Open AccessOceanography and life history predict contrasting genetic population structure in two A ntarctic fish species
Author(s) -
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.
Publication year - 2015
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.12259
Subject(s) - biological dispersal , biology , seascape , ecology , genetic structure , population , isolation by distance , adaptation (eye) , local adaptation , population genetics , evolutionary biology , genetic variation , demography , habitat , biochemistry , neuroscience , sociology , gene
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, C hampsocephalus gunnari and N otothenia 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.