Population genetics provides new insights into biomarker prevalence in dab (Limanda limanda L.): a key marine biomonitoring species
Version of Record online: 23 MAY 2013
© 2013 The Authors. Published by Blackwell Publishing Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Volume 6, Issue 6, pages 891–909, September 2013
How to Cite
Tysklind, N., Taylor, M. I., Lyons, B. P., Goodsir, F., McCarthy, I. D. and Carvalho, G. R. (2013), Population genetics provides new insights into biomarker prevalence in dab (Limanda limanda L.): a key marine biomonitoring species. Evolutionary Applications, 6: 891–909. doi: 10.1111/eva.12074
- Issue online: 27 AUG 2013
- Version of Record online: 23 MAY 2013
- Manuscript Accepted: 3 APR 2013
- Manuscript Received: 30 MAR 2013
- Cefas-Bangor University joint studentship
- Department for Environment Food and Rural Affairs (Defra). Grant Number: ME3206
- disease biology;
- population genetics;
- random forest;
- temporal genetic stability
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 = 0.007; = 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.