Evolutionary responses to climate change and contaminants: Evidence and experimental approaches
Abstract A fundamental objective within ecotoxicology lies in understanding and predicting effects of contaminants. This objective is made more challenging when global climate change is considered as an environmental stress that co-occurs with contaminant exposure. In this multi-stressor context, ev...
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Published in | Current zoology Vol. 61; no. 4; pp. 690 - 701 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
01.08.2015
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Online Access | Get full text |
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Summary: | Abstract
A fundamental objective within ecotoxicology lies in understanding and predicting effects of contaminants. This objective is made more challenging when global climate change is considered as an environmental stress that co-occurs with contaminant exposure. In this multi-stressor context, evolutionary processes are particularly important. In this paper, we consider several non-”omic” approaches wherein evolutionary responses to stress have been studied and discuss those amenable to a multiple stressor context. Specifically, we discuss common-garden designs, artificial and quasi-natural selection, and the estimation of adaptive potential using quantitative genetics as methods for studying evolutionary responses to contaminants and climate change in the absence of expensive molecular tools. While all approaches shed light on potential evolutionary impacts of stressor exposure, they also have limitations. These include logistical constraints, difficulty extrapolating to real systems, and responses tied strongly to specific taxa, populations, and/or testing conditions. The most effective way to lessen these inherent limitations is likely through inclusion of complementary physiological and molecular tools, when available. We believe that an evolutionary context to the study of contaminants and global climate change is a high priority in ecotoxicology and we outline methods that can be implemented by almost any researcher but will also provide valuable insights. |
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ISSN: | 1674-5507 2396-9814 |
DOI: | 10.1093/czoolo/61.4.690 |