Variation in reaction norms: statistical considerations and biological interpretation
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Analysis of reaction norms, the functions by which the phenotype produced by a given genotype depends on the environment, is critical to studying many aspects of phenotypic evolution. Different techniques are available for quantifying different aspects of reaction norm variation. We examine what biological inferences can be drawn from some of the more readily-applicable analyses for studying reaction norms. We adopt a strongly biologically-motivated view, but draw on statistical theory to highlight strengths and drawbacks of different techniques. In particular, consideration of some formal statistical theory leads to revision of some recently, and forcefully, advocated opinions on reaction norm analysis. We clarify what simple analysis of the slope between mean phenotype in two environments can tell us about reaction norms, explore the conditions under which polynomial regression can provide robust inferences about reaction norm shape, and explore how different existing approaches may be used to draw inferences about variation in reaction norm shape. We show how mixed model-based approaches can provide more robust inferences than more commonly-used multistep statistical approaches, and derive new metrics of the relative importance of variation in reaction norm intercepts, slopes, and curvatures.
Morrissey , M B & Liefting , M 2016 , ' Variation in reaction norms: statistical considerations and biological interpretation ' Evolution , vol 70 , no. 9 , pp. 1944-1959 . DOI: 10.1111/evo.13003
© 2016, The Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at onlinelibrary.wiley.com / https://dx.doi.org/10.1111/evo.13003
DescriptionM.B.M. is supported by a Royal Society (London) University Research Fellowship. M.L. is supported by the Netherlands Organisation for Scientific Research, VIDI grant nr. 864.03.003.
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