DNA metabarcoding confirms primary targets and breadth of diet for coral reef butterflyfishes
Coker, Darren et al. (2022), DNA metabarcoding confirms primary targets and breadth of diet for coral reef butterflyfishes, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gthtdb
Understanding species-specific resource requirements is paramount in managing and protecting biodiversity in a world where environmental quality is in decline. Dietary data can inform predator–prey relationships and how changes in prey availability impact different species. However, for many coral reef fishes, prey and predatory events can be difficult to observe and identify, both in situ and within examined stomach samples. Here we applied DNA metabarcoding of stomach content samples for eleven Red Sea butterflyfish species to identify the diversity of dietary components that these primarily benthic feeding fish consume across coral reefs. Detections based on 18S and COI sequences from partially digested stomach contents significantly increased the resolution and diversity of the known diet for this group of fish, which included cryptic prey that are difficult to visually document due to soft parts or morphological ambiguity. In addition to scleractinian corals and other Cnidaria, the obligate corallivore species fed on a wide range of benthic organisms, whereas facultative species displayed a broader diet with crustaceans, tunicates, and worms contributing to samples. While a number of individuals contained DNA that could not be confidently identified using this method, the proportion of unidentifiable sequences was relatively low across butterflyfish species. The COI marker identified the importance of soft corals in the diet for two hard coral specialists; Chaetodon melannotus and Chaetodon semilarvatus, with soft coral detected in over half of the individuals and contributing significantly to the number of DNA sequence reads within their gut. Notably, five prey items identified to the species level were detected that are currently not documented in the Red Sea. Our analysis revealed that the diet of different species of butterflyfish significantly overlaps, with all species deriving most of their diet from the phylum Cnidaria (hard and soft coral, anemones) and symbiotic Symbiodiniaceae algae. Furthermore, accumulation curves suggest that all study species may feed on an even greater fraction of the benthos, likely driven by the availability and diversity of each individual/pair’s associated territory. This approach increases the known dietary resolution and diversity of these key reef fishes and further enhances our understanding between butterflyfish and benthic organisms.
A universal primer set targeting 18S rRNA (V1-3 hypervariable region; 18S_uni_1F: 5′-GCCAGTAGTCATATGCTTGTCT-3′; 18S_uni_400R: 5′-GCCTGCTGCCTTCCTT-3′; Pochon et al. 2013) with an amplicon length of ~ 340–420 bp and COI (m1COIintF: 5′-GGWACWGGWTGAACWGTWTAYCCYCC-3′; jgHCO2198: 5′-TAIACYTCIGGRTGICCRAAR AAYCA-3′; Leray et al. 2013) with an amplicon length of 313 bp was used to maximize the eukaryotic fraction of diversity detected from stomach content samples. 18S and COI primers were selected for this study because they have been shown to work well across a diversity of marine invertebrates, and the combination of these two assays provides a greater probability of prey detection for individual butterflyfish (Leray et al. 2013; Casey et al. 2019, 2021). Samples from 126 individuals were analyzed using 18S and 67 samples were analyzed using COI (Table 1).
Quantitative PCR (qPCR) experiments were set up in a dedicated ultra-clean laboratory at Curtin University (Perth, Western Australia) designed for ancient DNA work using a QIAgility robotics platform (Qiagen, Germany). Given that low copy number and PCR inhibition can severely impact metabarcoding data (Murray et al. 2015), template input concentrations were optimized using a dilution qPCR series based on the reaction conditions described below. To reduce the likelihood of cross-contamination, chimera production, and index-tag jumping, amplification of target DNA was performed in a single round of PCR using fusion-tag primers, custom sequencing primers, and index combinations unique to this study. All qPCR reactions for each replicate were run in duplicate to control for amplification stochasticity. PCR reagents included 1 × AmpliTaq Gold® Buffer (Life Technologies, CA), 2 mM MgCl2, 0.25 μM dNTPs, 10 μg BSA, 5 pmol of each primer, 0.12 × SYBR® Green (Life Technologies, CA), 1 Unit AmpliTaq Gold DNA polymerase (Life Technologies, CA), 2 μl of DNA, and Ultrapure® Distilled Water (Life Technologies, CA) made up to 25 μl total volume. PCR was performed on a StepOnePlus Real-Time PCR System (Applied Biosystems, CA) under the following conditions: initial denaturation at 95 °C for 5 min, followed by 45 cycles of 30 s at 95 °C, 30 s at 52 °C (18S) or 51 °C (COI), and 45 s at 72 °C, with a final extension for 10 min at 72 °C. All extraction controls were combined into a single PCR reaction run in duplicate for each assay, hereafter referred to as “GutNC”.
Libraries for sequencing were prepared by pooling amplicons, separated by assay, into equimolar ratios based on the endpoint of qPCR amplification curves. Amplicons in each pooled library were size selected (160–600 bp) using a Pippin Prep (Sage Science, MA) and purified using the Qiaquick PCR Purification Kit (Qiagen, Germany). The volume of purified library added to the sequencing run was determined against DNA standards of known molarity on a LabChip GX Touch (PerkinElmer Health Sciences, MA). Final libraries were sequenced paired-end using 500 cycle MiSeq® V2 Reagent Kits on an Illumina MiSeq platform (Illumina, CA) located in the Trace and Environmental DNA (TrEnD) Laboratory at Curtin University (Perth, Western Australia).
Casey, J. M., Meyer, C. P., Morat, F., Brandl, S. J., Planes, S. & Parravicini, V. Reconstructing hyperdiverse food webs: gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. Methods in Ecology and Evolution 10, 1157–1170 (2019).
Casey, J. M., Ransome, E., Collins, A. G., Mahardini, A., Kurniasih, E. M., Sembiring, A., Schiettekatte, N. M., Cahyani, N. K., Wahyu Anggoro, A., Moore, M., Uehling, A. DNA metabarcoding marker choice skews perception of marine eukaryotic biodiversity. Environmental DNA 6, 1229–1246 (2021).
Leray, M. et al. 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. Frontiers in Zoology 10, 34 (2013).
Murray, D. C., Coghlan, M. L. & Bunce, M. From benchtop to desktop: important considerations when designing amplicon sequencing workflows. PLoS One 10, e0124671 (2015).
Pochon, X., Bott, N. J., Smith, K. F. & Wood, S. A. Evaluating detection limits of next-generation sequencing for the surveillance and monitoring of international marine pests. PloS one 8, e73935 (2013).
We here provide raw Illumina MiSeq sequencing runs (read 1 and read 2) in .fastq.gz format.
MSRun125_FTP85_S1_L001_R1_001.fastq.gz paired with MSRun125_FTP85_S1_L001_R2_001.fastq.gz
MSRun131_FTP89_S1_L001_R1_001.fastq.gz paired with MSRun131_FTP89_S1_L001_R2_001.fastq.gz
MSRun142_FTP100_S1_L001_R1_001.fastq.gz paired with MSRun142_FTP100_S1_L001_R2_001.fastq.gz
We additionally provide a text file ("Table_Sequence_Tags_Primers.txt") listing the type of run (i.e., paired end), paired sequence file names, run date, individual sample ID, TrEnD lab sample ID, primer pair, forward tag number, forward tag sequence, reverse tag number, reverse tag sequence, forward primer sequence, and reverse primer sequence for demultiplexing.
All data quality filtering steps that we used are outlined in the Coral Reefs publication:
Australian Research Council, Award: LP160100839