README: Data for: Multi-generational fitness effects of natural immigration indicate strong heterosis and epistatic breakdown in a wild bird population

This file is associated with the manuscript "Multi-generational fitness effects of natural immigration indicate strong heterosis and epistatic breakdown in a wild bird population" (Submitted to American Naturalist in March, 2023)

Authors: Lisa Dickel, Peter Arcese, Lukas F. Keller, Pirmin Nietlisbach, Debora Goedert, Henrik Jensen, Jane M. Reid

Study population: Song sparrow (Melospiza melodia) of XOX DEL (English name Mandarte island), British Columbia, Canada, (latitude 48.6329, longitude 123.2859, 0.06 km)

All code was written by Lisa Dickel and Jane M. Reid

Data collection was planned and overseen by P. Arcese, and P. Nietlisbach, L. F. Keller and J. M. Reid contributed to fieldwork.

Study summary

Data used come from a long-term study of song sparrows on Mandarte Island.

In these analyses we tested for fitness differences in different groups of immigrants and natives and their descendants.

Data and analysis structure

We analysed 5 different fitness components, namely:

• Lifetime reproductive success (LRS)
• Annual survival
• Annual reproductive success (ARS)
• Juvenile survival
• Zygote survival

These analyses relied on 4 different data sets, comprising:

• Lrs.data.csv (for Lifetime reproductive success, LRS)
• Annual_data.csv (for Annual survival and Annual reproductive success, ARS)
• Juvenile_data.csv (for juvenile survival)
• egg_data (for zygote survival)

There is one script for each of the 5 fitness component analysis, plus one script for overall fitness approximation.
One additional script contains the functions used to process model outputs.

Below, each fitness component is listed with the data set and coding script.

This is a Bayesian analysis, using Monte Carlo sampling. Therefore there is some sampling variance (i.e. Monte Carlo Error) in the results. This typically affects the second, and therefore due to rounding, sometimes the first decimal when re-running these models.

These models take up to 30 minutes to run on a standard comuputer.

The folder for these analyses contains two empty sub-folders to save model outputs and figures when the analyses are run, and the main folder (location of all R scripts) is set as the working directory.

Lifetime reproductive success

R script: LRS_analysis.Rmd

lrs_data: Data used to analyze lifetime reproductive success with the following variables (columns)

• ninecode: Individual identifyer for each bird (factor)
• natalyr2: Hatch year for each individual (integer)
• lrs.use: Count of banded offspring produced over the entire lifetime by an individual (integer)
• f.coef: Inbreeding coefficient pedigree f (numeric)
• sex.fac: Sex of each individual (factor)
• status_mother_father2: Status of both parents before reducing the levels, i.e. the "Other" group found in the "parent_status" column is still split into different sub grups (factor)
• parent_status: Filial group membership of both parents combined, equivalent grouping as in "names" which is created in the scripts (factor)
• pair_ID: Combined ID of both parents of each individual (factor) Annual adult fitness components i.e. annual reproductive success (ARS) and annual adult survival

R scripts: AnnualSurvival_analysis.Rmd

     ARS_analysis.Rmd 

Data: annual_data.csv Data

With the following variables (columns):

• ninecode: Individual identifyer for each bird (factor)
• syear: Observation year for annual traits (ARS and annual survival)
• natalyr2: Hatch year for each individual (integer)
• f.coef: Inbreeding coefficient pedigree f (numeric)
• ars: Annual reproductive success counted as number of banded offspring (integer)
• sex.fac: Sex of each individual (factor)
• age.cat: Category of young, middle, or old age (factor)
• parent_status: Filial group membership of both parents combined, equivalent grouping as in "names" which is created in the scripts (factor)
• pair_ID: Combined ID of both parents of each individual (factor)
• surv: Annual survival from April to April (binary) Juvenile survival

R script: JuvenileSurvival_analysis.Rmd

Data: juvenile_data.csv

With the following variables (columns)

• ninecode: Individual identifyer for each bird (factor)
• natalyr: Hatch year for each individual (integer)
• f.coef: Inbreeding coefficient pedigree f (numeric)
• surv.band.ad: Survival to banding at ~6 days of age
• nestrec: Nest ID identifying each group of nestlings raised in the same nest
• status_mother_father2: Status of both parents before reducing the levels, i.e. the "Other" group found in the "parent_status" column is still split into different sub grups (factor)
• sex.use: Sex of each individual (factor)
• parent_status: Filial group membership of both parents combined, equivalent grouping as in "names" which is created in the scripts (factor)
• pair_ID: Combined ID of both parents of each individual (factor)Zygote (i.e. egg) survival

R script: ZygoteSurvival_analysis.Rmd

Data: egg_data

• Year: Year of egg laid and observed to hatch (or not) (integer)
• parent_status: Filial group membership of both parents combined, equivalent grouping as in "names" which is created in the scripts (factor)
• nestrec: Nest ID identifying each group of nestlings raised in the same nest (factor)
• band: Count of eggs banded (integer)
• eggs_not_banded: Count of eggs not banded (integer)
• eggs: Total number of eggs counted in the clutch (integer)
• f.coef: Inbreeding coefficient pedigree f (numeric)
• status_offspring_par: Status of both parents before reducing the levels, i.e. the "Other" group found in the "parent_status" column is still split into different sub grups (factor)
• pair_ID: Combined ID of both parents of each individual (factor)

############### R session info ####################################3

R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
[1] LC_CTYPE=en_US.UTF-8[2] LC_NUMERIC=C[3] LC_TIME=nb_NO.UTF-8[4] LC_COLLATE=en_US.UTF-8[5] LC_MONETARY=nb_NO.UTF-8[6] LC_MESSAGES=en_US.UTF-8[7] LC_PAPER=nb_NO.UTF-8[8] LC_NAME=C[9] LC_ADDRESS=C[10] LC_TELEPHONE=C[11] LC_MEASUREMENT=nb_NO.UTF-8
[12] LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils[5] datasets methods base

other attached packages:
[1] ggdist_3.2.1 cowplot_1.1.1[3] ggpubr_0.6.0 scales_1.2.1[5] qwraps2_0.5.2 tidybayes_3.0.3
[7] bayesplot_1.10.0 MCMCglmm_2.34[9] ape_5.7 coda_0.19-4[11] Matrix_1.5-3 egg_0.4.5[13] gridExtra_2.3 forcats_1.0.0[15] stringr_1.5.0 dplyr_1.1.0[17] purrr_1.0.1 readr_2.1.4[19] tidyr_1.3.0 tibble_3.1.8[21] ggplot2_3.4.1 tidyverse_1.3.1

loaded via a namespace (and not attached):
[1] nlme_3.1-162 fs_1.6.1[3] bit64_4.0.5 lubridate_1.9.2[5] webshot_0.5.4 httr_1.4.4[7] tensorA_0.36.2 tools_4.2.2[9] backports_1.4.1 utf8_1.2.3[11] R6_2.5.1 DBI_1.1.3[13] colorspace_2.1-0 withr_2.5.0[15] tidyselect_1.2.0 bit_4.0.4[17] compiler_4.2.2 cli_3.6.0[19] rvest_1.0.3 arrayhelpers_1.1-0[21] xml2_1.3.3 labeling_0.4.2[23] posterior_1.3.1 checkmate_2.1.0[25] systemfonts_1.0.4 digest_0.6.31[27] rmarkdown_2.20 svglite_2.1.1[29] pkgconfig_2.0.3 htmltools_0.5.4[31] dbplyr_2.3.1 fastmap_1.1.0[33] rlang_1.0.6 readxl_1.4.2[35] rstudioapi_0.14 farver_2.1.1[37] generics_0.1.2 svUnit_1.0.6[39] jsonlite_1.8.4 vroom_1.6.1[41] car_3.1-1 distributional_0.3.1
[43] magrittr_2.0.3 kableExtra_1.3.4[45] Rcpp_1.0.10 munsell_0.5.0[47] fansi_1.0.4 abind_1.4-5[49] lifecycle_1.0.3 stringi_1.7.12[51] yaml_2.3.7 carData_3.0-5[53] grid_4.2.2 parallel_4.2.2[55] crayon_1.5.2 lattice_0.20-45[57] haven_2.5.2 hms_1.1.2[59] knitr_1.42 pillar_1.8.1[61] boot_1.3-28 cubature_2.0.4.6[63] ggsignif_0.6.4 corpcor_1.6.10[65] reprex_2.0.2 glue_1.6.2[67] evaluate_0.20 modelr_0.1.10[69] vctrs_0.5.2 tzdb_0.3.0[71] cellranger_1.1.0 gtable_0.3.1[73] xfun_0.37 broom_1.0.3[75] rstatix_0.7.2 viridisLite_0.4.1[77] timechange_0.2.0 ellipsis_0.3.2