Data and script for the manuscript 'Pollinator abundance shapes sexual selection in an angiosperm'

README — Data and analysis scripts for the manuscript "Pollinator abundance shapes sexual selection in an angiosperm" DESCRIPTION This repository contains the datasets and R scripts used to perform the analyses presented in: Barbot E, Dufaÿ M, Janicke T, Lapeyre B, Orcel D, Rousset F, Tonnabel J. 2026. Pollinator abundance shapes sexual selection in an angiosperm. The repository includes the datasets required to reproduce the main analyses and the variance partitioning analyses reported in the manuscript. CONTENTS The repository contains the following files: > script_ABPOLL_main_analysis.RR script used to perform the main statistical analyses presented in the manuscript.> script_ABPOLL_variance_partition.RR script used to perform the variance partitioning analyses.> data_ABPOLL_ID_resume.txtDataset used for the main statistical analyses and the variance partitioning analyses.> data_ABPOLL_flower_resume.txtDataset used for the main statistical analyses and the variance partitioning analyses.> data_ABPOLL_fix10_paternities.txtDataset used for the main statistical analyses and the variance partitioning analyses.> data_ABPOLL_ID_level_ID.txtDataset used for the main statistical analyses.> data_ABPOLL_flower_level_detID.txtDataset used for the variance partitioning analyses.> data_ABPOLL_ID_level_detflo.txtDataset used for the variance partitioning analyses.README.txtDescription of the datasets, analysis scripts, and instructions for reproducing the analyses. DATA The datasets provided in this repository contain the data required to reproduce the analyses reported in the manuscript. > data_ABPOLL_ID_resume.txt reports data at the individual level, and is mainly used with data_ABPOLL_fix10_paternities.txt (which correspond to results from paternity analyses with 10 genotyped seeds per individual) to generate formatted datasets for the main analyses (dt in the script). These final datasets dt are formatted with two lines by individual, one for the male function and another for the female function:- session: name of the pollinator observation session (independant population replicate)- ID_full: name of the individual- type: female (fem) or male (mal) function- poll_treat_factor: pollinator abundance treatment (low, medium or high)- SR_out: outcrossed reproductive success- SR_all: total reproductive success- gMS: genetic mating success- nb_part_ID_out_coXX: number of pre-pollination sexual partners based on pollinator observations (i.e. observational mating success), with XX indicating the carry-over used to infer effective pollen movement- mean_nb_dist_flo_out_coXX: number of flowers visited per mate, with XX the carry-over used- nb_dist_vis: number of flowers visited on the plant itself- nb_visit: total number of visits on the plant itselfIt contains also relativized variables for the estimation of Bateman gradient and cross-sec effects, based on outcrossed or total reproductive success:- r_SR_out: relativized outcrossed reproductive success- r_SR_all: relativized total reproductive success- r_gMS: relativized genetic mating success- r_nb_part_ID_out_coXX: relativized number of pre-pollination sexual partners based on pollinator observations (i.e. observational mating success), with XX the carry-over used- r_mean_nb_dist_flo_out_coXX: relativized number of flowers visited per mate, with XX the carry-over used- r_nb_dist_vis: relativized number of flowers visited on the plant itself- r_nb_visit: relativized total number of visits on the plant itself- r_pl_mean: relativized mean pollen load at the individual scaleThe same variables are used for the estimation of the Jones Index, with the observation session 3.FA2 removed due to a lack of variability. These data sets dt are also used to generate dt_opp and dt_opp_foc to estimate the opportunity for (sexual) selection at the scale of the pollinator observation session within sex, with:- session: name of the pollinator observation session (independant population replicate)- type: female (fem) or male (mal) function- poll_treat_factor: pollinator abundance treatment (low, medium or high)- I_SR_out: opportunity for selection based on outcrossed reproductive success- I_SR_all: opportunity for selection based on total reproductive success- Is_gMS: opportunity for sexual selection based on genetic mating success- Is_oMS_coXX: opportunity for sexual selection based on observational mating success with the corresponding carry-over XX- Is_adfvpm_coXX: opportunity for sexual selection based on the number of flowers visited per mate with the corresponding carry-over XX- Is_nb_dist_vis: opportunity for sexual selection based on the number of flowers visited on the plant itself- Is_nb_visit_tot: opportunity for sexual selection based on the total number of visited on the plant itself- Is_pl_mean: opportunity for sexual selection based on mean pollen load on the plantFinally, data_ABPOLL_ID_resume.txt is used for analyses on the proportion of selfed seeds with the column "prop_seed" > data_ABPOLL_ID_resume.txt and data_ABPOLL_fix10_paternities.txt are also used in the script for variance partitioning analyses, to estimate the various variance components based on genetic data (see script_ABPOLL_variance_partition.R for details about component estimation). > data_ABPOLL_flower_level_detID.txt (focus on the female function with each flower that get pollen from pollinators) and data_ABPOLL_ID_level_detflo.txt (focus on the male function with each flower reached by pollinators) are used in the script for variance partitioning analyses, to estimate the various variance components based on observational data (see script_ABPOLL_variance_partition.R for details about component estimation). > data_ABPOLL_flower_resume.txt reports data at the flower level, and is mainly used for analyses concerning pollen load on stigmas, with:- session: name of the pollinator observation session (independant population replicate)- ID_full: name of the individual- id_flow: unique identity of the flower- poll_treat_factor: pollinator abundance treatment (low, medium or high)- pl: pollen load as the number of pollen grains deposited on stigma (see supplementary material for pollen couting) > data_ABPOLL_ID_level_ID.txt represents pollen exchange as well as the resulting seeds between a focal individual and its partners, with:- session: name of the pollinator observation session (independant population replicate)- ID_full_foc: name of the focal individual- ID_full_part: name of the sexual partner- import_nb_visit_coXX: number of contacts between the focal individual and its partner based on pollinator observations, with the corresponding carry-over XX- seed_couple_on_foc: number of resulting seeds based on paternity analyses for the couple For detailed descriptions of the variables not used in the analyses if needed, please contact us. ANALYSIS SCRIPTS The analyses were conducted using R. script_ABPOLL_main_analysis.R:This script performs the main statistical analyses reported in the manuscript, including preliminary data correction for the binomial sampling bias, treatment effect on the measured variables, opportunity for (sexual) selection, Bateman's gradient, Jones index, cross-sex effects, and filtering of male sexual partners after pollination. script_ABPOLL_variance_partition.R:This script performs the variance partitioning analyses described in the manuscript, both on females and males. For each sex, analyses are conducted with genetic mating success (including or not selfing) and observational mating success. SOFTWARE REQUIREMENTS Analyses were performed using R version 4.5.2. The following R packages are required:- ggpmisc- tidyverse- lavaan- semPlot- piecewiseSEM- lme4- tidySEM- DHARMa- glmmTMB- semptools- semTools- sjPlot- ggsignif- cowplot- car- emmeans- performance- lmerTest- missMDA- FactoMineR- factoextra- data.table- flextable- spaMM- boot The scripts include the required package loading commands. REPRODUCING THE ANALYSES To reproduce the analyses: Download all files from this repository.Place the datasets and R scripts in the same working directory, or update the file paths in the scripts accordingly.Open R or RStudio.Install the required R packages, if necessary.Run script_ABPOLL_main_analysis.R to reproduce the main analyses.Run script_ABPOLL_variance_partition.R to reproduce the variance partitioning analyses. The scripts are organized so that the analyses can be run from beginning to end using the datasets provided in this repository. The scripts generate the statistical results reported in the manuscript, including the results presented in the main text, figures, and tables. CONTACT For questions regarding the data or analysis scripts, please contact: Estelle BarbotUniversité de [e-mail]