2022
DOI: 10.1038/s41598-022-19790-5
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Genetic and particle modelling approaches to assessing population connectivity in a deep sea lobster

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 Ne… Show more

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Cited by 6 publications
(2 citation statements)
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“…Genetic distance refers to the degree of gene (genomic) differences within a population or species [40]- [42]. Meanwhile, genetic diversity is important for population stability, adaptability, and resilience [43], [44]. For example, it can prevent individual fitness loss caused by inbreeding, which can lead to extinction due to uniformity.…”
Section: B Discussionmentioning
confidence: 99%
“…Genetic distance refers to the degree of gene (genomic) differences within a population or species [40]- [42]. Meanwhile, genetic diversity is important for population stability, adaptability, and resilience [43], [44]. For example, it can prevent individual fitness loss caused by inbreeding, which can lead to extinction due to uniformity.…”
Section: B Discussionmentioning
confidence: 99%
“…The connectivity patterns predicted by simulated models of larval dispersal can also be analysed together with empirical methods such as genetic connectivity (e.g. Coleman et al 2013, Liu et al 2020, van der Reis et al 2022. This multidisciplinary approach represents a branch of the growing field of seascape genetics (Jahnke & Jonsson 2022), which studies the relationship between environmental variables (in this case, ocean circulation) and patterns of genetic variation among populations (Selkoe et al 2016).…”
Section: Biophysical Particle Tracking Modelsmentioning
confidence: 99%