Time for a revision? Maureen O’Malley and Russell Powell on Major Transitions, part 1

The so-called ‘Major Transitions’ framework is an attempt to explain the hierarchical structure of life on Earth: genes within chromosomes, chromosomes within cells, cells within cells (eukaryotic cells), individuals within sexual partnerships, cells within multicellular organisms, and organisms within societies. The best-known attempt to unify the origins of these relationships is a book by John Maynard Smith* and Eörs SzathmáryThe Major Transitions in Evolution.

MajorTransitionsCover

First published in 1995, the book focused on the origins of these hierarchical levels, connecting them with the unifying theme that

…entities that were capable of independent replication before the transition can replicate only as part of a larger whole after it.

For example, after a transition from unicellular to multicellular organisms (there were several), cellular reproduction either contributes to the growth of the organism or to production of new multicellular organisms.

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Evolution of eusociality

Last month, two papers on the evolution of eusociality were published in high-profile journals: one by Karen M. Kapheim and colleagues in Science, the other by Sandra M. Rehan and Amy L. Toth in Trends in Ecology & Evolution (TREE). Social and eusocial insects are an attractive system for studying major transitions, sharing some of the key features that make the volvocine algae so good for this purpose: multiple, independent origins of traits thought to be important to the transition and extant species with intermediate levels of sociality. These features make the social insects, like the volvocine algae, well-suited for comparative studies.
Figure 1 from Rehan & Toth: (A) Overview of phylogeny of aculeate Hymenoptera (with the nonhymenopteran but eusocial termites as an outgroup), highlighting independent origins of sociality (colored branches), groups with species ranging from solitary to primitively social (green), primitively social to advanced eusocial (orange), solitary to advanced eusocial (blue), and all species advanced eusocial (grey). (B) The full range of the solitary to eusocial spectrum (blue) and predictions of which genomic mechanisms are hypothesized to operate at different transitional stages of social evolution (broken arrows).

Figure 1 from Rehan & Toth: (A) Overview of phylogeny of aculeate Hymenoptera (with the nonhymenopteran but eusocial termites as an outgroup), highlighting independent origins of sociality (colored branches), groups with species ranging from solitary to primitively social (green), primitively social to advanced eusocial (orange), solitary to advanced eusocial (blue), and all species advanced eusocial (grey). (B) The full range of the solitary to eusocial spectrum (blue) and predictions of which genomic mechanisms are hypothesized to operate at different transitional stages of social evolution (broken arrows).

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Pierrick Bourrat responds

[I invited Pierrick Bourrat to respond to my two posts about his new paper and to comments to those posts. He kindly agreed, and he provided the following guest post, which I have edited only for formatting.]

First of all, I would like to thank Matthew Herron for his interest in my work and his invitation to respond to his posts. Also, I would like to thank Rick Michod and Deborah Shelton for their comments.

I will respond to several issues pointed out both in the posts and the comments.

About the usefulness of the export of fitness view of ETI: I agree that it is a useful way of thinking about it, as long as it is used as a heuristic. This means that I am not inclined to think that building models with the assumption that the fitness of a cell would have been 0 had it been in an environment with not social partners will be able to explain in some deep sense ETIs (and even more so the origin of fitness at some level). In his comment to Matthew’s first post, Rick Michod claims that I somehow confuse realized fitness from a more counterfactual notion of fitness.  Well, to be honest, I do not see how one could simulate (I do not mean ‘explain’) the evolution of a process if the variables in the model do not correspond to realized properties of the system. If I want to model a particular phenomenon, I ought to use variables and parameters that represent the target system and clearly, at least for me, this counterfactual notion of fitness does not represent any properties the cells have because they always have social partners. It is common to use expected rather than realized fitness in models, but this assumption is justified when we can assume that population are large and the environment is overall not fluctuating too much. With the counterfactual notion of fitness, aside from being useful for explaining the ETIs, I fail to see how it could be successfully integrated in models (by successfully, I mean how it could represent meaningfully the target system).

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