Population genetics provides new insights into biomarker prevalence in dab (Limanda limanda L.): a key marine biomonitoring species

Evolutionary Applications

Niklas Tysklind³  Martin I. Taylor³  Brett P. Lyons¹  Freya Goodsir²  Ian D. McCarthy⁴ 

[1]Weymouth Cefas Laboratory, Weymouth, UK

[2]Lowestoft Cefas Laboratory, Lowestoft, UK

[3]Molecular Ecology and Fisheries Genetics Laboratory, Environment Centre Wales, School of Biological Sciences, Bangor University, Gwynedd, UK

[4]School of Ocean Sciences, Bangor University, Menai Bridge, UK

关键词: biomonitoring;    disease biology;    ecotoxicology;    fish;    microsatellite;    population genetics;    random forest;    temporal genetic stability;   

DOI  :  10.1111/eva.12074

来源: Wiley

【 摘 要 】

Abstract

Bioindicators are species for which some quantifiable aspect of its biology, a biomarker, is assumed to be sensitive to ecosystem health. However, there is frequently a lack of information on the underlying genetic and environmental drivers shaping the spatiotemporal variance in prevalence of the biomarkers employed. Here, we explore the relative role of potential variables influencing the spatiotemporal prevalence of biomarkers in dab, Limanda limanda, a species used as a bioindicator of marine contaminants. Firstly, the spatiotemporal genetic structure of dab around UK waters (39 samples across 15 sites for four years: 2005–2008) is evaluated with 16 microsatellites. Two temporally stable groups are identified corresponding to the North and Irish Seas (average between basin GST ' = 0.007; GST ″ = 0.022). Secondly, we examine the association between biomarker prevalence and several variables, including genetic structuring, age and contaminant exposure. Genetic structure had significant interactive effects, together with age and some contaminants, in the prevalence of some of the biomarkers considered, namely hyperpigmentation and liver lesions. The integration of these data sets enhanced our understanding of the relationship between biomarker prevalence, exposure to contaminants and population-specific response, thereby yielding more informative predictive models of response and prospects for environmental remediation.

【 授权许可】

CC BY   
© 2013 The Authors. Published by Blackwell Publishing Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.