A quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification
AuthorsMalvezzi, Alex J.
Murray, Christopher S.
Feldheim, Kevin A.
Gobler, Christopher J.
Chapman, Demian D.
KAUST DepartmentRed Sea Research Center (RSRC)
Online Publication Date2015-02-13
Print Publication Date2015-04
Permanent link to this recordhttp://hdl.handle.net/10754/346797
MetadataShow full item record
AbstractAssessing the potential of marine organisms to adapt genetically to increasing oceanic CO2 levels requires proxies such as heritability of fitness-related traits under ocean acidification (OA). We applied a quantitative genetic method to derive the first heritability estimate of survival under elevated CO2 conditions in a metazoan. Specifically, we reared offspring, selected from a wild coastal fish population (Atlantic silverside, Menidia menidia), at high CO2 conditions (~2300 μatm) from fertilization to 15 days posthatch, which significantly reduced survival compared to controls. Perished and surviving offspring were quantitatively sampled and genotyped along with their parents, using eight polymorphic microsatellite loci, to reconstruct a parent-offspring pedigree and estimate variance components. Genetically related individuals were phenotypically more similar (i.e., survived similarly long at elevated CO2 conditions) than unrelated individuals, which translated into a significantly nonzero heritability (0.20 ± 0.07). The contribution of maternal effects was surprisingly small (0.05 ± 0.04) and nonsignificant. Survival among replicates was positively correlated with genetic diversity, particularly with observed heterozygosity. We conclude that early life survival of M. menidia under high CO2 levels has a significant additive genetic component that could elicit an evolutionary response to OA, depending on the strength and direction of future selection.
CitationA quantitative genetic approach to assess the evolutionary potential of a coastal marine fish to ocean acidification 2015:n/a Evolutionary Applications
PubMed Central IDPMC4408146
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