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

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 bi...

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Bibliographic Details
Main Authors: Tysklind, Niklas, Taylor, Martin I., Lyons, Brett P., Goodsir, Freya, McCarthy, Ian D., Carvalho, Gary R.
Format: Article in Journal/Newspaper
Language:unknown
Published: 2013
Subjects:
Disease Biology
Ecotoxicology
Fisheries Management
Habitat Degradation
Population Genetics - Empirical
Wildlife Management
Ecological Genetics
Adaptation
North East Atlantic
Online Access:http://hdl.handle.net/10255/dryad.48734
https://doi.org/10.5061/dryad.j7c74
Description
Summary: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 G’ST =0.007; G’’ST=0.022). Secondly, we examine the association among 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 datasets 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.

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