| Summary: | The orange roughy, Hoplostethus atlanticus, has been one of the main targeted species in deep-sea fisheries worldwide. It occurs at depths of 450 – 1800 m and is abundant off the coasts of New Zealand, Australia, Namibia, Chile, and in the Northeast Atlantic Ocean. Like many other deep-sea fishes, orange roughy is vulnerable to over exploitation because they grow slow reaching maturity at about 30 years and live for more than 100 years. Their fecundity is low, which means they have low productivity. The individuals form predictable and dense spawning aggregations close to seamounts, plateaus and canyons. The trawl fishery for orange roughy started in seamounts around New Zealand in the late 1970s and progressively expanded off the coast of other countries and to the high seas (out of any Economic Exclusive Zone). Most stocks have been fished down to or below 30% pre-exploitation levels; as a consequence, fisheries have been closed or catches largely reduced. Currently, the only large scale fisheries operate off New Zealand. For effective fisheries management it is essential to define real biological units or “stocks”. There has been considerable research into the levels of population differentiation of orange roughy using a range of techniques at different geographic scales to attempt to differentiated stocks. However, there is no consensus about the level of connectivity among populations. In the present study, I investigated the levels of population differentiation in orange roughy using two types of neutral molecular markers at a global and fine-scales. Both markers revealed high levels of genetic diversity which is likely related with historically large population sizes. The analyses of 546 cytochrome c oxidase subunit I (COI) sequences revealed a lack of global genetic differentiation among samples from New Zealand, Australia, Namibia, and Chile. However, low but significant differentiation was found between the Southern hemisphere sites and two Northeast Atlantic sites. Mismatch distribution and Bayesian ...
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