DOI: 10.5061/dryad.g68d124
title: Data from: Parallel genetic evolution and speciation from standing variation

description: Adaptation often proceeds from standing variation, and natural selection
 acting on pairs of populations is a quantitative continuum ranging from
 parallel to divergent. Yet, it is unclear how the extent of parallel
 genetic evolution during adaptation from standing variation is affected by
 the difference in the direction of selection between populations. Nor is
 it clear whether the availability of standing variation for adaptation
 affects progress toward speciation in a manner that depends on the
 difference in the direction of selection. We conducted a theoretical study
 investigating these questions and have two primary findings. First, the
 extent of parallel genetic evolution between two populations rapidly
 declines as selection changes from fully parallel toward divergent, and
 this decline is steeper in organisms with more traits (i.e., greater
 dimensionality). This rapid decline happens because small differences in
 the direction of selection greatly reduce the fraction of alleles that are
 beneficial in both populations. For example, populations adapting to
 optima separated by an angle of 33° might have only 50% of potentially
 beneficial alleles in common. Second, relative to when adaptation is from
 only new mutation, adaptation from standing variation improves hybrid
 fitness under parallel selection and reduces hybrid fitness under
 divergent selection. Under parallel selection, genetic parallelism from
 standing variation reduces the phenotypic segregation variance in hybrids,
 thereby increasing mean fitness in the parental environment. Under
 divergent selection, larger pleiotropic effects of alleles fixed from
 standing variation lead to maladaptive transgressive phenotypes when
 combined in hybrids. Adaptation from standing genetic variation therefore
 slows progress toward speciation under parallel selection and facilitates
 progress toward speciation under divergent selection.
description: Simulation python
 script This is the simulation
 script used to generate results plotted in the main text. It was run using
 Python v3.6.4. MainTextSims.py
 
 Mathematica
 notebooks (zip) This is a zip folder
 containing Wolfram Mathematica Notebooks used to derive the analytical
 results. notebooks.zip
 
 Simulated data
 and R script This ZIP folder contains
 an RProject file, R script, and simulated data. The script grabs data from
 the 'data' folder to make the plots used in the
 paper. Analysis_and_figures_Rproject.zip
description: # Data from: Parallel genetic evolution and speciation from standing
 variation [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.g68d124) Simulation script,
 Mathematica notebooks, simulated data, and R scripts for plotting and
 analyzing the data. ## Description of the data and file structure ### Main
 Text Simulation Script The 'MainTextSims.py' file is the file
 that can be run to reproduce the main analysis. After installing Python,
 run: ```{eval=FALSE} python MainTextSims.py ``` Output files will go into
 a folder called 'data' in the present directory. To change, edit
 'data_dir' parameter in the script. ### Notebooks Folder The
 'Notebooks' folder (zipped) contains five Wolfram Mathematica
 notebooks that produce results in the paper. You must have Mathematica
 installed to view them. The Wolfram Player program can do this for free. *
 **som.nb**---Supporting Mathematica notebook for "Patterns of
 speciation and parallel genetic evolution under adaptation from standing
 variation". This is the key document. The remainder documents are
 supplementary and their contents are: *
 **fitnessvarload.nb**---investigates the effect of variance
 "load" on hybrid fitness * **HoC.nb**---analyzes the House of
 Cards model * **overlap.nb**---computes the fraction of overlap between
 circles and (hyper)spheres * **parallelism_testing.nb**---is a referenced
 workbook that computes parallelism ### Analysis and figures Rproject The
 'Analysis_and_figures_Rproject' folder (zipped) contains an
 RProject file for RStudio, and data and scripts subfolders. The files in
 'data' are all output by a run of the main text simulation
 script, and their filenames reveal the Figure to which they are plotted,
 and the parameters used in the run. These files are retrieved by the
 Rscripts in Scripts. * *n* is dimensionality * *N* is population size *
 *alpha* is mutational SD * *u* is mutation rate * *sigma* is strength of
 selection The file in 'scripts' (a single script) is an R script
 that reads in the data in the data folder. The script is annotated and
 describes which figures are being generated where. ## Code/Software All
 code and software versions are listed in the main text of the open-access
 paper.