Simulation code for: Clones on the run - the genomics of a recently expanded partially clonal species
Data files
May 05, 2023 version files 36.08 GB
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README.rtf
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simulationOutputs.zip
May 05, 2023 version files 36.08 GB
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README.md
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README.rtf
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simulationOutputs.zip
May 05, 2023 version files 36.08 GB
-
README.md
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simulationOutputs.zip
Abstract
Why species that in their core areas mainly reproduce sexually become enriched with clones in marginal populations ("geographic parthenogenesis") remains unclear. Earlier hypotheses have emphasised that selection might promote clonality because it protects locally adapted genotypes. On the other hand, it also hampers recombination and adaptation to changing conditions. The aim of the present study was to investigate the early stages of range expansion in a partially clonal species and what drives an increase in cloning during such expansion. We used genome-wide sequencing to investigate the origin and evolution of large clones formed in a macroalgal species (Fucus vesiculosus) during a recent expansion into the postglacial Baltic Sea. We found low but persistent clonality in core populations, while at range margins, large dominant clonal lineages had evolved repeatedly from different sexual populations. A range expansion model showed that even when asexual recruitment is less favourable than sexual recruitment in core populations, repeated bottlenecks at the expansion front can establish a genetically eroded clonal wave that spreads ahead of a sexual wave into the new area. Genetic variation decreased by drift following repeated bottlenecks at the expansion front. This resulted in the emerging clones having low expected heterozygosity, which corroborated our empirical observations. We conclude that Baker's Law (clones being favoured by uniparental reproductive assurance in new areas) can play an important role during range expansions in partially clonal species, resulting in a complex spatiotemporal mosaic of clonal and sexual lineages that might persist during thousands of generations.
README: Simulation code for: Clones on the run - the genomics of a recently expanded partially clonal species
https://doi.org/10.5061/dryad.qfttdz0mc
The simulations used in “Clones on the run - the genomics of a recently expanded partially clonal species” by R.T. Pereyra, M. Rafajlović, P. De Wit, M. Pinder, A. Kinnby, M. Töpel and K. Johannesson are written and executed in Matlab. This Matlab code (.m file) is named here ‘main_fixedMaxN.m’. The explanations of the notations used, and of the main steps in the model are explained in the comments in this .m file.
Description of the data and file structure
The raw simulation outputs for the model with both short- and long-range dispersal are contained in .mat files with names starting with ‘YesLong_’. Each filename further specifies parameter values for the corresponding simulation, i.e., MaxN, mean long-range dispersal distance (dist), death rate d, per-individual total dispersal probability (mT), probability that a migrant disperses by long-range dispersal (pL), and per-clone loss of sexual function (sLoss), with ‘p’ within numerals denoting ‘point’ so that ‘d0p1’ means that d=0.1 (and similarly for the other parameters). The explanations of the matrices and vectors in the files can be found in the comments in the main code (the above mentioned .m file).
The raw simulation results for the model with only short-range dispersal are contained in .mat files with names starting with ‘NoLong_’. The name format is the same as for ‘YesLong_’ files described above, except that here for each parameter set, the name also specifies the run counter (as for each parameter set, 200 independent runs were performed). This is specified at the end of the filename in the form ‘_runX’, with X ranging from 1 to 200. The explanations of the matrices and vectors in the files can be found in the comments in the main code (the above mentioned .m file).
Code/Software
The code is written in Matlab.
Methods
This dataset consists of a custom-made simulation code of the mathematical model employed in the study (written in Matlab; .m file), and the simulation outputs (.mat files). Matlab was used to run the simulations and analyze the simulation outputs.
Usage notes
Files can be accessed using MATLAB.