Tradeoff breaking as model of evolutionary transitions in individuality and the limits of the fitness-decoupling metaphor

Abstract
Data availability
Article and author information
Metrics

Abstract

Evolutionary transitions in individuality (ETIs) involve the formation of Darwinian collectives from Darwinian particles. The transition from cells to multicellular life is a prime example. During an ETI, collectives become units of selection in their own right. However, the underlying processes are poorly understood. One observation used to identify the completion of an ETI is an increase in collective-level performance accompanied by a decrease in particle-level performance, for example measured by growth rate. This seemingly counterintuitive dynamic has been referred to as 'fitness decoupling' and has been used to interpret both models and experimental data. Extending and unifying results from the literature, we show that fitness of particles and collectives can never decouple because calculations of particle and collective fitness performed over appropriate and equivalent time intervals are necessarily the same provided the population reaches a stable collective size distribution. By way of solution, we draw attention to the value of mechanistic approaches that emphasise traits, and tradeoffs among traits, as opposed to fitness. This trait-based approach is sufficient to capture dynamics that underpin evolutionary transitions. In addition, drawing upon both experimental and theoretical studies, we show that while early stages of transitions might often involve tradeoffs among particle traits, later—and critical-stages are likely to involve the rupture of such tradeoffs. Thus, when observed in the context of ETIs, tradeoff-breaking events stand as a useful marker for these transitions.

Data availability

The code implementing the models is publicly available on Zenodo (https://doi.org/10.5281/zenodo.5352208)For Figure 1: Protocol described and statistical analysis performed in Hammerschmidt et al. (2014). Dataset published as Rose et al. (2018). For Figure 6b: Data taken from Colon-Lopez et al (1997); Mohr et al (2013); Misra & Tuli (2000); Berman-Frank et al (2001); Popa et al. (2007) and standardised.For Figure 6c: Data taken from the dataset published as Rose et al. (2018).

The following previously published data sets were used

(2018) Meta-population structure and the evolutionary transition to multicellularity
Zenodo DOI: 10.1101/407163.

https://zenodo.org/record/3748416#.YUgyT7gzaUl

Article and author information

Author details

Pierrick Bourrat

Philosophy Department, Macquarie University, Sydney, Australia

For correspondence
p.bourrat@gmail.com

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0002-4465-6015
Guilhem Doulcier

Philosophy Department, Macquarie University, Sydney, Australia

For correspondence
guihem.doulcier@normalesup.org

Competing interests
No competing interests declared.
Caroline J Rose

Centre d'Écologie Fonctionnelle et Évolutive, CNRS, Montpellier, France

Competing interests
No competing interests declared.
Paul B Rainey

Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany

Competing interests
Paul B Rainey, Reviewing editor, eLife.

"This ORCID iD identifies the author of this article:" 0000-0003-0879-5795
Katrin Hammerschmidt

Institute of Microbiology, Kiel University, Kiel, Germany

For correspondence
katrinhammerschmidt@googlemail.com

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-0172-8995

Funding

John Templeton Foundation (62220)

Pierrick Bourrat
Guilhem Doulcier
Katrin Hammerschmidt

Max Planck Institute for Evolutionary Biology (open access funding)

Paul B Rainey

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.