The genomic basis and environmental correlates of local adaptation in the Atlantic horse mackerel (

Understanding how populations adapt to their environment is increasingly important to prevent biodiversity loss due to overexploitation and climate change. Here we studied the population structure and genetic basis of local adaptation of Atlantic horse mackerel, a commercially and ecologically important marine fish that has one of the widest distributions in the eastern Atlantic. We analyzed whole-genome sequencing and environmental data of samples collected from the North Sea to North Africa and the western Mediterranean Sea. Our genomic approach indicated low population structure with a major split between the Mediterranean Sea and the Atlantic Ocean and between locations north and south of mid-Portugal. Populations from the North Sea are the most genetically distinct in the Atlantic. We discovered that most population structure patterns are driven by a few highly differentiated putatively adaptive loci. Seven loci discriminate the North Sea, two the Mediterranean Sea, and a large putative inversion (9.9 Mb) on chromosome 21 underlines the north-south divide and distinguishes North Africa. A genome-environment association analysis indicates that mean seawater temperature and temperature range, or factors correlated to them, are likely the main environmental drivers of local adaptation. Our genomic data broadly support the current stock divisions, but highlight areas of potential mixing, which require further investigation. Moreover, we demonstrate that as few as 17 highly informative SNPs can genetically discriminate the North Sea and North African samples from neighboring populations. Our study highlights the importance of both, life history and climate-related selective pressures in shaping population structure patterns in marine fish. It also supports that chromosomal rearrangements play a key role in local adaptation with gene flow. This study provides the basis for more accurate delineation of the horse mackerel stocks and paves the way for improving stock assessments.