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Comparison of kinship-identification methods for robust stock assessment using close-kin mark--recapture data for Pacific bluefin tuna
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  • Yohei Tsukahara,
  • Reiichiro Nakamichi,
  • Aiko Matsuura,
  • Tetsuya Akita,
  • Atushi Fujiwara,
  • Nobuaki Suzuki
Yohei Tsukahara
Japan Fisheries Research and Education Agency

Corresponding Author:tsukahara_yohei35@fra.go.jp

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Reiichiro Nakamichi
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Aiko Matsuura
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Tetsuya Akita
Japan Fisheries Research and Education Agency
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Atushi Fujiwara
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Nobuaki Suzuki
Japan Fisheries Research and Education Agency
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Abstract

Several attempts have been made to understand the population dynamics of fishery resources, such as tuna species using an integrated analysis model with multiple data sources. However, estimating absolute abundance levels in practical stock assessments remains a challenge. Close-kin mark–recapture (CKMR) methods provide information about the number of adults in a population using close-kinship pairs identified by genetic markers and statistical methods. In this study, we compared three methods for kinship identification using different algorithms in samples of wild Pacific bluefin tuna genotyped across 5,029 genome-wide single nucleotide polymorphisms in 4,108 samples. The fraRF method we developed employs pairwise identity-by-descent values as inputs for random-forest classification. The other two methods were CKMRsim and COLONY, which have been published and applied in several studies. These three methods were applied to the actual genotyping data with moderate missing genotypes, in addition to the pseudo-generated genotyping data for the simulation test. The simulation test mimicked genotyping data with physical linkage as well as genetic characteristics similar to those of actual samples. The three methods resulted in different numbers of inferred kinship pairs for both generated and actual data. Particularly for the half-sibling pairs, considerable number of false-positive and false-negative existed in the identification results. The differences in kinship identification results were interpreted based on a simulation test. This study may promote the understanding of behavior in each software when applying the software to SNP data with moderate missing genotypes as in the case of this study.
02 Sep 2024Submitted to Population Ecology
04 Sep 2024Submission Checks Completed
04 Sep 2024Assigned to Editor
04 Sep 2024Review(s) Completed, Editorial Evaluation Pending
09 Sep 2024Reviewer(s) Assigned
16 Oct 2024Editorial Decision: Revise Minor
30 Oct 20241st Revision Received
30 Oct 2024Submission Checks Completed
30 Oct 2024Assigned to Editor
30 Oct 2024Review(s) Completed, Editorial Evaluation Pending
20 Nov 2024Editorial Decision: Accept