Quantitative genetics approaches to study evolutionary processes in ecotoxicology; a perspective from research on the evolution of resistance View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-05

AUTHORS

Paul L. Klerks, Lingtian Xie, Jeffrey S. Levinton

ABSTRACT

Quantitative genetic approaches are often used to study evolutionary processes in ecotoxicology. This paper focuses on the evolution of resistance to environmental contaminants-an important evolutionary process in ecotoxicology. Three approaches are commonly employed to study the evolution of resistance: (1) Assessing whether a contaminant-exposed population has an increased resistance relative to a control population, using either spatial or temporal comparisons. (2) Estimating a population's heritability of resistance. (3) Investigating responses in a laboratory selection experiment. All three approaches provide valuable information on the potential for contaminants to affect a population's evolutionary trajectory via natural selection. However, all three approaches have inherent limitations, including difficulty in separating the various genetic and environmental variance components, responses being dependent on specific population and testing conditions, and inability to fully capture natural conditions in the laboratory. In order to maximize insights into the long-term consequences of adaptation, it is important to not just look at resistance itself, but also at the fitness consequences and at correlated responses in characteristics other than resistance. The rapid development of molecular genetics has yielded alternatives to the "black box" approach of quantitative genetics, but the presence of different limitations and strengths in the two fields means that they should be viewed as complementary rather than exchangeable. Quantitative genetics is benefiting from the incorporation of molecular tools and remains an important field for studying evolutionary toxicology. More... »

PAGES

513-523

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10646-011-0640-2

DOI

http://dx.doi.org/10.1007/s10646-011-0640-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007056035

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21516382


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