Selection on Sporulation Strategies in a Metapopulation Can Lead to Coexistence
Data files
Nov 22, 2024 version files 10.68 KB
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pip-fitness.csv
8.79 KB
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README.md
1.89 KB
Abstract
In constant environments the coexistence of similar species or genotypes is generally limited. In a metapopulation context, however, types that utilize the same resource but are distributed along a competition-colonization trade-off, can coexist. Much thought in this area focuses on a generic trade-off between within-deme competitive ability and between-deme dispersal ability. We point out that the sporulation program in yeasts and other microbes can create a natural trade-off such that strains which initiate sporulation at higher rates suffer in terms of within-deme competition but benefit in terms of between deme dispersal. We develop metapopulation models where the within-deme behavior follows chemostat dynamics. We first show that the rate of sporulation determines the colonization ability of the strain, with colonization ability increasing with sporulation rate up to a point. Metapopulation stability of a single strain exists in a defined range of sporulation rates. We then use pairwise invasability plots to show that coexistence of strains with different sporulation rates generally occurs, but that the set of sporulation rates that can potentially coexist is smaller than the set that allows for stable metapopulations. We extend our pairwise results to show how a continuous set of strains can coexist and verify our conclusions with numerical calculations and stochastic simulations. Our results show that stable variation in sporulation rates is expected under a wide range of ecological conditions.
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https://doi.org/10.5061/dryad.18931zd6m
Description of the data and file structure
Numerical calculations were performed using Wolfram Mathematica.
Stochastic simulations were performed in both Wolfram Mathematica and using the Rust programing language.
Files and variables
- pip-fitness.csv is a comma separated value file that includes output from the Rust code. It has 3 columns, rho1, rho2, and value. Rho1 is the resident sporulation rate (cells/unit time), rho2 is the mutant sporulation rate (cells/unit time), and value is the simulated result for the growth factor of the mutant population. Values greater than 1 indicate that the mutant increases numerically over one generation when starting from a single mutant patch, on average. Values less than 1 indicate that fewer than 1 patch, on average, is present at the next timestep.
Code/software
Files are available on Zenodo: https://doi.org/10.5281/zenodo.14042760
- FigureCodeAndSupplementaryCalculations.nb is the main Mathematica notebook. This contains code to calculate within patch equilibrium values and to determine invasion of new/mutant sporulation strategies. Data from the Rust program is loaded in this notebook and used to make figures that combine Rust output and Mathematica calculations.
- main.rs is the main Rust code file. This includes code to run the simulations and export data to a .csv file
- Cargo.toml is an ancillary Rust file that is necessary to run the code.
The Mathematica code requires users to have and install Wolfram Mathematica.
The Rust code requires users to have a working Rust environment.
Access information
Other publicly accessible locations of the data:
- N/A
Data was derived from the following sources:
- N/A
Data for this paper consist of numerical and stochastic simulations performed using Mathematica and the Rust programming language. Some data files were stored as .csv files to transfer between simulation platforms.