Oxygen concentration drives local adaptation in the greenlip abalone (Haliotis laevigata)

Understanding adaptation has become one of the major biological questions especially in the light of rapid environmental changes induced by climate change. Ocean temperatures are rising which triggers massive changes in water chemistry and thereby alters the living environment of all marine organisms. Studying adaptation, however, can be tricky because spatial genetic patterns might also occur due to random effects, for example, genetic drift. Genetic drift is reduced in very large and well-connected populations, such as in broadcast marine spawning organisms. Here, spatial genetic divergence is likely to be produced by selection. In this issue of Molecular Ecology, Sandoval-Castillo et al. (2018) investigated patterns of spatial genetic divergence and their association with environmental factors in the greenlip abalone (Haliotis laevigata). This commercially important species of mollusc is a broadcast spawner with large population sizes, rendering genetic drift an unlikely factor in the genetic divergence of wild populations. Sandoval-Castillo et al. (2018) used a ddRAD genomic approach to test for genetic divergence between sampled populations while also measuring different environmental factors, for example, water temperature and oxygen content. The majority of identified SNPs was putatively neutral and showed only low levels of genetic divergence between field sites. However, 323 candidate adaptive markers were identified that clearly separated the individuals into five different clusters. These genetic clusters correlated with environmental clusters mainly determined by water temperature and (correlated) oxygen concentration. Gene annotation of the candidate SNPs revealed a large proportion of loci being involved in biological processes influenced by oxygen availability. The study by Sandoval-Castillo et al. (2018) in this issue of Molecular Ecology exemplifies the benefits of combining genomic studies with ecological data. It is a great starting point for more detailed (gene function, physiology) as well as broader (biodiversity) investigations that might help us to better understand adaptation and predict ecosystems' resilience and resistance to environmental disturbances. In addition, this information can be applied to implement optimal conservation regime policies and sustainable harvesting strategies, hopefully protecting biodiversity as well as commercial interests in marine life.