DOI: 10.5061/dryad.79cnp5hxt
title: Data from: Experimental evolution of a pheromone signal

description: Sexual signals are important in speciation, but understanding their
 evolution is complex, as these signals are often composed of multiple,
 genetically interdependent components. To understand how signals evolve,
 we thus need to consider selection responses in multiple components and
 account for the genetic correlations among components. One intriguing
 possibility is that selection changes the genetic covariance structure of
 a multicomponent signal in a way that facilitates a response to selection.
 However, this hypothesis remains largely untested empirically. In this
 study, we investigate the evolutionary response of the multicomponent
 female sex pheromone blend of the moth Heliothis subflexa to 10
 generations of artificial selection. We observed a selection response of
 about 3/4s of a phenotypic standard deviation in the components under
 selection. Interestingly, other pheromone components that are
 biochemically and genetically linked to the components under selection did
 not change. We also found that after the onset of selection, the genetic
 covariance structure diverged, resulting in the disassociation of
 components under selection and components not under selection across the
 first two genetic principal components. Our findings provide rare
 empirical support for an intriguing mechanism by which a sexual signal can
 respond to selection without possible constraints from indirect selection
 responses.
description: # Data from: Experimental evolution of a pheromone signal Reference
 Information --- * File name: README_Dataset-ECE-2022-04-00591.txt *
 Authors: Thomas Blankers * Other contributors: Elise Fruitet, Emily
 Burdfield-Steel, Astrid T. Groot * Date created: 2022-05-24 * Date
 modified: 2025-12-12 #### Description of the Data and file structure *
 pedigreeH.RDS Description: R data file with the pedigree data for the high
 line (selected for high acetate rates, see manuscript for details).
 Variables:  * animal - individual ID * sire - father of the individual *
 dam - mother of the individual * sex - sex of the individual * generation
 - selection line generation * family - family identifier * pedigreeL.RDS
 Description: R data file with the pedigree data for the low line (selected
 for high acetate rates, see manuscript for details). Variables : * animal
 - individual ID * sire - father of the individual * dam - mother of the
 individual * sex - sex of the individual * generation - selection line
 generation * family - family identifier * phenodat.txt Description:
 tab-delimited file with the following columns Variables: order -
 continuing order of individuals, sample - sample ID, family - family ID
 eggs.collected - when eggs were collected for this individual gen -
 generation that the individual was part of generation - generation that
 the individual was part of line - whether it belonged to the high or low
 selected line of the back-up (control) line line..14.22. - qualification
 if individual fits low or high lines criteria (&lt; 14% is low, &gt; 22%
 is high) line..16.22. - qualification if individual fits low or high lines
 criteria (&lt; 16% is low, &gt; 22% is high) line..14.24. - qualification
 if individual fits low or high lines criteria (&lt; 14% is low, &gt; 24%
 is high) line..12.26. - qualification if individual fits low or high lines
 criteria (&lt; 12% is low, &gt; 26% is high) total.acetates - total amount
 of acetates as a percentage of the total pheromone blend X14.Ald - each of
 the following columns has relative amounts of pheromone components. These
 have been calculated from chromatograms of the full blend Z9.14.Ald
 X16.Ald Z7.16.Ald Z9.16.Ald Z11.16.Ald Z7.16.Ac Z9.16.Ac Z11.16.Ac
 Z9.16.OH Z11.16.OH Total.amount - total amount of pheromone measured
 across the components listed above. #### Code/Software --- * Analyses.r R
 code for all analyses in the manuscript. In-line comments provide
 additional details. #### Acknowledgements --- * Funding sources: This
 study was funded by the following grants: the European Union's
 Horizon 2020 research and innovation program under the Marie
 Skłodowska-Curie grant agreement no. 794254 awarded to TB, NWO grant award
 ALWOP.2015.075 awarded to ATG, an IMPRS grant of the Max Planck Society
 awarded to EF, and an ASAB Research grant awarded to EBS.