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Mate-choice copying accelerates species range expansion

Cite this dataset

Sapage, Manuel et al. (2024). Mate-choice copying accelerates species range expansion [Dataset]. Dryad. https://doi.org/10.5061/dryad.bzkh189jh

Abstract

Mate-choice copying is a type of social learning in which females can change their mate preference after observing the choice of others. This behaviour can potentially affect population evolution and ecology, namely through increased dispersal and reduced local adaptation. Here, we simulated the effects of mate-choice copying in populations expanding across an environmental gradient to understand whether it can accelerate or retard the expansion process. Two mate-choice copying strategies were used: when females target a single individual, and when females target similar individuals. We also simulated cases where the male trait singled out by females with mate-choice maps perfectly onto their genotype or is influenced by genotype-by-environment interactions. These rules have different effects on the results. When a trait is determined by genotype alone, populations where copier females target all similar males expand faster, and the number of potential copiers increases. However, when preference is determined by genotype-by-environment interactions, populations where copier females target a single male have higher dispersal and also expand faster, but the potential number of copiers decreases. The results show that mate-choice copying can accelerate the expansion process, although its adaptiveness depends on the information animals use in different contexts.

README: Mate-choice copying accelerates species range expansion

https://doi.org/10.5061/dryad.bzkh189jh

This database contains the code used to run the individual-based simulations used to study the effects of mate-choice copying in a population expanding through an environmental gradient. These simulations show that different preference rules and mate-choice copying strategies can affect the speed of population range expansion.

Description of the data and file structure

The simulation code is contained in 5 different files:

  • main.c: This is the main file for the simulation. It contains the fixed parameters for the simulation (described in the comments of the code itself), the link to all the other files, and the main code to run each generation loop and the simulation itself.
  • input.h: This is the file with the parameters that were changed during the different simulations. The file also contains a description of each parameter as a comment.
  • initialize.h: This part of the code controls the initialization of the simulation, namely the declaration of the different parameters, the creation of the environmental gradient, and the creation and placement of the individuals in the environment. This part of the code also controls memory allocation and free space management.
  • output.h: This part of the code controls the sampling of the variables and the creation of the output files. The output files are described below and in the file itself.
  • dynamics.h: This part of the code controls the population dynamics within each generation. This file contains all the code related to individual viability, dispersal, mate choice, and reproduction.

After running the simulations an output folder will be created. Note that some of the folders may not be created depending on the options set on input.h. The name of the output folder is also set in this file. The code attributes a number to each simulation within the same output folder starting with 0001. If simulation 0001 is already present, the simulation will create simulation 0002, and so on. This number is checked from the log file, so changing the filename of the log file will override the output. The output has the following structure, with **** representing the four-digit simulation number:

Column -> Folder for the variables by column (x coordinate)

            Copy_sim_s****.txt -> Column mean copy allele

            Disp_sim_s****.txt -> Column mean dispersal allele

            Population_n_sim_s****.txt -> Adult population size per column

            Pref_sim_s****.txt -> Column mean preference allele

            Sex_sim_s****.txt -> Column mean sex ratio: 0 = female; 1 = male)        

            Trait_sim_s****.txt -> Column mean trait allele

Local -> Folder for the variables by patch

            Copy_sim_s****.txt -> Patch mean copy allele

            Disp_sim_s****.txt -> Patch mean dispersal allele

            Population_n_sim_s****.txt -> Adult population size per patch

            Pref_sim_s****.txt -> Patch mean preference allele

            Sex_sim_s****.txt -> Patch mean sex ratio: 0 = female; 1 = male)

            Trait_sim_s****.txt -> Patch mean trait allele

Main -> Main raw output sampling all individuals - each line in the file is a sampled generation)

            Coord_sim_s***.txt -> Individual's positions

            Copy_sim_s****.txt -> Individual's copy allele

            Disp_sim_s****.txt -> Individual's dispersal allele

            Pref_sim_s****.txt -> Individual's preference allele

            Sex_sim_s****.txt -> Individual's sex: 0 = female; 1 = male)

            Trait_sim_s****.txt -> Individual's trait allele

Environment_sim_s****.txt -> Environmental matrix

Log_name_s****.log -> Log file

Summary_sim_s****.log -> Summary statistics for each generation

Code/Software

The current code is written in C and runs in Cygwin with GNU scientific library 2.3 (GSL). Instructions to run the simulations:

1)      Install Cygwin and GSL

2)      Change the input.h file

3)      Get to the directory where the file is stored and type the following code in the console:

gcc -c main.c -lm

gcc main.c -lgsl -lgslcblas -lm -o main.exe

main.exe

Funding

Fundação para a Ciência e Tecnologia, Award: UIDB/00329/2020, DOI: 10.54499/UIDB/00329/2020

Fundação para a Ciência e Tecnologia, Award: PD/BD/128349/2017

Fundação para a Ciência e Tecnologia, Award: COVID/BD/152111/2021

Fundação para a Ciência e Tecnologia, Award: PTDC/BIA-COM/31887/2017

Ministerio de Ciencia, Innovación y Universidades, Award: PID2021-127107NB-I00

Government of Catalonia, Award: 2021 SGR 00526

Hungarian Academy of Sciences, Distinguished Guest Scientists Fellowship Programme