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

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.The programs required to open the data files, generate the scenarios and reproduce the computations are Python 3.0 (open-source; https://www.python.org/downloads/) and Matlab (proprietary; MATLAB and Statistics Toolbox Release 2019b, 9.7.0.1190202 (R2019b). Natick, Massachusetts, The MathWorks Inc.). The Matlab files can be opened directly using Python as shown the Python code (Pop_Evo_multifarious_evolution_free_energy_Fokker_Planck_equation.py). An open-source alternative to run the Matlab routines and generate/modify the scenarios (cf. README.md) is Octave (https://octave.org/).Funding provided by: Maria Zambrano Fellowship, requalification of the Spanish university system, NextGeneration EU (ALRC)*Crossref Funder Registry ID: Award Number: Funding provided by: Universidad de las Américas*Crossref Funder Registry ID: Award Number: FGE.JY.22.01All the data are computational and generated with the code provided