Open AccessPopulation genetics provides new insights into biomarker prevalence in dab ( L imanda limanda L .): a key marine biomonitoring species
Author(s) -
Tysklind Niklas,
Taylor Martin I.,
Lyons Brett P.,
Goodsir Freya,
McCarthy Ian D.,
Carvalho Gary R.
Publication year - 2013
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.12074
Subject(s) - biomonitoring , biology , bioindicator , biomarker , population , ecology , limanda , computational biology , genetics , evolutionary biology , zoology , environmental health , medicine , fishery , fish <actinopterygii> , flatfish
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, L imanda 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 N orth and I rish S eas (average between basinG ST' = 0.007;G ST″ = 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.