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

机译:人口遗传学为重要的海洋生物监测物种dab(Limanda limanda L.)中的生物标志物流行提供了新见解。

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摘要

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.
机译:生物指示剂是指认为其生物学的某些可量化方面(即生物标记)对生态系统健康敏感的物种。然而,通常缺乏有关影响所使用生物标志物流行的时空变异的潜在遗传和环境驱动因素的信息。在这里,我们探讨了可能的变量对相对生物标志物时空流行的影响的相对作用,该物种是海洋污染物的生物指示物,Limanda limanda。首先,用16个微卫星评估了英国水域周围dab的时空遗传结构(在15个地点进行了39个采样,为期4年:2005-2008年)。确定了与北海和爱尔兰海相对应的两个时间上稳定的组(盆地之间的平均值= 0.007; = 0.022)。其次,我们检查了生物标志物患病率与几个变量之间的关联,其中包括遗传结构,年龄和污染物暴露。遗传结构与年龄和某些污染物一起,在考虑的某些生物标志物(即色素沉着过度和肝病)的流行中具有重要的相互作用。这些数据集的整合增强了我们对生物标志物流行,暴露于污染物和特定人群反应之间关系的理解,从而产生了反应更丰富的预测模型以及环境修复的前景。

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