. Colours correspond to fish family</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**FIGURE 6** Simplified trophic network of 22 fish species (top) and their OTUs (i.e. prey items; bottom), including 233 OTUs with a ≥ 1% relative abundance across fishes. The relative proportion of the OTU corresponds to the width of the arrow at its origin. We ordered fish species by family, and we separated OTUs by phylum and class and aggregated them into bars on the bottom axis. Within Arthropoda, we further divided aggregated bars into order or infraorder (for the order Decapoda) due to the high relative abundance of prey items in these groups. The relative proportion of each OTU class (or order or infraorder) across all fish species corresponds to the width of the bar on the bottom axis. Phyla are colour-coordinated, and prominent phyla are labelled. 'Algae' includes all OTUs identified as Rhodophyta</figcaption></figure>

the disproportionate importance of a few frequently ingested, highly
abundant invertebrate prey items (Figure 7). In other words, we found
that the most common OTUs were consumed across a greater number
of fishes and likewise across a greater number of fish families. The OTU
with the highest relative abundance across fish species was *Parupeneus
barberinus* (family: Mullidae), a goatfish. The OTU with the highest fre-
quency across fish species was *Chlorodiella* sp. (family: Xanthidae), a
brachyuran crab. Other high hits in terms of relative abundance and
frequency include a polychaete worm in the family Terebellidae, a
snapping shrimp in the genus *Alpheus* (family: Alpheidae), and two
brachyuran crabs, including *Etisus electra* and *Pilodius* sp. (family:
Xanthidae). A Spearman's rank correlation indicated that there is a
positive, significant relationship between OTU relative abundance and
OTU frequency across fish species (r<sub>s</sub> = 0.466, p < 0.01).

4 | DISCUSSION

Examining trophic interactions among species in hyperdiverse eco-
systems provides key insights into species' functional roles and the
potential impact of anthropogenic stressors on food web structure.
With the molecular toolbox provided by metabarcoding, we were
able to pinpoint detailed trophic information that considerably im-
proves traditional, broad-scale trophic assignments (Sheppard &
Harwood, 2005). Our results suggest that broad *a priori* trophic
groups and activity patterns may be poor predictors of fish diets,
while taxonomic group (family) performs slightly better. In addition,
we reveal substantial niche partitioning even among species that are
closely related and commonly placed in synonymous trophic groups.
Lastly, we provide evidence that a small handful of OTUs may
<figure><img src="image_11.png" /><figcaption>FIGURE 7 OTU relative abundance versus OTU frequency across fish species, with OTU taxonomic assignment to the level of phyla. This includes the 233 OTUs with a ≥ 1% relative abundance. Colours correspond to the 12 phyla of invertebrate OTUs (or 'Algae' for Rhodophyta) and one unidentified category. The relative size of the points corresponds to the OTU frequency across fish families. A Spearman's rank correlation test indicates a significant correlation between OTU relative abundance and frequency across fish species ($r_S = 0.466, p < 0.01$).</figcaption></figure>

dominate the relative abundance and frequency of prey items across fish species, suggesting that stochastic events such as spawning (i.e. the availability of eggs, sperm and larvae) may also dictate fish diet due to the large degree of omnivory and opportunism embedded into the life history strategies of coral reef fishes (Thompson, Hemberg, Starzomski, & Shurin, 2007).

Our results highlight the utility of metabarcoding to understand trophic interactions in highly diverse systems. In our case study, current characterizations of marine fish functional ecology misrepresent important functional differences among species. We found that taxonomic group (family) was consistently a better predictor of fish diet than trophic group. In one recent study (Burns & Strauss, 2011), the authors experimentally validated a link between phylogenetic relatedness and ecological similarity in plant communities, which corroborates our findings: more closely related coral reef fish species express some degree of niche conservationism in dietary composition. Furthermore, our findings relate to recent results that question the convergence between phylogenetic diversity and functional diversity, with the latter defined by broad categorical traits (Mazel et al., 2018). Our results support the mismatch between phylogeny and trophic categories; however, when compared to empirical functional roles, broad phylogenetic groupings appear to be more meaningful than pre-defined trophic traits. Nonetheless, more rigorous within-species sampling as well as a concerted sampling effort across the entire food web is needed to further pinpoint the relationship between phylogenetic and functional diversity.

Marine systems are reported to harbour low levels of functional redundancy, so the utilization of broad trophic groups to indicate ecological functional traits may be insufficient (Brandl & Bellwood, 2014; Micheli & Halpern, 2005; Rasher, Hoey, & Hay, 2013). Although family-level groups perform better than trophic groups to predict empirical trophic relationships, we found little evidence of functional redundancy within families; in fact, we show highly distinct ingestion patterns among fish species, even within the same family. Of 4,341 OTUs, 3,246 (75%) were specific to a particular fish individual, and even of the most abundant OTUs, 46 (20%) of 233 OTUs were ingested by only one species. The prevalence of dietary specificity indicates a high degree of dietary partitioning; however, increasing within-species sample sizes may dampen the extent of this partitioning. This is further exemplified by the clear differences among congeners that are typically assigned identical broad functional traits. For instance, the two wrasses *Cheilinus trilobatus* and *Cheilinus chlorourus* are both solitary, diurnal, benthic invertebrate feeders in the family Labridae (Froese & Pauly, 2000). Thus, the two species are invariably described by the same functional entities in trait-based assessments (e.g. Parravicini et al., 2014). Our data demonstrate that even at a coarse taxonomic level of OTUs (i.e. phylum), the gut contents of these two species differ substantially (Figure 6). Therefore, the assumption of functional equivalency between species may be untenable at the resolution obtained from molecular techniques, and the application of gut content metabarcoding to redefine precise functional traits may be highly valuable.

The two most dominant taxonomic OTU categories detected in our study, brachyuran crabs and caridean shrimps, are abundant organisms in standardized surveys of marine biodiversity as well as prevalent OTUs across marine fish diets (Brandl, Goatley, Bellwood, & Tornabene, 2018; Harmelin-Vivien, Kaim-Malka, Ledoyer, & Jacob-Abraham, 1989; Plaisance, Caley, Brainard, & Knowlton, 2011; Randall, 1967). While most studies list these prey items simply as brachyuran crabs and caridean shrimps, our data identify 300 distinct OTUs classified as brachyuran crabs and 286 distinct OTUs classified as caridean shrimps. These taxa are often identified to the level of genus or species, revealing a deep trove of cryptic diversity within these dominant taxonomic groups. Thus, with large-scale fish gut content metabarcoding across a food web, we can pinpoint cryptic species within the realm of trophic interactions and make predictions about ecosystem functioning based on these fine-scale detections (De Meester, Gingold, Rigaux, Derycke, & Moens, 2016).

While metabarcoding provides unrivalled taxonomic resolution of fish diet, it comes with several caveats. Amplification bias (the tendency for certain sequences to not bind and amplify with the selected primer due to sequence mismatches while other sequences preferentially bind and amplify more efficiently) can skew our perception of fish diet (Deagle, Jarman, Coissac, Pompanon, & Taberlet, 2014). Consequently, amplification product is not a perfect representation of the original DNA, and one should understand the intrinsic biases associated with primer choice when interpreting metabarcoding data. COI is a suitable universal primer across most invertebrate and vertebrate fauna; however, it does not amplify
well for a number of eukaryotic organisms, such as Chlorophyta (green algae) and Nematoda (round worms) (Leray, Yang, et al., 2013; Sherwood & Presting, 2007). Therefore, describing an entire marine food web will require the use of several primers in combination (e.g. see Devloo-Delva et al., 2018).

In addition, gut content metabarcoding data are most valuable if a well-curated DNA barcode library exists for the prey items (Cristescu, 2014). DNA metabarcoding often results in many sequences with unknown identities, which creates a biased perception of prey items that neglects rare and cryptic fauna and flora (Leray & Knowlton, 2017). In our case, we were fortunate to have the Mo'orea BIOCODE database, which contains DNA barcodes for the majority of invertebrate and vertebrate organisms around Mo'orea (Leray & Knowlton, 2015, 2016; Meyer, 2016; Plaisance et al., 2011; Ransome et al., 2017). Consequently, we identified the vast majority of our sequences to the level of phylum, with many taxonomic identifications to the genus or species level. However, there remain certain groups of organisms (e.g. meiofauna that are difficult to visually detect) that are under-represented by metabarcoding data simply due to the absence of barcoding data (Bohmann et al., 2014). The expansion of basic DNA-based biodiversity inventories will empower the future applications of DNA metabarcoding across many ecosystems and biogeographical regions.

One analytical caveat is the conversion of sequence reads to dietary data, which may skew our perception of a consumer's diet. Across metabarcoding studies, both RRA and occurrence metrics are commonly used, but both have biases (Deagle et al., 2018). The RRA metric is more sensitive to taxa-specific biases that result from sequencing errors (e.g. see Alberdi, Aizpurua, Gilbert, & Bohmann, 2017). Conversely, occurrence metrics (FOO, POO and wPOO) are potentially less accurate representations of diet because they artificially inflate rare taxa (Deagle et al., 2018). Although eukaryotic dietary metabarcoding studies preference the use occurrence data, most of these studies are undertaken on one or a small number of consumer species with low dietary breadth (e.g. killer whales that consume eight fish species: six salmonid species, halibut and herding (Ford et al., 2016)). In hyperdiverse communities such as coral reefs, however, consumer diets are often extremely diverse, resulting in a plethora of rare dietary prey OTUs (Leray, Yang, et al., 2013). Consequently, dietary metabarcoding data sourced from tropical reef fishes may be vulnerable to rare taxa inflation when converted to occurrence data. Although we evaluate our data with both RRA and occurrence metrics and detect few differences at the community level, comparing the RRA and wPOO metrics indeed showed significant inflation of rare taxa (Figure 4). We therefore posit that the RRA metric may be more appropriate for hyperdiverse communities, particularly when examining trophic links across an entire food web.

Our inability to detect stochastic environmental factors may also impact our assessment of fish diet. Sequence databases do not reveal whether a sequence is from an adult, juvenile, larvae or egg (Leray et al., 2015). For example, the OTU with the highest relative abundance across fish species was identified as *Parupeneus barberinus*, a goatfish. The average length of *P. barberinus* is either greater

than or equal to the length of all the fish species that consumed it (FishBase; Froese & Pauly, 2000). Thus, it is likely that these fishes were feeding on the eggs or larvae of *P. barberinus* during a stochastic spawning event rather than feeding on an adult goatfish (unless the consumer fishes were scavenging). It is essential to interpret fish gut content metabarcoding as a single snapshot of gut contents, and fishes should be widely sampled over space and time to capture maximum heterogeneity in dietary breadth and prevent site and time-specific biases. In this context, we recommend employing multiple techniques when examining trophic linkages; coupling metabarcoding (i.e. a high resolution snapshot) with stable isotope analysis (i.e. long-term trophic position) represents an adept approach to capture food web dynamics.

Our study upholds the notion that metabarcoding is unparalleled in its ability to provide high-resolution dietary information across a diverse range of species. Across 22 fish species, we were able to define over 4,000 prey OTUs, often to genus or species with a high level of confidence. This would have been impossible to achieve without a molecular toolbox and demonstrates the high potential of fish gut content metabarcoding to trace trophic interactions and permit large-scale, cohesive food web reconstruction. Our results substantiate a multifaceted approach to untangle hyperdiverse food webs: combining gut content metabarcoding with niche-based modelling elucidates otherwise hidden trophic interactions and provides high-resolution niche space data, an indispensable metric that advances our understanding of energy fluxes in diverse ecosystems. Gut content metabarcoding represents an invaluable emerging technique that will play a critical role in our ability to tease apart the functional roles of species in highly diverse systems (Mouchet, Villéger, Mason, & Mouillot, 2010).

## ACKNOWLEDGEMENTS

This work was funded by Labex CORAIL (NECTAR Project), the BNP Paribas Foundation (Reef Services Project) and the French National Agency for Research (REEFLUX project; ANR-17-CE32-0006). J.M.C. was supported by a Make Our Planet Great Again Postdoctoral Grant (mopga-pdf-0000000144) and a National Science Foundation Partnerships for International Research and Education Grant (1243541). We thank Marc Besson, René Galzin and Vetea Liao for assistance catching fishes, Nao Nakamura for help with fish dissections, Caroline Dubé for assistance with fish gut content processing, Kenan Matterson for support with MiSeq sequencing and Katrina Lohan for help with pipeline bioinformatics.

## AUTHORS' CONTRIBUTIONS

J.M.C., C.P.M. and V.P. conceived the project; F.M. and V.P. collected the fishes and performed dissections; J.M.C. processed the fish guts, carried out molecular lab work and conducted bioinformatics; J.M.C., S.J.B. and V.P. analysed the data; J.M.C. led the writing of the manuscript. All authors contributed to writing and approved the final draft for publication.
## DATA AVAILABILITY STATEMENT

Raw Illumina MiSeq FASTA files and the final OTU table are available in the Dryad Digital Repository https://doi.org/10.5061/dryad.t7pd371 (Casey et al., 2019).

## ORCID

Jordan M. Casey https://orcid.org/0000-0002-2434-7207

Simon J. Brandl https://orcid.org/0000-0002-6649-2496

## REFERENCES

Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. *Methods in Ecology and Evolution, **9**, 134-147. https://doi.org/10.1111/2041-210X.12849

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. *Journal of Molecular Biology*, **215**, 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2

Arias-González, J. E., Delsalle, B., Salvat, B., & Galzin, R. (1997). Trophic functioning of the Tiahura reef sector, Moorea Island, French Polynesia. *Coral Reefs*, **16**, 231-246. https://doi.org/10.1007/s003380050079

Barlow, J., França, F., Gardner, T. A., Hicks, C. C., Lennox, G. D., Berenguer, E., ... Graham, N. A. J. (2018). The future of hyperdiverse tropical ecosystems. *Nature*, **559**, 517-526. https://doi.org/10.1038/s41586-018-0301-1

Bascompte, J., Melián, C. J., & Sala, E. (2005). Interaction strength combinations and the overfishing of a marine food web. *Proceedings of the National Academy of Sciences*, **15**, 5443-5447. https://doi.org/10.1073/pnas.0501562102

Blankenship, L. E., & Yayanos, A. A. (2005). Universal primers and PCR of gut contents to study marine invertebrate diets. *Molecular Ecology*, **14**, 891-899. https://doi.org/10.1111/j.1365-294X.2005.02448.x

Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C., & Finnegan, S. (2013). Climate change and the past, present, and future of biotic interactions. *Science*, **341**, 499-504. https://doi.org/10.1126/science.1237184

Bohmann, K., Evans, A., Gilbert, M. T. P., Carvalho, G. R., Creer, S., Knapp, M., ... de Bruyn, M. (2014). Environmental DNA for wildlife biology and biodiversity monitoring. *Trends in Ecology and Evolution*, **29**, 358-367. https://doi.org/10.1016/j.tree.2014.04.003

Braga, R. R., Bornatowski, H., & Simões Vitule, J. R. (2012). Feeding ecology in fishes: An overview of worldwide publications. *Reviews in Fish Biology and Fisheries*, **22**, 915-929.

Brandl, S. J., & Bellwood, D. R. (2014). Individual-based analyses reveal limited functional overlap in a coral reef fish community. *Journal of Animal Ecology*, **83**, 661-670. https://doi.org/10.1111/1365-2656.12171

Brandl, S. J., Goatley, C. H. R., Bellwood, D. R., & Tornabene, L. (2018). The hidden half: Ecology and evolution of cryptobenthic fishes on coral reefs. *Biological Reviews*, **93**, 1846-1873. https://doi.org/10.1111/brv.12423

Brandl, S. J., Robbins, W. D., & Bellwood, D. R. (2015). Exploring the nature of ecological specialization in a coral reef fish community: Morphology, diet and foraging microhabitat use. *The Proceedings of the Royal Society B*, **282**, 20151147. https://doi.org/10.1098/rspb.2015.1147

Burns, J. H., & Strauss, S. Y. (2011). More closely related species are more ecologically similar in an experimental test. *Proceedings of the National Academy of Sciences*, **13**, 5302-5307. https://doi.org/10.1073/pnas.1013003108

Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., ... Naeem, S. (2012). Biodiversity loss and its impact on humanity. *Nature*, **486**, 59-67. https://doi.org/10.1038/nature11148

Casey, J. M., Baird, A. H., Brandl, S. J., Hoogenboom, M. O., Rizzari, J. R., Frisch, A. J., ... Connolly, S. R. (2016). A test of trophic cascade theory: Fish and benthic assemblages across a predator density gradient on coral reefs. *Oecologia*, **183**, 161-175. https://doi.org/10.1007/s00442-016-3753-8

Casey, J. M., Meyer, C. P., Morat, F., Brandl, S. J., Planes, S., & Parravicini, V. (2019). Data from: Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. *Dryad Digital Repository*. https://doi.org/10.5061/dryad.t7pd371

Choat, J. H., Clements, K. D., & Robbins, W. D. (2002). The trophic status of herbivorous fishes on coral reefs. *Marine Biology*, **140**, 613-623. https://doi.org/10.1007/s00227-001-0715-3

Cornwell, W. K., Schwilk, D. W., & Ackerly, D. D. (2006). A trait-based test for habitat filtering: Convex hull volume. *Ecology*, **87**, 1465-1471.

Corse, E., Costerdoat, C., Chappaz, R., Pech, N., Martin, J. F., & Gilles, A. (2010). A PCR-based method for diet analysis in freshwater organisms using 18S rDNA barcoding on faeces. *Molecular Ecology Resources*, **10**, 96-108. https://doi.org/10.1111/j.1755-0998.2009.02795.x

Cristescu, M. E. (2014). From barcoding single individuals to metabarcoding biological communities: Towards an integrative approach to the study of global biodiversity. *Trends in Ecology and Evolution*, **10**, 566-571. https://doi.org/10.1016/j.tree.2014.08.001

De Meester, N., Gingold, R., Rigaux, A., Derycke, S., & Moens, T. (2016). Cryptic diversity and ecosystem functioning: A complex tale of differential effects on decomposition. *Oecologia*, **182**, 559-571. https://doi.org/10.1007/s00442-016-3677-3

Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F., & Taberlet, P. (2014). DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. *Biology Letters*, **10**, 20140562. https://doi.org/10.1098/rsbl.2014.0562

Deagle, B. E., Thomas, A. C., McInnes, J. C., Clarke, L. J., Vesterinen, E. J., Clare, E. L., ... Eveson, J. P. (2018). Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? *Molecular Ecology*, **28**, 391-406.

Devloo-Delva, F., Huerlimann, R., Chua, G., Matley, J. K., Heupel, M. R., Simpfendorfer, C. A., & Maes, G. E. (2018). How does marker choice affect your diet analysis: Comparing genetic markers and digestion levels for diet metabarcoding of tropical-reef piscivores. *Marine and Freshwater Research*, **70**, 8-18. https://doi.org/10.1071/MF17209

Dormann, C. F., Fruend, J., Bluethgen, N., & Gruber, B. (2009). Indices, graphs and null models: Analyzing bipartite ecological networks. *The Open Ecology Journal*, **2**, 7-24. https://doi.org/10.2174/1874213000902010007

du Toit, J. T., Walker, B. H., & Campbell, B. M. (2004). Conserving tropical nature: Current challenges for ecologists. *Trends in Ecology and Evolution*, **19**, 12-17. https://doi.org/10.1016/j.tree.2003.09.018

Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. *Bioinformatics*, **26**, 2460-2461. https://doi.org/10.1093/bioinformatics/btq461

Eurich, J. G., Matley, J. K., Baker, R., McCormick, M. I., & Jones, G. P. (2019). Stable isotope analysis reveals trophic diversity and partitioning in territorial damselfishes on a low-latitude coral reef. *Marine Biology*, **166**, 17. https://doi.org/10.1007/s00227-018-3463-3

Ford, M. J., Hempelmann, J., Hanson, M. B., Ayres, K. L., Baird, R. W., Emmons, C. K., ... Park, L. K. (2016). Estimation of a killer whale (*Orcinus orca*) population's diet using sequencing analysis of DNA from feces. *PLoS ONE*, **11**, e0144956. https://doi.org/10.1371/journal.pone.0144956

Froese, R., & Pauly, D. (2000). *FishBase 2000: Concepts, design and data sources* (p. 344). Los Baños, Laguna, Philippines: ICLARM.
Geller, J. B., Meyer, C. P., Parker, M., & Hawk, H. (2013). Redesign of PCR primers for mitochondrial Cytochrome c. oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. *Molecular Ecology Resources*, **13**, 851–861.

Harmelin-Vivien, M. L., Kaim-Malka, R. A., Ledoyer, M., & Jacob-Abraham, S. S. (1989). Food partitioning among scorpaenid fishes in Mediterranean seagrass beds. *Journal of Fish Biology*, **34**, 75–734.
https://doi.org/10.1111/j.1095-8649.1989.tb03352.x

Hughes, T. P., Kerry, J. T., Baird, A. H., Connolly, S. R., Dietzel, A., Eakin, C. M., ... Torda, G. (2018). Global warming transforms coral reef assemblages. *Nature*, **556**, 492–496. https://doi.org/10.1038/s41586-018-0041-2

Kahle, D., & Wickham, H. (2013). ggmap: Spatial Visualization with ggplot2. *The R Journal*, **5**, 144–161. https://doi.org/10.32614/RJ-2013-014

Kulbicki, M., Bozec, Y. M., Labrosse, P., Letourneur, Y., Mou-Tham, G., & Wantiez, L. (2005). Diet composition of carnivorous fishes from coral reef lagoons of New Caledonia. *Aquatic Living Resources*, **18**, 231–250. https://doi.org/10.1051/alr:2005029

Larsson, J. (2019). euerr: Area-proportional Euler and Venn diagrams with elipses. R Package, 5.1.0. Retrieved from https://cran.r-proje ct.org/package=euerr

Leray, M., Agudelo, N., Mills, S. C., & Meyer, C. P. (2013). Effectiveness of annealing blocking primers versus restriction enzymes for characterization of generalist diets: Unexpected prey revealed in the gut contents of two coral reef fish species. *PLoS ONE*, 8, e58076. https:// doi.org/10.1371/journal.pone.0058076

Leray, M., Alldredge, A. L., Yang, J. Y., Meyer, C. P., Holbrook, S. J., Schmitt, R. J., ... Brooks, A. J. (2019). Dietary partitioning promotes the coexistence of planktivorous species on coral reefs. *Molecular Ecology*, https://doi.org/10.1111/mec.15090

Leray, M., Boehm, J. T., Mills, S. C., & Meyer, C. P. (2012). Moorea BIOCODE barcode library as a tool for understanding predator-prey interactions: Insights into the diet of common predatory coral reef fishes. *Coral Reefs*, **31**, 383–388. https://doi.org/10.1007/s00338-011-0845-0

Leray, M., & Knowlton, N. (2015). DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. *Proceedings of the National Academy of Science*, **112**, 2076–2081.
https://doi.org/10.1073/pnas.1424997112

Leray, M., & Knowlton, N. (2016). Censusing marine eukaryotic diversity in the twenty-first century. *Philosophical Transactions of the Royal Society B*, **371**, 20150331. https://doi.org/10.1098/rstb.2015.0331

Leray, M., & Knowlton, N. (2017). Random sampling causes the low reproducibility of rare eukaryotic OTUs in Illumina COI metabarcoding. *PeerJ*, 5, e3006. https://doi.org/10.7717/peerj.3006

Leray, M., Meyer, C. P., & Mills, S. C. (2015). Metabarcoding dietary analysis of coral dwelling predatory fish demonstrates the minor contribution of coral mutualists to their highly partitioned, generalist diet. *Peer J*, 3, e1047. https://doi.org/10.7717/peerj.1047

Leray, M., Yang, J. Y., Meyer, C. P., Mills, S. C., Agudelo, N., Ranwez, V., ... Machida, R. J. (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. *BMC Frontiers in Zoology*, **10**, 34. https://doi.org/10.1186/1742-9994-10-34

Li, H. (2015). BFC: Correcting Illumina sequencing errors. *Bioinformatics*, **17**, 2885–2887. https://doi.org/10.1093/bioinformatics/btv290

Longenecker, K. (2007). Devil in the details: High-resolution dietary analysis contradicts a basic assumption of reef-fish diversity models. *Copeia*, **2007**, 543–555.

Loreau, M., Naeem, S., Inchausti, P., Bengtsson, J., Grime, J. P., Hector, A., ... Wardle, D. A. (2001). Biodiversity and ecosystem functioning: Current knowledge and future challenges. *Science*, **294**, 804–808.
https://doi.org/10.1126/science.1064088

Matley, J. K., Maes, G. E., Devloo-Delva, F., Huerlimann, R., Chua, G., Tobin, A. J., ... Heupel, M. R. (2018). Integrating complementary methods to improve diet analysis in fishery-targeted species. *Ecology and Evolution*, **8**, 9503–9515. https://doi.org/10.1002/ece3.4456

Mazel, F., Pennell, M. W., Cadotte, M. W., Diaz, S., Dalla Riva, G. V., Grenyer, R., ... Pearse, W. D. (2018). Prioritizing phylogenetic diversity captures functional diversity unreliably. *Nature Communications*, **9**, 1–9. https://doi.org/10.1038/s41467-018-05126-3

Meyer, C. (2016). Moorea Biocode Project FASTA data. *California Digital Library*. Retrieved from http://n2t.net/ark:/13030/m5478zfg

Micheli, F., & Halpern, B. S. (2005). Low functional redundancy in coastal marine assemblages. *Ecology Letters*, **8**, 391–400. https://doi.org/10.1111/j.1461-0248.2005.00731.x

Morales-Castilla, I., Matias, M. G., Gravel, D., & Araújo, M. B. (2015). Inferring biotic interactions from proxies. *Trends in Ecology and Evolution*, **30**, 347–356. https://doi.org/10.1016/j.tree.2015.03.014

Mouchet, M.A., Villéger, S., Mason, N.W.H., & Mouillot, D. (2010). Functional diversity measures: An overview of their redundancy and their ability to discriminate community assembly rules. *Functional Ecology*, **24**, 867–876. https://doi.org/10.1111/j.1365-2435.2010.01695.x

Mouillot, D., Villeger, S., Parravicini, V., Kulbicki, M., Arias-Gonzalez, J. E., Bender, M., ... Bellwood, D. R. (2014). Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. *Proceedings of the National Academy of Sciences*, **111**, 13757–13762. https://doi.org/10.1073/pnas.1317625111

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., ... Wagner, H. (2018). vegan: Community Ecology Package. R package version 2.5-2. Retrieved from https://CRAN.R-project.org/pack age=vegan

Parravicini, V., Villéger, S., McClanahan, T. R., Arias-González, J. E., Bellwood, D. R., Belmaker, J., ... Mouillot, D. (2014). Global mismatch between species richness and vulnerability of reef fish assemblages. *Ecology Letters*, **17**, 1101–1110. https://doi.org/10.1111/ele.12316

Peres-Neto, P., Legendre, P., Dray, S., & Borcard, D. (2006). Variation partitioning of species data matrices: Estimation and comparison of fractions. *Ecology*, **87**, 2614–2625.

Plaisance, L., Caley, M. J., Brainard, R. E., & Knowlton, N. (2011). The diversity of coral reefs: What are we missing? *PLoS ONE*, 6, e25026. https://doi.org/10.1371/journal.pone.0025026

Polis, G. A., & Strong, D. R. (1996). Food web complexity and community dynamics. *The American Naturalist*, **147**, 813–846. https://doi.org/10.1086/285880

R Core Team. (2018). *R: A language and environment for statistical computing*. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

Randall, J. E. (1967). Food habits of reef fishes of the West Indies. *Studies in Tropical Oceanography*, **5**, 655–847.

Ransome, E., Geller, J. B., Timmers, M., Leray, M., Mahardini, A., Sembiring, A., ... Meyer, C. P. (2017). The importance of standardization for biodiversity comparisons: A case study using autonomous reef monitoring structures (ARMS) and metabarcoding to measure cryptic diversity on Mo'orea coral reefs, French Polynesia. *PLoS ONE*, **12**, e0175066. https://doi.org/10.1371/journ al.pone.0175066

Rasher, D. B., Hoey, A. S., & Hay, M. E. (2013). Consumer diversity interacts with prey defenses to drive ecosystem function. *Ecology*, **94**, 1347–1358. https://doi.org/10.1890/12-0389.1

Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: A versatile open source tool for metagenomics. *PeerJ*, 4, e2584. https://doi.org/10.7717/peerj.2584

Roopnarine, P. D., & Hertog, R. (2013). Detailed food web networks of three greater Antillean coral reef systems: The Cayman Islands, Cuba, and Jamaica. *Dataset Papers in Ecology*, 2013, 857470. https://doi.org/10.7167/2013/857470
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., ... Weber, C. F. (2009). Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. *Applied and Environmental Microbiology, **75*, 7537-7541. https://doi.org/10.1128/AEM.01541-09

Sheppard, S. K., & Harwood, J. D. (2005). Advances in molecular ecology: Tracking trophic links through predator-prey food-webs. *Functional Ecology*, *19*,751-762. https://doi.org/10.1111/j.1365-2435.2005.01041.x

Sherwood, A. R., & Presting, G. G. (2007). Universal primers amplify a 23S rDNA plastid marker in eukaryotic algae and cyanobacteria. *Journal of Phycology*, **43**, 605–608. https://doi.org/10.1111/j.1529-8817.2007.00341.x

Thompson, R. M., Hemberg, M., Starzomski, B. M., & Shurin, J. B. (2007). Trophic levels and trophic tangles: The prevalence of omnivory in real food webs. *Ecology*, **88**, 612–617. https://doi.org/10.1890/05-1454

Valiente-Banuet, A., Aizen, M. A., Alcántara, J. M., Arroyo, J., Cocucci, A., Galetti, M., ... Zamora, R. (2015). Beyond species loss: The extinction of ecological interactions in a changing world. *Functional Ecology*, **29**, 299–307. https://doi.org/10.1111/1365-2435.12356

**How to cite this article:** Casey JM, Meyer CP, Morat F, Brandl SJ, Planes S, Parravicini V. Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. *Methods Ecol Evol.* 2019;10:1157-1170. https://doi.org/10.1111/2041-210X.13206