Increasing fishing effort, including bycatch and discard practices, are impacting marine biodiversity, particularly among slow-to-reproduce taxa such as elasmobranchs, and specifically sharks. While some fisheries involving sharks are sustainably managed, collateral mortalities continue, contributing towards > 35% of species being threatened with extinction. To effectively manage shark stocks, life-history information, including resource use and feeding ecologies is pivotal, especially among those species with wide-ranging distributions. Two cosmopolitan sharks bycaught off eastern Australia are the common blacktip shark (Carcharhinus limbatus; globally classified as Near Threatened) and great hammerhead (Sphyrna mokarran; Critically Endangered). We opportunistically sampled the digestive tracts of these two species (and also any whole prey; termed the ‘Russian-doll’ approach), caught in bather-protection gillnets off northern New South Wales, to investigate the capacity for DNA metabarcoding to simultaneously determine predator and prey regional feeding ecologies. While sample sizes were small, S. mokkaran fed predominantly on stingrays and skates (Myliobatiformes and Rajiformes), but also teleosts, while C. limbatus mostly consumed teleosts. Metabarcoding assays showed extensive intermixing of taxa from the digestive tracts of predators and their whole prey, likely via the predator’s stomach chyme, negating the opportunity to distinguish between primary and secondary predation. This Russian-doll effect requires further investigation in DNA metabarcoding studies focusing on dietary preferences and implies that any outcomes will need to be interpreted concomitant with traditional visual approaches.

The DNA extracts from each gut sample were amplified, tagged separately, and then pooled for sequencing. Two group-specific mini-barcode primers were selected for the amplification of teleost and crustacean DNA, targeting 12S (MiFish^: Miya et al., 2015) and 16S (Crust16S short: Berry et al., 2017) mitochondrial DNA genes, respectively. We also used a universal 18S primer set (Zhan et al., 2013) targeting the hypervariable V4 region of the nuclear small subunit ribosomal DNA to amplify templates from a broader fraction of marine metazoans. Polymerase chain reaction (PCR) was performed using the AmpliTaq Gold 360 protocol and thermocycling conditions recommended in (Taberlet, Bonin, Zinger, & Coissac, 2018).

The PCR hybridization temperatures were 50, 51 and 50 ^oC for MiFish, Crust16S, and Uni18S primer sets, respectively, and products were run on a 1% agarose gel to confirm amplification of the correct target size (MiFish = ±170 bp; Crust16S = ±170 bp, Uni18S = ±220 bp). A second round of PCR was undertaken on the cleaned PCR products using unique dual-indexed primers for each sample, which included the Illumina-specific sequencing adaptors. PCR products were sent to the Ramaciotti Centre for Genomics at the University of New South Wales for cleaning, normalising, and pooling prior to paired-end sequencing, which was performed using a 500 cycle MiSeq V3 Reagent Kit on an Illumina MiSeq platform (Illumina, San Diego, CA, USA). Sample demultiplexing based on the incorporated indexes was conducted by the sequencing centre.

Berry, T. E., Osterrieder, S. K., Murray, D. C., Coghlan, M. L., Richardson, A. J., Grealy, A. K., et al. (2017). DNA metabarcoding for diet analysis and biodiversity: A case study using the endangered Australian sea lion (Neophoca cinerea). Ecology and Evolution, 7(14), 5435–5453.

Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. Roy. Soc. Open Sci. 2(7), 150088 (2015).

Taberlet, P., Bonin A., Zinger L., Coissac E. Environmental DNA for Biodiversity Research and Monitoring. Oxford, UK. Oxford University Press (2018).

Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods in Ecology and Evolution, 4(6), 558–565 (2013).

We have provided raw demultiplexed reads for each sample to allow users to quality filter the data as required. The .fastq file naming convention is "SampleName_Assay_Read" with "1BT_MIFISH_R1.fastq" as one example.