Environmental features can alter the behaviours and phenotypes of organisms evolving within them influencing the dynamics of natural and sexual selection. Experimental environmental manipulation, particularly when conducted in experiments where the dynamics of the purging of deleterious alleles are compared, has demonstrated both direct and indirect effects on the strength and direction of selection. However, many of these experiments are conducted with fairly simplistic environments when it is not always clear how or why particular forms of spatial heterogeneity influence behaviour or selection. Using Drosophila melanogaster, we tested three different spatial environments designed to determine if spatial constraint of critical resources influences the efficiency of natural and sexual selection. We conducted two allele purging experiments to 1) assess effects of these spatial treatments on selective dynamics of six recessive mutations, and 2) determine how these dynamics changed when sexual selection was relaxed and spatial area reduced for 2 of the mutants. Allele purging dynamics depended on spatial environment, however the patterns of purging rates between the environments differed across distinct deleterious mutations. We also tested two of the mutant alleles, and demonstrate sexual selection increased the purging rate.

A detailed description is provided in the paper. In brief, this data represents the counts of individuals (by sex and genotype) from a series of deleterious allele purging experiments (short-term experimental evolution) in Drosophila melanogaster. Details of the rearing conditions within and between populations, source populations and introgression of deleterious alleles, and experimental crosses are all described in detail in the manuscript.

We provide a short readme detailing variable descriptions and with the R scripts to replicate analyses.

Information is provided in the readme file.

We also have the data and scripts available on github (https://github.com/idworkin/Wilson2021_Evolution_Data)

The few columns with missing data either are empty, or have NA.