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Ecological scaffolding and the evolution of individuality

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Abstract

Evolutionary transitions in individuality are central to the emergence of biological complexity. Recent experiments provide glimpses of processes underpinning the transition from single cells to multicellular life and draw attention to the critical role of ecology. Here, we emphasize this ecological dimension and argue that its current absence from theoretical frameworks hampers development of general explanatory solutions. Using mechanistic mathematical models, we show how a minimal ecological structure comprising patchily distributed resources and between-patch dispersal can scaffold Darwinian-like properties on collectives of cells. This scaffolding causes cells to participate directly in the process of evolution by natural selection as if they were members of multicellular collectives, with collectives participating in a death–birth process arising from the interplay between the timing of dispersal events and the rate of resource use by cells. When this timescale is sufficiently long and new collectives are founded by single cells, collectives experience conditions that favour evolution of a reproductive division of labour. Together our simple model makes explicit key events in the major evolutionary transition to multicellularity. It also makes predictions concerning the life history of certain pathogens and serves as an ecological recipe for experimental realization of evolutionary transitions.

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Fig. 1: Scaffolding Darwinian properties.
Fig. 2: Within-patch dynamics.
Fig. 3: Effect of dispersal timescale on properties of cells and patches.
Fig. 4: Evolution of patch size under slow and fast regimes.
Fig. 5: Asynchronicity in dispersal time.
Fig. 6: Simulations of the S–G model.

Data availability

All data are available within the manuscript.

Code availability

Simulation codes for the models presented in this paper are available at the GitHub repository https://github.com/andxblack/eco-scaff-paper-code under an MIT licence.

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Acknowledgements

We thank J. Wietz for critical review of the manuscript and valuable comment. We thank S. De Monte, G. Doulcier, M. Diard and members of our respective teams for lively discussion. A.J.B. acknowledges an Australian Research Council (ARC) DECRA fellowship (grant no. DE160100690) and support from both the ARC Centre of Excellence for Mathematical and Statistical Frontiers (CoE ACEMS) and the Australian Government NHMRC Centre for Research Excellence in Policy Relevant Infectious diseases Simulation and Mathematical Modelling (CRE PRISM2). P.B. acknowledges a Macquarie University Research Fellowship and a grant from the John Templeton Foundation (grant no. ID 60811). P.B.R. acknowledges generous financial support from MPG core funding and previously from the Marsden Fund Council from New Zealand Government funding, administered by the Royal Society of New Zealand.

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P.B.R. and P.B. developed the main concepts. A.J.B. contributed the mathematical models and ran simulations to clarify initial ideas. All authors wrote and revised the manuscript.

Corresponding authors

Correspondence to Andrew J. Black or Paul B. Rainey.

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Extended data

Extended Data Fig. 1 Genealogy of patches under slow (A) and fast (B) dispersal regimes.

The simulations have only 10 patches and modified mutational parameters compared with those in Fig. 2 of the main text. This is to allow a clearer visualization of the process, which otherwise requires many more generations for change to be apparent. Video versions of these are also included in the supplementary material. As in Figs. 4 and 5 of the main text, the cell numbers in each patch are proportional to the area of the circles and the growth rates are indicated by the colours, as shown by the colour bar in Fig. 4 of the main text. The mutational parameters are larger for these simulations (µ=0.05, p = 0.05) so evolution occurs on a quicker timescale as compared with the results shown in Fig. 3 of the main text.

Extended Data Fig. 2 Simulations of the model with sterile types over 2000 generations show convergence to equilibrium.

The comparative slowness of this convergence, as well as the large fluctuations in the mean value of q for single realizations, can be attributed to the flatness of the fitness landscape about the equilibrium as shown in the Supplementary Information.

Extended Data Fig. 3 Change in the position of the equilibrium as a function of the dispersal assistance provided by S cells assistance ρ for slow dispersal.

Fitness landscapes showing the effect of changes in the assistance, ρ, given to dispersing G cells by non-dispersing S cells on the equilibrium (*) relationship between cell growth rate, β, and the rate of production of S cells, q, under the slow (T = 30) dispersal regime.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, text and references.

Reporting Summary

Supplementary Video 1

Single realization of the model under the slow dispersal regime as shown in Fig 4A.

Supplementary Video 2

Single realization of the model under the fast dispersal regime as shown in Fig 4B.

Supplementary Video 3

Evolutionary fate of 10 lineages under the slow dispersal regime as shown in Extended Data 1A.

Supplementary Video 4

Evolutionary fate of 10 lineages under the fast dispersal regime as shown in Extended Data 1B.

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Black, A.J., Bourrat, P. & Rainey, P.B. Ecological scaffolding and the evolution of individuality . Nat Ecol Evol 4, 426–436 (2020). https://doi.org/10.1038/s41559-019-1086-9

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