Data for: Outcomes of multifarious selection on the evolution of visual signals

Yeager, Justin1 ; Penacchio, Olivier2

Published Mar 22, 2023; Updated Oct 03, 2023 on Dryad. https://doi.org/10.5061/dryad.8sf7m0ct8

Abstract

Multifarious sources of selection shape visual signals and can produce phenotypic divergence. Theory predicts variance in warning signals should be minimal due to purifying selection, yet polymorphism is abundant. While in some instances divergent signals can evolve into discrete morphs, continuously variable phenotypes are also encountered in natural populations. Notwithstanding, we currently have an incomplete understanding of how combinations of selection shape fitness landscapes, particularly those which produce polymorphism. We modeled how combinations of natural and sexual selection act on aposematic traits within a single population to gain insights into what combinations of selection favor the evolution and maintenance of phenotypic variation. With a rich foundation of studies on selection and phenotypic divergence, we reference the poison frog genus Oophaga to model signal evolution. Multifarious selection on aposematic traits created the topology of our model’s fitness landscape by approximating different scenarios found in natural populations. Combined, the model produced all types of phenotypic variation found in frog populations, namely monomorphism, continuous variation, and discrete polymorphism. Our results afford advances into how multifarious selection shapes phenotypic divergence, which, along with additional modelling enhancements, will allow us to further our understanding of visual signal evolution.

README: Data for: Outcomes of multifarious selection on the evolution of visual signals

Yeager, Justin and Penacchio, Olivier, Proceedings of the Royal Society B, March 2023

This repository contains all the experimental data to reproduce the simulations, results and analysis of the paper "Outcomes of multifarious selection on the evolution of visual signals", https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2023.0327.

Description of the Data and file structure

Summary:

File count: 9
Total file size: 4.08 MB
Range of individual file sizes: 35 kB - 801 kB
File formats: .mat

File naming:

The Matlab files (.mat) that give the fitness landscape (total_free_energy_landscape_for_Fokker_Plank_equation_) are named after the different scenarios contemplated in the manuscript, namely scenarios A, B1, B2, and C, and include information on the size of the phenotypic space (Siz50); each files provides a fitness landscape represented by a 50 x 50 matrix of real values.

The Matlab files (.mat) that gives the output of the model across time (cInput_across_time_ie_population_distribution_evolution_*) are also named after the different scenarios, with added information regarding the starting location of the population for scenario C ((12,20) vs (15,15)); each file provides the evolution of the population in the corresponding scenario and is given by a 41 x 50 x 50 matrix, where the first entry corresponds to 41 time steps between date 0 (start) and 1000 (no more evolution).

Table of content:

total_free_energy_landscape_for_Fokker_Plank_equation_scenarioA_Siz50.mat
total_free_energy_landscape_for_Fokker_Plank_equation_scenarioB1_Siz50.mat
total_free_energy_landscape_for_Fokker_Plank_equation_scenarioB2_Siz50.mat
total_free_energy_landscape_for_Fokker_Plank_equation_scenarioC_Siz50.mat
cInput_across_time_ie_population_distribution_evolution_scenarioA.mat
cInput_across_time_ie_population_distribution_evolution_scenarioB1.mat
cInput_across_time_ie_population_distribution_evolution_scenarioB2.mat
cInput_across_time_ie_population_distribution_evolution_scenarioC_12_20.mat
cInput_across_time_ie_population_distribution_evolution_scenarioC_15_15.mat

Related files:

Code/scripts

The Matlab and Python code to define the fitness landscape and run the simulations can be found on Zenodo

Overall, the code tracks the evolution of the distribution of phenotypes in a population with a constant number of individuals in different scenarios. Depending on the different forces at play operate synergistically or in opposition, the distribution of phenotypes evolves towards all types of phenotypic variation, including monomorphism, continuous variation, and discrete polymorphism.

The Matlab function create_landscape_for_scenarios_ABC.m generates the three components that make the fitness landscape, namely W_cost, W_nat and W_sex, and exports them to build the total fitness landscape W_total = W_cost + W_nat + W_sex.

The Python function Pop_Evo_multifarious_evolution_free_energy_Fokker_Planck_equation.py models the temporal evolution of the distribution of phenotypes given a random component (the diffusion term, D) and the fitness landscape F_total. This is done using a Fokker-Planck equation in its equivalent formulation using free energy equivalent (see text for details). The code finds the distribution of phenotypes that minimizes free energy, which corresponds to the distribution that maximizes the fitness on the fitness landscape F_total given the processes of diffusion driven by D.

Figures

The figures and animations of the evolutionary trajectories of the distributions of phenotypes can be found on Zenodo

The evolutionary trajectories of the distribution of phenotypes for all scenarios are provided by videos movie_scenarioA_compressed.mp4, movie_scenarioB1_compressed.mp4, movie_scenarioC_case1_compressed.mp4, and movie_scenarioC_case2_compressed.mp4.

End of README file