Universal closed-tube barcoding for monitoring the shark and ray trade in megadiverse conservation hotspots

Andhika P Prasetyo; Marine Cusa; Joanna M Murray; Firdaus Agung; Efin Muttaqin; Stefano Mariani; Allan D McDevitt

doi:10.1016/j.isci.2023.107065

Universal closed-tube barcoding for monitoring the shark and ray trade in megadiverse conservation hotspots

iScience. 2023 Jun 7;26(7):107065. doi: 10.1016/j.isci.2023.107065. eCollection 2023 Jul 21.

Authors

Andhika P Prasetyo^{1

2

3}, Marine Cusa¹, Joanna M Murray⁴, Firdaus Agung⁵, Efin Muttaqin⁶, Stefano Mariani⁷, Allan D McDevitt^{1

8}

Affiliations

¹ School of Science, Engineering and Environment, University of Salford, Salford, UK.
² Centre Fisheries Research, Ministry for Marine Affairs and Fisheries, Jakarta, Indonesia.
³ Research Centre for Conservation of Marine and Inland Water Resources, National Research and Innovation Agency, Bogor, Indonesia.
⁴ Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, UK.
⁵ Directorate for Conservation and Marine Biodiversity, Ministry for Marine Affairs and Fisheries, Jakarta, Indonesia.
⁶ Wildlife Conservation Society Indonesia Program, Bogor, Indonesia.
⁷ School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK.
⁸ Department of Natural Resources and Environment, School of Science and Computing, Atlantic Technological University, Galway, Ireland.

Abstract

Trade restrictions for endangered elasmobranch species exist to disincentivise their exploitation and curb their declines. However, trade monitoring is challenging due to product variety and the complexity of import/export routes. We investigate the use of a portable, universal, DNA-based tool which would greatly facilitate in-situ monitoring. We collected shark and ray samples across the Island of Java, Indonesia, and selected 28 commonly encountered species (including 22 CITES-listed species) to test a recently developed real-time PCR single-assay originally developed for screening bony fish. In the absence of a bespoke elasmobranch identification online platform in the original FASTFISH-ID model, we employed a deep learning algorithm to recognize species based on DNA melt-curve signatures. By combining visual and machine-learning assignment methods, we distinguished 25/28 species, 20 of which were CITES-listed. With further refinement, this method can improve monitoring of the elasmobranch trade worldwide, without a lab or species-specific assays.

Keywords: Aquatic biology; Aquatic science; Ichthyology; Nature conservation.