Oceanography and life history predict contrasting genetic population structure in two Antarctic fish species

• Published in: Evolutionary Applications
• Main Authors: Young, Emma F., Belchier, Mark, Hauser, Lorenz, Horsburgh, Gavin J., Meredith, Michael P., Murphy, Eugene J., Pascoal, Sonia, Rock, Jennifer, Tysklind, Niklas, Carvalho, Gary R.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2015
• Subjects: Antarctic; Antarc*; Notothenia rossii
• Online Access: http://nora.nerc.ac.uk/id/eprint/508391/; https://nora.nerc.ac.uk/id/eprint/508391/1/Young_et_al-2015-Evolutionary_Applications.pdf

Understanding the key drivers of population connectivity in the marine environment is essential for the effective management of natural resources. Although several different approaches to evaluating connectivity have been used, they are rarely integrated quantitatively. Here, we use a ‘seascape genetics’ approach, by combining oceanographic modelling and microsatellite analyses, to understand the dominant influences on the population genetic structure of two Antarctic fishes with contrasting life histories, Champsocephalus gunnari and Notothenia rossii. The close accord between the model projections and empirical genetic structure demonstrated that passive dispersal during the planktonic early life stages is the dominant influence on patterns and extent of genetic structuring in both species. The shorter planktonic phase of C. gunnari restricts direct transport of larvae between distant populations, leading to stronger regional differentiation. By contrast, geographic distance did not affect differentiation in N. rossii, whose longer larval period promotes long-distance dispersal. Interannual variability in oceanographic flows strongly influenced the projected genetic structure, suggesting that shifts in circulation patterns due to climate change are likely to impact future genetic connectivity and opportunities for local adaptation, resilience and recovery from perturbations. Further development of realistic climate models is required to fully assess such potential impacts.