Establishing the evolutionary compatibility of potential sources of colonizers for overfished stocks: a population genomics approach
Da Silva, AG and Appleyard, SA and Upston, J, Establishing the evolutionary compatibility of potential sources of colonizers for overfished stocks: a population genomics approach, Molecular Ecology, 24, (3) pp. 564-579. ISSN 0962-1083 (2015) [Refereed Article]
Identifying fish stock structure is fundamental to pinpoint stocks that might contribute colonizers to overfished stocks. However, a stock's potential to contribute to rebuilding hinges on demographic connectivity, a challenging parameter to measure. With genomics as a new tool, fisheries managers can detect signatures of natural selection and thus identify fishing areas likely to contribute evolutionarily compatible colonizers to an overfished area (i.e. colonizers that are not at a fitness disadvantage in the overfished area and able to reproduce at optimal rates). Identifying evolutionarily compatible stocks would help narrow the focus on establishing demographic connectivity where it matters. Here, we genotype 4723 SNPs in 616 orange roughy (Hoplostethus atlanticus) across five fishing areas off the Tasmanian coast in Australia. We ask whether these areas form a single genetic unit, and test for signatures of local adaptation. Results from amova, structure, discriminant analysis of principal components, bayesass and isolation by distance suggest that sampled locations are subjected to geneflow amounts that are above what is needed to establish ‘drift connectivity’. However, it remains unclear whether there is a single panmictic population or several highly connected populations. Most importantly, we did not find any evidence of local adaptation, suggesting that the examined orange roughy stocks are evolutionarily compatible. The data have helped test an assumption of the orange roughy management programme and to formulate hypotheses regarding stock demographic connectivity. Overall, our results demonstrate the potential of genomics to inform fisheries management, even when evidence for stock structure is sparse.
conservation, deep sea, fisheries management, orange roughy, population genomics