Molecular signatures of resource competition: Clonal interference favors ecological diversification and can lead to incipient speciation
Amicone, Massimo; Gordo, Isabel Mendes (2021), Molecular signatures of resource competition: Clonal interference favors ecological diversification and can lead to incipient speciation, Dryad, Dataset, https://doi.org/10.5061/dryad.15dv41nxt
Microbial ecosystems harbor an astonishing diversity that can persist for long times. To understand how such diversity is structured and maintained, ecological and evolutionary processes need to be integrated at similar timescales. Here, we study a model of resource competition that allows for evolution via de novo mutation, and focus on rapidly adapting asexual populations with large mutational inputs, as typical of many bacteria species. We characterize the adaptation and diversification of an initially maladapted population and show how the eco-evolutionary dynamics are shaped by the interaction between simultaneously emerging lineages – clonal interference. We find that in large populations, more intense clonal interference can foster diversification under sympatry, increasing the probability that phenotypically and genetically distinct clusters coexist. In smaller populations, the accumulation of deleterious and compensatory mutations can push further the diversification process and kick-start speciation. Our findings have implications beyond microbial populations, providing novel insights about the interplay between ecology and evolution in clonal populations.
The dataset has been produced via numerical simulations. These were performed on R with the script in "Amicone_EcoEvo_Code.R" following the model and assumptions described in the paper (Amicone & Gordo 2021).
The code parameters can be customized. Change the corresponding values in lines 9-30.
The simulation will then produce data files that can be further analysed with the script in "Amicone_EcoEvo_Analysis.R".
See the corresponding README.txt file for more info about the code and the dataset.
Fundação para a Ciência e a Tecnologia, Award: PD/BD/138735/2018