Genetic and particle modelling approaches to assessing population connectivity in a deep sea lobster.

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.

Combining genotypic and phenotypic variation in a geospatial framework to identify sources of mussels in northern New Zealand.

The New Zealand green-lipped mussel aquaculture industry is largely dependent on the supply of young mussels that wash up on Ninety Mile Beach (so-called Kaitaia spat), which are collected and trucked to aquaculture farms. The locations of source populations of Kaitaia spat are unknown and this lack of knowledge represents a major problem because spat supply may be irregular. We combined genotypic (microsatellite) and phenotypic (shell geochemistry) data in a geospatial framework to determine if this new approach can help identify source populations of mussels collected from two spat-collecting and four non-spat-collecting sites further south. Genetic analyses resolved differentiated clusters (mostly three clusters), but no obvious source populations. Shell geochemistry analyses resolved six differentiated clusters, as did the combined genotypic and phenotypic data. Analyses revealed high levels of spatial and temporal variability in the geochemistry signal. Whilst we have not been able to identify the source site(s) of Kaitaia spat our analyses indicate that geospatial testing using combined genotypic and phenotypic data is a powerful approach. Next steps should employ analyses of single nucleotide polymorphism markers with shell geochemistry and in conjunction with high resolution physical oceanographic modelling to resolve the longstanding question of the origin of Kaitaia spat.

Comparative influence of genetics, ontogeny and the environment on elemental fingerprints in the shell of Perna canaliculus.

The trace elemental composition of biogenic calcium carbonate (CaCO3) structures is thought to reflect environmental conditions at their time of formation. As CaCO3 structures such as shell are deposited incrementally, sequential analysis of these structures allows reconstructions of animal movements. However, variation driven by genetics or ontogeny may interact with the environment to influence CaCO3 composition. This study examined how genetics, ontogeny, and the environment influence shell composition of the bivalve Perna canaliculus. We cultured genetically distinct families at two sites in situ and in the laboratory. Analyses were performed on shell formed immediately prior to harvest on all animals as well as on shell formed early in life only on animals grown in the laboratory. Discriminant analysis using 8 elements (Co, Ti, Li, Sr, Mn, Ba, Mg, Pb, Ci, Ni) classified 80% of individuals grown in situ to their family and 92% to growth site. Generalised linear models showed genetics influenced all elements, and ontogeny affected seven of eight elements. This demonstrates that although genetics and ontogeny influence shell composition, environmental factors dominate. The location at which shell material formed can be identified if environmental differences exist. Where no environmental differences exist, genetically isolated populations can still be identified.