Linking gene expression patterns with survival studies elucidates adaptive potential in changing environments

• Published in: Molecular ecology Vol. 29; no. 6; pp. 1031 - 1034
• Main Author: Meek, Mariah H.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.03.2020
• Subjects: Adaptation; Atlantic salmon; Biological evolution; Cardiovascular system; Changing environments; Conservation biology; Ecology; Environmental changes; Evolutionary conservation; experimental transcriptomics; Gene expression; Genetic diversity; Phenotypic variations; Phenotyping; Salmo salar; Salmon; Survival; Thiamine; Vitamin B; Wildlife conservation; experimental transcriptomics; thiamine; adaptation; Atlantic salmon
• ISSN: 0962-1083; 1365-294X
• DOI: 10.1111/mec.15389

A major goal in ecology, evolution and conservation biology is understanding how species adapt to changing conditions and using that information to improve conservation actions. Primary to advancing our understanding of adaptation and adaptive potential is determining the causes and consequences of the genetic and phenotypic intraspecific variation that allows for adaptation. However, few studies have been able to link the variation present in molecular process under differing conditions to intraspecific variation in survival. In a From the Cover article in this issue of Molecular Ecology, Harder et al explore the relationship between molecular process and survival to understand the adaptive variation underlying tolerance to low thiamine (vitamin B1, an essential micronutrient) conditions in Atlantic salmon (Salmo salar). Thiamine is required for metabolic functioning, including energy production and nervous and cardiovascular system functioning. By combining controlled breeding and phenotyping with a survival study and transcriptomics, the authors are able to quantify among‐family differences in survival under low thiamine conditions. They find wide variation in survival among families, and this survival is linked to differences in gene expression patterns. Their study elucidates the importance of combining different data types to characterize within‐population phenotypic variation and understand how that variation may lead to genetic adaptation under stressful conditions.