Experimental Evolution, Ecology and Behaviour (EXEB)

Requirements for the evolution of evolvability

2017-05-26T11:36:00.001-07:00

We have aready discussed how selection can bias variation to be more advantageous, but what are the the requirements for this to happen?
This paper from our colleagues at Southampton models gene regulatory network evolution to address which factors allow the generation of phenotypic variation "tailored" to environmental variation.

10:00 in Darwin's, with fika.

Paper available here

Abstract

One of the most intriguing questions in evolution is how organisms exhibit suitable pheno- typic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour develop- mental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regulari- ties, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the sub- sequent failure to generalise.Wesupport the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that exist- ing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well- developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

On the non-random effects of random mutation

2017-05-19T05:05:00.000-07:00

The idea that evolution produces (developmental) genetic architectures that make the effects of mutations biased towards particular phenotypes have come up a few times in recent discussions. Next week we will look a little closer at one of the models that have been used to explore the how and why of this problem.

10.00 in Darwin. Fika and entertainment provided.

You can find the paper here

Epistasis and natural selection shape the mutational architecture of complex traits

Adam G Jones, Reinhard Burger & Stevan Arnold

The evolutionary trajectories of complex traits are constrained by levels of genetic variation as well as genetic correlations among traits. As the ultimate source of all genetic variation is mutation, the distribution of mutations entering populations profoundly affects standing variation and genetic correlations. Here we use an individual-based simulation model to investigate how natural selection and gene interactions (that is, epistasis) shape the evolution of mutational processes affecting complex traits. We find that the presence of epistasis allows natural selection to mould the distribution of mutations, such that mutational effects align with the selection surface. Consequently, novel mutations tend to be more compatible with the current forces of selection acting on the population. These results suggest that in many cases mutational effects should be seen as an outcome of natural selection rather than as an unbiased source of genetic variation that is independent of other evolutionary processes.

Sex difference in lifespan and sexual conflict

2017-05-11T02:00:00.001-07:00

For the next week's lab meeting, I would like to use this Drosophila paper as a case, to discuss the evolution of sexual dimorphism in lifespan.

Hope to hear your thoughts over fika!

Photo by Qinyang Li

Manipulation of feeding regime alters sexual dimorphism for lifespan and reduces sexual conflict in Drosophila melanogaster

24.04.2017, 15:58 byElizabeth M. L. DuxburyWayne G. RostantTracey Chapman

Sexual dimorphism for lifespan (SDL) is widespread, but poorly understood. A leading hypothesis, which we test here, is that strong SDL can reduce sexual conflict, by allowing each sex to maximize its sex-specific fitness. We used replicated experimental evolution lines of the fruit fly, Drosophila melanogaster, which had been maintained for over 360 generations on either unpredictable ‘Random’ or predictable ‘Regular’ feeding regimes. This evolutionary manipulation of feeding regime led to robust, enhanced SDL in Random over control, Regular lines. Enhanced SDL was associated with a significant increase in the fitness of focal males, tested with wild-type (WT) females. This was due to sex-specific changes to male life history, manifested as increased early reproductive output and reduced survival. In contrast, focal female fitness, tested with WT males, did not differ across regimes. Hence increased SDL was associated with a reduction in sexual conflict, which increased male fitness and maintained fitness in females. Differences in SDL were not associated with developmental time or developmental survival. Overall, the results showed that the expression of enhanced SDL, resulting from experimental evolution of feeding regimes, was associated with male-specific changes in life history, leading to increased fitness and reduced sexual conflict.