You have full text access to this OnlineOpen article
Evolutionary genetics of ageing in the wild: empirical patterns and future perspectives
Article first published online: 16 MAY 2008
DOI: 10.1111/j.1365-2435.2008.01412.x
© 2008 The Authors. Journal compilation © 2008 British Ecological Society
Additional Information
How to Cite
Wilson, A. J., Charmantier, A. and Hadfield, J. D. (2008), Evolutionary genetics of ageing in the wild: empirical patterns and future perspectives. Functional Ecology, 22: 431–442. doi: 10.1111/j.1365-2435.2008.01412.x
Publication History
- Issue published online: 16 MAY 2008
- Article first published online: 16 MAY 2008
- Received 7 November 2007; accepted 19 March 2008; Handling Editor: Daniel Nussey
- Abstract
- Article
- References
- Cited By
Keywords:
- ageing;
- senescence;
- quantitative genetics
Summary
- 1Classical evolutionary theory states that senescence should arise as a consequence of the declining force of selection late in life. Although the quantitative genetic predictions of hypotheses derived from this theory have been extensively tested in laboratory studies of invertebrate systems, relatively little is known about the genetics of ageing in the wild.
- 2Data from long-term ecological studies is increasingly allowing quantitative genetic approaches to be used in studies of senescence in free-living populations of vertebrates. We review work to date and argue that the patterns are broadly consistent with theoretical predictions, although there is also a clear need for more empirical work.
- 3We argue that further advances in this field of research might be facilitated by increased use of reaction norm models, and a decreased emphasis on attempting to discriminate between mutation accumulation and antagonistic pleiotropy models of senescence. We also suggest a framework for the better integration of environmental and genetic effects on ageing.
- 4Finally, we discuss some of the difficulties in applying quantitative genetic models to studies of senescence outside the laboratory. In particular we highlight the problems that viability selection can cause for an accurate estimation of parameters used in the prediction of age-trajectory evolution.

1365-2435/asset/olbannerleft.gif?v=1&s=c8b848a8f001fdfa90240fe2ab26b1f04b6fe8e4)
1365-2435/asset/olbannerright.gif?v=1&s=2cf6e00d281371851f86902da3937ac5884bcfe0)
