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The population genomics of yellowfin tuna (Thunnus albacares) at global geographic scale challenges current stock delineation.

ABSTRACT: Yellowfin tuna, Thunnus albacares, is one of the most important seafood commodities in the world. Despite its great biological and economic importance, conflicting evidence arises from classical genetic and tagging studies concerning the yellowfin tuna population structure at local and global oceanic scales. Access to more powerful and cost effective genetic tools would represent the first step towards resolving the population structure of yellowfin tuna across its distribution range. Using a panel of 939 neutral Single Nucleotide Polymorphisms (SNPs), and the most comprehensive data set of yellowfin samples available so far, we found genetic differentiation among the Atlantic, Indian and Pacific oceans. The genetic stock structure analysis carried out with 33 outlier SNPs, putatively under selection, identified discrete populations within the Pacific Ocean and, for the first time, also within the Atlantic Ocean. Stock assessment approaches that consider genetic differences at neutral and adaptive genomic loci should be routinely implemented to check the status of the yellowfin tuna, prevent illegal trade, and develop more sustainable management measures.

SUBMITTER: Pecoraro C

PROVIDER: S-EPMC6141456 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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The population genomics of yellowfin tuna (Thunnus albacares) at global geographic scale challenges current stock delineation.

Pecoraro Carlo C Babbucci Massimiliano M Franch Rafaella R Rico Ciro C Papetti Chiara C Chassot Emmanuel E Bodin Nathalie N Cariani Alessia A Bargelloni Luca L Tinti Fausto F

Scientific reports 20180917 1

Yellowfin tuna, Thunnus albacares, is one of the most important seafood commodities in the world. Despite its great biological and economic importance, conflicting evidence arises from classical genetic and tagging studies concerning the yellowfin tuna population structure at local and global oceanic scales. Access to more powerful and cost effective genetic tools would represent the first step towards resolving the population structure of yellowfin tuna across its distribution range. Using a pa ...[more]

PMID: 30224658

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Project description:Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the sagittal otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation.

Dataset Information

The population genomics of yellowfin tuna (Thunnus albacares) at global geographic scale challenges current stock delineation.

Publications

The population genomics of yellowfin tuna (Thunnus albacares) at global geographic scale challenges current stock delineation.

Similar Datasets

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