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Genetic and particle modelling approaches to assessing population connectivity in a deep sea lobster
- Source :
- Scientific Reports, Vol 12, Iss 1, Pp 1-16 (2022)
- Publication Year :
- 2022
- Publisher :
- Nature Portfolio, 2022.
-
Abstract
- Abstract The emergence of high resolution population genetic techniques, such as genotyping-by-sequencing (GBS), in combination with recent advances in particle modelling of larval dispersal in marine organisms, can deliver powerful new insights to support fisheries conservation and management. In this study, we used this combination to investigate the population connectivity of a commercial deep sea lobster species, the New Zealand scampi, Metanephrops challengeri, which ranges across a vast area of seafloor around New Zealand. This species has limited dispersal capabilities, including larvae with weak swimming abilities and short pelagic duration, while the reptant juvenile/adult stages of the lifecycle are obligate burrow dwellers with limited home ranges. Ninety-one individuals, collected from five scampi fishery management areas around New Zealand, were genotyped using GBS. Using 983 haplotypic genomic loci, three genetically distinct groups were identified: eastern, southern and western. These groups showed significant genetic differentiation with clear source-sink dynamics. The direction of gene flow inferred from the genomic data largely reflected the hydrodynamic particle modelling of ocean current flow around New Zealand. The modelled dispersal during pelagic larval phase highlights the strong connectivity among eastern sampling locations and explains the low genetic differentiation detected among these sampled areas. Our results highlight the value of using a transdisciplinary approach in the inference of connectivity among populations for informing conservation and fishery management.
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 12
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0a8ee8cf25f4baaa99a3a332ad39610
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41598-022-19790-5