Biodiversity inventory of the grey mullets (Actinopterygii: Mugilidae) of the Indo‐Australian Archipelago through the iterative use of DNA‐based species delimitation and specimen assignment methods

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Ifremer

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Ifremer

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info:eu-repo/semantics/openAccess , restricted use


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Coral Triangle cryptic diversity DNA barcoding reference library taxonomic gap

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Speciation (Biology)

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Delrieu‐trottin Erwan et al., « Biodiversity inventory of the grey mullets (Actinopterygii: Mugilidae) of the Indo‐Australian Archipelago through the iterative use of DNA‐based species delimitation and specimen assignment methods », Archimer, archive institutionnelle de l'Ifremer, ID : 10.1111/eva.12926


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DNA barcoding opens new perspectives on the way we document biodiversity. Initially proposed to circumvent the limits of morphological characters to assign unknown individuals to known species, DNA barcoding has been used in a wide array of studies where collecting species identity constitutes a crucial step. The assignment of unknowns to knowns assumes that species are already well identified and delineated, making the assignment performed reliable. Here, we used DNA‐based species delimitation and specimen assignment methods iteratively to tackle the inventory of the Indo‐Australian Archipelago grey mullets, a notorious case of taxonomic complexity that requires DNA‐based identification methods considering that traditional morphological identifications are usually not repeatable and sequences mislabeling are common in international sequence repositories. We first revisited a DNA barcode reference library available at the global scale for Mugilidae through different DNA‐based species delimitation methods to produce a robust consensus scheme of species delineation. We then used this curated library to assign unknown specimens collected throughout the Indo‐Australian Archipelago to known species. A second iteration of OTUs delimitation and specimen assignment was then performed. We show the benefits of using species delimitation and specimen assignment methods iteratively to improve the accuracy of specimen identification and propose a workflow to do so.

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