Published in Proceedings of the Royal Society B

Article DOI: http://dx.doi.org/10.1098/rspb.2020.0575

Load all required packages

library(tidyverse) # data re-shaping, ggplot, stringr and more
library(png) # to load images
library(grid) # to plot images
library(lme4) # for the lmer and glmer mixed model functions
library(lmerTest) # Used to get p-values for lmer models using simulation. It over-writes lmer() with a new version
library(glmmTMB) # for zero-inflated or hurdle glms
library(MuMIn) # for model selection and averaging
library(emmeans) # for pairwise comparisons
library(ggpubr) # for the ggarrange function
library(ggbeeswarm) # violin plots with data points
library(kableExtra) # nice tables that can scroll
library(pander) # more nice tables
library(groupdata2) # for assigning rows in data-frames to groups

Supplementary methods

Schematic for replacement of the unknown Sxl-GFP nuclear background with the isogenic w1118 background

img <- readPNG("Crossing_scheme.png")
 grid.raster(img)

Figure S1: Crossing scheme used to create a standard homozygous w1118-GFP line. Males from this line were crossed with females carrying a specific mitochondrial haplotype, to create experimental mt-strains. These newly produced strains carried the mitochondrial haplotype of the female and were heterozygous for the Sxl-GFP construct. G1 = the first generation of the cross.

Table S1: Recipe for food medium used in our experiment. The provided quantities make ~ 1 litre of food.

tibble("Ingredients" = c("Soy flour", "Cornmeal", "Yeast", "Dextrose", "Agar", "Water", "Tegosept", "Acid mix (4 ml orthophosphoric acid, 41 ml propionic acid, 55 ml water to make 100 mls)"),
       "Quantity" = c("20 g", "73 g", "35 g", "75 g", "6 g", "1000 mls", "17 mls", "14 mls")) %>% 
  pander(split.cell = 20, split.table = Inf)
Ingredients Quantity
Soy flour 20 g
Cornmeal 73 g
Yeast 35 g
Dextrose 75 g
Agar 6 g
Water 1000 mls
Tegosept 17 mls
Acid mix (4 ml orthophosphoric acid, 41 ml propionic acid, 55 ml water to make 100 mls) 14 mls

Data analysis and supplementary results

Here we include all code used to run our analysis and create Figure 1 and 2, our rationale behind the modelling approaches, and tables S2-9.

Read in the data and create some helpful functions

# Read in data frame and add Dyad_ID column

all_data <- read.csv("mtDNA_larval_competition_data.csv") %>% 
  arrange(Individual) %>%
  group(n = 2, method = "greedy") %>% rename(Dyad_ID = .groups)

# helper function for saving large model objects and naming the file object.rds

save_it <- function(object){
  saveRDS(get(object), file = paste(object, ".rds", sep = ""))}

# Create a function for standard error

SE <- function(x) sd(x)/sqrt(length(x))

Data preparation for all responses

# Clean the dataset up for analysis

# Select the columns we're interested in and rename them

fitness_data <- dplyr::select(all_data, Individual, Block, Strain,  Dyad_ID, Sex, Focal.haplotype, Social.haplotype, Mortality, Development.time..hrs., Wing.size..mm., Female.offspring, Male.offspring, Total.female.assay, Total.red.all.vials, Total.bw.all.vials, Proportion.red.all.vials) %>% 
  
rename(Block = Block, Survived = Mortality, Focal_haplotype = Focal.haplotype, Social_haplotype = Social.haplotype, Dev_time = Development.time..hrs., Wing_length = Wing.size..mm., Maternal_female_offspring = Female.offspring, Maternal_male_offspring = Male.offspring, Maternal_total_offspring = Total.female.assay, Paternal_focal_offspring = Total.red.all.vials, Paternal_bw_offspring = Total.bw.all.vials, Proportion_focal = Proportion.red.all.vials)

# Define new levels for mortality to make renaming possible 

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "NO")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "YES")

# Rename the mortality responses
# L means died as larva, P means died as pupae, N means did not die (i.e. eclosed as an adult)

fitness_data$Survived[fitness_data$Survived == 'L'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'P'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'N'] <- 'YES'

# Now that it makes sense change "YES" to 1 and "NO" to 0 so we can fit a binomial GLM.

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "1")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "0")

fitness_data$Survived[fitness_data$Survived == "YES"] <- 1
fitness_data$Survived[fitness_data$Survived == "NO"] <- 0

# Make the factor numeric 

fitness_data$Survived <- as.numeric(as.character(fitness_data$Survived))


# Create specific datasets for each fitness trait

# Remove all rows that contain an NA value in the survival column. The NAs mean things like the GFP sorting did not work, or the vial was never set up due to a shortage of larvae. They are not meaningful data, and we remove them here.

survival <- fitness_data %>% filter(!is.na(Survived)) 
  
# Remove all rows that contain an NA value in the development time column. This instances represent flies where we failed to measure development time. 

larval_development <- fitness_data %>% filter(!is.na(Dev_time)) 

# Remove all rows that contain an NA value in the wing length column. Wing length was not measured in Blocks 1 and 2.

body_size <- fitness_data %>% filter(!is.na(Wing_length)) 

# Remove all rows that contain an NA value in the female reproductive output column (e.g. all the males), and where females did not survive to adulthood (coded as producing 0 offspring). 

female_reproductive_output <- fitness_data %>% filter(!is.na(Maternal_total_offspring), Survived == 1)


# Male adult fitness

# First remove females from the dataset.

Male_fitness <- fitness_data %>% filter(!is.na(Paternal_focal_offspring)) 

# Create an offspring counted column so that the data is correctly formatted for a binomial success-failure model.

Male_fitness$Offspring_counted <- Male_fitness$Paternal_focal_offspring + Male_fitness$Paternal_bw_offspring

# Now lets remove vials where the female produced 0 offspring (this includes trials where the male died in development), as we cannot determine paternity from these vials. The tidy up the dataframe by removing unneccessary columns

Male_fitness <- Male_fitness %>% filter(!(Offspring_counted == 0)) %>% 
  select(-Maternal_female_offspring, -Maternal_male_offspring, -Maternal_total_offspring) %>% 
  rename(Focal_male_offspring = Paternal_focal_offspring, bw_male_offspring = Paternal_bw_offspring)

Modelling approach

We analysed the data using linear and generalised linear mixed models in the lmer andglmmTMB packages for R.

Fixed effects

For the analysis of fitness traits expressed in both sexes (survival, development time and body size), we are interested in the effect of an individual’s focal mtDNA, the mtDNA of a social competitor and the effect of sex on fitness. To measure these potential effects each model contained the following fixed effects and the three-way interaction between these variables:

Focal haplotype: the mtDNA haplotype that an individual carries.

Social haplotype: the mtDNA haplotype carried by a social partner during larval development.

Sex: the sex of the focal individual. The social partner’s sex was always opposite to that of the focal individual.

Random effects

Duplicate strain: Each haplotype has been introgressed alongside the w1118 nuclear background in two independent duplicates, creating 10 total strains. Within each block we ran multiple replicates that were made up of flies from the first set of strains (i.e. Barcelona 1, Brownsville 1 etc.), while the other half used only flies from the second set of strains. This random effect accounts for any residual differences in the nuclear genome, epigenome, microbiome or vial environment that may have arisen between duplicates.

Block: accounts for differences in the response variable between experimental blocks (e.g. to variance in temperature or composition of the fly food). In our experiment a block contained multiple replicates and a replicate was made up of 25 different cells each housing a pair of larvae.

Dyad ID: accounts for differences in the quality of the larval environment between pairs of larvae. For example, the moisture content of the food varied between pipette tips, despite our best efforts to keep this variable constant.

Model evaluation

Each model was evaluated and ranked by AICc values using the dredge function, from the Mumin package. There was rarely a single model that was unequivocally the best fit to the data, so we conducted model averaging for the set of models where delta was < 6, as suggested by Symonds and Moussalli (2011). The present study is a planned experiment to measure the effect of mtDNA on fitness, so we derived model estimates from the conditional model averages.

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Larval fitness measures

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Egg to adult viability analysis

The model:

Survival ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

# Fit the global model

survival_model <- lme4::glmer(Survived ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

Model evaluation

Table S2: Evaluation of the survivorship model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors duplicate strain, block and dyad ID. As there was no clear top model, the final model was calculated via model averaging.

# Compare all possible combinations of models (from the global model)

if(file.exists("survival_dredge.rds")){ # If already done, just load the results
  survival_dredge <- readRDS("survival_dredge.rds")
} else {survival_dredge <- dredge(survival_model) # If not already done, run all the models and save the results
lapply(c("survival_dredge"), save_it)
}


survival_table <- subset(survival_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(survival_table)[names(survival_table) == "(Intercept)"] <- "Intercept"
names(survival_table)[names(survival_table) == "Focal_haplotype"] <- "Focal haplotype"
names(survival_table)[names(survival_table) == "Sex"] <- "Sex"
names(survival_table)[names(survival_table) == "Social_haplotype"] <- "Social haplotype"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(survival_table)[names(survival_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(survival_table)[names(survival_table) == "df"] <- "Degrees of freedom"
names(survival_table)[names(survival_table) == "logLik"] <- "Log likelihood"
names(survival_table)[names(survival_table) == "AICc"] <- "AICc"
names(survival_table)[names(survival_table) == "delta"] <- "Delta"
names(survival_table)[names(survival_table) == "weight"] <- "Weight"

pander(survival_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
1 -0.225 NA NA NA NA NA NA NA 4 -1304 2617 0 0.4529
3 -0.2715 NA + NA NA NA NA NA 5 -1304 2618 1.066 0.2658
2 -0.1177 + NA NA NA NA NA NA 8 -1302 2619 2.469 0.1318
4 -0.1639 + + NA NA NA NA NA 9 -1301 2621 3.557 0.0765
5 -0.2554 NA NA + NA NA NA NA 8 -1303 2622 5.416 0.0302

\(~\)

Relative variable importance for each of the predictors and interactions in the survival model set. RVI can be interpreted as the likelihood the model term is present in the best performing model from the initial full set of possible models.

# present relative variable importance in a table 

sw(survival_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
  RVI
Sex 0.377
Focal_haplotype 0.231
Social_haplotype 0.065
Focal_haplotype:Sex 0.009
Sex:Social_haplotype 0.004
Focal_haplotype:Social_haplotype 0
Focal_haplotype:Sex:Social_haplotype 0

\(~\)

Model averaging

Table S3: Effects of mtDNA and sex on egg-to-adult viability. Conditional estimates from model averaging the full generalised linear mixed model are shown. Models were included in the averaging subset if delta < 6. Bold rows indicate significant effects.

# Model average

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict(), fitted() or eemeans()

top_survival_models <- get.models(survival_dredge, subset = delta < 6)

survival_avgm <- model.avg(top_survival_models)

# average the models with delta < 6

survival_CIs <- confint(survival_avgm) %>% as.data.frame()

survival_estimate <- coefTable(survival_avgm) %>% as.data.frame()

survival_p_values <- summary(survival_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

survival_model_avg <- data.frame(survival_estimate, survival_CIs, survival_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(survival_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(survival_model_avg)[names(survival_model_avg) == "Estimate"] <- "Conditional average estimate"
names(survival_model_avg)[names(survival_model_avg) == "Std..Error"] <- "Standard Error"
names(survival_model_avg)[names(survival_model_avg) == "X2.5.."] <- "2.5% Interval"
names(survival_model_avg)[names(survival_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(survival_model_avg, split.cell = 40, split.table = Inf, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval p
Intercept -0.219 0.309 -0.825 0.387 0.478
Sex: Male 0.093 0.095 -0.094 0.279 0.331
Focal haplotype: Brownsville -0.084 0.151 -0.38 0.213 0.58
Focal haplotype: Dahomey 0.08 0.151 -0.216 0.376 0.597
Focal haplotype: Israel -0.261 0.153 -0.561 0.039 0.088
Focal haplotype: Sweden -0.214 0.154 -0.515 0.088 0.164
Social haplotype: Brownsville -0.073 0.152 -0.371 0.224 0.628
Social haplotype: Dahomey 0.039 0.152 -0.258 0.336 0.797
Social haplotype: Israel 0.167 0.152 -0.13 0.464 0.27
Social haplotype: Sweden 0.021 0.153 -0.279 0.321 0.891 
# The full average provides a parameter average across all models considered, including ones where the parameter coefficient is set to 0. The conditional average reports coefficents for only the models where the parameter is included.

Comparison of focal and social haplotype effect sizes

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on viability are of similar magnitudes.

Focal haplotype effect sizes
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

survival_emmeans <- glmer(Survived ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(survival_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df z.ratio p.value
Barcelona / Brownsville 1.09 0.1646 Inf 0.5692 0.9795
Barcelona / Dahomey 0.9206 0.1391 Inf -0.5477 0.9823
Barcelona / Israel 1.29 0.1969 Inf 1.668 0.4539
Barcelona / Sweden 1.235 0.1895 Inf 1.373 0.6451
Brownsville / Dahomey 0.8447 0.1281 Inf -1.112 0.8001
Brownsville / Israel 1.184 0.1812 Inf 1.102 0.8058
Brownsville / Sweden 1.133 0.1745 Inf 0.8104 0.9275
Dahomey / Israel 1.401 0.2147 Inf 2.202 0.179
Dahomey / Sweden 1.341 0.2068 Inf 1.904 0.3151
Israel / Sweden 0.9571 0.1487 Inf -0.282 0.9986
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(survival_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df z.ratio p.value
Barcelona / Brownsville 1.077 0.1637 Inf 0.4863 0.9886
Barcelona / Dahomey 0.9589 0.1459 Inf -0.2758 0.9987
Barcelona / Israel 0.8495 0.1291 Inf -1.073 0.8203
Barcelona / Sweden 0.9798 0.1503 Inf -0.1329 0.9999
Brownsville / Dahomey 0.8906 0.136 Inf -0.7589 0.9422
Brownsville / Israel 0.7889 0.1204 Inf -1.553 0.5276
Brownsville / Sweden 0.91 0.1401 Inf -0.6126 0.9731
Dahomey / Israel 0.8859 0.135 Inf -0.7948 0.9322
Dahomey / Sweden 1.022 0.1573 Inf 0.14 0.9999
Israel / Sweden 1.153 0.1773 Inf 0.9283 0.886

\(~\)

Development time analysis

The model:

Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

# Fit the linear model

dev_model <- lmer(Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

Model evaluation

Table S4: Evaluation of the development time model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex as well as the random factors duplicate strain, block and dyad ID. As there was no clear top model, the final model was calculated via model averaging.

# Use dredge to compare all possible models derived from the global model

Dev_dredge <- dredge(dev_model)

development_table <- subset(Dev_dredge, delta < 6, recalc.weights = FALSE)  %>% as.data.frame()

names(development_table)[names(development_table) == "(Intercept)"] <- "Intercept"
names(development_table)[names(development_table) == "Focal_haplotype"] <- "Focal haplotype"
names(development_table)[names(development_table) == "Social_haplotype"] <- "Social haplotype"
names(development_table)[names(development_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(development_table)[names(development_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(development_table)[names(development_table) == "df"] <- "Degrees of freedom"
names(development_table)[names(development_table) == "logLik"] <- "Log likelihood"
names(development_table)[names(development_table) == "AICc"] <- "AICc"
names(development_table)[names(development_table) == "delta"] <- "Delta"
names(development_table)[names(development_table) == "weight"] <- "Weight"

pander(development_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
3 260.5 NA + NA NA NA NA NA 6 -2998 6008 0 0.7286
1 261.6 NA NA NA NA NA NA NA 5 -3001 6012 3.858 0.1059
4 261.9 + + NA NA NA NA NA 10 -2996 6012 3.98 0.09962
7 260.1 NA + + NA NA NA NA 10 -2997 6014 5.995 0.03637

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Relative variable importance for each of the predictors and interactions in the development time model set.

# present relative variable importance in a table 

sw(Dev_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
  RVI
Sex 0.874
Focal_haplotype 0.121
Social_haplotype 0.051
Focal_haplotype:Sex 0.003
Sex:Social_haplotype 0.001
Focal_haplotype:Social_haplotype 0
Focal_haplotype:Sex:Social_haplotype 0

\(~\)

Model averaging

Table S5: Effects of mtDNA and sex on egg-to-adult development time. Conditional estimates from model averaging the full generalised linear mixed model are shown. Models were included in the averaging subset if delta < 6. Bold rows indicate significant effects.

# Model averaging

Dev_time_avg <- (model.avg(Dev_dredge, subset = delta < 6))

Dev_CIs <- confint(Dev_time_avg) %>% as.data.frame()

Dev_estimate <- coefTable(Dev_time_avg) %>% as.data.frame()

Dev_p_values <- summary(Dev_time_avg)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Dev_model_avg <- data.frame(Dev_estimate, Dev_CIs, Dev_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Dev_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Dev_model_avg)[names(Dev_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Dev_model_avg)[names(Dev_model_avg) == "Std..Error"] <- "Standard Error"
names(Dev_model_avg)[names(Dev_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Dev_model_avg)[names(Dev_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(Dev_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval p
Intercept 260.8 2.353 256.2 265.4 0
Sex: Male 2.173 0.891 0.427 3.919 0.015
Focal haplotype: Brownsville -1.943 1.45 -4.784 0.899 0.18
Focal haplotype: Dahomey -2.688 1.417 -5.466 0.09 0.058
Focal haplotype: Israel -1.914 1.503 -4.86 1.031 0.203
Focal haplotype: Sweden -0.063 1.512 -3.025 2.9 0.967
Social haplotype: Brownsville 1.007 1.522 -1.977 3.991 0.508
Social haplotype: Dahomey -0.574 1.467 -3.45 2.302 0.696
Social haplotype: Israel 0.673 1.443 -2.156 3.502 0.641
Social haplotype: Sweden 1.376 1.479 -1.523 4.275 0.352

Comparison of focal and social haplotype effect sizes

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on development time are of similar magnitudes.

Focal haplotype effect sizes
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

dev_time_emmeans <- lmer(Dev_time ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(dev_time_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast estimate SE df t.ratio p.value
Barcelona - Brownsville 1.802 2.047 14.99 0.8804 0.8997
Barcelona - Dahomey 2.717 2.032 14.82 1.337 0.6737
Barcelona - Israel 1.861 2.069 14.6 0.8993 0.8926
Barcelona - Sweden -0.2396 2.124 15.62 -0.1128 1
Brownsville - Dahomey 0.9151 2.04 15.13 0.4486 0.9908
Brownsville - Israel 0.05866 2.086 14.93 0.02812 1
Brownsville - Sweden -2.042 2.134 15.88 -0.9567 0.8702
Dahomey - Israel -0.8565 2.064 14.6 -0.415 0.9931
Dahomey - Sweden -2.957 2.111 15.53 -1.401 0.636
Israel - Sweden -2.101 2.157 15.42 -0.974 0.8628
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(dev_time_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast estimate SE df t.ratio p.value
Barcelona - Brownsville -0.942 1.538 746.4 -0.6126 0.9731
Barcelona - Dahomey 0.7562 1.48 746.9 0.5109 0.9863
Barcelona - Israel -0.5576 1.452 742.6 -0.3841 0.9954
Barcelona - Sweden -1.404 1.498 752.5 -0.9371 0.8824
Brownsville - Dahomey 1.698 1.555 745.2 1.092 0.8107
Brownsville - Israel 0.3843 1.525 744.9 0.252 0.9991
Brownsville - Sweden -0.4619 1.568 749.2 -0.2945 0.9984
Dahomey - Israel -1.314 1.472 748.5 -0.8924 0.8997
Dahomey - Sweden -2.16 1.508 744.7 -1.432 0.6069
Israel - Sweden -0.8462 1.493 753.9 -0.5669 0.9798

\(~\)

Adult fitness measures

\(~\)

Body size analysis

We use wing length as a proxy for adult body size.

The model:

Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

body_size_model <- lmer(Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Model evaluation

Table S6: Evaluation of the wing length model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors duplicate strain, block and dyad ID. There was a clear top model; coefficients are displayed in Table S7.

# Compare all possible combinations of models (from the global model)

body_size_dredge <- dredge(body_size_model)

size_table <- subset(body_size_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()


names(size_table)[names(size_table) == "(Intercept)"] <- "Intercept"
names(size_table)[names(size_table) == "Focal_haplotype"] <- "Focal haplotype"
names(size_table)[names(size_table) == "Sex"] <- "Sex"
names(size_table)[names(size_table) == "Social_haplotype"] <- "Social haplotype"
names(size_table)[names(size_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(size_table)[names(size_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(size_table)[names(size_table) == "df"] <- "Degrees of freedom"
names(size_table)[names(size_table) == "logLik"] <- "Log likelihood"
names(size_table)[names(size_table) == "AICc"] <- "AICc"
names(size_table)[names(size_table) == "delta"] <- "Delta"
names(size_table)[names(size_table) == "weight"] <- "Weight"

pander(size_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
3 1.063 NA + NA NA NA NA NA 6 440.6 -869 0 0.9201

\(~\)

Relative variable importance for each of the predictors and interactions in the wing length model set.

# present relative variable importance in a table 

sw(body_size_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
  RVI
Sex 1
Social_haplotype 0.044
Focal_haplotype 0.038
Sex:Social_haplotype 0.004
Focal_haplotype:Sex 0.002
Focal_haplotype:Social_haplotype 0
Focal_haplotype:Sex:Social_haplotype 0

\(~\)

Best fitting model

One model was retained in the delta < 6 subset; model averaging is not required.

Table S7: Effects of mtDNA and sex on wing length. Results from the best fitting generalised linear mixed model are shown. Bold rows indicate significant effects.

# Fit the top model

body_size_model_final <- lmer(Wing_length ~ Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Size_CIs <- confint(body_size_model_final) %>%
  as.data.frame() %>% 
  slice(5:6)

Size_estimate <- coefTable(body_size_model_final) %>% as.data.frame()

Size_p_values <- summary(body_size_model_final)$coefficients[, 5] %>% as.data.frame() %>% rename(p = ".")

Size_model_avg <- data.frame(Size_estimate, Size_CIs, Size_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Size_model_avg) <- c("Intercept", "Sex: Male")

names(Size_model_avg)[names(Size_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Size_model_avg)[names(Size_model_avg) == "Std..Error"] <- "Standard Error"
names(Size_model_avg)[names(Size_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Size_model_avg)[names(Size_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Size_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = (2), round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval p
Intercept 1.063 0.015 1.024 1.1 0
Sex: Male -0.086 0.007 -0.1 -0.072 0

\(~\)

Comparison of focal and social haplotype effect sizes

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on body size are minimal and of similar magnitudes.

Focal haplotype effect sizes
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

size_emmeans <- lmer(Wing_length ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(size_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast estimate SE df t.ratio p.value
Barcelona - Brownsville 0.002352 0.01389 2.719 0.1693 0.9997
Barcelona - Dahomey -0.008054 0.01357 2.17 -0.5934 0.9645
Barcelona - Israel -0.005097 0.0138 2.547 -0.3694 0.9936
Barcelona - Sweden 0.006442 0.01434 3.083 0.4492 0.9876
Brownsville - Dahomey -0.01041 0.01381 2.434 -0.7535 0.9269
Brownsville - Israel -0.007449 0.01377 2.818 -0.5411 0.9756
Brownsville - Sweden 0.00409 0.01428 3.191 0.2864 0.9977
Dahomey - Israel 0.002957 0.01353 2.217 0.2186 0.9991
Dahomey - Sweden 0.0145 0.01387 2.682 1.045 0.8238
Israel - Sweden 0.01154 0.01412 2.992 0.817 0.9098
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(size_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast estimate SE df t.ratio p.value
Barcelona - Brownsville 0.009373 0.01331 409.4 0.7043 0.9555
Barcelona - Dahomey 0.01224 0.01304 403.8 0.939 0.8815
Barcelona - Israel -0.001812 0.01253 405.3 -0.1446 0.9999
Barcelona - Sweden 0.007855 0.0133 409.5 0.5904 0.9765
Brownsville - Dahomey 0.002868 0.01314 407.3 0.2183 0.9995
Brownsville - Israel -0.01118 0.01258 407.6 -0.8891 0.9009
Brownsville - Sweden -0.001517 0.01323 410.5 -0.1147 1
Dahomey - Israel -0.01405 0.01233 404.2 -1.14 0.7854
Dahomey - Sweden -0.004385 0.01301 409.6 -0.337 0.9972
Israel - Sweden 0.009667 0.01266 410.4 0.7634 0.9409

\(~\)

Female reproductive output

To effectively accommodate zero-inflation, we modelled female offspring production using the glmmTMB package (Brooks et al. 2017). This package allows us to fit hurdle models and zero-inflated models.

We analysed the number of offspring produced by females using a hurdle model with negative binomial errors. This approach allowed us to answer two questions: (1) did mtDNA affect the incidence of failing to produce any offspring? and (2) for females that produced at least one offspring, was the number of offspring produced affected by mtDNA?

The model:

Maternal_total_offspring ~ Focal_haplotype * Social_haplotype + (1|Strain) + (1|Block)

female_hurdle_model <- glmmTMB(Maternal_total_offspring ~ Social_haplotype * Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)

Model evaluation

Table S8: Evaluation of the female reproductive output model. All possible models were evaluated from the global model that included an interaction between focal haplotype and social haplotype and the random factors strain and block. As there was no clear top model, the final model was calculated via model averaging. The zero-inflated results relate to whether a female produced any offspring, while the conditional results relate to the number of offspring produced by fertile females.

# Compare all possible combinations of models (from the global model)

if(file.exists("female_dredge.rds")){ # If already done, just load the results
  female_dredge <- readRDS("female_dredge.rds")
} else {female_dredge <- dredge(female_hurdle_model)                  # If not already done, run all the models and save the results
lapply(c("female_dredge"), save_it)
}


female_table <- subset(female_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(female_table)[names(female_table) == "cond((Int))"] <- "Conditional intercept"
names(female_table)[names(female_table) == "zi((Int))"] <- "Zero-inflated intercept"
names(female_table)[names(female_table) == "disp((Int))"] <- "Dispersion factor intercept"
names(female_table)[names(female_table) == "cond(Focal_haplotype)"] <- "Conditional (Focal haplotype)"
names(female_table)[names(female_table) == "cond(Social_haplotype)"] <- "Conditional (Social haplotype)"
names(female_table)[names(female_table) == "cond(Focal_haplotype:Social_haplotype)"] <- "Conditional (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype)"] <- "Zero-inflated (Focal haplotype)"
names(female_table)[names(female_table) == "zi(Social_haplotype)"] <- "Zero-inflated (Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype:Social_haplotype)"] <- "Zero-inflated (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "df"] <- "Degrees of freedom"
names(female_table)[names(female_table) == "logLik"] <- "Log likelihood"
names(female_table)[names(female_table) == "AICc"] <- "AICc"
names(female_table)[names(female_table) == "delta"] <- "Delta"
names(female_table)[names(female_table) == "weight"] <- "Weight"

pander(female_table, split.cell = 40, split.table = Inf)
  Conditional intercept Zero-inflated intercept Dispersion factor intercept Conditional (Focal haplotype) Conditional (Social haplotype) Conditional (Focal haplotype x Social haplotype) Zero-inflated (Focal haplotype) Zero-inflated (Social haplotype) Zero-inflated (Focal haplotype x Social haplotype) Degrees of freedom Log likelihood AICc Delta Weight
18 3.932 -1.245 + + NA NA NA + NA 15 -1546 3124 0 0.5844
17 3.839 -1.245 + NA NA NA NA + NA 11 -1552 3126 2.057 0.209
2 3.932 -0.783 + + NA NA NA NA NA 11 -1553 3128 4.256 0.06959
26 3.932 -1.156 + + NA NA + + NA 19 -1545 3129 4.796 0.05313

\(~\)

Relative variable importance for each of the predictors and interactions in the female reproductive output model set.

# present relative variable importance in a table 

sw(female_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
  RVI
zi(Social_haplotype) 0.895
cond(Focal_haplotype) 0.736
zi(Focal_haplotype) 0.086
cond(Social_haplotype) 0.031
zi(Focal_haplotype:Social_haplotype) 0
cond(Focal_haplotype:Social_haplotype) 0

\(~\)

Model averaging

Zi (zero-hurdle requirement) and conditional (after hurdle) model coefficients, standard error and 95% confidence limits listed in Table 1 are shown for the female offspring production averaged model. Bold rows indicate significant effects.

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict()

top_female_models <- get.models(female_dredge, subset = delta < 6)

female_avgm <- model.avg(top_female_models)

# extract useful info

Female_CIs <- confint(female_avgm) %>% as.data.frame()

Female_estimate <- coefTable(female_avgm) %>% as.data.frame()

Female_p_values <- summary(female_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Female_model_avg <- data.frame(Female_estimate, Female_CIs, Female_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Female_model_avg) <- c("Conditional intercept", "Conditional focal haplotype: Brownsville", "Conditional focal haplotype: Dahomey", "Conditional focal haplotype: Israel", "Conditional focal haplotype: Sweden", "Zi intercept", "Zi social haplotype: Brownsville", "Zi social haplotype: Dahomey", "Zi social haplotype: Israel", "Zi social haplotype: Sweden", "Zi focal haplotype: Brownsville", "Zi focal haplotype: Dahomey", "Zi focal haplotype: Israel", "Zi focal haplotype: Sweden")


names(Female_model_avg)[names(Female_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Female_model_avg)[names(Female_model_avg) == "Std..Error"] <- "Standard Error"
names(Female_model_avg)[names(Female_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Female_model_avg)[names(Female_model_avg) == "X97.5.."] <- "97.5% Interval"

Female_model_avg %>%
  pander(split.cell = 40, split.table = Inf, emphasize.strong.rows = c(2, 5, 9), round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval p
Conditional intercept 3.911 0.076 3.762 4.059 0
Conditional focal haplotype: Brownsville -0.191 0.08 -0.347 -0.035 0.016
Conditional focal haplotype: Dahomey -0.084 0.076 -0.234 0.065 0.27
Conditional focal haplotype: Israel -0.005 0.081 -0.163 0.152 0.947
Conditional focal haplotype: Sweden -0.213 0.083 -0.377 -0.049 0.011
Zi intercept -1.205 0.285 -1.764 -0.645 0
Zi social haplotype: Brownsville 0.552 0.359 -0.151 1.255 0.124
Zi social haplotype: Dahomey 0.188 0.353 -0.504 0.88 0.595
Zi social haplotype: Israel 1.051 0.335 0.395 1.707 0.002
Zi social haplotype: Sweden 0.371 0.361 -0.337 1.079 0.305
Zi focal haplotype: Brownsville 0.003 0.325 -0.635 0.641 0.993
Zi focal haplotype: Dahomey -0.28 0.333 -0.933 0.373 0.401
Zi focal haplotype: Israel 0.214 0.327 -0.427 0.855 0.513
Zi focal haplotype: Sweden -0.396 0.364 -1.109 0.318 0.277

\(~\)

Comparison of focal and social haplotype effect sizes

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the indirect effects on female reproductive output are of similar magnitudes to, or exceed the size of, focal haplotype effects.

Did the female produce offspring? (Hurdle component of the model)
Focal haplotype effect sizes
# Fit a simplified version of the model without interactions that can be used with the emmeans() function. For the hurdle component of the model we simply fit a binary model with the response variable: did the female produce >= 1 offspring. This produces slightly different estimates from the full hurdle model but the effects sizes are extremely similar.

female_reproductive_output_zi <- female_reproductive_output %>% 
  mutate(produced_offspring = if_else(Maternal_total_offspring == 0, 0, 1))

female_emmeans_h <- glmmTMB(produced_offspring ~ Social_haplotype + Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output_zi, family = binomial, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(female_emmeans_h, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df t.ratio p.value
Barcelona / Brownsville 1.003 0.3253 413 0.008458 1
Barcelona / Dahomey 0.7557 0.2511 413 -0.843 0.917
Barcelona / Israel 1.238 0.4039 413 0.6557 0.9655
Barcelona / Sweden 0.6733 0.2443 413 -1.09 0.8117
Brownsville / Dahomey 0.7536 0.2499 413 -0.8531 0.9136
Brownsville / Israel 1.235 0.4014 413 0.6495 0.9667
Brownsville / Sweden 0.6715 0.243 413 -1.1 0.8063
Dahomey / Israel 1.639 0.5432 413 1.49 0.5695
Dahomey / Sweden 0.891 0.3269 413 -0.3146 0.9979
Israel / Sweden 0.5437 0.1961 413 -1.69 0.4415
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(female_emmeans_h, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df t.ratio p.value
Barcelona / Brownsville 1.726 0.62 413 1.519 0.5506
Barcelona / Dahomey 1.186 0.4195 413 0.4824 0.9889
Barcelona / Israel 2.848 0.956 413 3.119 0.01657
Barcelona / Sweden 1.394 0.5056 413 0.9153 0.8909
Brownsville / Dahomey 0.6872 0.2355 413 -1.095 0.8093
Brownsville / Israel 1.65 0.535 413 1.545 0.5336
Brownsville / Sweden 0.8076 0.2844 413 -0.6067 0.974
Dahomey / Israel 2.401 0.7643 413 2.753 0.04831
Dahomey / Sweden 1.175 0.407 413 0.466 0.9903
Israel / Sweden 0.4894 0.1611 413 -2.171 0.1929
The number of offspring produced by fertile females (conditional component of the model)
Focal haplotype effect sizes
# Fit a simplified version of the model without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

female_emmeans <- glmmTMB(Maternal_total_offspring ~ Social_haplotype + Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(female_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast ratio SE df t.ratio p.value
Barcelona / Brownsville 1.211 0.09641 401 2.403 0.1167
Barcelona / Dahomey 1.093 0.08359 401 1.161 0.7734
Barcelona / Israel 1.011 0.08162 401 0.1393 0.9999
Barcelona / Sweden 1.236 0.1043 401 2.506 0.09122
Brownsville / Dahomey 0.9026 0.07126 401 -1.298 0.6925
Brownsville / Israel 0.8352 0.06957 401 -2.162 0.1966
Brownsville / Sweden 1.021 0.0882 401 0.2353 0.9993
Dahomey / Israel 0.9254 0.07353 401 -0.9762 0.8658
Dahomey / Sweden 1.131 0.09338 401 1.487 0.5714
Israel / Sweden 1.222 0.1059 401 2.312 0.1432
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(female_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast ratio SE df t.ratio p.value
Barcelona / Brownsville 1.002 0.08302 401 0.02839 1
Barcelona / Dahomey 1.004 0.077 401 0.05686 1
Barcelona / Israel 0.9262 0.07605 401 -0.9337 0.8837
Barcelona / Sweden 0.9444 0.07505 401 -0.7196 0.9519
Brownsville / Dahomey 1.002 0.08278 401 0.0243 1
Brownsville / Israel 0.924 0.08113 401 -0.9 0.8968
Brownsville / Sweden 0.9422 0.08003 401 -0.7009 0.9562
Dahomey / Israel 0.9222 0.07511 401 -0.9948 0.8576
Dahomey / Sweden 0.9403 0.07402 401 -0.7817 0.9358
Israel / Sweden 1.02 0.08622 401 0.2305 0.9994

\(~\)

Create Figure 1

# Plotting with model predictions

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be averaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype in the Zi formula. These were found by inspection of the top model list above.

focal_female_zi_models <- get.models(female_dredge, subset = "26")

# Note that only model "26' contains focal haplotype in the Zi formula. No averaging takes place and estimates are derived straight from this model. The conditional averaged estimates from the female_avgm object are identical to the estimates in model "26".

# fit model "26"

focal_zi_female_avg <- glmmTMB(Maternal_total_offspring ~ Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block), na.action = na.fail, REML = FALSE)


# Now average models that contain the social haplotype predictor in the Zi formula.

social_female_zi_models <- get.models(female_dredge, subset = c("18", "17", "26"))

social_zi_female_avg <- model.avg(social_female_zi_models)

# The conditional averaged estimates from the female_avgm object are identical to the zi social haplotype estimates from the "full model "social_zi_female_avg" object.

# Now average models that contain the focal haplotype predictor in the conditional formula.

focal_female_con_models <- get.models(female_dredge, subset = c("18", "2", "26"))

focal_con_female_avg <- model.avg(focal_female_con_models)

# Estimates match female_avg

# Make a new dataframe, for which we will derive predictions. It's the same as the old data, except that we set Focal haplotype, block and duplicate to the same value for all observations. The re.form = NA argument sets random effects to 0, meaning population means are calculated.
 
new_data <- female_reproductive_output %>%
  ungroup() %>%
  select(Focal_haplotype, Strain, Block) %>%
  mutate(Social_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# First lets get predictions for the average number of offspring produced by females that produced at least one progeny, split by focal haplotype.

pred <- predict(focal_con_female_avg, se.fit = TRUE, type = "conditional", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred1 <- pred %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred2 <- pred %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred <- cbind(new_data, pred1, pred2) %>%
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE) %>%
  rename(Maternal_total_offspring = mean_estimate)

# Load the data for each individual female that produced offspring so that this can be plotted

female_cond_plot_data <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0) %>%
  ungroup() %>% 
  select(Individual, Focal_haplotype, Maternal_total_offspring)

# Now lets plot these predictions

female_focal_cond_plot <- female_cond_plot_data %>%
  ggplot(aes(x = Focal_haplotype, y = Maternal_total_offspring, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = female_cond_plot_data, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred, aes(x = Focal_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = pred, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now lets get the Zi predictions for focal haplotype

pred_ZI <- predict(focal_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_1 <- pred_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_2 <- pred_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI <- cbind(new_data, pred_ZI_1, pred_ZI_2) %>%
  transmute(Focal_haplotype, Strain, Block, Social_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  

# Plot
  
female_focal_zi_plot <- pred_focal_ZI %>%
  ggplot(aes(x = Focal_haplotype, y = mean_estimate, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_errorbar(aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Focal_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Female mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now create the newdata for social haplotype predictions

new_data_social <- female_reproductive_output %>%
  ungroup() %>%
  select(Social_haplotype, Strain, Block) %>%
  mutate(Focal_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# Get zi social haplotype predictions

pred_social_ZI <- predict(social_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data_social) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_social_1 <- pred_social_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_social_2 <- pred_social_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI_social <- cbind(new_data_social, pred_ZI_social_1, pred_ZI_social_2) %>% 
  transmute(Social_haplotype, Strain, Block, Focal_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
  # Plot 
  
female_social_zi_plot <- pred_focal_ZI_social %>%
  ggplot(aes(x = Social_haplotype, y = mean_estimate, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_errorbar(aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Social_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Male mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Lets calculate mean estimates from the raw data for the number of offspring produced by females split by social haplotype. We can't use model predictions here because social haplotype is not retained in the conditional part of the model 

female_reproductive_output_cond <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0)

female_social_raw_cond <- female_reproductive_output_cond %>% 
  dplyr::group_by(Social_haplotype) %>%
  dplyr::summarise(Mean_offspring = sum(Maternal_total_offspring) / length(Maternal_total_offspring), Lower = (Mean_offspring - SE(Maternal_total_offspring)), Upper = (Mean_offspring + SE(Maternal_total_offspring)), n = n()) %>% 
  rename(Maternal_total_offspring = Mean_offspring)

female_social_cond_plot <- female_reproductive_output_cond %>%
  ggplot(aes(x = Social_haplotype, y = Maternal_total_offspring, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = female_reproductive_output_cond, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
geom_point(data = female_social_raw_cond, aes(x = Social_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = female_social_raw_cond, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


ggarrange(female_focal_zi_plot, female_social_zi_plot, female_focal_cond_plot, female_social_cond_plot, labels = c("a", "b", "c", "d"))

Figure 1: mtDNA directly and indirectly affects female fitness. Panels a and b show model predictions of the mean proportion of females that produced offspring (the zero-inflated or hurdle component of the model) across a female focal mtDNA haplotypes and b social male mtDNA haplotypes. Panels c and d show the direct and indirect effect of mtDNA on the number of offspring produced by a female. Black points show model predictions of the mean for each haplotype in c and mean estimates from the raw data in d, while coloured points represent offspring produced by individual females. Model predictions were not calculated for d because social haplotype was not retained as a predictor for the conditional component of the averaged hurdle model. Error bars depict standard errors in all plots.

\(~\)

Male reproductive fitness

Our measure of male fitness involves both pre- and post-copulatory competitive ability; that is we assess in one measure the combination of 1) the ability of a male to inseminate a female in the presence of another male and 2) the competitive ability of his sperm within females that have been inseminated by another male.

We analyse male fitness as the proportion of offspring produced by mt-strain males competing against a standard bw male competitor. The data contains many 0 or 1 values - corresponding to a monopoly of female fertilisation by one of the males. To model this process we specify a beta-binomial distribution, which allows greater flexibility when modelling the distribution of the response.

The Brownsville haplotype renders males sterile alongside the w1118 nuclear background and sub-fertile alongside all other tested backgrounds. In our experiment, we find that Brownsville males are able to produce offspring but to a very limited capacity. Due to this, our model is unable to produce reliable estimates when the interaction between focal and social haplotype is included. We do not include the interaction in the full model.

We include an additional random effect - MALE ID - in the model to account for repeated measures of each pair of focal and rival males.

(Focal_male_offspring, bw_offspring) ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block) + (1|Male_ID)

response <- cbind(Male_fitness$Focal_male_offspring, Male_fitness$bw_male_offspring)

Male_fitness <- 
  Male_fitness %>% 
  rename(Male_ID = Individual)

male_model <- glmmTMB(response ~ Focal_haplotype + Social_haplotype + (1|Block) + (1|Strain) + (1|Male_ID), data = Male_fitness, family = "betabinomial", na.action = na.fail)

Model evaluation

Table S9: Evaluation of the male adult fitness model. All possible models were evaluated from the global model that included focal haplotype, social haplotype and the random factors strain, block and individual. As there was no clear top model, the final model was calculated via model averaging.

male_dredge <- dredge(male_model)

Male_table <- subset(male_dredge, delta < 6) %>% as.data.frame()


names(Male_table)[names(Male_table) == "(Intercept)"] <- "Intercept"
names(Male_table)[names(Male_table) == "Focal_haplotype"] <- "Focal haplotype"
names(Male_table)[names(Male_table) == "Social_haplotype"] <- "Social haplotype"
names(Male_table)[names(Male_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(Male_table)[names(Male_table) == "df"] <- "Degrees of freedom"
names(Male_table)[names(Male_table) == "logLik"] <- "Log likelihood"
names(Male_table)[names(Male_table) == "AICc"] <- "AICc"
names(Male_table)[names(Male_table) == "delta"] <- "Delta"
names(Male_table)[names(Male_table) == "weight"] <- "Weight"

pander(Male_table, split.cell = 40, split.table = Inf)
  cond((Int)) disp((Int)) cond(Focal_haplotype) cond(Social_haplotype) Degrees of freedom Log likelihood AICc Delta Weight
2 -0.3087 + + NA 9 -835.2 1689 0 0.906
4 -0.1068 + + + 13 -833.2 1693 4.531 0.09401

\(~\)

Relative variable importance for each of the predictors and interactions in the male reproductive fitness model set.

# present relative variable importance in a table 

sw(male_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
  RVI
cond(Focal_haplotype) 0.994
cond(Social_haplotype) 0.094

\(~\)

Model averaging

Model coefficients, standard error and 95% confidence limits listed in Table 2 are shown for the male adult fitness averaged model. Bold rows indicate significant effects.

# Model average

top_male_models <- get.models(male_dredge, subset = delta < 6)

male_avgm <- model.avg(top_male_models)

# extract useful information

# summary(model.avg(male_binary_dredge, subset = delta < 6))

Male_CIs <- confint(male_avgm) %>% as.data.frame()

Male_estimate <- coefTable(male_avgm) %>% as.data.frame()

Male_p_values <- summary(male_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Male_model_avg <- data.frame(Male_estimate, Male_CIs, Male_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Male_model_avg) <- c("Intercept", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Male_model_avg)[names(Male_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Male_model_avg)[names(Male_model_avg) == "Std..Error"] <- "Standard Error"
names(Male_model_avg)[names(Male_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Male_model_avg)[names(Male_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Male_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval p
Intercept -0.29 0.276 -0.83 0.251 0.294
Focal haplotype: Brownsville -2.614 0.479 -3.552 -1.676 0
Focal haplotype: Dahomey -0.355 0.285 -0.914 0.205 0.214
Focal haplotype: Israel -0.075 0.314 -0.691 0.54 0.81
Focal haplotype: Sweden 0.143 0.303 -0.452 0.737 0.638
Social haplotype: Brownsville -0.546 0.321 -1.175 0.084 0.089
Social haplotype: Dahomey -0.065 0.326 -0.704 0.574 0.841
Social haplotype: Israel -0.233 0.316 -0.852 0.385 0.46
Social haplotype: Sweden -0.066 0.323 -0.699 0.568 0.839

\(~\)

Comparison of focal and social haplotype effect sizes

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct effects on male reproductive competitive ability are much larger than the indirect effects.

Focal haplotype effect sizes
# We can use our original model

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(male_model, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df t.ratio p.value
Barcelona / Brownsville 14.27 7.073 343 5.364 1.489e-06
Barcelona / Dahomey 1.425 0.4305 343 1.173 0.7667
Barcelona / Israel 1.11 0.3844 343 0.3028 0.9982
Barcelona / Sweden 0.8743 0.2845 343 -0.4129 0.9939
Brownsville / Dahomey 0.09987 0.04649 343 -4.949 1.158e-05
Brownsville / Israel 0.07781 0.03733 343 -5.322 1.843e-06
Brownsville / Sweden 0.06126 0.02963 343 -5.773 1.739e-07
Dahomey / Israel 0.7791 0.2371 343 -0.8203 0.9243
Dahomey / Sweden 0.6134 0.1823 343 -1.645 0.4699
Israel / Sweden 0.7873 0.248 343 -0.7592 0.942
Social haplotype effect sizes
# Now for social haplotype

pairs(emmeans(male_model, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)
contrast odds.ratio SE df t.ratio p.value
Barcelona / Brownsville 1.726 0.5522 343 1.705 0.4323
Barcelona / Dahomey 1.067 0.3466 343 0.2009 0.9996
Barcelona / Israel 1.263 0.3972 343 0.742 0.9464
Barcelona / Sweden 1.068 0.3438 343 0.204 0.9996
Brownsville / Dahomey 0.6186 0.1971 343 -1.508 0.5582
Brownsville / Israel 0.7318 0.2286 343 -0.9996 0.8554
Brownsville / Sweden 0.6189 0.1957 343 -1.517 0.5519
Dahomey / Israel 1.183 0.3768 343 0.5279 0.9845
Dahomey / Sweden 1 0.3202 343 0.001441 1
Israel / Sweden 0.8456 0.2642 343 -0.5367 0.9835

\(~\)

Create Figure 2

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype. These were found by inspection of the top model list above.

focal_male_models <- get.models(male_dredge, subset = c("2", "4"))

focal_male_avg <- model.avg(focal_male_models)

# Note that the conditional averaged estimates from the male_avgm object are identical to the full averaged estimates for the focal_male_avg object for focal haplotype.

# Now average models that contain the social haplotype predictor.

social_male_models <- get.models(male_dredge, subset = c("4"))

# Note that there is only one model (the original full model) that contains social haplotype in the < 6 delta subset, so estimates are calculated directly from this model - no averaging occurs. The conditional averaged estimates from the male_avgm object are identical to the estimates from the full model.



# Focal new data

new_data_male <- Male_fitness %>%
  ungroup() %>%
  select(Focal_haplotype, Block, Strain, Male_ID) %>%
  mutate(Social_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Male_ID = "4") %>% 
  distinct() 


pred_male_focal <- predict(focal_male_avg, newdata = new_data_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_focal_1 <- pred_male_focal %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_focal_2 <- pred_male_focal %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_focal_male <- cbind(new_data_male, pred_male_focal_1, pred_male_focal_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)

# Plot

Male_focal_plot <- Male_fitness %>%
  ggplot(aes(x = Focal_haplotype, y = Proportion_focal, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_focal_male, aes(x = Focal_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_focal_male, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Social new data

new_data_social_male <- Male_fitness %>%
  ungroup() %>%
  select(Social_haplotype, Block, Strain, Male_ID) %>%
  mutate(Focal_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Male_ID = "4") %>% 
  distinct()

# predict.averaging works over the full average rather than the conditional average that we present. I use a workaround where I create another model average object but only using the models in the < 6 delta subset that include social haplotype. Here only two models make the cut - the full model is the only one containing social haplotype as a predictor so no averaging is neccessary. Plug the full model into the predict function.

pred_male_social <- predict(male_model, newdata = new_data_social_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_social_1 <- pred_male_social %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_social_2 <- pred_male_social %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_male_social <- cbind(new_data_social_male, pred_male_social_1, pred_male_social_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)
  

# Plot

Male_social_plot <- Male_fitness %>%
  ggplot(aes(x = Social_haplotype, y = Proportion_focal, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
 scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_male_social, aes(x = Social_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_male_social, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(Male_focal_plot, Male_social_plot, labels = c("a", "b"))

Figure 2: The proportion of offspring produced by mt-strain males competing with standard bw males. a shows the direct effect of mtDNA on male fitness. b shows the indirect genetic effect of female mtDNA on male fitness. Coloured points represent individual males, with larger points indicating a higher number of offspring produced in the vial (sired by either male). Black points show model predictions of the mean proportion of offspring sired by the mt-strain male, with associated standard errors.

\(~\)

Raw data and reproducibility

Table of raw data

For the purposes of completeness, transparency and data archiving, we include the raw data in this report.

Table S10: the raw data-set used in the present study, with NA values resulting from data collection mistakes removed (i.e. two females placed in competitive environment, no value recorded for whether the fly survived, flies that escaped during the experiment etc.).

kable(fitness_data %>% filter(!is.na(Survived)), "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "800px")
Individual Block Strain Dyad_ID Sex Focal_haplotype Social_haplotype Survived Dev_time Wing_length Maternal_female_offspring Maternal_male_offspring Maternal_total_offspring Paternal_focal_offspring Paternal_bw_offspring Proportion_focal
1 1 Barcelona 1 1 F Barcelona Barcelona 1 NA NA 35 24 59 NA NA NA
2 1 Barcelona 1 1 M Barcelona Barcelona 0 NA NA NA NA NA 0 0 0.0000000
3 1 Brownsville 1 2 F Brownsville Barcelona 1 249 NA 36 45 81 NA NA NA
4 1 Barcelona 1 2 M Barcelona Brownsville 1 NA NA NA NA NA 0 41 0.0000000
5 1 Dahomey 1 3 F Dahomey Barcelona 1 249 NA 17 23 40 NA NA NA
6 1 Barcelona 1 3 M Barcelona Dahomey 1 242 NA NA NA NA 42 0 1.0000000
7 1 Israel 1 4 F Israel Barcelona 1 NA NA 0 0 0 NA NA NA
8 1 Barcelona 1 4 M Barcelona Israel 1 242 NA NA NA NA 32 26 0.5517241
9 1 Sweden 1 5 F Sweden Barcelona 1 NA NA 7 15 22 NA NA NA
10 1 Barcelona 1 5 M Barcelona Sweden 1 267 NA NA NA NA 0 0 0.0000000
11 1 Barcelona 1 6 F Barcelona Brownsville 0 NA NA 0 0 0 NA NA NA
12 1 Brownsville 1 6 M Brownsville Barcelona 0 NA NA NA NA NA 0 0 0.0000000
15 1 Dahomey 1 8 F Dahomey Brownsville 1 242 NA 23 31 54 NA NA NA
16 1 Brownsville 1 8 M Brownsville Dahomey 1 245 NA NA NA NA 0 63 0.0000000
17 1 Israel 1 9 F Israel Brownsville 1 243 NA NA NA NA NA NA NA
18 1 Brownsville 1 9 M Brownsville Israel 1 NA NA NA NA NA 0 0 0.0000000
19 1 Sweden 1 10 F Sweden Brownsville 1 266 NA 26 39 65 NA NA NA
20 1 Brownsville 1 10 M Brownsville Sweden 0 NA NA NA NA NA 0 0 0.0000000
21 1 Barcelona 1 11 F Barcelona Dahomey 1 NA NA 8 21 29 NA NA NA
22 1 Dahomey 1 11 M Dahomey Barcelona 1 267 NA NA NA NA 11 26 0.2972973
23 1 Brownsville 1 12 F Brownsville Dahomey 1 242 NA 11 22 33 NA NA NA
24 1 Dahomey 1 12 M Dahomey Brownsville 1 NA NA NA NA NA 0 13 0.0000000
25 1 Dahomey 1 13 F Dahomey Dahomey 1 265 NA NA NA NA NA NA NA
26 1 Dahomey 1 13 M Dahomey Dahomey 1 NA NA NA NA NA 128 0 1.0000000
27 1 Israel 1 14 F Israel Dahomey 0 NA NA 0 0 0 NA NA NA
28 1 Dahomey 1 14 M Dahomey Israel 1 248 NA NA NA NA 163 0 1.0000000
29 1 Sweden 1 15 F Sweden Dahomey 1 245 NA 16 27 43 NA NA NA
30 1 Dahomey 1 15 M Dahomey Sweden 1 248 NA NA NA NA 105 0 1.0000000
31 1 Barcelona 1 16 F Barcelona Israel 0 NA NA 0 0 0 NA NA NA
32 1 Israel 1 16 M Israel Barcelona 1 249 NA NA NA NA 25 25 0.5000000
38 1 Israel 1 19 M Israel Israel 1 243 NA NA NA NA 0 58 0.0000000
41 1 Barcelona 1 21 F Barcelona Sweden 1 NA NA 0 0 0 NA NA NA
42 1 Sweden 1 21 M Sweden Barcelona 1 NA NA NA NA NA 0 0 0.0000000
43 1 Brownsville 1 22 F Brownsville Sweden 1 270 NA 31 22 53 NA NA NA
44 1 Sweden 1 22 M Sweden Brownsville 1 267 NA NA NA NA 0 0 0.0000000
45 1 Dahomey 1 23 F Dahomey Sweden 1 NA NA 23 32 55 NA NA NA
46 1 Sweden 1 23 M Sweden Dahomey 1 242 NA NA NA NA 0 52 0.0000000
47 1 Israel 1 24 F Israel Sweden 1 NA NA 18 22 40 NA NA NA
48 1 Sweden 1 24 M Sweden Israel 1 NA NA NA NA NA 91 14 0.8666667
49 1 Sweden 1 25 F Sweden Sweden 1 273 NA 18 19 37 NA NA NA
50 1 Sweden 1 25 M Sweden Sweden 1 NA NA NA NA NA 68 4 0.9444444
51 1 Barcelona 1 26 F Barcelona Barcelona 0 NA NA 0 0 0 NA NA NA
52 1 Barcelona 1 26 M Barcelona Barcelona 1 245 NA NA NA NA 138 0 1.0000000
53 1 Brownsville 1 27 F Brownsville Barcelona 1 269 NA 9 17 26 NA NA NA
54 1 Barcelona 1 27 M Barcelona Brownsville 1 269 NA NA NA NA 74 0 1.0000000
55 1 Dahomey 1 28 F Dahomey Barcelona 1 267 NA 25 19 44 NA NA NA
56 1 Barcelona 1 28 M Barcelona Dahomey 1 283 NA NA NA NA 0 25 0.0000000
57 1 Israel 1 29 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
58 1 Barcelona 1 29 M Barcelona Israel 1 288 NA NA NA NA 0 0 0.0000000
59 1 Sweden 1 30 F Sweden Barcelona 1 283 NA 28 19 47 NA NA NA
60 1 Barcelona 1 30 M Barcelona Sweden 1 283 NA NA NA NA 70 46 0.6034483
61 1 Barcelona 1 31 F Barcelona Brownsville 1 269 NA 0 0 0 NA NA NA
62 1 Brownsville 1 31 M Brownsville Barcelona 1 NA NA NA NA NA 0 32 0.0000000
63 1 Brownsville 1 32 F Brownsville Brownsville 1 NA NA 27 22 49 NA NA NA
64 1 Brownsville 1 32 M Brownsville Brownsville 1 NA NA NA NA NA 0 0 0.0000000
65 1 Dahomey 1 33 F Dahomey Brownsville 1 270 NA 13 16 29 NA NA NA
66 1 Brownsville 1 33 M Brownsville Dahomey 0 NA NA NA NA NA 0 0 0.0000000
67 1 Israel 1 34 F Israel Brownsville 1 273 NA 0 0 0 NA NA NA
68 1 Brownsville 1 34 M Brownsville Israel 1 270 NA NA NA NA 0 52 0.0000000
69 1 Sweden 1 35 F Sweden Brownsville 1 269 NA 9 10 19 NA NA NA
70 1 Brownsville 1 35 M Brownsville Sweden 1 271 NA NA NA NA 0 21 0.0000000
71 1 Barcelona 1 36 F Barcelona Dahomey 1 NA NA 0 0 0 NA NA NA
72 1 Dahomey 1 36 M Dahomey Barcelona 1 269 NA NA NA NA 0 0 0.0000000
73 1 Brownsville 1 37 F Brownsville Dahomey 1 243 NA 18 29 47 NA NA NA
74 1 Dahomey 1 37 M Dahomey Brownsville 1 248 NA NA NA NA 0 49 0.0000000
75 1 Dahomey 1 38 F Dahomey Dahomey 0 NA NA 0 0 0 NA NA NA
76 1 Dahomey 1 38 M Dahomey Dahomey 1 251 NA NA NA NA 0 0 0.0000000
77 1 Israel 1 39 F Israel Dahomey 1 NA NA 35 20 55 NA NA NA
78 1 Dahomey 1 39 M Dahomey Israel 1 248 NA NA NA NA 81 8 0.9101124
79 1 Sweden 1 40 F Sweden Dahomey 0 NA NA 0 0 0 NA NA NA
80 1 Dahomey 1 40 M Dahomey Sweden 1 274 NA NA NA NA 1 23 0.0416667
81 1 Barcelona 1 41 F Barcelona Israel 0 NA NA 0 0 0 NA NA NA
82 1 Israel 1 41 M Israel Barcelona 1 269 NA NA NA NA 0 55 0.0000000
85 1 Dahomey 1 43 F Dahomey Israel 0 NA NA 0 0 0 NA NA NA
86 1 Israel 1 43 M Israel Dahomey 1 267 NA NA NA NA 200 0 1.0000000
87 1 Israel 1 44 F Israel Israel 0 NA NA 0 0 0 NA NA NA
88 1 Israel 1 44 M Israel Israel 1 248 NA NA NA NA 0 0 0.0000000
89 1 Sweden 1 45 F Sweden Israel 1 288 NA 0 0 0 NA NA NA
90 1 Israel 1 45 M Israel Sweden 1 270 NA NA NA NA 22 49 0.3098592
91 1 Barcelona 1 46 F Barcelona Sweden 1 NA NA 19 34 53 NA NA NA
92 1 Sweden 1 46 M Sweden Barcelona 1 NA NA NA NA NA 75 0 1.0000000
93 1 Brownsville 1 47 F Brownsville Sweden 1 267 NA 0 0 0 NA NA NA
94 1 Sweden 1 47 M Sweden Brownsville 1 NA NA NA NA NA 104 2 0.9811321
95 1 Dahomey 1 48 F Dahomey Sweden 0 NA NA 0 0 0 NA NA NA
96 1 Sweden 1 48 M Sweden Dahomey 1 NA NA NA NA NA 0 0 0.0000000
99 1 Sweden 1 50 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
100 1 Sweden 1 50 M Sweden Sweden 1 NA NA NA NA NA 47 0 1.0000000
101 1 Barcelona 1 51 F Barcelona Barcelona 1 249 NA 23 27 50 NA NA NA
102 1 Barcelona 1 51 M Barcelona Barcelona 1 249 NA NA NA NA 0 0 0.0000000
103 1 Brownsville 1 52 F Brownsville Barcelona 1 269 NA 0 0 0 NA NA NA
104 1 Barcelona 1 52 M Barcelona Brownsville 1 NA NA NA NA NA 141 15 0.9038462
105 1 Dahomey 1 53 F Dahomey Barcelona 1 246 NA 22 21 43 NA NA NA
106 1 Barcelona 1 53 M Barcelona Dahomey 1 NA NA NA NA NA 3 27 0.1000000
107 1 Israel 1 54 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
108 1 Barcelona 1 54 M Barcelona Israel 0 NA NA NA NA NA 0 0 0.0000000
109 1 Sweden 1 55 F Sweden Barcelona 1 NA NA 8 8 16 NA NA NA
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1932 6 Israel 1 966 M Israel Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1933 6 Brownsville 1 967 F Brownsville Israel 1 NA 1.094 0 0 0 NA NA NA
1934 6 Israel 1 967 M Israel Brownsville 0 NA NA NA NA NA 0 0 0.0000000
1935 6 Dahomey 1 968 F Dahomey Israel 0 NA NA 0 0 0 NA NA NA
1936 6 Israel 1 968 M Israel Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1937 6 Israel 1 969 F Israel Israel 1 270 NA 0 0 0 NA NA NA
1938 6 Israel 1 969 M Israel Israel 1 268 0.844 NA NA NA 73 36 0.6697248
1939 6 Sweden 1 970 F Sweden Israel 1 253 1.068 28 18 46 NA NA NA
1940 6 Israel 1 970 M Israel Sweden 1 271 0.900 NA NA NA 39 16 0.7090909
1941 6 Barcelona 1 971 F Barcelona Sweden 1 245 1.040 21 25 46 NA NA NA
1942 6 Sweden 1 971 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1943 6 Brownsville 1 972 F Brownsville Sweden 1 241 NA 0 0 0 NA NA NA
1944 6 Sweden 1 972 M Sweden Brownsville 0 NA NA NA NA NA 0 0 0.0000000
1945 6 Dahomey 1 973 F Dahomey Sweden 1 251 NA 1 1 2 NA NA NA
1946 6 Sweden 1 973 M Sweden Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1951 6 Barcelona 1 976 F Barcelona Barcelona 0 NA NA 0 0 0 NA NA NA
1952 6 Barcelona 1 976 M Barcelona Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1953 6 Brownsville 1 977 F Brownsville Barcelona 0 NA NA 0 0 0 NA NA NA
1954 6 Barcelona 1 977 M Barcelona Brownsville 0 NA NA NA NA NA 0 0 0.0000000
1955 6 Dahomey 1 978 F Dahomey Barcelona 1 257 0.985 22 32 54 NA NA NA
1956 6 Barcelona 1 978 M Barcelona Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1957 6 Israel 1 979 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
1958 6 Barcelona 1 979 M Barcelona Israel 1 277 NA NA NA NA 0 66 0.0000000
1959 6 Sweden 1 980 F Sweden Barcelona 0 NA NA 0 0 0 NA NA NA
1960 6 Barcelona 1 980 M Barcelona Sweden 0 NA NA NA NA NA 0 0 0.0000000
1961 6 Barcelona 1 981 F Barcelona Brownsville 1 255 0.989 8 8 16 NA NA NA
1962 6 Brownsville 1 981 M Brownsville Barcelona 1 287 0.919 NA NA NA 0 66 0.0000000
1963 6 Brownsville 1 982 F Brownsville Brownsville 0 NA NA 0 0 0 NA NA NA
1964 6 Brownsville 1 982 M Brownsville Brownsville 1 247 0.999 NA NA NA 0 90 0.0000000
1965 6 Dahomey 1 983 F Dahomey Brownsville 0 NA NA 0 0 0 NA NA NA
1966 6 Brownsville 1 983 M Brownsville Dahomey 1 NA 0.910 NA NA NA 0 14 0.0000000
1967 6 Israel 1 984 F Israel Brownsville 0 NA NA 0 0 0 NA NA NA
1968 6 Brownsville 1 984 M Brownsville Israel 0 NA NA NA NA NA 0 0 0.0000000
1969 6 Sweden 1 985 F Sweden Brownsville 0 NA NA 0 0 0 NA NA NA
1970 6 Brownsville 1 985 M Brownsville Sweden 1 240 0.999 NA NA NA 0 0 0.0000000
1971 6 Barcelona 1 986 F Barcelona Dahomey 0 NA NA 0 0 0 NA NA NA
1972 6 Dahomey 1 986 M Dahomey Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1973 6 Brownsville 1 987 F Brownsville Dahomey 1 242 1.024 29 30 59 NA NA NA
1974 6 Dahomey 1 987 M Dahomey Brownsville 0 NA NA NA NA NA 0 0 0.0000000
1975 6 Dahomey 1 988 F Dahomey Dahomey 0 NA NA 0 0 0 NA NA NA
1976 6 Dahomey 1 988 M Dahomey Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1977 6 Israel 1 989 F Israel Dahomey 1 242 NA 0 0 0 NA NA NA
1978 6 Dahomey 1 989 M Dahomey Israel 0 NA NA NA NA NA 0 0 0.0000000
1979 6 Sweden 1 990 F Sweden Dahomey 0 NA NA 0 0 0 NA NA NA
1980 6 Dahomey 1 990 M Dahomey Sweden 0 NA NA NA NA NA 0 0 0.0000000
1981 6 Barcelona 1 991 F Barcelona Israel 0 NA NA 0 0 0 NA NA NA
1982 6 Israel 1 991 M Israel Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1983 6 Brownsville 1 992 F Brownsville Israel 1 245 0.996 23 18 41 NA NA NA
1984 6 Israel 1 992 M Israel Brownsville 0 NA NA NA NA NA 0 0 0.0000000
1985 6 Dahomey 1 993 F Dahomey Israel 1 250 NA 0 0 0 NA NA NA
1986 6 Israel 1 993 M Israel Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1987 6 Israel 1 994 F Israel Israel 1 272 0.994 2 4 6 NA NA NA
1988 6 Israel 1 994 M Israel Israel 0 NA NA NA NA NA 0 0 0.0000000
1989 6 Sweden 1 995 F Sweden Israel 0 NA NA 0 0 0 NA NA NA
1990 6 Israel 1 995 M Israel Sweden 1 243 1.012 NA NA NA 0 4 0.0000000
1991 6 Barcelona 1 996 F Barcelona Sweden 0 NA NA 0 0 0 NA NA NA
1992 6 Sweden 1 996 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
1993 6 Brownsville 1 997 F Brownsville Sweden 1 242 1.033 26 28 54 NA NA NA
1994 6 Sweden 1 997 M Sweden Brownsville 1 257 0.906 NA NA NA 48 60 0.4444444
1995 6 Dahomey 1 998 F Dahomey Sweden 1 275 0.908 0 0 0 NA NA NA
1996 6 Sweden 1 998 M Sweden Dahomey 0 NA NA NA NA NA 0 0 0.0000000
1997 6 Israel 1 999 F Israel Sweden 0 NA NA 0 0 0 NA NA NA
1998 6 Sweden 1 999 M Sweden Israel 1 248 0.987 NA NA NA 124 23 0.8435374
1999 6 Sweden 1 1000 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
2000 6 Sweden 1 1000 M Sweden Sweden 0 NA NA NA NA NA 0 0 0.0000000
2001 6 Barcelona 1 1001 F Barcelona Barcelona 0 NA NA 0 0 0 NA NA NA
2002 6 Barcelona 1 1001 M Barcelona Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2003 6 Brownsville 1 1002 F Brownsville Barcelona 1 269 1.042 23 15 38 NA NA NA
2004 6 Barcelona 1 1002 M Barcelona Brownsville 1 271 0.963 NA NA NA 97 24 0.8016529
2005 6 Dahomey 1 1003 F Dahomey Barcelona 0 NA NA 0 0 0 NA NA NA
2006 6 Barcelona 1 1003 M Barcelona Dahomey 1 244 1.059 NA NA NA 58 9 0.8656716
2007 6 Israel 1 1004 F Israel Barcelona 1 250 NA 27 21 48 NA NA NA
2008 6 Barcelona 1 1004 M Barcelona Israel 0 NA NA NA NA NA 0 0 0.0000000
2009 6 Sweden 1 1005 F Sweden Barcelona 1 248 1.149 23 20 43 NA NA NA
2010 6 Barcelona 1 1005 M Barcelona Sweden 0 NA NA NA NA NA 0 0 0.0000000
2011 6 Barcelona 1 1006 F Barcelona Brownsville 1 274 NA 0 0 0 NA NA NA
2012 6 Brownsville 1 1006 M Brownsville Barcelona 1 242 1.030 NA NA NA 0 32 0.0000000
2013 6 Brownsville 1 1007 F Brownsville Brownsville 1 254 NA 0 0 0 NA NA NA
2014 6 Brownsville 1 1007 M Brownsville Brownsville 1 243 0.947 NA NA NA 0 0 0.0000000
2015 6 Dahomey 1 1008 F Dahomey Brownsville 1 253 1.157 48 26 74 NA NA NA
2016 6 Brownsville 1 1008 M Brownsville Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2017 6 Israel 1 1009 F Israel Brownsville 0 NA NA 0 0 0 NA NA NA
2018 6 Brownsville 1 1009 M Brownsville Israel 0 NA NA NA NA NA 0 0 0.0000000
2019 6 Sweden 1 1010 F Sweden Brownsville 1 266 0.998 1 2 3 NA NA NA
2020 6 Brownsville 1 1010 M Brownsville Sweden 1 288 NA NA NA NA 0 0 0.0000000
2021 6 Barcelona 1 1011 F Barcelona Dahomey 1 247 1.092 16 26 42 NA NA NA
2022 6 Dahomey 1 1011 M Dahomey Barcelona 1 250 1.060 NA NA NA 45 3 0.9375000
2023 6 Brownsville 1 1012 F Brownsville Dahomey 1 272 1.005 14 21 35 NA NA NA
2024 6 Dahomey 1 1012 M Dahomey Brownsville 1 284 1.021 NA NA NA 4 0 1.0000000
2025 6 Dahomey 1 1013 F Dahomey Dahomey 1 245 1.074 38 32 70 NA NA NA
2026 6 Dahomey 1 1013 M Dahomey Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2027 6 Israel 1 1014 F Israel Dahomey 1 274 0.967 0 0 0 NA NA NA
2028 6 Dahomey 1 1014 M Dahomey Israel 1 241 1.042 NA NA NA 0 54 0.0000000
2029 6 Sweden 1 1015 F Sweden Dahomey 1 240 NA 33 31 64 NA NA NA
2030 6 Dahomey 1 1015 M Dahomey Sweden 0 NA NA NA NA NA 0 0 0.0000000
2031 6 Barcelona 1 1016 F Barcelona Israel 1 248 1.106 22 30 52 NA NA NA
2032 6 Israel 1 1016 M Israel Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2033 6 Brownsville 1 1017 F Brownsville Israel 1 245 1.063 7 9 16 NA NA NA
2034 6 Israel 1 1017 M Israel Brownsville 1 272 0.876 NA NA NA 0 96 0.0000000
2035 6 Dahomey 1 1018 F Dahomey Israel 1 252 0.985 18 21 39 NA NA NA
2036 6 Israel 1 1018 M Israel Dahomey 1 263 0.904 NA NA NA 0 58 0.0000000
2037 6 Israel 1 1019 F Israel Israel 1 231 1.053 28 25 53 NA NA NA
2038 6 Israel 1 1019 M Israel Israel 0 NA NA NA NA NA 0 0 0.0000000
2039 6 Sweden 1 1020 F Sweden Israel 1 238 1.205 0 0 0 NA NA NA
2040 6 Israel 1 1020 M Israel Sweden 1 247 NA NA NA NA NA NA NA
2041 6 Barcelona 1 1021 F Barcelona Sweden 1 273 1.106 31 38 69 NA NA NA
2042 6 Sweden 1 1021 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2043 6 Brownsville 1 1022 F Brownsville Sweden 1 275 NA 0 0 0 NA NA NA
2044 6 Sweden 1 1022 M Sweden Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2045 6 Dahomey 1 1023 F Dahomey Sweden 0 NA NA 0 0 0 NA NA NA
2046 6 Sweden 1 1023 M Sweden Dahomey 1 252 NA NA NA NA 0 19 0.0000000
2049 6 Sweden 1 1025 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
2050 6 Sweden 1 1025 M Sweden Sweden 1 268 NA NA NA NA 0 0 0.0000000
2051 6 Barcelona 1 1026 F Barcelona Barcelona 1 257 1.090 21 26 47 NA NA NA
2052 6 Barcelona 1 1026 M Barcelona Barcelona 1 255 0.995 NA NA NA 90 0 1.0000000
2053 6 Brownsville 1 1027 F Brownsville Barcelona 1 270 NA 0 0 0 NA NA NA
2054 6 Barcelona 1 1027 M Barcelona Brownsville 1 255 0.995 NA NA NA 0 16 0.0000000
2055 6 Dahomey 1 1028 F Dahomey Barcelona 0 NA NA 0 0 0 NA NA NA
2056 6 Barcelona 1 1028 M Barcelona Dahomey 1 255 0.973 NA NA NA 70 68 0.5072464
2057 6 Israel 1 1029 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
2058 6 Barcelona 1 1029 M Barcelona Israel 1 275 NA NA NA NA 0 0 0.0000000
2059 6 Sweden 1 1030 F Sweden Barcelona 0 NA NA 0 0 0 NA NA NA
2060 6 Barcelona 1 1030 M Barcelona Sweden 1 268 0.965 NA NA NA 81 33 0.7105263
2063 6 Brownsville 1 1032 F Brownsville Brownsville 1 245 1.165 39 34 73 NA NA NA
2064 6 Brownsville 1 1032 M Brownsville Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2065 6 Dahomey 1 1033 F Dahomey Brownsville 0 NA NA 0 0 0 NA NA NA
2066 6 Brownsville 1 1033 M Brownsville Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2067 6 Israel 1 1034 F Israel Brownsville 1 250 1.070 24 25 49 NA NA NA
2068 6 Brownsville 1 1034 M Brownsville Israel 1 254 NA NA NA NA 0 99 0.0000000
2069 6 Sweden 1 1035 F Sweden Brownsville 1 258 0.994 0 0 0 NA NA NA
2070 6 Brownsville 1 1035 M Brownsville Sweden 1 274 0.902 NA NA NA 0 43 0.0000000
2071 6 Barcelona 1 1036 F Barcelona Dahomey 1 241 1.103 22 23 45 NA NA NA
2072 6 Dahomey 1 1036 M Dahomey Barcelona 1 254 0.981 NA NA NA 14 29 0.3255814
2073 6 Brownsville 1 1037 F Brownsville Dahomey 1 230 0.916 20 20 40 NA NA NA
2074 6 Dahomey 1 1037 M Dahomey Brownsville 1 242 0.995 NA NA NA 0 17 0.0000000
2075 6 Dahomey 1 1038 F Dahomey Dahomey 1 252 1.102 27 28 55 NA NA NA
2076 6 Dahomey 1 1038 M Dahomey Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2077 6 Israel 1 1039 F Israel Dahomey 1 259 1.076 19 18 37 NA NA NA
2078 6 Dahomey 1 1039 M Dahomey Israel 1 275 0.944 NA NA NA 121 0 1.0000000
2079 6 Sweden 1 1040 F Sweden Dahomey 1 269 1.003 24 23 47 NA NA NA
2080 6 Dahomey 1 1040 M Dahomey Sweden 0 NA NA NA NA NA 0 0 0.0000000
2081 6 Barcelona 1 1041 F Barcelona Israel 1 245 1.212 0 0 0 NA NA NA
2082 6 Israel 1 1041 M Israel Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2083 6 Brownsville 1 1042 F Brownsville Israel 0 NA NA 0 0 0 NA NA NA
2084 6 Israel 1 1042 M Israel Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2087 6 Israel 1 1044 F Israel Israel 0 NA NA 0 0 0 NA NA NA
2088 6 Israel 1 1044 M Israel Israel 0 NA NA NA NA NA 0 0 0.0000000
2089 6 Sweden 1 1045 F Sweden Israel 1 276 0.940 6 13 19 NA NA NA
2090 6 Israel 1 1045 M Israel Sweden 0 NA NA NA NA NA 0 0 0.0000000
2091 6 Barcelona 1 1046 F Barcelona Sweden 0 NA NA 0 0 0 NA NA NA
2092 6 Sweden 1 1046 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2093 6 Brownsville 1 1047 F Brownsville Sweden 1 254 0.897 8 14 22 NA NA NA
2094 6 Sweden 1 1047 M Sweden Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2095 6 Dahomey 1 1048 F Dahomey Sweden 1 252 1.011 17 27 44 NA NA NA
2096 6 Sweden 1 1048 M Sweden Dahomey 1 262 0.970 NA NA NA 106 8 0.9298246
2097 6 Israel 1 1049 F Israel Sweden 0 NA NA 0 0 0 NA NA NA
2098 6 Sweden 1 1049 M Sweden Israel 1 286 NA NA NA NA 0 0 0.0000000
2099 6 Sweden 1 1050 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
2100 6 Sweden 1 1050 M Sweden Sweden 0 NA NA NA NA NA 0 0 0.0000000
2101 6 Barcelona 1 1051 F Barcelona Barcelona 0 NA NA 0 0 0 NA NA NA
2102 6 Barcelona 1 1051 M Barcelona Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2103 6 Brownsville 1 1052 F Brownsville Barcelona 1 252 1.085 27 28 55 NA NA NA
2104 6 Barcelona 1 1052 M Barcelona Brownsville 1 277 0.917 NA NA NA 0 82 0.0000000
2105 6 Dahomey 1 1053 F Dahomey Barcelona 0 NA NA 0 0 0 NA NA NA
2106 6 Barcelona 1 1053 M Barcelona Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2107 6 Israel 1 1054 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
2108 6 Barcelona 1 1054 M Barcelona Israel 0 NA NA NA NA NA 0 0 0.0000000
2109 6 Sweden 1 1055 F Sweden Barcelona 1 259 0.936 10 9 19 NA NA NA
2110 6 Barcelona 1 1055 M Barcelona Sweden 0 NA NA NA NA NA 0 0 0.0000000
2111 6 Barcelona 1 1056 F Barcelona Brownsville 0 NA NA 0 0 0 NA NA NA
2112 6 Brownsville 1 1056 M Brownsville Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2113 6 Brownsville 1 1057 F Brownsville Brownsville 0 NA NA 0 0 0 NA NA NA
2114 6 Brownsville 1 1057 M Brownsville Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2115 6 Dahomey 1 1058 F Dahomey Brownsville 0 NA NA 0 0 0 NA NA NA
2116 6 Brownsville 1 1058 M Brownsville Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2117 6 Israel 1 1059 F Israel Brownsville 0 NA NA 0 0 0 NA NA NA
2118 6 Brownsville 1 1059 M Brownsville Israel 0 NA NA NA NA NA 0 0 0.0000000
2119 6 Sweden 1 1060 F Sweden Brownsville 0 NA NA 0 0 0 NA NA NA
2120 6 Brownsville 1 1060 M Brownsville Sweden 0 NA NA NA NA NA 0 0 0.0000000
2121 6 Barcelona 1 1061 F Barcelona Dahomey 0 NA NA 0 0 0 NA NA NA
2122 6 Dahomey 1 1061 M Dahomey Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2123 6 Brownsville 1 1062 F Brownsville Dahomey 0 NA NA 0 0 0 NA NA NA
2124 6 Dahomey 1 1062 M Dahomey Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2125 6 Dahomey 1 1063 F Dahomey Dahomey 0 NA NA 0 0 0 NA NA NA
2126 6 Dahomey 1 1063 M Dahomey Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2127 6 Israel 1 1064 F Israel Dahomey 0 NA NA 0 0 0 NA NA NA
2128 6 Dahomey 1 1064 M Dahomey Israel 0 NA NA NA NA NA 0 0 0.0000000
2129 6 Sweden 1 1065 F Sweden Dahomey 0 NA NA 0 0 0 NA NA NA
2130 6 Dahomey 1 1065 M Dahomey Sweden 0 NA NA NA NA NA 0 0 0.0000000
2131 6 Barcelona 1 1066 F Barcelona Israel 0 NA NA 0 0 0 NA NA NA
2132 6 Israel 1 1066 M Israel Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2133 6 Brownsville 1 1067 F Brownsville Israel 0 NA NA 0 0 0 NA NA NA
2134 6 Israel 1 1067 M Israel Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2135 6 Dahomey 1 1068 F Dahomey Israel 1 256 0.999 28 18 46 NA NA NA
2136 6 Israel 1 1068 M Israel Dahomey 1 275 0.827 NA NA NA 0 30 0.0000000
2137 6 Israel 1 1069 F Israel Israel 0 NA NA 0 0 0 NA NA NA
2138 6 Israel 1 1069 M Israel Israel 0 NA NA NA NA NA 0 0 0.0000000
2139 6 Sweden 1 1070 F Sweden Israel 0 NA NA 0 0 0 NA NA NA
2140 6 Israel 1 1070 M Israel Sweden 0 NA NA NA NA NA 0 0 0.0000000
2141 6 Barcelona 1 1071 F Barcelona Sweden 0 NA NA 0 0 0 NA NA NA
2142 6 Sweden 1 1071 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2143 6 Brownsville 1 1072 F Brownsville Sweden 0 NA NA 0 0 0 NA NA NA
2144 6 Sweden 1 1072 M Sweden Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2145 6 Dahomey 1 1073 F Dahomey Sweden 0 NA NA 0 0 0 NA NA NA
2146 6 Sweden 1 1073 M Sweden Dahomey 1 274 NA NA NA NA 0 49 0.0000000
2147 6 Israel 1 1074 F Israel Sweden 0 NA NA 0 0 0 NA NA NA
2148 6 Sweden 1 1074 M Sweden Israel 0 NA NA NA NA NA 0 0 0.0000000
2149 6 Sweden 1 1075 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
2150 6 Sweden 1 1075 M Sweden Sweden 0 NA NA NA NA NA 0 0 0.0000000
2151 6 Barcelona 1 1076 F Barcelona Barcelona 0 NA NA 0 0 0 NA NA NA
2152 6 Barcelona 1 1076 M Barcelona Barcelona 1 244 NA NA NA NA 0 0 0.0000000
2153 6 Brownsville 1 1077 F Brownsville Barcelona 0 NA NA 0 0 0 NA NA NA
2154 6 Barcelona 1 1077 M Barcelona Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2155 6 Dahomey 1 1078 F Dahomey Barcelona 1 256 1.080 13 4 17 NA NA NA
2156 6 Barcelona 1 1078 M Barcelona Dahomey 1 245 1.017 NA NA NA 46 38 0.5476190
2157 6 Israel 1 1079 F Israel Barcelona 0 NA NA 0 0 0 NA NA NA
2158 6 Barcelona 1 1079 M Barcelona Israel 1 243 1.059 NA NA NA 50 2 0.9615385
2159 6 Sweden 1 1080 F Sweden Barcelona 0 NA NA 0 0 0 NA NA NA
2160 6 Barcelona 1 1080 M Barcelona Sweden 1 261 0.952 NA NA NA 107 53 0.6687500
2161 6 Barcelona 1 1081 F Barcelona Brownsville 0 NA NA 0 0 0 NA NA NA
2162 6 Brownsville 1 1081 M Brownsville Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2163 6 Brownsville 1 1082 F Brownsville Brownsville 0 NA NA 0 0 0 NA NA NA
2164 6 Brownsville 1 1082 M Brownsville Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2165 6 Dahomey 1 1083 F Dahomey Brownsville 0 NA NA 0 0 0 NA NA NA
2166 6 Brownsville 1 1083 M Brownsville Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2167 6 Israel 1 1084 F Israel Brownsville 0 NA NA 0 0 0 NA NA NA
2168 6 Brownsville 1 1084 M Brownsville Israel 1 269 NA NA NA NA 0 79 0.0000000
2169 6 Sweden 1 1085 F Sweden Brownsville 0 NA NA 0 0 0 NA NA NA
2170 6 Brownsville 1 1085 M Brownsville Sweden 1 260 0.993 NA NA NA 0 45 0.0000000
2171 6 Barcelona 1 1086 F Barcelona Dahomey 1 258 1.057 25 28 53 NA NA NA
2172 6 Dahomey 1 1086 M Dahomey Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2173 6 Brownsville 1 1087 F Brownsville Dahomey 0 NA NA 0 0 0 NA NA NA
2174 6 Dahomey 1 1087 M Dahomey Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2175 6 Dahomey 1 1088 F Dahomey Dahomey 0 NA NA 0 0 0 NA NA NA
2176 6 Dahomey 1 1088 M Dahomey Dahomey 1 NA NA NA NA NA 0 0 0.0000000
2177 6 Israel 1 1089 F Israel Dahomey 0 NA NA 0 0 0 NA NA NA
2178 6 Dahomey 1 1089 M Dahomey Israel 1 262 0.988 NA NA NA 0 48 0.0000000
2179 6 Sweden 1 1090 F Sweden Dahomey 0 NA NA 0 0 0 NA NA NA
2180 6 Dahomey 1 1090 M Dahomey Sweden 0 NA NA NA NA NA 0 0 0.0000000
2181 6 Barcelona 1 1091 F Barcelona Israel 0 NA NA 0 0 0 NA NA NA
2182 6 Israel 1 1091 M Israel Barcelona 1 257 NA NA NA NA NA NA NA
2183 6 Brownsville 1 1092 F Brownsville Israel 0 NA NA 0 0 0 NA NA NA
2184 6 Israel 1 1092 M Israel Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2185 6 Dahomey 1 1093 F Dahomey Israel 1 275 NA 13 18 31 NA NA NA
2186 6 Israel 1 1093 M Israel Dahomey 0 NA NA NA NA NA 0 0 0.0000000
2187 6 Israel 1 1094 F Israel Israel 1 245 1.100 19 24 43 NA NA NA
2188 6 Israel 1 1094 M Israel Israel 0 NA NA NA NA NA 0 0 0.0000000
2189 6 Sweden 1 1095 F Sweden Israel 1 251 1.124 21 45 66 NA NA NA
2190 6 Israel 1 1095 M Israel Sweden 0 NA NA NA NA NA 0 0 0.0000000
2191 6 Barcelona 1 1096 F Barcelona Sweden 0 NA NA 0 0 0 NA NA NA
2192 6 Sweden 1 1096 M Sweden Barcelona 0 NA NA NA NA NA 0 0 0.0000000
2193 6 Brownsville 1 1097 F Brownsville Sweden 1 275 NA 0 0 0 NA NA NA
2194 6 Sweden 1 1097 M Sweden Brownsville 0 NA NA NA NA NA 0 0 0.0000000
2195 6 Dahomey 1 1098 F Dahomey Sweden 0 NA NA 0 0 0 NA NA NA
2196 6 Sweden 1 1098 M Sweden Dahomey 1 253 1.071 NA NA NA 58 81 0.4172662
2197 6 Israel 1 1099 F Israel Sweden 1 NA 1.114 9 14 23 NA NA NA
2198 6 Sweden 1 1099 M Sweden Israel 1 278 NA NA NA NA 0 0 0.0000000
2199 6 Sweden 1 1100 F Sweden Sweden 0 NA NA 0 0 0 NA NA NA
2200 6 Sweden 1 1100 M Sweden Sweden 1 274 NA NA NA NA 0 0 0.0000000

\(~\)

Columns represent:

Individual: the focal fly being tested.

Block: the experiment was completed in six separate blocks, identified here 1-6.

Strain: which of the 10 combinations of haplotype and duplicate strain was the individual from?

Dyad_ID: this identifies the pipette tip environment that the individual developed in.

Sex: the sex of the focal individual.

Focal_haplotype: the mtDNA haplotype carried by the focal individual.

Social_haplotype: the mtDNA haplotype carried by the focal individual’s competitor.

Survived: did the focal individual survive to adulthood (1) or die during larval development (0)?

Dev_time: the hours taken for the focal individual to progress from an egg to an adult. NA values indicate where individuals did not survive or development time could not be measured.

Wing_length: the length in mm of the focal individual’s right wing.

Maternal_female_offspring: the number of adult female offspring the focal mt-strain female produced over a two day period.

Maternal_male_offspring: the number of adult male offspring the focal mt-strain female produced over a two day period.

Maternal_total_offspring: the total number of adult offspring the focal mt-strain female produced over a two day period.

Paternal_focal_offspring: the number of red-eye phenotype offspring sired by a mt-strain male in the adult male fitness assay.

Paternal_bw_offspring: the number of brown-eye phenotype offspring sired by a bw competitor male in the adult male fitness assay.

Proportion focal: the proportion of offspring produced by the mt-strain male in the adult male fitness assay.

R session information

This section provides information on the operating system and R packages attached during the production of this document, to allow easier replication of the analysis.

sessionInfo() %>% pander

R version 3.6.2 (2019-12-12)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8

attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: groupdata2(v.1.1.2), pander(v.0.6.3), kableExtra(v.1.1.0), ggbeeswarm(v.0.6.0), ggpubr(v.0.2.5), magrittr(v.1.5), emmeans(v.1.4.4), MuMIn(v.1.43.15), glmmTMB(v.1.0.1), lmerTest(v.3.1-1), lme4(v.1.1-21), Matrix(v.1.2-18), png(v.0.1-7), forcats(v.0.5.0), stringr(v.1.4.0), dplyr(v.0.8.5), purrr(v.0.3.3), readr(v.1.3.1), tidyr(v.1.0.2), tibble(v.2.1.3), ggplot2(v.3.3.0) and tidyverse(v.1.3.0)

loaded via a namespace (and not attached): nlme(v.3.1-144), fs(v.1.3.1), pbkrtest(v.0.4-7), lubridate(v.1.7.4), webshot(v.0.5.2), httr(v.1.4.1), numDeriv(v.2016.8-1.1), tools(v.3.6.2), TMB(v.1.7.16), backports(v.1.1.5), R6(v.2.4.1), vipor(v.0.4.5), DBI(v.1.1.0), colorspace(v.1.4-1), withr(v.2.1.2), tidyselect(v.1.0.0), compiler(v.3.6.2), cli(v.2.0.1), rvest(v.0.3.5), xml2(v.1.2.2), labeling(v.0.3), scales(v.1.1.0), mvtnorm(v.1.0-12), digest(v.0.6.23), minqa(v.1.2.4), rmarkdown(v.2.1), base64enc(v.0.1-3), pkgconfig(v.2.0.3), htmltools(v.0.4.0), highr(v.0.8), dbplyr(v.1.4.2), rlang(v.0.4.4), readxl(v.1.3.1), rstudioapi(v.0.11), farver(v.2.0.3), generics(v.0.0.2), jsonlite(v.1.6.1), Rcpp(v.1.0.3), munsell(v.0.5.0), fansi(v.0.4.1), lifecycle(v.0.1.0), stringi(v.1.4.5), yaml(v.2.2.1), MASS(v.7.3-51.5), plyr(v.1.8.5), parallel(v.3.6.2), crayon(v.1.3.4), lattice(v.0.20-38), cowplot(v.1.0.0), haven(v.2.2.0), splines(v.3.6.2), hms(v.0.5.3), knitr(v.1.28), pillar(v.1.4.3), boot(v.1.3-24), estimability(v.1.3), ggsignif(v.0.6.0), stats4(v.3.6.2), reprex(v.0.3.0), glue(v.1.3.1), evaluate(v.0.14), modelr(v.0.1.5), vctrs(v.0.2.2), nloptr(v.1.2.1), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.12), xtable(v.1.8-4), broom(v.0.5.4), coda(v.0.19-3), viridisLite(v.0.3.0) and beeswarm(v.0.2.3)

References

Brooks, Mollie E, Kasper Kristensen, Koen J van Benthem, Arni Magnusson, Casper W Berg, Anders Nielsen, Hans J Skaug, Martin Maechler, and Benjamin M Bolker. 2017. “Modeling Zero-Inflated Count Data with glmmTMB.” Journal Article. BioRxiv, 132753.

Symonds, Matthew R. E., and Adnan Moussalli. 2011. “A Brief Guide to Model Selection, Multimodel Inference and Model Averaging in Behavioural Ecology Using Akaike’s Information Criterion.” Journal Article. Behavioral Ecology and Sociobiology 65 (1): 13–21. https://doi.org/10.1007/s00265-010-1037-6.

---
title: "Sibling rivalry vs mother’s curse: can kin competition facilitate a response to selection on male mitochondria?"  
author: "Thomas A Keaney, Heidi WS Wong, Damian K Dowling, Therésa M Jones and Luke Holman"
bibliography: "supp_references.bib"
subtitle: Supplementary material
output:
  html_document:
    code_folding: hide
    depth: 1
    number_sections: no
    theme: yeti
    toc: yes
    toc_float: yes
    code_download: true
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = FALSE, warning = FALSE, message = FALSE)
```

Published in _Proceedings of the Royal Society B_

Article DOI: http://dx.doi.org/10.1098/rspb.2020.0575

# Load all required packages

```{r, load packages}

library(tidyverse) # data re-shaping, ggplot, stringr and more
library(png) # to load images
library(grid) # to plot images
library(lme4) # for the lmer and glmer mixed model functions
library(lmerTest) # Used to get p-values for lmer models using simulation. It over-writes lmer() with a new version
library(glmmTMB) # for zero-inflated or hurdle glms
library(MuMIn) # for model selection and averaging
library(emmeans) # for pairwise comparisons
library(ggpubr) # for the ggarrange function
library(ggbeeswarm) # violin plots with data points
library(kableExtra) # nice tables that can scroll
library(pander) # more nice tables
library(groupdata2) # for assigning rows in data-frames to groups

```


# Supplementary methods

Schematic for replacement of the unknown _Sxl-GFP_ nuclear background with the isogenic _w^1118^_ background

```{r fig.width=6, fig.height=8}

img <- readPNG("Crossing_scheme.png")
 grid.raster(img)
```

**Figure S1**: Crossing scheme used to create a standard homozygous _w^1118-GFP^_ line. Males from this line were crossed with females carrying a specific mitochondrial haplotype, to create experimental _mt_-strains. These newly produced strains carried the mitochondrial haplotype of the female and were heterozygous for the _Sxl-GFP_ construct. G1 = the first generation of the cross.



**Table S1**: Recipe for food medium used in our experiment. The provided quantities make ~ 1 litre of food.

```{r, food recipe}

tibble("Ingredients" = c("Soy flour", "Cornmeal", "Yeast", "Dextrose", "Agar", "Water", "Tegosept", "Acid mix (4 ml orthophosphoric acid, 41 ml propionic acid, 55 ml water to make 100 mls)"),
       "Quantity" = c("20 g", "73 g", "35 g", "75 g", "6 g", "1000 mls", "17 mls", "14 mls")) %>% 
  pander(split.cell = 20, split.table = Inf)

```



# Data analysis and supplementary results


Here we include all code used to run our analysis and create Figure 1 and 2, our rationale behind the modelling approaches, and tables S2-9.

#### Read in the data and create some helpful functions


```{r data and functions}

# Read in data frame and add Dyad_ID column

all_data <- read.csv("mtDNA_larval_competition_data.csv") %>% 
  arrange(Individual) %>%
  group(n = 2, method = "greedy") %>% rename(Dyad_ID = .groups)

# helper function for saving large model objects and naming the file object.rds

save_it <- function(object){
  saveRDS(get(object), file = paste(object, ".rds", sep = ""))}

# Create a function for standard error

SE <- function(x) sd(x)/sqrt(length(x))

```


#### Data preparation for all responses

```{r data cleaning}

# Clean the dataset up for analysis

# Select the columns we're interested in and rename them

fitness_data <- dplyr::select(all_data, Individual, Block, Strain,  Dyad_ID, Sex, Focal.haplotype, Social.haplotype, Mortality, Development.time..hrs., Wing.size..mm., Female.offspring, Male.offspring, Total.female.assay, Total.red.all.vials, Total.bw.all.vials, Proportion.red.all.vials) %>% 
  
rename(Block = Block, Survived = Mortality, Focal_haplotype = Focal.haplotype, Social_haplotype = Social.haplotype, Dev_time = Development.time..hrs., Wing_length = Wing.size..mm., Maternal_female_offspring = Female.offspring, Maternal_male_offspring = Male.offspring, Maternal_total_offspring = Total.female.assay, Paternal_focal_offspring = Total.red.all.vials, Paternal_bw_offspring = Total.bw.all.vials, Proportion_focal = Proportion.red.all.vials)

# Define new levels for mortality to make renaming possible 

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "NO")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "YES")

# Rename the mortality responses
# L means died as larva, P means died as pupae, N means did not die (i.e. eclosed as an adult)

fitness_data$Survived[fitness_data$Survived == 'L'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'P'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'N'] <- 'YES'

# Now that it makes sense change "YES" to 1 and "NO" to 0 so we can fit a binomial GLM.

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "1")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "0")

fitness_data$Survived[fitness_data$Survived == "YES"] <- 1
fitness_data$Survived[fitness_data$Survived == "NO"] <- 0

# Make the factor numeric 

fitness_data$Survived <- as.numeric(as.character(fitness_data$Survived))


# Create specific datasets for each fitness trait

# Remove all rows that contain an NA value in the survival column. The NAs mean things like the GFP sorting did not work, or the vial was never set up due to a shortage of larvae. They are not meaningful data, and we remove them here.

survival <- fitness_data %>% filter(!is.na(Survived)) 
  
# Remove all rows that contain an NA value in the development time column. This instances represent flies where we failed to measure development time. 

larval_development <- fitness_data %>% filter(!is.na(Dev_time)) 

# Remove all rows that contain an NA value in the wing length column. Wing length was not measured in Blocks 1 and 2.

body_size <- fitness_data %>% filter(!is.na(Wing_length)) 

# Remove all rows that contain an NA value in the female reproductive output column (e.g. all the males), and where females did not survive to adulthood (coded as producing 0 offspring). 

female_reproductive_output <- fitness_data %>% filter(!is.na(Maternal_total_offspring), Survived == 1)


# Male adult fitness

# First remove females from the dataset.

Male_fitness <- fitness_data %>% filter(!is.na(Paternal_focal_offspring)) 

# Create an offspring counted column so that the data is correctly formatted for a binomial success-failure model.

Male_fitness$Offspring_counted <- Male_fitness$Paternal_focal_offspring + Male_fitness$Paternal_bw_offspring

# Now lets remove vials where the female produced 0 offspring (this includes trials where the male died in development), as we cannot determine paternity from these vials. The tidy up the dataframe by removing unneccessary columns

Male_fitness <- Male_fitness %>% filter(!(Offspring_counted == 0)) %>% 
  select(-Maternal_female_offspring, -Maternal_male_offspring, -Maternal_total_offspring) %>% 
  rename(Focal_male_offspring = Paternal_focal_offspring, bw_male_offspring = Paternal_bw_offspring)

```


## Modelling approach

We analysed the data using linear and generalised linear mixed models in the `lmer` and`glmmTMB` packages for R.

**Fixed effects**

For the analysis of fitness traits expressed in both sexes (survival, development time and body size), we are interested in the effect of an individual’s focal mtDNA, the mtDNA of a social competitor and the effect of sex on fitness. To measure these potential effects each model contained the following fixed effects and the three-way interaction between these variables:

Focal haplotype: the mtDNA haplotype that an individual carries.

Social haplotype: the mtDNA haplotype carried by a social partner during larval development.

Sex: the sex of the focal individual. The social partner's sex was always opposite to that of the focal individual.

**Random effects**

Duplicate strain: Each haplotype has been introgressed alongside the _w^1118^_ nuclear background in two independent duplicates, creating 10 total strains. Within each block we ran multiple replicates that were made up of flies from the first set of strains (i.e. Barcelona 1, Brownsville 1 etc.), while the other half used only flies from the second set of strains. This random effect accounts for any residual differences in the nuclear genome, epigenome, microbiome or vial environment that may have arisen between duplicates.

Block: accounts for differences in the response variable between experimental blocks (e.g. to variance in temperature or composition of the fly food). In our experiment a block contained multiple replicates and a replicate was made up of 25 different cells each housing a pair of larvae.

Dyad ID: accounts for differences in the quality of the larval environment between pairs of larvae. For example, the moisture content of the food varied between pipette tips, despite our best efforts to keep this variable constant.  

**Model evaluation**

Each model was evaluated and ranked by AICc values using the `dredge` function, from the `Mumin` package. There was rarely a single model that was unequivocally the best fit to the data, so we conducted model averaging for the set of models where delta was < 6, as suggested by Symonds and Moussalli [-@RN455]. The present study is a planned experiment to measure the effect of mtDNA on fitness, so we derived model estimates from the conditional model averages.

$~$


## Larval fitness measures

$~$


### Egg to adult viability analysis
* * *

The model:

_Survival ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_

```{r survival model}

# Fit the global model

survival_model <- lme4::glmer(Survived ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

```

#### Model evaluation

**Table S2**: Evaluation of the survivorship model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors duplicate strain, block and dyad ID. As there was no clear top model, the final model was calculated via model averaging.

```{r survival dredge table}

# Compare all possible combinations of models (from the global model)

if(file.exists("survival_dredge.rds")){ # If already done, just load the results
  survival_dredge <- readRDS("survival_dredge.rds")
} else {survival_dredge <- dredge(survival_model) # If not already done, run all the models and save the results
lapply(c("survival_dredge"), save_it)
}


survival_table <- subset(survival_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(survival_table)[names(survival_table) == "(Intercept)"] <- "Intercept"
names(survival_table)[names(survival_table) == "Focal_haplotype"] <- "Focal haplotype"
names(survival_table)[names(survival_table) == "Sex"] <- "Sex"
names(survival_table)[names(survival_table) == "Social_haplotype"] <- "Social haplotype"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(survival_table)[names(survival_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(survival_table)[names(survival_table) == "df"] <- "Degrees of freedom"
names(survival_table)[names(survival_table) == "logLik"] <- "Log likelihood"
names(survival_table)[names(survival_table) == "AICc"] <- "AICc"
names(survival_table)[names(survival_table) == "delta"] <- "Delta"
names(survival_table)[names(survival_table) == "weight"] <- "Weight"

pander(survival_table, split.cell = 40, split.table = Inf)
```

$~$


Relative variable importance for each of the predictors and interactions in the survival model set. RVI can be interpreted as the likelihood the model term is present in the best performing model from the initial full set of possible models.

```{r, survival RVI}

# present relative variable importance in a table 

sw(survival_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
```

$~$


#### Model averaging

**Table S3**: Effects of mtDNA and sex on egg-to-adult viability.  Conditional estimates from model averaging the full generalised linear mixed model are shown. Models were included in the averaging subset if delta < 6. Bold rows indicate significant effects. 

```{r survival model averaging}

# Model average

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict(), fitted() or eemeans()

top_survival_models <- get.models(survival_dredge, subset = delta < 6)

survival_avgm <- model.avg(top_survival_models)

# average the models with delta < 6

survival_CIs <- confint(survival_avgm) %>% as.data.frame()

survival_estimate <- coefTable(survival_avgm) %>% as.data.frame()

survival_p_values <- summary(survival_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

survival_model_avg <- data.frame(survival_estimate, survival_CIs, survival_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(survival_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(survival_model_avg)[names(survival_model_avg) == "Estimate"] <- "Conditional average estimate"
names(survival_model_avg)[names(survival_model_avg) == "Std..Error"] <- "Standard Error"
names(survival_model_avg)[names(survival_model_avg) == "X2.5.."] <- "2.5% Interval"
names(survival_model_avg)[names(survival_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(survival_model_avg, split.cell = 40, split.table = Inf, round = 3)

# The full average provides a parameter average across all models considered, including ones where the parameter coefficient is set to 0. The conditional average reports coefficents for only the models where the parameter is included.

```

#### Comparison of focal and social haplotype effect sizes {.tabset}

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on viability are of similar magnitudes.

##### Focal haplotype effect sizes

```{r}
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

survival_emmeans <- glmer(Survived ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(survival_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

##### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(survival_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```


$~$


### Development time analysis
* * *

The model:

_Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_

```{r dev time model}

# Fit the linear model

dev_model <- lmer(Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

```

#### Model evaluation

**Table S4**: Evaluation of the development time model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex as well as the random factors duplicate strain, block and dyad ID. As there was no clear top model, the final model was calculated via model averaging.

```{r dev time dredge table}
# Use dredge to compare all possible models derived from the global model

Dev_dredge <- dredge(dev_model)

development_table <- subset(Dev_dredge, delta < 6, recalc.weights = FALSE)  %>% as.data.frame()

names(development_table)[names(development_table) == "(Intercept)"] <- "Intercept"
names(development_table)[names(development_table) == "Focal_haplotype"] <- "Focal haplotype"
names(development_table)[names(development_table) == "Social_haplotype"] <- "Social haplotype"
names(development_table)[names(development_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(development_table)[names(development_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(development_table)[names(development_table) == "df"] <- "Degrees of freedom"
names(development_table)[names(development_table) == "logLik"] <- "Log likelihood"
names(development_table)[names(development_table) == "AICc"] <- "AICc"
names(development_table)[names(development_table) == "delta"] <- "Delta"
names(development_table)[names(development_table) == "weight"] <- "Weight"

pander(development_table, split.cell = 40, split.table = Inf)

```

$~$


Relative variable importance for each of the predictors and interactions in the development time model set.

```{r, dev time RVI}

# present relative variable importance in a table 

sw(Dev_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
```

$~$


#### Model averaging

**Table S5**: Effects of mtDNA and sex on egg-to-adult development time.  Conditional estimates from model averaging the full generalised linear mixed model are shown. Models were included in the averaging subset if delta < 6. Bold rows indicate significant effects.

```{r dev time model averaging}

# Model averaging

Dev_time_avg <- (model.avg(Dev_dredge, subset = delta < 6))

Dev_CIs <- confint(Dev_time_avg) %>% as.data.frame()

Dev_estimate <- coefTable(Dev_time_avg) %>% as.data.frame()

Dev_p_values <- summary(Dev_time_avg)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Dev_model_avg <- data.frame(Dev_estimate, Dev_CIs, Dev_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Dev_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Dev_model_avg)[names(Dev_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Dev_model_avg)[names(Dev_model_avg) == "Std..Error"] <- "Standard Error"
names(Dev_model_avg)[names(Dev_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Dev_model_avg)[names(Dev_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(Dev_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)

```

#### Comparison of focal and social haplotype effect sizes {.tabset}

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on development time are of similar magnitudes.

##### Focal haplotype effect sizes

```{r}
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

dev_time_emmeans <- lmer(Dev_time ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(dev_time_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

##### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(dev_time_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```


$~$


## Adult fitness measures

$~$


### Body size analysis
* * *

We use wing length as a proxy for adult body size.

The model:

_Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_


```{r size model}

body_size_model <- lmer(Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

```

#### Model evaluation

**Table S6**: Evaluation of the wing length model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors duplicate strain, block and dyad ID. There was a clear top model; coefficients are displayed in Table S7.
```{r size dredge table}

# Compare all possible combinations of models (from the global model)

body_size_dredge <- dredge(body_size_model)

size_table <- subset(body_size_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()


names(size_table)[names(size_table) == "(Intercept)"] <- "Intercept"
names(size_table)[names(size_table) == "Focal_haplotype"] <- "Focal haplotype"
names(size_table)[names(size_table) == "Sex"] <- "Sex"
names(size_table)[names(size_table) == "Social_haplotype"] <- "Social haplotype"
names(size_table)[names(size_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(size_table)[names(size_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(size_table)[names(size_table) == "df"] <- "Degrees of freedom"
names(size_table)[names(size_table) == "logLik"] <- "Log likelihood"
names(size_table)[names(size_table) == "AICc"] <- "AICc"
names(size_table)[names(size_table) == "delta"] <- "Delta"
names(size_table)[names(size_table) == "weight"] <- "Weight"

pander(size_table, split.cell = 40, split.table = Inf)

```

$~$


Relative variable importance for each of the predictors and interactions in the wing length model set.

```{r, size RVI}

# present relative variable importance in a table 

sw(body_size_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
```

$~$



#### Best fitting model

One model was retained in the delta < 6 subset; model averaging is not required.

**Table S7**: Effects of mtDNA and sex on wing length. Results from the best fitting generalised linear mixed model are shown. Bold rows indicate significant effects.

```{r size best model}

# Fit the top model

body_size_model_final <- lmer(Wing_length ~ Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Size_CIs <- confint(body_size_model_final) %>%
  as.data.frame() %>% 
  slice(5:6)

Size_estimate <- coefTable(body_size_model_final) %>% as.data.frame()

Size_p_values <- summary(body_size_model_final)$coefficients[, 5] %>% as.data.frame() %>% rename(p = ".")

Size_model_avg <- data.frame(Size_estimate, Size_CIs, Size_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Size_model_avg) <- c("Intercept", "Sex: Male")

names(Size_model_avg)[names(Size_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Size_model_avg)[names(Size_model_avg) == "Std..Error"] <- "Standard Error"
names(Size_model_avg)[names(Size_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Size_model_avg)[names(Size_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Size_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = (2), round = 3)

```

$~$

#### Comparison of focal and social haplotype effect sizes {.tabset}

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct and indirect effects on body size are minimal and of similar magnitudes.

##### Focal haplotype effect sizes

```{r}
# Fit a simplified version of the mode without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

size_emmeans <- lmer(Wing_length ~ Focal_haplotype + Social_haplotype + Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(size_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

##### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(size_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```


$~$


### Female reproductive output
* * *

To effectively accommodate zero-inflation, we modelled female offspring production using the `glmmTMB` package [@RN602]. This package allows us to fit hurdle models and zero-inflated models.

We analysed the number of offspring produced by females using a hurdle model with negative binomial errors. This approach allowed us to answer two questions: (1) did mtDNA affect the incidence of failing to produce any offspring? and (2) for females that produced at least one offspring, was the number of offspring produced affected by mtDNA?

The model:

_Maternal_total_offspring ~ Focal_haplotype * Social_haplotype + (1|Strain) + (1|Block)_

```{r female model}
female_hurdle_model <- glmmTMB(Maternal_total_offspring ~ Social_haplotype * Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)
```

#### Model evaluation

**Table S8**: Evaluation of the female reproductive output model. All possible models were evaluated from the global model that included an interaction between focal haplotype and social haplotype and the random factors strain and block. As there was no clear top model, the final model was calculated via model averaging. The zero-inflated results relate to whether a female produced any offspring, while the conditional results relate to the number of offspring produced by fertile females.
```{r female dredge table}
# Compare all possible combinations of models (from the global model)

if(file.exists("female_dredge.rds")){ # If already done, just load the results
  female_dredge <- readRDS("female_dredge.rds")
} else {female_dredge <- dredge(female_hurdle_model)                  # If not already done, run all the models and save the results
lapply(c("female_dredge"), save_it)
}


female_table <- subset(female_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(female_table)[names(female_table) == "cond((Int))"] <- "Conditional intercept"
names(female_table)[names(female_table) == "zi((Int))"] <- "Zero-inflated intercept"
names(female_table)[names(female_table) == "disp((Int))"] <- "Dispersion factor intercept"
names(female_table)[names(female_table) == "cond(Focal_haplotype)"] <- "Conditional (Focal haplotype)"
names(female_table)[names(female_table) == "cond(Social_haplotype)"] <- "Conditional (Social haplotype)"
names(female_table)[names(female_table) == "cond(Focal_haplotype:Social_haplotype)"] <- "Conditional (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype)"] <- "Zero-inflated (Focal haplotype)"
names(female_table)[names(female_table) == "zi(Social_haplotype)"] <- "Zero-inflated (Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype:Social_haplotype)"] <- "Zero-inflated (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "df"] <- "Degrees of freedom"
names(female_table)[names(female_table) == "logLik"] <- "Log likelihood"
names(female_table)[names(female_table) == "AICc"] <- "AICc"
names(female_table)[names(female_table) == "delta"] <- "Delta"
names(female_table)[names(female_table) == "weight"] <- "Weight"

pander(female_table, split.cell = 40, split.table = Inf)

```

$~$


Relative variable importance for each of the predictors and interactions in the female reproductive output model set.

```{r, female RVI}

# present relative variable importance in a table 

sw(female_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
```

$~$



#### Model averaging

Zi (zero-hurdle requirement) and conditional (after hurdle)  model coefficients, standard error and 95% confidence limits listed in **Table 1** are shown for the female offspring production averaged model. Bold rows indicate significant effects. 

```{r female model averaging}

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict()

top_female_models <- get.models(female_dredge, subset = delta < 6)

female_avgm <- model.avg(top_female_models)

# extract useful info

Female_CIs <- confint(female_avgm) %>% as.data.frame()

Female_estimate <- coefTable(female_avgm) %>% as.data.frame()

Female_p_values <- summary(female_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Female_model_avg <- data.frame(Female_estimate, Female_CIs, Female_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Female_model_avg) <- c("Conditional intercept", "Conditional focal haplotype: Brownsville", "Conditional focal haplotype: Dahomey", "Conditional focal haplotype: Israel", "Conditional focal haplotype: Sweden", "Zi intercept", "Zi social haplotype: Brownsville", "Zi social haplotype: Dahomey", "Zi social haplotype: Israel", "Zi social haplotype: Sweden", "Zi focal haplotype: Brownsville", "Zi focal haplotype: Dahomey", "Zi focal haplotype: Israel", "Zi focal haplotype: Sweden")


names(Female_model_avg)[names(Female_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Female_model_avg)[names(Female_model_avg) == "Std..Error"] <- "Standard Error"
names(Female_model_avg)[names(Female_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Female_model_avg)[names(Female_model_avg) == "X97.5.."] <- "97.5% Interval"

Female_model_avg %>%
  pander(split.cell = 40, split.table = Inf, emphasize.strong.rows = c(2, 5, 9), round = 3)

```

$~$


#### Comparison of focal and social haplotype effect sizes 

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the indirect effects on female reproductive output are of similar magnitudes to, or exceed the size of, focal haplotype effects.

##### Did the female produce offspring? (Hurdle component of the model) {.tabset}


###### Focal haplotype effect sizes


```{r}
# Fit a simplified version of the model without interactions that can be used with the emmeans() function. For the hurdle component of the model we simply fit a binary model with the response variable: did the female produce >= 1 offspring. This produces slightly different estimates from the full hurdle model but the effects sizes are extremely similar.

female_reproductive_output_zi <- female_reproductive_output %>% 
  mutate(produced_offspring = if_else(Maternal_total_offspring == 0, 0, 1))

female_emmeans_h <- glmmTMB(produced_offspring ~ Social_haplotype + Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output_zi, family = binomial, na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(female_emmeans_h, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

###### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(female_emmeans_h, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```


##### The number of offspring produced by fertile females (conditional component of the model) {.tabset}


###### Focal haplotype effect sizes

```{r}
# Fit a simplified version of the model without interactions that can be used with the emmeans() function. This model is reasonable as we detected no evidence for an interaction between any of our fixed effects in the above analysis.

female_emmeans <- glmmTMB(Maternal_total_offspring ~ Social_haplotype + Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(female_emmeans, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

###### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(female_emmeans, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

$~$


#### Create Figure 1

```{r female figure, fig.width=11, fig.height=11}

# Plotting with model predictions

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be averaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype in the Zi formula. These were found by inspection of the top model list above.

focal_female_zi_models <- get.models(female_dredge, subset = "26")

# Note that only model "26' contains focal haplotype in the Zi formula. No averaging takes place and estimates are derived straight from this model. The conditional averaged estimates from the female_avgm object are identical to the estimates in model "26".

# fit model "26"

focal_zi_female_avg <- glmmTMB(Maternal_total_offspring ~ Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block), na.action = na.fail, REML = FALSE)


# Now average models that contain the social haplotype predictor in the Zi formula.

social_female_zi_models <- get.models(female_dredge, subset = c("18", "17", "26"))

social_zi_female_avg <- model.avg(social_female_zi_models)

# The conditional averaged estimates from the female_avgm object are identical to the zi social haplotype estimates from the "full model "social_zi_female_avg" object.

# Now average models that contain the focal haplotype predictor in the conditional formula.

focal_female_con_models <- get.models(female_dredge, subset = c("18", "2", "26"))

focal_con_female_avg <- model.avg(focal_female_con_models)

# Estimates match female_avg

# Make a new dataframe, for which we will derive predictions. It's the same as the old data, except that we set Focal haplotype, block and duplicate to the same value for all observations. The re.form = NA argument sets random effects to 0, meaning population means are calculated.
 
new_data <- female_reproductive_output %>%
  ungroup() %>%
  select(Focal_haplotype, Strain, Block) %>%
  mutate(Social_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# First lets get predictions for the average number of offspring produced by females that produced at least one progeny, split by focal haplotype.

pred <- predict(focal_con_female_avg, se.fit = TRUE, type = "conditional", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred1 <- pred %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred2 <- pred %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred <- cbind(new_data, pred1, pred2) %>%
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE) %>%
  rename(Maternal_total_offspring = mean_estimate)

# Load the data for each individual female that produced offspring so that this can be plotted

female_cond_plot_data <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0) %>%
  ungroup() %>% 
  select(Individual, Focal_haplotype, Maternal_total_offspring)

# Now lets plot these predictions

female_focal_cond_plot <- female_cond_plot_data %>%
  ggplot(aes(x = Focal_haplotype, y = Maternal_total_offspring, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = female_cond_plot_data, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred, aes(x = Focal_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = pred, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now lets get the Zi predictions for focal haplotype

pred_ZI <- predict(focal_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_1 <- pred_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_2 <- pred_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI <- cbind(new_data, pred_ZI_1, pred_ZI_2) %>%
  transmute(Focal_haplotype, Strain, Block, Social_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  

# Plot
  
female_focal_zi_plot <- pred_focal_ZI %>%
  ggplot(aes(x = Focal_haplotype, y = mean_estimate, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_errorbar(aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Focal_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Female mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now create the newdata for social haplotype predictions

new_data_social <- female_reproductive_output %>%
  ungroup() %>%
  select(Social_haplotype, Strain, Block) %>%
  mutate(Focal_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# Get zi social haplotype predictions

pred_social_ZI <- predict(social_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data_social) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_social_1 <- pred_social_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_social_2 <- pred_social_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI_social <- cbind(new_data_social, pred_ZI_social_1, pred_ZI_social_2) %>% 
  transmute(Social_haplotype, Strain, Block, Focal_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
  # Plot 
  
female_social_zi_plot <- pred_focal_ZI_social %>%
  ggplot(aes(x = Social_haplotype, y = mean_estimate, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_errorbar(aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Social_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26') +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Male mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Lets calculate mean estimates from the raw data for the number of offspring produced by females split by social haplotype. We can't use model predictions here because social haplotype is not retained in the conditional part of the model 

female_reproductive_output_cond <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0)

female_social_raw_cond <- female_reproductive_output_cond %>% 
  dplyr::group_by(Social_haplotype) %>%
  dplyr::summarise(Mean_offspring = sum(Maternal_total_offspring) / length(Maternal_total_offspring), Lower = (Mean_offspring - SE(Maternal_total_offspring)), Upper = (Mean_offspring + SE(Maternal_total_offspring)), n = n()) %>% 
  rename(Maternal_total_offspring = Mean_offspring)

female_social_cond_plot <- female_reproductive_output_cond %>%
  ggplot(aes(x = Social_haplotype, y = Maternal_total_offspring, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = female_reproductive_output_cond, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
geom_point(data = female_social_raw_cond, aes(x = Social_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = female_social_raw_cond, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


ggarrange(female_focal_zi_plot, female_social_zi_plot, female_focal_cond_plot, female_social_cond_plot, labels = c("a", "b", "c", "d"))
  
```

**Figure 1:** mtDNA directly and indirectly affects female fitness. Panels **a** and **b** show model predictions of the mean proportion of females that produced offspring (the zero-inflated or hurdle component of the model) across **a** female focal mtDNA haplotypes and **b** social male mtDNA haplotypes.  Panels **c** and **d** show the direct and indirect effect of mtDNA on the number of offspring produced by a female. Black points show model predictions of the mean for each haplotype in **c** and mean estimates from the raw data in **d**, while coloured points represent offspring produced by individual females. Model predictions were not calculated for **d** because social haplotype was not retained as a predictor for the conditional component of the averaged hurdle model. Error bars depict standard errors in all plots.


$~$


### Male reproductive fitness
* * *

Our measure of male fitness involves both pre- and post-copulatory competitive ability; that is we assess in one measure the combination of 1) the ability of a male to inseminate a female in the presence of another male and 2) the competitive ability of his sperm within females that have been inseminated by another male.

We analyse male fitness as the proportion of offspring produced by _mt_-strain males competing against a standard _bw_ male competitor. The data contains many 0 or 1 values - corresponding to a monopoly of female fertilisation by one of the males. To model this process we specify a beta-binomial distribution, which allows greater flexibility when modelling the distribution of the response. 

The Brownsville haplotype renders males sterile alongside the _w^1118^_ nuclear background and sub-fertile alongside all other tested backgrounds. In our experiment, we find that Brownsville males are able to produce offspring but to a very limited capacity. Due to this, our model is unable to produce reliable estimates when the interaction between focal and social haplotype is included. We do not include the interaction in the full model.

We include an additional random effect - MALE ID - in the model to account for repeated measures of each pair of focal and rival males.

_(Focal_male_offspring, bw_offspring) ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block) + (1|Male_ID)_ 

```{r male model}

response <- cbind(Male_fitness$Focal_male_offspring, Male_fitness$bw_male_offspring)

Male_fitness <- 
  Male_fitness %>% 
  rename(Male_ID = Individual)

male_model <- glmmTMB(response ~ Focal_haplotype + Social_haplotype + (1|Block) + (1|Strain) + (1|Male_ID), data = Male_fitness, family = "betabinomial", na.action = na.fail)

```

#### Model evaluation


**Table S9**: Evaluation of the male adult fitness model. All possible models were evaluated from the global model that included focal haplotype, social haplotype and the random factors strain, block and individual. As there was no clear top model, the final model was calculated via model averaging.

```{r male dredge table}

male_dredge <- dredge(male_model)

Male_table <- subset(male_dredge, delta < 6) %>% as.data.frame()


names(Male_table)[names(Male_table) == "(Intercept)"] <- "Intercept"
names(Male_table)[names(Male_table) == "Focal_haplotype"] <- "Focal haplotype"
names(Male_table)[names(Male_table) == "Social_haplotype"] <- "Social haplotype"
names(Male_table)[names(Male_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(Male_table)[names(Male_table) == "df"] <- "Degrees of freedom"
names(Male_table)[names(Male_table) == "logLik"] <- "Log likelihood"
names(Male_table)[names(Male_table) == "AICc"] <- "AICc"
names(Male_table)[names(Male_table) == "delta"] <- "Delta"
names(Male_table)[names(Male_table) == "weight"] <- "Weight"

pander(Male_table, split.cell = 40, split.table = Inf)

```

$~$


Relative variable importance for each of the predictors and interactions in the male reproductive fitness model set.

```{r, male RVI}

# present relative variable importance in a table 

sw(male_dredge) %>%
  as.data.frame() %>%
  pander(split.cell = 40, split.table = Inf, round = 3, col.names = "RVI")
```

$~$



#### Model averaging

Model coefficients, standard error and 95% confidence limits listed in **Table 2** are shown for the male adult fitness averaged model. Bold rows indicate significant effects. 

```{r male model averaging}

# Model average

top_male_models <- get.models(male_dredge, subset = delta < 6)

male_avgm <- model.avg(top_male_models)

# extract useful information

# summary(model.avg(male_binary_dredge, subset = delta < 6))

Male_CIs <- confint(male_avgm) %>% as.data.frame()

Male_estimate <- coefTable(male_avgm) %>% as.data.frame()

Male_p_values <- summary(male_avgm)$coefmat.subset[, 5] %>% as.data.frame() %>% rename(p = ".")

Male_model_avg <- data.frame(Male_estimate, Male_CIs, Male_p_values) %>% select(Estimate, Std..Error,  X2.5.., X97.5.., p)

row.names(Male_model_avg) <- c("Intercept", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Male_model_avg)[names(Male_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Male_model_avg)[names(Male_model_avg) == "Std..Error"] <- "Standard Error"
names(Male_model_avg)[names(Male_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Male_model_avg)[names(Male_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Male_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)

```

$~$


#### Comparison of focal and social haplotype effect sizes {.tabset}

While our experiment was not designed to calculate estimates of conventional selection and social selection, we present the standardised effect size for the difference in mean fitness between haplotype pairs, for direct and indirect fitness effects. Our aim is to illustrate that the size of the direct effects on male reproductive competitive ability are much larger than the indirect effects.

##### Focal haplotype effect sizes

```{r}
# We can use our original model

# Now create the pairwise comparisons for focal haplotype

pairs(emmeans(male_model, ~ Focal_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

##### Social haplotype effect sizes

```{r}
# Now for social haplotype

pairs(emmeans(male_model, ~ Social_haplotype, type = "response")) %>% 
  as_tibble() %>% 
  pander(split.cell = 40, split.table = Inf)

```

$~$


#### Create Figure 2

```{r male figure, fig.width= 8.5, fig.height= 6}

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype. These were found by inspection of the top model list above.

focal_male_models <- get.models(male_dredge, subset = c("2", "4"))

focal_male_avg <- model.avg(focal_male_models)

# Note that the conditional averaged estimates from the male_avgm object are identical to the full averaged estimates for the focal_male_avg object for focal haplotype.

# Now average models that contain the social haplotype predictor.

social_male_models <- get.models(male_dredge, subset = c("4"))

# Note that there is only one model (the original full model) that contains social haplotype in the < 6 delta subset, so estimates are calculated directly from this model - no averaging occurs. The conditional averaged estimates from the male_avgm object are identical to the estimates from the full model.



# Focal new data

new_data_male <- Male_fitness %>%
  ungroup() %>%
  select(Focal_haplotype, Block, Strain, Male_ID) %>%
  mutate(Social_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Male_ID = "4") %>% 
  distinct() 


pred_male_focal <- predict(focal_male_avg, newdata = new_data_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_focal_1 <- pred_male_focal %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_focal_2 <- pred_male_focal %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_focal_male <- cbind(new_data_male, pred_male_focal_1, pred_male_focal_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)

# Plot

Male_focal_plot <- Male_fitness %>%
  ggplot(aes(x = Focal_haplotype, y = Proportion_focal, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_focal_male, aes(x = Focal_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_focal_male, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Social new data

new_data_social_male <- Male_fitness %>%
  ungroup() %>%
  select(Social_haplotype, Block, Strain, Male_ID) %>%
  mutate(Focal_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Male_ID = "4") %>% 
  distinct()

# predict.averaging works over the full average rather than the conditional average that we present. I use a workaround where I create another model average object but only using the models in the < 6 delta subset that include social haplotype. Here only two models make the cut - the full model is the only one containing social haplotype as a predictor so no averaging is neccessary. Plug the full model into the predict function.

pred_male_social <- predict(male_model, newdata = new_data_social_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_social_1 <- pred_male_social %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_social_2 <- pred_male_social %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_male_social <- cbind(new_data_social_male, pred_male_social_1, pred_male_social_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)
  

# Plot

Male_social_plot <- Male_fitness %>%
  ggplot(aes(x = Social_haplotype, y = Proportion_focal, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
 scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_male_social, aes(x = Social_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_male_social, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(Male_focal_plot, Male_social_plot, labels = c("a", "b"))

```

**Figure 2**: The proportion of offspring produced by _mt_-strain males competing with standard _bw_ males. **a** shows the direct effect of mtDNA on male fitness. **b** shows the indirect genetic effect of female mtDNA on male fitness. Coloured points represent individual males, with larger points indicating a higher number of offspring produced in the vial (sired by either male). Black points show model predictions of the mean proportion of offspring sired by the _mt_-strain male, with associated standard errors.


$~$


# Raw data and reproducibility

### Table of raw data

For the purposes of completeness, transparency and data archiving, we include the raw data in this report.


**Table S10**: the raw data-set used in the present study, with NA values resulting from data collection mistakes removed (i.e. two females placed in competitive environment, no value recorded for whether the fly survived, flies that escaped during the experiment etc.).
```{r raw data}
kable(fitness_data %>% filter(!is.na(Survived)), "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "800px")
```


$~$


Columns represent:

**Individual:** the focal fly being tested.

**Block:** the experiment was completed in six separate blocks, identified here 1-6.

**Strain:** which of the 10 combinations of haplotype and duplicate strain was the individual from?

**Dyad_ID:** this identifies the pipette tip environment that the individual developed in.

**Sex:** the sex of the focal individual.

**Focal_haplotype:** the mtDNA haplotype carried by the focal individual.

**Social_haplotype:** the mtDNA haplotype carried by the focal individual's competitor.

**Survived:** did the focal individual survive to adulthood (1) or die during larval development (0)?

**Dev_time:** the hours taken for the focal individual to progress from an egg to an adult. NA values indicate where individuals did not survive or development time could not be measured.

**Wing_length:** the length in mm of the focal individual's right wing.

**Maternal_female_offspring:** the number of adult female offspring the focal _mt_-strain female produced over a two day period.

**Maternal_male_offspring:** the number of adult male offspring the focal _mt_-strain female produced over a two day period.

**Maternal_total_offspring:** the total number of adult offspring the focal _mt_-strain female produced over a two day period.

**Paternal_focal_offspring:** the number of red-eye phenotype offspring sired by a _mt_-strain male in the adult male fitness assay.

**Paternal_bw_offspring:** the number of brown-eye phenotype offspring sired by a _bw_ competitor male in the adult male fitness assay.

**Proportion focal:** the proportion of offspring produced by the _mt_-strain male in the adult male fitness assay.


### R session information

This section provides information on the operating system and R packages attached during the production of this document, to allow easier replication of the analysis.

```{r session info}
sessionInfo() %>% pander
```


# References
