Original FASTQ files of: Global genetic diversity and historical demography of the Bull Shark
Authors/Creators
-
Postaire, Bautisse1
- Devloo-Delva, Floriaan2
- Brunnschweiler, Juerg M.3
- Charvet, Patricia4
- Chen, Xiao5
- Cliff, Geremy6
- Daly, Ryan7
- Drymon, Marcus J.8
- Espinoza, Mario9
- Fernando, Daniel10
- Glaus, Kerstin11
- Grant, Michael I.12
- Hernandez, Sebastian13
- Hyodo, Susumu14
- Jabado, Rima W.15
- Jaquemet, Sébastien1
- Johnson, Grant16
- Naylor, Gavin P.17
- Nevill, John E.G.18
- Pathirana, Buddhi M.10
- Pillans, Richard D.2
- Smoothey, Amy F.19
- Tachihara, Katsunori20
- Tillet, Bree J.21
- Valerio-Vargas, Jorge A.9
- Lesturgie, Pierre22
- Magalon, Hélène1
- Feutry, Pierre2
- Mona, Stefano23
- 1. University of La Réunion
- 2. CSIRO Environment*
- 3. Independent researcher*
- 4. Universidade Federal do Ceará
- 5. South China Agricultural University
- 6. University of KwaZulu-Natal
- 7. South African Institute for Aquatic Biodiversity
- 8. Mississippi State University
- 9. University of Costa Rica
- 10. Blue Resources Trust
- 11. University of the South Pacific
- 12. James Cook University
- 13. Universidad Veritas
- 14. University of Tokyo
- 15. Elasmo Project
- 16. Trade and Tourism, Aquatic Resource Research Unit*
- 17. University of Florida
- 18. Environment Seychelles*
- 19. Sydney Institute of Marine Science
- 20. University of the Ryukyus
- 21. University of Queensland
- 22. National Museum of Natural History
- 23. École Pratique des Hautes Études
Description
Aim
Biogeographic boundaries and genetic structuring have important effects on the inferences and interpretation of effective population size (Ne) temporal variations, a key genetics parameter. We reconstructed the historical demography and divergence history of a vulnerable coastal high-trophic shark using population genomics and assessed our ability to detect recent bottlenecks events.
Location
Western and Central Indo-Pacific (IPA), Western Tropical Atlantic (WTA), Eastern Tropical Pacific (EPA)
Taxon
Carcharhinus leucas (Müller & Henle, 1839)
Methods
A DArTcapTM approach was used to sequence 475 samples and assess global genetic structuring. Three demographic models were tested on each population, using an ABC-RF framework coupled with coalescent simulations, to investigate within-cluster structure. Divergence times between clusters were computed, testing multiple scenarios, with fastsimcoal. Ne temporal variations were reconstructed with STAIRWAYPLOT. Coalescent simulations were performed to determine the detectability of recent bottleneck under the estimated historical trend for datasets of this size.
Results
Three genetic clusters corresponding to the IPA, WTA and EPA regions were identified, agreeing with previous studies. The IPA presented the highest genetic diversity and was consistently identified as the oldest. No significant within-cluster structuring was detected. Ne increased globally, with an earlier onset in the IPA, during the last glacial period. Coalescent simulations showed that weak and recent bottlenecks could not be detected with our dataset, while old and/or strong bottlenecks would erase the observed ancestral expansion.
Main conclusions
This study further confirms the role of marine biogeographic breaks in shaping the genetic history of large mobile marine predator. Ne Historical increases of Ne are potentially linked to extended coastal habitat availability. The limited within-cluster population structuring suggests that Ne can be monitored over ocean basins. Due to insufficient amount of available genetic data, it cannot be concluded whether overfishing is impacting Bull Shark genetic diversity, calling for whole genome sequencing.
Notes
Funding provided by: Laboratoire d'Excellence Corail*
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Funding provided by: University of Tasmania
Crossref Funder Registry ID: https://ror.org/01nfmeh72
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Funding provided by: CSIRO Oceans and Atmosphere
Crossref Funder Registry ID: https://ror.org/026nh4520
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Funding provided by: Sea World Research and Rescue Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100009034
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Funding provided by: Ord River Research Offset*
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Funding provided by: Australian Government
Crossref Funder Registry ID: https://ror.org/0314h5y94
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Funding provided by: DEAL, La Reunion*
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Methods
Sample collection and DNA extraction
A subsample of the dataset of Devloo-Delva et al. (2023) was used for this study, representing 475 C. leucas sampled between 1985 and 2019 from 18 locations covering its distribution (except for West Africa; Supplementary Material 1). DNA was extracted with the Qiagen Blood and Tissue kit, following standard protocol (Qiagen Inc., Valencia, California, USA). After bait design and bioinformatic filtering (see following sections), the dataset comprised 16 sampling locations with at least five individuals (309 individuals; Fig. 1, Table 1) covering the WTA, IPA, and EPA. Sampling locations with mostly adults were preferentially selected to limit relatedness effects.
SNP selection for bait design
The approach used for bait design is described in Devloo-Delva et al. (2023). Briefly, a subset of 219 sample libraries were genotyped using the DArTseqTM approach (Cruz et al., 2013; Feutry et al., 2017, 2020, Supplementary material 1). From this dataset, 3,400 loci of 70 bp were randomly selected for DNA-capture bait development. The DArTcapTM enriched libraries were sequenced on a Illumina HiSeq 2500.