Genomic differentiation and demographic histories of Atlantic and Indo-Pacific yellowfin tuna (Thunnus albacares) populations
Open accessAbstract
Recent developments in the field of genomics have provided new and powerful insights into population structure and dynamics that are essential for the conservation of biological diversity. As a commercially highly valuable species, the yellowfin tuna (Thunnus albacares) is intensely exploited throughout its distribution in tropical oceans around the world, and is currently classified as near threatened. However, conservation efforts for this species have so far been hampered by limited knowledge of its population structure, due to incongruent results of previous investigations. Here, we use whole-genome sequencing in concert with a draft genome assembly to decipher the global population structure of the yellowfin tuna, and to investigate its demographic history. We detect significant differentiation of Atlantic and Indo-Pacific yellowfin tuna populations as well as the possibility of a third diverged yellowfin tuna group in the Arabian Sea. We further observe evidence for past population expansion as well as asymmetric gene flow from the Indo-Pacific to the Atlantic.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Genomic analyses have revealed asymmetrical dispersal from the Indian Ocean yellowfin tuna into the Atlantic and highlighted that yellowfin tuna found along all the South African coast are derived from the Indian population.
“Moreover, genomic analyses have revealed asymmetrical dispersal from the Indian Ocean yellowfin tuna into the Atlantic and highlighted that yellowfin tuna found along all the South African coast are derived from the Indian population (Barth et al., 2017; Mullins et al., 2018).”
From A review of the fisheries, life history and stock structure of tropical tuna (skipjack Katsuwonus pelamis, yellowfin Thunnus albacares and bigeye Thunnus obesus) in the Indian Ocean (2021) · cites this paper as Barth et al., 2017
The Discussion section, “Demographic Histories and Asymmetric Gene Flow,” reports migration estimates about ten times higher from the Indo-Pacific to the Atlantic than in the reverse direction. But the Sample Collection section lists only one South African site, Cape Town, and the Results section says all except one of its seven specimens grouped with the Indo-Pacific clade. The paper therefore does not establish that yellowfin tuna along all of South Africa’s coast derive from the Indian population. (LLM, conf=0.89)
Experimental — accuracy not yet measuredUsing genome wide SNPs, Barth et al. (2017) found genetic differentiation of yellowfin tuna from the Arabian Sea with respect to those from the Atlantic and Indo-Pacific.
“Using genome wide SNPs, Barth et al. (2017) found genetic differentiation of yellowfin tuna from the Arabian Sea with respect to those from the Atlantic and Indo-Pacific.”
From A review of the fisheries, life history and stock structure of tropical tuna (skipjack Katsuwonus pelamis, yellowfin Thunnus albacares and bigeye Thunnus obesus) in the Indian Ocean (2021) · cites this paper as Barth et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · cited 1× · see citing works
“90 beta (https://www.cog-genomics.org/plink2; last accessed April 7, 2017; Purcell et al. 2007), which allows larger “chromosome” numbers with the flag “- -aec” (PLINK_HIGH_CONTIG, available from the software authors): We excluded SNPs displaying a minor-allele count< 2 across all populations and…”
BackgroundRAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies
Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu033 · Cited by 32,291 · see citing works
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895 · see citing works
IUCN Red List of Threatened Species
Ragle, J., & Remsen, D. (2010) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.46240 · Cited by 26,893 · cited 3× · see citing works
“According to IUCN, the generation time of yellowfin tuna is 2.2–3.5 years (Collette et al. 2011).”
BackgroundResultInfluentialDetecting the number of clusters of individuals using the software structure: a simulation study
Evanno, G., Regnaut, S., & Goudet, J. (2005) · Molecular Ecology · doi:10.1111/j.1365-294x.2005.02553.x · Cited by 21,540 · cited 1× · see citing works
“S4b, Supplementary Material online; Evanno et al. 2005).”
BackgroundESTIMATING F -STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE
Weir, B. S., & Cockerham, C. C. (1984) · Evolution · doi:10.1111/j.1558-5646.1984.tb05657.x · Cited by 18,909 · cited 1× · see citing works
“Genetic Differentiation Pairwise fixation indices (FST) were estimated according to Weir and Cockerham (1984) by applying the “- -weir-fstpop” flag in VCFTOOLS v0.”
MethodMAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform
Katoh, K. (2002) · Nucleic Acids Research · doi:10.1093/nar/gkf436 · Cited by 17,027 · see citing works
Molecular Evolutionary Genetics
Nei, M. (1987) · Columbia University Press eBooks · doi:10.7312/nei-92038 · Cited by 16,583 · cited 1× · see citing works
“1.14 (Danecek et al. 2011), and according to Nei (1987) using the package HIERFSTAT v0.”
MethodInfluentialANALYZING TABLES OF STATISTICAL TESTS
Rice, W. R. (1989) · Evolution · doi:10.1111/j.1558-5646.1989.tb04220.x · Cited by 14,361 · cited 1× · see citing works
“%) and P values for Weir & Cockerham’s FST estimates using the R package STAMPP (Pembleton et al. 2013), and P values were adjusted for multiple testing by applying sequential Bonferroni correction (Rice 1988).”
MethodA framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,216 · cited 1× · see citing works
“3.2 (DePristo et al. 2011).”
Influentialadegenet: a R package for the multivariate analysis of genetic markers
Jombart, T. (2008) · Bioinform. · doi:10.1093/bioinformatics/btn129 · Cited by 7,788 · see citing works
Li, H. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr509 · Cited by 6,283 · cited 4× · see citing works
“We used the pairwise sequential Markovian coalescent (PSMC; Li and Durbin 2011) on single representatives of the Atlantic (ATL) and Indo-Pacific (PAC) Oceans to infer the demographic history of the two populations.”
BackgroundMethodInfluentialPopulation Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280 · cited 1× · see citing works
“Multivariate analyses like PCA and DAPC describe the largest variation of the data, but they do not take advantage of population genetic models (Patterson et al. 2006).”
MethodHaplotype-based variant detection from short-read sequencing
Garrison, E., & Marth, G. (2012) · arXiv · doi:10.48550/arxiv.1207.3907 · Cited by 3,700 · cited 1× · see citing works
“9.14 (Garrison and Marth 2012) and GATK v3.”
InfluentialAliView: a fast and lightweight alignment viewer and editor for large datasets
Larsson, A. O. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu531 · Cited by 3,623 · see citing works
Genetic consequences of climatic oscillations in the Quaternary.
Hewitt, G. (2004) · Philosophical transactions of the Royal Society of London. Series B, Biological sciences · doi:10.1098/rstb.2003.1388 · Cited by 3,522 · cited 2× · see citing works
“Repeated glacial cycles and associated habitat changes in the Pleistocene have influenced the distribution and abundance of many species (Hewitt 2004) including tropical marine fish (Ludt and Rocha 2014), and have had genomic consequences for these species due to population bottlenecks and…”
BackgroundInferring weak population structure with the assistance of sample group information
HUBISZ, M. J., FALUSH, D., STEPHENS, M., & PRITCHARD, J. K. (2009) · Molecular Ecology Resources · doi:10.1111/j.1755-0998.2009.02591.x · Cited by 3,475 · cited 1× · see citing works
“Applying a sampling location prior (Hubisz et al. 2009) to assist clustering in STRUCTURE did not improve cluster assignment (data not shown).”
MethodA likelihood approach to estimating phylogeny from discrete morphological character data
Lewis, P. O. (2001) · Systematic Biology · doi:10.1080/106351501753462876 · Cited by 2,929 · cited 1× · see citing works
“RAXML was used to construct the maximum-likelihood tree under the GTR model with ascertainment bias correction for the absence of invariant sites (“-m ASC_GTRCAT”; Lewis 2001).”
MethodHIERFSTAT , a package for R to compute and test hierarchical F -statistics
Goudet, J. (2005) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2004.00828.x · Cited by 2,730 · cited 1× · see citing works
“04-22 (Goudet 2005) in R, based on SNPs with MAF> 1% and at most 10% missing genotypes (supplementary table S2, Supplementary Material online).”
BackgroundInference of human population history from individual whole-genome sequences
Li, H., & Durbin, R. (2011) · Nature · doi:10.1038/nature10231 · Cited by 2,590 · see citing works
Genomics and the future of conservation genetics
Allendorf, F. W., Hohenlohe, P. A., & Luikart, G. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2844 · Cited by 1,586 · cited 1× · see citing works
“…gene flow and low population genetic structuring, it is now possible to precisely identify biologically relevant populations by their signatures of local adaptation, and to decipher the demographic imprints caused by population size changes or dispersal (Nielsen et al. 2009; Allendorf et al. 2010).”
BackgroundRobust Demographic Inference from Genomic and SNP Data
Excoffier, L., Dupanloup, I., Huerta‐Sánchez, E., Sousa, V. C., & Foll, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003905 · Cited by 1,434 · cited 1× · see citing works
“…rates of gene flow between Atlantic and Indo-Pacific populations, we performed coalescent simulations with the software FASTSIMCOAL2 v.2.5.2 (Excoffier et al. 2013) according to an evolutionary scenario in which an ancestral population splits into two extant populations that are connected…”
MethodInfluentialHarnessing genomics for delineating conservation units
Funk, W. C., McKay, J., Hohenlohe, P. A., & Allendorf, F. W. (2012) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2012.05.012 · Cited by 1,121 · cited 1× · see citing works
“Differentiation between populations connected by gene flow is often limited to genomic regions underlying traits involved in local adaptation (Funk et al. 2012) that are easily missed when only few markers are employed.”
BackgroundHo, S., Phillips, M., Cooper, A., & Drummond, A. (2005) · Molecular biology and evolution · doi:10.1093/molbev/msi145 · Cited by 1,065 · see citing works
Puechmaille, S. J. (2016) · Molecular Ecology Resources · doi:10.1111/1755-0998.12512 · Cited by 986 · cited 2× · see citing works
“To account for putative biases in the inference of the true number of K by uneven sample sizes (Puechmaille 2016), cluster membership was also investigated with an even sampling scheme of 5 individuals each from CAP, SOU, OMA, and JAP, 1088 Genome Biol.”
BackgroundMethodPembleton, L., Cogan, N., & Forster, J. W. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12129 · Cited by 817 · see citing works
The utility of single nucleotide polymorphisms in inferences of population history
Brumfield, R. T., Beerli, P., Nickerson, D. A., & Edwards, S. V. (2003) · Trends in Ecology & Evolution · doi:10.1016/s0169-5347(03)00018-1 · Cited by 694 · cited 1× · see citing works
“Generally, nuclear SNPs are assumed to have mutation rates on the order of 10 8 to 10 9 substitutions/site/generation (Brumfield et al. 2003), and the human mutation rate is estimated to be 2.5 10 8 substitutions/site/ generation (Nachman and Crowell 2000), which would place our estimates at the…”
BackgroundInfluentialReiss, H., Hoarau, G., Dickey-Collas, M., & Wolff, W. (2009) · Fish and Fisheries · doi:10.1111/j.1467-2979.2008.00324.x · Cited by 535 · cited 1× · see citing works
“Any mismatch will exacerbate the increasing trend of unsustainably harvested fish stocks that suffer severe population declines or even collapse (Mullon et al. 2005; Reiss et al. 2009).”
BackgroundSea-level variability over five glacial cycles
Grant, K., Rohling, E., Ramsey, C., Cheng, H., Edwards, R., Florindo, F., Heslop, D., Marra, F., Roberts, A. P., Tamisiea, M., et al. (2014) · Nature Communications · doi:10.1038/ncomms6076 · Cited by 481 · see citing works
Conservation genetics in transition to conservation genomics
Ouborg, N. J., Pertoldi, C., Loeschcke, V., Bijlsma, R., & Hedrick, P. W. (2010) · Trends in Genetics · doi:10.1016/j.tig.2010.01.001 · Cited by 409 · see citing works
The Indonesian seas and their role in the coupled ocean–climate system
Sprintall, J., Gordon, A., Koch-Larrouy, A., Lee, T., Potemra, J., Pujiana, K., & Wijffels, S. (2014) · Nature Geoscience · doi:10.1038/ngeo2188 · Cited by 375 · cited 1× · see citing works
“0.26 (Stamatakis 2014), and node support was assessed with up to 1,000 bootstrap replicates (option “autoMRE”; supplementary fig.”
InfluentialThe dynamics of collapse in world fisheries
Mullon, C., Fréon, P., & Cury, P. M. (2005) · Fish and Fisheries · doi:10.1111/j.1467-2979.2005.00181.x · Cited by 338 · see citing works
Population genomics of marine fishes: identifying adaptive variation in space and time
Nielsen, E. E., Hemmer‐Hansen, J., LARSEN, P. F., & Bekkevold, D. (2009) · Molecular Ecology · doi:10.1111/j.1365-294x.2009.04272.x · Cited by 326 · see citing works
Ludt, W., & Rocha, L. (2015) · Journal of Biogeography · doi:10.1111/jbi.12416 · Cited by 271 · see citing works
Coalescent Processes When the Distribution of Offspring Number Among Individuals Is Highly Skewed
Eldon, B., & Wakeley, J. (2006) · Genetics · doi:10.1534/genetics.105.052175 · Cited by 251 · cited 1× · see citing works
“Such differences in reproductive success can cause overestimates of the timing of population expansion as well as Ne (Eldon and Wakeley 2006; Grant 2016).”
BackgroundEvolution of the immune system influences speciation rates in teleost fishes
Malmstrøm, M., Matschiner, M., Tørresen, O. K., Star, B., Snipen, L. G., Hansen, T. F., Baalsrud, H. T., Nederbragt, A. J., Hanel, R., Salzburger, W., et al. (2016) · Nature Genetics · doi:10.1038/ng.3645 · Cited by 235 · see citing works
Self-citationTranslation, decay and splitting of Agulhas rings in the southeastern Atlantic Ocean
Schouten, M., de Ruijter, W. P., van Leeuwen, P. V., & Lutjeharms, J. (2000) · Journal of Geophysical Research Atmospheres · doi:10.1029/1999jc000046 · Cited by 197 · see citing works
Western and Central Pacific Fisheries Commission
Aqorau, T. (2009) · The International Journal of Marine and Coastal Law · doi:10.1163/092735209x12462520578696 · Cited by 190 · see citing works
Productivity collapses in the Arabian Sea during glacial cold phases
Singh, A., Jung, S. J. A., Darling, K., Ganeshram, R., Ivanochko, T., & Kroon, D. (2011) · Paleoceanography · doi:10.1029/2009pa001923 · Cited by 182 · see citing works
Global population trajectories of tunas and their relatives
Juan‐Jordá, M. J., Mosqueira, I., Cooper, A. B., Freire, J., & Dulvy, N. K. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1107743108 · Cited by 163 · see citing works
Ely, B., Viñas, J., Bremer, J. A. A., Black, D. R., Lucas, L., Covello, K., Labrie, A. V., & Thelen, E. (2005) · BMC Evolutionary Biology · doi:10.1186/1471-2148-5-19 · Cited by 154 · cited 13× · see citing works
“Ely et al. (2005) calculated the Ne of females with mismatch distributions of D-loop sequences to be about ten million individuals, with a historical population expansion around 500 ka.”
BackgroundMethodResultInfluentialShould genes with missing data be excluded from phylogenetic analyses?
Jiang, W., Chen, S., Wang, H., Li, D.-Z., & Wiens, J. J. (2014) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2014.08.006 · Cited by 142 · see citing works
Evolutionary changes of multiple visual pigment genes in the complete genome of Pacific bluefin tuna
Nakamura, Y., Mori, K., Saitoh, K., Oshima, K., Mekuchi, M., Sugaya, T., Shigenobu, Y., Ojima, N., Muta, S., Fujiwara, A., et al. (2013) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1302051110 · Cited by 139
The genomics of ecological vicariance in threespine stickleback fish
Roesti, M., Kueng, B., Moser, D., & Berner, D. (2015) · Nature Communications · doi:10.1038/ncomms9767 · Cited by 134 · see citing works
Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution
Dueri, S., Bopp, L., & Maury, O. (2014) · Global Change Biology · doi:10.1111/gcb.12460 · Cited by 131 · cited 1× · see citing works
“Abundance and distribution of tuna have been connected with climatic changes and weather oscillations (Kumar et al. 2014; Dueri et al. 2014), and the ongoing ocean warming may thus lead to range shifts in some populations.”
BackgroundGraves, J., Ferris, S., & Dizon, A. (1984) · Marine Biology · doi:10.1007/bf00393264 · Cited by 127 · cited 1× · see citing works
“…found for the southern bluefin tuna (T. maccoyii) with a circumglobal distribution in the temperate waters of the southern Atlantic and Indo-Pacific (Grewe et al. 1997), and for the closest relative of the genus Thunnus, the skipjack tuna (Katsuwonus pelamis; Graves et al. 1984; Ely et al. 2005).”
BackgroundGrewe, P., Feutry, P., Hill, P. L., Gunasekera, R., Schaefer, K., Itano, D., Fuller, D., Foster, S., & Davies, C. (2015) · Scientific Reports · doi:10.1038/srep16916 · Cited by 120 · cited 2× · see citing works
“Intraoceanic differentiation has been described between the western, central, and eastern Pacific (Ward et al. 1994; Grewe et al. 2015).”
BackgroundAgulhas retroflection rings in the South Atlantic Ocean: an overview
Rae, C. M. D. (1991) · doi:10.2989/025776191784287574 · Cited by 110
Durand, J.-D., Collet, A., Chow, S., Guinand, B., & Borsa, P. (2005) · Marine Biology · doi:10.1007/s00227-005-1564-2 · Cited by 108 · cited 2× · see citing works
“…for warm-water species into the Atlantic, and could thus be the cause for the observed directionality of gene flow in yellowfin tuna, as well as other circumtropically distributed fishes, including the bigeye tuna (Chow et al. 2000; Durand et al. 2005; Gonzalez et al. 2008; Gaither et al. 2016).”
BackgroundFishes that rule the world: circumtropical distributions revisited
Gaither, M. R., Bowen, B. W., Rocha, L. A., & Briggs, J. C. (2015) · Fish and Fisheries · doi:10.1111/faf.12136 · Cited by 100 · cited 1× · see citing works
“…for warm-water species into the Atlantic, and could thus be the cause for the observed directionality of gene flow in yellowfin tuna, as well as other circumtropically distributed fishes, including the bigeye tuna (Chow et al. 2000; Durand et al. 2005; Gonzalez et al. 2008; Gaither et al. 2016).”
BackgroundWard, R., Elliott, N. G., Grewe, P., & Smolenski, A. (1994) · doi:10.1007/bf00347499 · Cited by 95 · cited 1×
“Intraoceanic differentiation has been described between the western, central, and eastern Pacific (Ward et al. 1994; Grewe et al. 2015).”
BackgroundWard, R., Elliott, N. G., Innes, B. H., Smolenski, A., & Grewe, P. (1997) · Oceanographic Literature Review · doi:10.1016/s0967-0653(97)88982-x · Cited by 92 · see citing works
Chow, S., Okamoto, H., Miyabe, N., Hiramatsu, K., & Barut, N. C. (2000) · Molecular Ecology · doi:10.1046/j.1365-294x.2000.00851.x · Cited by 89 · cited 8× · see citing works
“Genetic divergence between Atlantic and Indo-Pacific stocks of bigeye tuna (Thunnus obesus) and admixture around South Africa.”
BackgroundInfluentialGenetic diversity and historical demography of Atlantic bigeye tuna (Thunnus obesus)
Martínez, P., González, E., Castilho, R., & Zardoya, R. (2005) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2005.07.022 · Cited by 84 · see citing works
Missing Data Estimation in Morphometrics: How Much is Too Much?
Clavel, J., Merceron, G., & Escarguel, G. (2013) · Systematic Biology · doi:10.1093/sysbio/syt100 · Cited by 83 · cited 2× · see citing works
“…and Bayesian clustering analyses are constrained either computationally (in the case of STRUCTURE), or by the method (in the case of PCA; Clavel et al. 2014), we additionally inferred the yellowfin tuna population structure using maximum-likelihood phylogenetic clustering with a less…”
MethodMoura, A., van Rensburg, C. J., Pilot, M., Tehrani, A., Best, P., Thornton, M., Plön, S., de Bruyn, P. D., Worley, K., Gibbs, R., et al. (2014) · Molecular biology and evolution · doi:10.1093/molbev/msu058 · Cited by 77 · see citing works
Population Genomics of Marine Fishes: Next-Generation Prospects and Challenges
Hemmer‐Hansen, J., Therkildsen, N. O., & Pujolar, J. M. (2014) · Biological Bulletin · doi:10.1086/bblv227n2p117 · Cited by 71 · cited 1× · see citing works
“…sequencing technology is a valuable tool for the conservation and management of species and populations (Ouborg et al. 2010) enabling the discovery of fine-scale genetic variation and thus the deduction of population divergences with drastically increased accuracy (Hemmer-Hansen et al. 2014).”
BackgroundHenriques, R., Potts, W. M., Santos, C., Sauer, W., & Shaw, P. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087907 · Cited by 70 · cited 1× · see citing works
“However, this route may not be favorable for the migration of warm-water fishes as it would involve the crossing of the strong Benguela Current, which transports cold upwelled water northward along the southwest coast of Africa and is a potential barrier to gene flow (Henriques et al. 2014).”
BackgroundFitchi: haplotype genealogy graphs based on the Fitch algorithm
Matschiner, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv717 · Cited by 65 · see citing works
Self-citationGenetic structuring and migration patterns of Atlantic bigeye tuna, Thunnus obesus (Lowe, 1839)
Gonzalez, E. G., Beerli, P., & Zardoya, R. (2008) · BMC Evolutionary Biology · doi:10.1186/1471-2148-8-252 · Cited by 63 · cited 6× · see citing works
“On the other hand, a subsequent investigation found no evidence of differentiation based on nuclear DNA and the authors attributed the incongruence of mitochondrial and nuclear patterns to separation during glacial maxima followed by secondary contact (Gonzalez et al. 2008).”
BackgroundInfluentialFonteneau, A., & Hallier, J.-P. (2015) · Fisheries Research · doi:10.1016/j.fishres.2014.03.022 · Cited by 60 · cited 3× · see citing works
“…of yellowfin tuna population structure has been controversial, as long-distance movements are known to occur in this highly migratory species (Itano and Williams 1992; Ortiz 2001; Fonteneau and Hallier 2015) which could potentially lead to a homogenization of between-ocean genetic differentiation.”
BackgroundEl Niño Southern Oscillation (ENSO) impact on tuna fisheries in Indian Ocean
Kumar, P. S., Pillai, G. N., & Manjusha, U. (2014) · SpringerPlus · doi:10.1186/2193-1801-3-591 · Cited by 59 · see citing works
Kunal, S. P., Kumar, G., Menezes, M. R., & Meena, R. M. (2013) · Conservation Genetics · doi:10.1007/s10592-013-0445-3 · Cited by 55 · see citing works
Pecoraro, C., Babbucci, M., Villamor, A., Franch, R., Papetti, C., Leroy, B. M., Ortega‐García, S., Muir, J. A., Rooker, J. R., Arocha, F., et al. (2015) · Marine Genomics · doi:10.1016/j.margen.2015.12.002 · Cited by 52 · see citing works
Laconcha, U., Iriondo, M., Arrizabalaga, H., Manzano, C., Markaide, P., Montes, I., Zarraonaindia, I., Velado, I., Bilbao, E., Goñi, N., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128247 · Cited by 51 · see citing works
Pedrosa-Gerasmio, I. R., Babaran, R., & Santos, M. D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035604 · Cited by 49
Stéquert, B. (2001) · Aquatic Living Resources · doi:10.1016/s0990-7440(01)01126-3 · Cited by 48 · see citing works
Albaina, A., Iriondo, M., Velado, I., Laconcha, U., Zarraonaindia, I., Arrizabalaga, H., Pardo, M. Á., Lutcavage, M. E., Grant, W. S., & Estonba, A. (2013) · Animal Genetics · doi:10.1111/age.12051 · Cited by 47 · cited 1× · see citing works
“…differentiation between Atlantic and Indo-Pacific groups has also been described for two other circumtropical members of the genus Thunnus: albacore (T. alalunga; Albaina et al. 2013; Laconcha et al. 2015) and bigeye tuna (T. obesus; Chow et al. 2000; Martı́nez et al. 2006; Gonzalez et al. 2008).”
BackgroundGenetic population structure of southern bluefin tuna (Thunnus maccoyii )
Grewe, P., Elliott, N. G., Innes, B. H., & Ward, R. D. (1997) · Marine Biology · doi:10.1007/s002270050045 · Cited by 43 · cited 20× · see citing works
“Demographic parameters were also estimated through coalescent simulations with FASTSIMCOAL2, comparing simulated SFS with the observed joint SFS of Atlantic and Indo-Pacific yellowfin tuna populations.”
BackgroundInfluentialNomura, S., Kobayashi, T., Agawa, Y., Margulies, D., Scholey, V. P., Sawada, Y., & Yagishita, N. (2014) · Fisheries Science · doi:10.1007/s12562-014-0789-8 · Cited by 18
Paradigm Shifts in the Phylogeographic Analysis of Seaweeds
Grant, W. S. (2016) · Springer eBooks · doi:10.1007/978-94-017-7534-2_2 · Cited by 10 · cited 1× · see citing works
“Such differences in reproductive success can cause overestimates of the timing of population expansion as well as Ne (Eldon and Wakeley 2006; Grant 2016).”
BackgroundGuo, L., Li, M., Zhang, H., Yang, S., Chen, X., Meng, Z.-N., & Lin, H.-R. (2014) · Mitochondrial DNA. Part A, DNA mapping, sequencing, and analysis · doi:10.3109/19401736.2014.982570 · Cited by 3
Flagged first, then by citations
23 of the 95 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
- Benson DA Cavanaugh M Clark K, 2013 GenBank. Nucleic Acids Res. 41: 36– 42. PMC3531190 23193287
- Collette BB Nauen CE. 1983. FAO Species Catalogue, Vol. 2: Scombrids of the World. An annotated and illustrated catalogue of tunas, mackerels, bonitos and related species known to date. FAO Fisheries Synopsis. 125.
- Danecek P Auton A Abecasis G, 2011 The variant call format and VCFtools. Bioinformatics 27: 2156– 2158. 21653522 PMC3137218
- Falush D Stephens M Pritchard JK. 2003 Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567– 1587. 12930761 PMC1462648
- Hauser L Ward RD. 1998. Population identification in pelagic fish: the limits of molecular markers. In: Carvalho, G, editor. Advances in molecular ecology. Nato Advanced Science Institutes Series A, Life Sciences. Amsterdam: IOS Press. Vol. 306. p.191–224.
- Itano DG Williams PG. 1992. Analyses of yellowfin tagging data and related information collected by the skipjack survey and assessment programme. South Pacific Commission, Tuna and Billfish Assessment Programme. Vol. 28. p. 1–39.
- Jombart T Devillard S Balloux F. 2010 Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11: 94. 20950446 PMC2973851
- Li H Durbin R. 2009 Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754– 1760. 19451168 PMC2705234
- López MD Uribe Alcocer M Díaz Jaimes M. 2010 Phylogeography and historical demography of the Pacific Sierra mackerel (Scomberomorus sierra) in the Eastern Pacific. BMC Genet. 11: 34. 20438637 PMC2876057
- Nachman MW Crowell SL. 2000 Estimate of the mutation rate per nucleotide in humans. Genetics 156: 297– 304. 10978293 PMC1461236
- Ortiz M. 2001 Review of tag-releases and recaptures for yellowfin tuna from the US CTC program. ICCAT Collect Vol Sci Pap. 52: 215– 221.
- Pritchard JK Stephens M Donnelly P. 2000 Inference of population structure using multilocus genotype data. Genetics 155: 945– 959. 10835412 PMC1461096
- Quinlan AR Hall IM. 2010 BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26: 841– 842. 20110278 PMC2832824
Cited by
Sorted by citations
Patton, A. H., Margres, M., Stahlke, A. R., Hendricks, S. A., Lewallen, K., Hamede, R., Ruiz-Aravena, M., Ryder, O., McCallum, H., Jones, M. E., et al. (2019) · Molecular biology and evolution · doi:10.1093/molbev/msz191 · Cited by 117 · see citing works
“Examples of their use include investigations of the decline of the Sumatran rhinoceros (Mays et al. 2018), the demographic history of yellow-fin tuna (Barth et al. 2017), and the low genomic diversity in the brown hyena (Westbury et al. 2018).”
BackgroundMethodInfluentialDisentangling structural genomic and behavioural barriers in a sea of connectivity
Barth, J. M. I., Villegas‐Ríos, D., Freitas, C., Moland, E., Star, B., André, C., Knutsen, H., Bradbury, I., Dierking, J., Petereit, C., et al. (2019) · Molecular Ecology · doi:10.1111/mec.15010 · Cited by 102 · see citing works
Self-citationPecoraro, C., Babbucci, M., Franch, R., Rico, C., Papetti, C., Chassot, E., Bodin, N., Cariani, A., Bargelloni, L., & Tinti, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-32331-3 · Cited by 80 · see citing works
A Fast, Reproducible, High-throughput Variant Calling Workflow for Population Genomics
Mirchandani, C., Shultz, A. J., Thomas, G. W. C., Smith, S. J., Baylis, M., Arnold, B., Corbett-Detig, R. B., Enbody, E. D., & Sackton, T. B. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad270 · Cited by 83 · see citing works
Mullins, R. B., McKeown, N. J., Sauer, W. H. H., & Shaw, P. W. (2018) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsy102 · Cited by 46 · see citing works
“Recent genomic studies have confirmed genetic differences and restricted interbreeding among regional groups (Pecoraro et al., 2016; Barth et al., 2017).”
BackgroundMamoozadeh, N., Graves, J., & McDowell, J. (2019) · Evolutionary Applications · doi:10.1111/eva.12892 · Cited by 30 · see citing works
Mattingsdal, M., Jorde, P. E., Knutsen, H., Jentoft, S., Stenseth, N. C., Sodeland, M., Robalo, J. I., Hansen, M. M., André, C., & Gonzalez, E. B. (2019) · Molecular Ecology · doi:10.1111/mec.15310 · Cited by 31 · see citing works
Self-citationNimit, K., Masuluri, N. K., Berger, A. M., Bright, R. P., Prakash, S., Udayabhaskar, T., Srinivasa Kumar, T., Rohit, P., Tiburtius, A., Ghosh, S., et al. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1707903 · Cited by 39 · see citing works
“A larger study with sample from all the oceans using whole-genome sequencing in concert with a draft genome assembly also indicated the possibility of a distinct yellow fi n tuna population in the Arabian Sea in addition to Atlantic and Indo-Paci fi c populations (Barth et al. 2017).”
BackgroundDíaz‐Arce, N., Gagnaire, P., Richardson, D. E., Walter, J. F., Arnaud‐Haond, S., Fromentin, J., Brophy, D., Lutcavage, M. E., Addis, P., Alemany, F., et al. (2023) · Molecular Ecology · doi:10.1111/mec.17188 · Cited by 29 · see citing works
“…adaptation to local environments (Bernatchez et al., 2017 ; Ovenden et al., 2015), even when genetic differentiation is low, as observed in highly migratory fish like striped marlin (Mamoozadeh et al., 2020 ), blue shark (Nikolic et al., 2023 ) and yellowfin tuna (Barth, Damerau, et al., 2017 ).”
BackgroundAsaduzzaman, M., Igarashi, Y., Wahab, M. A., Nahiduzzaman, M., Rahman, M. J., Phillips, M., Huang, S.-Q., Asakawa, S., Rahman, M. M., & Wong, L.-L. (2019) · Genes · doi:10.3390/genes11010046 · Cited by 24
“Previous studies have also reported that local adaptation in fish populations is related to the presence of linked alleles, associated with life history traits [71], which is important in defining the structuring pattern of Hilsa shad.”
BackgroundExploring the legacy of Central European historical winter wheat landraces
Cseh, A., Poczai, P., Kiss, T., Balla, K., Berki, Z., Horváth, Á., Kuti, C., & Karsai, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-03261-4 · Cited by 20 · see citing works
Nikolic, N., Montes, I., Lalire, M., Puech, A., Bodin, N., Arnaud‐Haond, S., Kerwath, S., Corse, E., Gaspar, P., Hollanda, S. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72369-w · Cited by 20 · see citing works
The Mitogenome Relationships and Phylogeography of Barn Swallows (Hirundo rustica)
Lombardo, G., Migliore, N. R., Colombo, G., Capodiferro, M., Formenti, G., Caprioli, M., Moroni, E., Caporali, L., Lancioni, H., Secomandi, S., et al. (2022) · Molecular biology and evolution · doi:10.1093/molbev/msac113 · Cited by 19 · see citing works
“…for humans and many other animal species (Achilli et al. 2008, 2012; Behar et al. 2012; Miao et al. 2013; Morin et al. 2015; Battaglia et al. 2016; Barth et al. 2017; Peng et al. 2018; Cole et al. 2019; de Manuel et al. 2020; Niedziałkowska et al. 2021) and recently pursued also in H. rustica…”
BackgroundFisheries genomics of snapper ( Chrysophrys auratus ) along the west Australian coast
Bertram, A., Fairclough, D. V., Sandoval‐Castillo, J., Brauer, C. J., Fowler, A. J., Wellenreuther, M., & Beheregaray, L. B. (2022) · Evolutionary Applications · doi:10.1111/eva.13439 · Cited by 20 · see citing works
“For example, in the broadcast spawning and highly migratory yellowfin tuna (Thunnus albacares), homogeneity likely occurs at spatial scales as extensive as entire ocean basins (Barth et al. 2017, Pecoraro et al. 2018).”
BackgroundBernard, A., Finnegan, K. A., Bitar, P. P. P., Stanhope, M., & Shivji, M. (2021) · Journal of Heredity · doi:10.1093/jhered/esab046 · Cited by 18
Complex Dispersal of Adult Yellowfin Tuna From the Main Hawaiian Islands
Lam, C., Tam, C., Kobayashi, D., & Lutcavage, M. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00138 · Cited by 16 · see citing works
“Sampling at each collection site/ area so far has remained small scale (n ≤ 11, Barth et al., 2017; n ≤ 24, Grewe et al., 2015; n ≤ 45, Pecoraro et al., 2018; n ≤ 80, Anderson et al., 2019), and the number of sites in the Pacific are limited (n= 2, Barth et al., 2017; n = 3, Grewe et al., 2015; n =…”
BackgroundInfluentialPhylogeny, biology, and evolution of acanthopterygian fish clades
Thacker, C., & Near, T. (2025) · Reviews in Fish Biology and Fisheries · doi:10.1007/s11160-025-09935-w · Cited by 14
“Scombriformes are pelagic and often found in schools, and some undertake long distance migrations across ocean basins (Miya et al. 2013), yielding high connectivity among populations (Thiesen et al. 2008; Barth et al. 2017).”
Li, J., Bian, C., Yi, Y., Yu, H., You, X.-X., & Shi, Q. (2021) · BMC Genomics · doi:10.1186/s12864-021-07816-7 · Cited by 13 · see citing works
Jatmiko, I., Zedta, R. R., Agustina, M., & Setyadji, B. (2019) · ILMU KELAUTAN: Indonesian Journal of Marine Sciences · doi:10.14710/ik.ijms.24.2.61-68 · Cited by 12 · see citing works
D'Urban-Jackson, J., Bruford, M., Székely, T., DaCosta, J., Sorenson, M., Russo, I., Maher, K., Cruz-López, M., Galindo-Espinosa, D., Palacios, E., et al. (2020) · Conservation Genetics · doi:10.1007/s10592-020-01256-8 · Cited by 12 · see citing works
“…produce data from hundreds or thousands rather than tens of loci distributed throughout the genome, and can reveal previously uncharacterised genetic structure (e.g. Ruegg et al. 2014; Saenz-Agudelo et al. 2015; Barth et al. 2017; Vendrami et al. 2017; Younger et al. 2017; Sadanandan et al. 2019).”
Artetxe-Arrate, I., Fraile, I., Farley, J. H., Darnaude, A. M., Clear, N., Dettman, D. L., Davies, C. R., Marsac, F., & Murua, H. (2021) · Oceans · doi:10.3390/oceans2030026 · Cited by 13 · see citing works
“There are no known spawning grounds of yellowfin tuna in this area, although the possibility of a separate population of yellowfin tuna in the Arabian Sea has been proposed [60,61].”
BackgroundMachado, A., Gomes-dos-Santos, A., Fonseca, M. M., da Fonseca, R. D., Veríssimo, A., Felício, M., Capela, R., Alves, N., Santos, M., Salvador-Caramelo, F., et al. (2021) · bioRxiv · doi:10.1101/2021.11.19.468211 · Cited by 10
Backenstose, N. J. C., MacGuigan, D. J., Osborne, C. A., Bernal, M. A., Thomas, E. K., Normandeau, É., Yule, D. L., Stott, W., Ackiss, A. S., Albert, V. A., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06503-z · Cited by 11 · see citing works
Coelho, J. F. R., Mendes, L. D. F., Dario, F. D., Carvalho, P., Dias, R., Lima, S. M. Q., Verba, J., & Pereira, R. J. (2024) · Proceedings of the Royal Society B · doi:10.1098/rspb.2023.2746 · Cited by 8 · see citing works
Whole genome sequencing of the fast-swimming Southern bluefin tuna (Thunnus maccoyii)
Zhao, X., Huang, Y., Bian, C., You, X.-X., Zhang, X.-H., Chen, J.-M., Wang, M., Hu, C., Xu, Y.-L., Xu, J.-M., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1020017 · Cited by 8 · see citing works
“, 2018) and yellowfin tuna (Barth et al., 2017) have been published.”
BackgroundMuñoz‐Abril, L., Torres, M., Valle, C. A., Rubianes-Landázuri, F., Galván-Magaña, F., Canty, S. W. J., Terán, M., Brandt, M., Chaves, J., & Grewe, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272713 · Cited by 8 · see citing works
“[8] estimated the demographic history of yellowfin tuna, detecting significant differentiation of Atlantic and Indo-Pacific yellowfin tuna populations and showing that the true values of effective size (Ne) are far below those estimated by Ely [13], which may suggest greater genetic differentiation within populations.”
BackgroundTuna fish identification using mtDNA markers
Amaral, C., Maciel, V. A., Goldenberg-Barbosa, R., Silva, D., Amorim, A., & Carvalho, E. (2017) · doi:10.1016/j.fsigss.2017.09.193 · Cited by 8
Akita, T. (2022) · bioRxiv · doi:10.1111/1755-0998.13682 · Cited by 7
Universal primer design for crustacean and bivalve-mollusc authenticity based on cytochrome-b gene
Dwiyitno, D., Hoffman, S., Parmentier, K., & Keer, C. V. (2021) · Biodiversitas Journal of Biological Diversity · doi:10.13057/biodiv/d230103 · Cited by 7
Haro-Bilbao, I., Riginos, C., Baldwin, J., Zischke, M., Tibbetts, I., & Thia, J. A. (2021) · Journal of Biogeography · doi:10.1111/jbi.14135 · Cited by 7 · see citing works
“…with long- standing isolation, intriguingly, genomic investigations of yellowfin tuna have detected ongoing connections with substantial migration into Atlantic Ocean populations over evolutionary time frames (Barth et al., 2017) and amongst present- day populations (Mullins et al., 2018).”
BackgroundInfluentialMendoza-Portillo, V., Galván-Tirado, C., Portnoy, D., Valenzuela-Quiñonez, F., Domínguez-Domínguez, O., Durand, J., Pérez-Urbiola, J. C., & León, F. G. G.-D. (2020) · Journal of Fish Biology · doi:10.1111/jfb.14450 · Cited by 7 · see citing works
Artetxe-Arrate, I., Fraile, I., Luque, P. L., Farley, J. H., Clear, N., Shahid, U., Razzaque, S. A., Ahusan, M., Vidot, A., Parker, D. S., et al. (2024) · Fisheries Research · doi:10.1016/j.fishres.2024.107241 · Cited by 7 · see citing works
Eighani, M., Cope, J., Raoufi, P., Naderi, R., & Bach, P. (2021) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsab114 · Cited by 6
Guo, L., Wang, S., Wei, J., Peng, R., Shen, Z.-Y., Zhou, L.-J., Kang, J., Rao, K., Yang, Y., Chen, Z.-N., et al. (2026) · Aquaculture · doi:10.1016/j.aquaculture.2025.743298 · Cited by 6 · see citing works
Genomic differentiation and interoceanic population structure of two large pelagic scombrid species
Weist, P., Kusche, H., Tørresen, O., Hermida, M., Lopes, E. P., Jentoft, S., & Hanel, R. (2024) · Global Ecology and Conservation · doi:10.1016/j.gecco.2024.e03117 · Cited by 5 · see citing works
Self-citationAnderson, G., Holland, E. A., & Rico, C. (2019) · Reviews in Fisheries Science & Aquaculture · doi:10.1080/23308249.2019.1633270 · Cited by 5 · see citing works
“The mitochondrial and nuclear genomes are distinctly different, in everything from overall length (Barth et al., 2017; Bayona-V asquez et al., 2018) to proportion of coding and noncoding regions (Bazin et al.”
BackgroundMethodLe, M.-H., Morgan, B., Lu, M., Moctezuma, V., Burgos, O., & Huang, J.-P. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13908 · Cited by 3 · see citing works
“Genomes of different species not only contain information about variations in the sequence and structure of important functional genes but can also help with inferring past demographic dynamics of different species/populations (Barth et al., 2017 ; Paez et al., 2022).”
BackgroundPember, B. M., Chaplin, J., Braccini, M., & Loneragan, N. (2023) · Fisheries Research · doi:10.1016/j.fishres.2023.106779 · Cited by 3
Surachat, K., Narkthewan, P., Thotsagotphairee, C., Wonglapsuwan, M., & Thongpradub, W. (2022) · Animals · doi:10.3390/ani12141769 · Cited by 3 · see citing works
“R. brachysoma 21 62 680.14 0.813 8.30 R. kanagurta 21 62 688.82 0.808 9.30 Roughly speaking, the genome sizes of R. brachysoma and R. kanagurta are 680–700 Mbp, and the genome of R. brachysoma is slightly smaller than that of R. kanagurta.”
BackgroundInfluentialExcavating ghost footprints and tangled trees from modern genomes
Jacobs, A., & Therkildsen, N. (2019) · Molecular Ecology · doi:10.1111/mec.15141 · Cited by 3 · see citing works
Srioktoviana, S. K., Zainuddin, M., Hidayat, R., Yuniar, A., Farhum, A., Safruddin, Mustapha, M. A., Abudarda, A. R. F., & Ridwan, M. (2026) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2026.104830 · Cited by 2 · see citing works
Chromosome-level genome assembly of Indo-Pacific king mackerel (Scomberomorus guttatus)
Gao, Y., Liu, S.-L., Gutang, Q., Li, C., Lin, X., Liang, B., Li, P., Lin, J.-Q., & Liu, W.-H. (2024) · Scientific Data · doi:10.1038/s41597-024-04110-5 · Cited by 2
Faulks, L., Kaushik, P., Taniguchi, S., Sekino, M., Nakamichi, R., Yamamoto, Y., Fujimori, H., Okamoto, C., Kodama, S., Daryani, A., et al. (2021) · bioRxiv · doi:10.1101/2021.11.18.468312 · Cited by 2
Mzingirwa, F., McKeown, N., Okemwa, G., Sauer, W., Halafo, J., Mshana, J., Kamau, J., & Shaw, P. (2025) · African Journal of Marine Science · doi:10.2989/1814232x.2024.2429515 · Cited by 1
“…either genetic homogeneity and high gene flow across large geographical areas (Mullins et al. 2018; Natasha et al. 2022) or revealing differentiation and stock structure in previously assumed single mixed populations (Barth et al. 2017; Mullins et al. 2018; Pecoraro et al. 2018; Vaux et al. 2021).”
Lelono, T. D., Bintoro, G., Setyanto, A., Sutjipto, D. O., Tumulyadi, A., Adhihapsari, W., Sari, W. K., Rihmi, M. K., Aliviyanti, D., Isdianto, A., et al. (2024) · Biodiversitas Journal of Biological Diversity · doi:10.13057/biodiv/d250905 · Cited by 1
“Thunnus albacares (Bonnaterre, 1788), found in the Atlantic, Indian, and Pacific Oceans, is commercially important but classified as near-threatened due to intense fishing (Barth et al. 2017; Artetxe-Arrate et al. 2021).”
Morphometric variations of Kawakawa (Euthynnus affinis Cantor, 1849) in Malaysian and Yemeni waters
Binashikhbubkr, K., Setyawan, A., & Naim, D. M. (2023) · Nusantara Bioscience · doi:10.13057/nusbiosci/n150214 · Cited by 1
“…of the Arabian Sea, it exhibits various features that can lead to differences in the fish body shape, including high productivity, food availability, salinity, biodiversity, a wide range of temperatures (Jawad 2021), geographical barriers, and discrete spawning time (Barth et al. 2017).”
BackgroundBinashikhbubkr, K., & Naim, D. M. (2023) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1139/1/012004 · Cited by 1 · see citing works
“In contrast, a previous genetic study reported that the Yellowfin tuna Thunnus albacares collected from the Arabian sea was distinct from others obtained from the Atlantic, Indo-Pacific regions, and the Bay of Bengal [33].”
ResultGoddard, B. K., Willis, C., & Tracey, S. (2026) · Fisheries Research · doi:10.1016/j.fishres.2026.107859
Regional variability in growth and biometrics of yellowfin tuna in Indian waters
Kar, C., & Purkayastha, A. (2026) · Academia Biology · doi:10.20935/acadbiol8495
Jenkins, S. F., & Rhode, C. (2026) · Marine Genomics · doi:10.1016/j.margen.2026.101265
Global genome-wide patterns of genetic diversity and population structure in Coryphaena hippurus.
Jaimes, P. D., Mendoza‐Portillo, V., Hinojosa-Alvarez, S., Magallón-Gayón, E., Chávez-Santoscoy, R., Hernandez-Perez, J., Mar-Silva, A. F., Ochoa-Zavala, M., Ortega-García, S., Lao, O., et al. (2026) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2026.108694
Genomic metrics should inform formal conservation threat assessments.
Jeon, J. Y., Mularo, A., Allen, N. M., Heenkenda, E., Bickham, J. W., Black, A., & DeWoody, J. (2026) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2026.06.015
Anakotta, A. R. F., Saputra, S., Fitri, A., Sabdaningsih, A., Akbar, N., Pertiwi, N., & Sari, F. (2026) · Ecological Genetics and Genomics · doi:10.1016/j.egg.2026.100493
Quaresma, M., Rezende, B. V., Silva-Almeida, J., Pereira, G., Lucio, H., Ventoso, B., Gonçalves, H., Nunes, M. L., & Roseiro, C. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1796270
Jaskiel, J. E., Aichelman, H. E., Anderson, G., Claassens, L., Fifer, J. E., Hernández, C. M., Huzar, A. K., Llopiz, J. K., Witting, J. W., Mullen, S. P., et al. (2026) · Molecular Ecology · doi:10.1111/mec.70335
“…based on our calculations of θ W and the equation N e = θ W /4 μ , using the estimated nuclear mutation rate for yellowfin of 7.3 × 10 − 9 (Barth et al. 2017) with approximate adjustments to this rate for skipjack and bigeye based on differences in life history traits like generation…”
Influentialal., M. M. A. E. (2022) · Egyptian Journal of Aquatic Biology and Fisheries · doi:10.21608/ejabf.2022.246467
“Julia et al. (2017) reported the recent developments in the field of genomics have provided new and powerful insights into population structure and dynamics that are essential for the conservation of biological diversity.”
Background
- Authors
- Barth, J. M. I., Damerau, M., Matschiner, M., Jentoft, S., & Hanel, R.
- Affiliations
- University of Oslo (NO)
- Johann Heinrich von Thünen-Institut (DE)
- Corresponding author
- Barth, Julia M.I.
- Hanel, Reinhold
- Venue
- Genome Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-04
- Volume
- 9
- Issue
- 4
- Pages
- 1084-1098
- ISSN
- 1759-6653 (ISSN-L)
- PMC
- PMC5408087
- OpenAlex
- W2605858675
- Cited by
- 62
- References
- 95
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 5 · 2023: 8 · 2022: 9 · 2021: 9 · 2020: 6 · 2019: 8 · 2018: 2 · 2017: 1
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome Biology and Evolution (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Norwegian Sequencing Centre
- Universitetet i Oslo
- Bundesministerium für Ernährung und Landwirtschaft
- Bundesamt für Landwirtschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
- Keywords
- yellowfin tuna, population structure, demographic history, genetic differentiation, demographic expansion, asymmetric gene flow, Atlantic populations, tuna conservation, whole genome sequencing, draft genome assembly
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
