Using a Meta-Barcoding Method for Studying Population Dynamics of Larval Invasive and Native Fishes in the Eastern Mediterranean

Kolker M Ghatan, Y Belmaker, M Kiflawi, S Meiri, R Holzman

Research output: Contribution to journalMeeting Abstractpeer-review


Accurate species-level identification, along with unbiased quantitative sampling, are the pillars of large-scale community ecology. However, species level enumeration is challenging in planktonic fish larvae, whose species-specific morphologies are often undeveloped, and their identification based on meristic criteria is often impractical. Meanwhile, PCR based identification methods of
pooled samples result in biased estimates of species abundance. We present an innovative metabarcoding method for studying population dynamics of fish larvae within the Eastern Mediterranean, a highly disturbed marine habitat, where the number of invasive Indo-Pacific species doubled every 20 years since the beginning of the 20th century. We sampled ichthyoplankton off the Israeli coast monthly during 2018-2019. We sequenced 96 samples containing 5642 larvae using a high-throughput next-generation method, resulting in
coverage of the mitochondrial CO1 barcoding gene. We BLASTed CO1 sequences found in our samples against an adult CO1 database containing ~1200 species, including >90% of the native Mediterranean species and the Red Sea species who are likely to have invaded the Mediterranean. We found that the relative fraction
of CO1 reads derived from each individual larva was proportional to the relative biomass of that larva from the sample, estimated from silhouette imaging of the sample, and used this relationship to assess the abundance of the species in the sample. This method enables direct species-level identification of the high-volume samples, with robust estimations of species abundances. It thus allows for exploration of species composition and richness as well as identification of invasive species.
Original languageEnglish
Article numberP1-122
Pages (from-to)E359-E359
Number of pages1
JournalIntegrative and Comparative Biology
Issue numberSupplement 1
StatePublished - 12 Mar 2020


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