Doi: to be updated upon acceptance.

This is the data and code accompanying the publication of " Inheritance of material wealth in a natural population ", submitted for consideration at Ecology Letters. This record contains all datasets and code necessary to reproduce the analyses, which consists in natural records of phenotypic traits, access to resources (food) and dispersal behaviour, quantitative genetic (full pedigree) and genomic analyses (GWAS on 38705 SNPs) on wild collared flycatchers (Ficedula albicollis) in the long-term monitoring population of Öland, Sweden.

Throughout the files, "NA" indicates "no data available" for that specific cell.

Description of the data and file structure

Analyses

1. Genome Wide Association Analysis (GWAS) of wealth among 800 collared flycatchers after filtering steps

1.1. Scripts

wealth_GWAS.R : main GWAS on material wealth in collared flycatchers, taking into account sex and year the individual was measured

getSEh2.R: script used to extract heritability value

vlookup_R.R

1.2 Datasets:

• new_phenotype_Mar1.xls: phenotypic information for 1996 individuals. Variables: ring (unique identifier), helper (ring number and year combined), sex_cha (sex as f= female and m= male), sex (sex as numeric, 1=female, 0 = male), year, NestID (unique identifier for each nest), area (3 letters abreviation for forest areas in the monitored population), box (nestbox number), Y_projection (GPS coordinate for latitude), laydate (day the first egg was laid, 1 = May 1st)
• meanphenos_Mar2.txt: data on wealth for measured individuals, variables: id (unique identifier), sex (sex as numeric, 1=female, 0 = male), habitat (relative measure of caterpillar abundance in a territory), dd (caterpillar biomass in a territory in dl/m3)
• CFgenotype.raw: genotypic information (SNPs position and genotype across the genome)

2. LASSO: Are the top SNPs (lowest p-values) from the GWAS analysis on material wealth correlated with:

• forehead patch size
• laying date

2.1. Script wealth_GWAS.R: needs to be ran after "wealth_GWAS.R" to get the top 100 SNPs as output

2.2 Dataset

• flycatchers_adults_20022020.csv: information on forehead patch size and laying date for individuals measured between 2002 and 2020.

variables: breeding_id (unique identifier for each breeding pair), year , ring_nb (unique identifier for each individual bird), patch_h (forehead patch height in mm), patch_b (forehead patch width in mm), laying_date (day the first egg was laid, 1 = May 1st), clutch_size (number of eggs in a clutch), chicks_hatched (number of chicks that successfully hatched), chicks_fledged (number of chicks that successfully fledged), wealth (relative measure of caterpillar abundance in a territory)

• yearmeans.csv: mean laying date (LD) and standard deviation across years for the whole population, variables: year, meanLD (mean date where the first egg was laid averaged across the population), sdLD (standard deviation on meanLD)

3. Gene Set over-representation analysis:

This analysis uses the 100 top SNPs from a GWAS on material wealth in collared flycatchers and compares the occurrence of specific gene functions among those genes to the functions of the whole genome, identifying gene sets over-represented in this subset.

3.1. Scripts

gene_functions.rmd

3.2 Datasets

3.2.1 gwas_top100_genes.csv

Variables: SNP_id: name of the SNP, ensembl_gene_id: ensembl database unique gene identifier, external_gene_name: external gene name, start and end position in the collared flycatcher genome

4. Animal models

• Animal model on material wealth to estimate heritability from the full pedigree
• Animal models to determine genetic correlation between material wealth and
• forehead patch size (dominance status in males)
• laying date (when the first egg of the clutch was laid)
• hatching success (number of eggs that hatched)
• fledging success (number of nestlings that left the nest alive)

4.1. Scripts:

####### !!! Note that these models are highly demanding in terms of computing power. They were ran on a cluster with 200G memory per cpu !!! ###############

wealth_animalmodel_20240702.R: main animal model to calculate heritability of material wealth

wealth_animalmodel_patch_20240703.R: genetic correlation between material wealth and forehead patch size

wealth_animalmodel_laydate_20240703.R: genetic correlation between material wealth and laying date

wealth_animalmodel_hatch_20240703.R: genetic correlation between material wealth and hatching success

wealth_animalmodel_fledge_20240703.R: genetic correlation between material wealth and fledging success

4.2 Datasets:

• pruned_pedigree_for_wealth_animalmodel.csv: social pedigree of collared flycatchers on Öland. variables: id (unique individual identifier), sire (unique identifier of the social father of that individual), dam ((unique identifier of the mother of that individual))
• collared_data_for_wealth_animalmodel.csv: phenotypic information for animals in the pedigree. Variables: ring_nb (unique individual identifier), sex (female or male), species (CF = collared flycatcher), age (years), area (3 letters code for forest area in the monitored population), box_name (nestbox number), day_real_hatch (date where the eggs hatched, 1 = May 1st), year, nestbox (area and box_name combined), wealth (relative measure of caterpillar abundance), species2 (species, CF = collared flycatcher), animal (unique individual identifier), indatabase (check that the animal is in the pedigree)

5. Phenotypic correlations

Models with phenotypes for recruiting individuals,born in our population and coming back to breed as adults

Testing for phenotypic correlations between material wealth of the breeding territory and

• forehead patch size (patcharea)
• wing length
• tarsus length
• mass
• age
• laying date (relative to the peak of laying for each year, ld_centered)
• mass as a nestling (12 days old, mass_d12.natal)
• sex

Random effects controlling for natal nest to take into account recruiting siblings, as well as year and person measuring the phenotypic traits

All variables centered around the mean.

5.1. Script

phenotypic_correlations.Rmd

5.2 Dataset

• recruits_May2024.csv: individuals born in our population that recruited as adults to breed.

variables:

nestbox.breed: unique identifier for the breeding nestbox

year.breed: year the bird bred as an adult

sex.breed: sex (male or female)

measurer.breed: initials of the person measuring the bird

tarsus.breed: adult tarsus length (mm)

wing.breed: adult wing length (mm)

mass.breed: adult mass (g)

latitude.breed: latitude of the breeding nestbox

longitude.breed: longitude of the breeding nestbox

material_wealth.breed: relative caterpillar abundance on the breeding territory

breeding_id.natal: unique identifier for the natal nest

nestbox.natal: unique identifier for the natal nestbox

year.natal: year the individual was born

mass_d12.natal: mass (g) of the individual at 12 days of age

foster.natal: was the nestling cross-fostered (0 = no, 1 = yes)

latitude.natal: latitude of the natal nest

longitude.natal: longitude of the natal nest

material_wealth.natal: relative caterpillar abundance of the natal territory

material_wealth_biol.natal: relative caterpillar abundance of the biological parents' territory

disp_dist: distance dispersed from natal to breeding nest (m)

ld_centered: relative laying date (day the first egg was laid, 1 = May 1st)

estimated_age: age in years

patcharea: forehead patch size area (mm2)

6. Cross-fostering experiments

This analysis compares the material wealth of recruiting adult individuals (breeding_wealth) that had been cross-fostered between several nests as nestlings to the material wealth of their biological parents (where they were born, bionest_wealth) and that of their foster parents (where they were raised, fosternest_wealth), controlling for both of these nest environments as random effects (foster_nestbox and bionest_nestbox).

6.1. Scripts

foster_nestlings_material_wealth.rmd


6.2 Datasets

• foster_recruits_May2024.csv: individuals born in our population and cross-fostered as eggs or chicks that recruited as adults to breed

variables: bionest_nestbox (unique identifier for the nest of the biological parents), foster_nestbox (unique identifier for the nest were the chicks were raised), breeding_wealth (relative caterpillar abundance on the breeding territory), fosternest_wealth (relative caterpillar abundance on the territory where the individual was raised as a chick), bionest_wealth (relative caterpillar abundance on the territory of the biological parents)

7.natal dispersal

Does dispersal distance influence the link between natal and breeding territory material wealth?

Analysis and simulations of the correlation between material wealth of the natal territory (material_wealth.natal) and that of the first recorded breeding territory as an adult (material_wealth.breed) at different dispersal distances. Do individuals that disperse far away look for similar habitats to where they grew up?

7.1 Scripts

natal_dispersal_and_simulations.r

7.2 Datasets

• recruits_May2024.csv: see variable descriptions under 5.2

8. Frass

Caterpillar frass biomass (i.e. frass) collected for 11 tree species in 2013 and 2014, sampled every fourth day. The data was used to produce supplementary figure S1 and Figure 2B, where 3 trees representing low, medium and high caterpillar productivity where chosen (...oak_birch_pine... dataset)

8.1 script: no code was used, figures produced in excel

8.2 Datasets

• Caterpilla_biomass_11_tree_species_2013_2014.xlsx: caterpillar biomass produced by 11 tree species in 2013 and 2014

variables:

year: year

Maydate: sampling date, 1= 1st of May, frass sampled every 4th day

alder_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for alder

ash_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for ash

birch_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for birch

elm_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for elm

hazel_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for hazel

hornbeam_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for hornbeam

lime_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for lime

maple_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for maple

oak_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for oak

pine_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for pine

spruce_raw: caterpillar biomass estimated as amount of frass (mg), sampled every fourth day, for spruce

• Caterpillar biomass oak_birch_pine_2013_2014.xlsx: mean of caterpillar frass biomass (estimated as frass, mg/d/m3) of all sampled tree individuals during each sampling period (4 days) for oak, birch, and pine in May 2013 and 2014

variables:

mean_Maydate_2014: average sampling date in 2014, 1= 1st of May, mean of Maydate of the sampling period of 4 days

Tree_species_2014: tree species

mean_sp_period_2014: mean of caterpillar frass biomass (estimated as frass, mg/d/m3 ) of all sampled tree individuals during each sampling period (4 days) in 2014

mean_Maydate_2013:average sampling date in 2013, 1= 1st of May, mean of Maydate of the sampling period of 4 days

Tree_species_2013: tree species

mean_sp_period_2013: mean of caterpillar frass biomass (estimated as frass, mg/d/m3 ) of all sampled tree individuals during each sampling period (4 days) in 2013

9. Material wealth variation

Variograms comparing material wealth between territories at different scales across Öland, mirroring the scales found in Figure 2A: Horns Kungsgård and Halltorps Hage (two big forest patches on Öland), Northern and Southern Öland, as well as across all nestboxes on the island.

9.1 script

variogram.r

9.2 Datasets

• nestboxes.csv: location and material wealth data for all nestboxes on the island, grouped by forest patch (area)

variables

nestbox: unique identifier for the nestbox

area: 3 letters abreviation for forest patch in the studied population

box: name of the nestbox

latitude: latitude

longitude: longitude

x_proj: coordinate representing latitude

y_proj: coordinate representing longitude

material_wealth: relative abundance of caterpillar on the territory

10. Distribution of material wealth

Figure 2A: map of territories and estimation of material wealth

10.1 script

wealth_figure2a_map.Rmd

10.2 Dataset

• nestboxes.csv: see variable descriptions under 9.2

##Code/Software

Code was produced using R (version 4.3.1 for most analyses, version 4.1.2 for the script "wealth_GWAS.R"), in R studio (version 2023.06.1)

The following R packages were used:

• AnnotationForge (v. 1.42.2)
• AnnotationHub (v. 3.8.0)
• BiocManager (v. 1.30.22)
• biomaRt (v.2.56.1 ), date accessed: 26 June 2024
• clusterProfiler (v. 4.8.2)
• cowplot (v.1.1.1)
• dplyr (v. 1.1.2)
• geosphere (v. 1.5.18)
• ggplot2 (v.3.4.4 )
• ggspatial (v.1.1.9)
• glmnet (v.4.1.8 )
• lme4 (v.1.1.35.1 )
• lmerTest (v. 3.1.3)
• MCMCglmm (v.2.35 )
• plyr (v.1.8.8 )
• RCurl (v.1.98.1.12 )
• RepeatABEL (v.1.1)
• rnaturalearth (v.0.3.4)
• rnaturalearthdata (v.0.1.0)
• rnaturalearthhires (v.0.2.1)
• sf (v.1.0.14 )
• sjPlot (v. 2.8.15)
• Stringr (v.1.5.0 )
• tidyverse (v.2.0.0 )