The assessment of the genetic structuring of biodiversity is crucial for management and conservation. This is particularly critical for widely distributed and highly mobile deep-water teleosts, such as hoki (Macruronus novaezelandiae). This species is significant to Māori people and supports the largest commercial fishery in New Zealand, but uncertainty about its stock structure presents a challenge for management. Here, we apply a comprehensive genomic analysis to shed light on the demographic structure of this species by (1) assembling the genome, (2) generating a catalogue of genome-wide SNPs to infer the stock structure and (3) identifying regions of the genome under selection. The final genome assembly used short and long reads and is near complete, representing 93.8% of BUSCO genes, and consisting of 566 contigs totalling 501 Mb. Whole-genome re-sequencing of 510 hoki sampled from 14 locations around New Zealand and Australia, at a read depth greater than 10×, produced 227,490 filtered SNPs. Analyses of these SNPs were able to resolve the stock structure of hoki into two genetically and geographically distinct clusters, one including the Australian and the other one all New Zealand locations, indicating genetic exchange between these regions is limited. Location differences within New Zealand samples were much more subtle (global F _ST = 0.0006), and while small and significant differences could be detected, they did not conclusively identify additional substructures. Ten putative adaptive SNPs were detected within the New Zealand samples, but these also did not geographically partition the dataset further. Contemporary and historical N _e estimation suggest the current New Zealand population of hoki is large yet declining. Overall, our study provides the first genomic resources for hoki and provides detailed insights into the fine-scale population genetic structure to inform the management of this species.

#	Concept	H-Index	Publications	Citations
1	Health Sciences	1	1	35
2	Medicine	1	1	35
3	Cell Biology	1	1	35
4	Cancer Cell Biology	1	1	35
5	Drug Resistance	1	1	35

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