Male Fitness
Anastasia Shavrova
01/11/2021
Do male morphs effect offspring fitness?
The goal of this study is to explore how polyphenism can affect offspring fitness and mating decisions.
Q1: Sex Ratio/ Ratio of polyphenism
H1: Fighters = more sons, scramblers = more daughters
H2: Fighters = more fighters; scramblers = more scramblers
Q2: Longevity
H1: fighter males > scrambler males
H2: sons = positive correlation with fathers; daughters = negative correlation with fathers
Q3: Correlations of Longevity
H2: sons = positive correlation with fathers; daughters = negative correlation with fathers
H3: fighters = positive correlation with fighters, neg with scramblers; no difference with scrambler fathers
Q4: Daughter Fecundity
H1: father like = more fecundity; father un-like = less fecundity
Description
Male fathers of different morphs were observed for their fitness (longevity), and their offspring was observed for fitness (longevity, and daughter fecundity mated with either a father-like morph, or opposite).
Q1: Ratios between father morphs
Q1: Sex Ratio/ Ratio of polyphenism
Sex:
H1: Fighters = more sons, scramblers = more daughters
Sex Ratio between father morph
#sex ratio
#for plot
sex_prop <- prop_off %>%
group_by(father_morph) %>%
mutate(prop_male=male/total) %>%
mutate(prop_female=female/total)
male <- ggplot(sex_prop, aes(father_morph,prop_male, fill=father_morph)) + geom_boxplot() + scale_fill_uchicago() + theme_classic() + ylim(0,1)
female <- ggplot(sex_prop, aes(father_morph, prop_female, fill=father_morph))+ geom_boxplot() + scale_fill_uchicago() + theme_classic()+ ylim(0,1)
ggarrange(male, female, labels=c("A", "B"))
#Did sex ratio differ between fighters and scramblers?
sex_ratio_mod <- glmer(cbind(female,male)~father_morph + trial + (1|fatherID), family="binomial", data=prop_off)
summary(sex_ratio_mod)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cbind(female, male) ~ father_morph + trial + (1 | fatherID)
## Data: prop_off
##
## AIC BIC logLik deviance df.resid
## 194.0 201.4 -93.0 186.0 43
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83538 -0.51961 -0.02429 0.59408 1.96508
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 0.1309 0.3618
## Number of obs: 47, groups: fatherID, 47
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.55870 0.28910 1.933 0.0533 .
## father_morphscrambler -0.01875 0.21331 -0.088 0.9299
## trial -0.19266 0.10308 -1.869 0.0616 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fthr_m
## fthr_mrphsc -0.322
## trial -0.853 -0.058emmeans(sex_ratio_mod, pairwise~father_morph, type="response")## $emmeans
## father_morph prob SE df asymp.LCL asymp.UCL
## fighter 0.520 0.0378 Inf 0.446 0.593
## scrambler 0.515 0.0373 Inf 0.442 0.587
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## fighter / scrambler 1.02 0.217 Inf 1 0.088 0.9299
##
## Tests are performed on the log odds ratio scalesex_ratio_mod_male <- glmer(cbind(male,female)~father_morph + trial + (1|fatherID), family="binomial", data=prop_off)
emmeans(sex_ratio_mod_male, pairwise~father_morph, type="response")## $emmeans
## father_morph prob SE df asymp.LCL asymp.UCL
## fighter 0.480 0.0378 Inf 0.407 0.554
## scrambler 0.485 0.0373 Inf 0.413 0.558
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
##
## $contrasts
## contrast odds.ratio SE df null z.ratio p.value
## fighter / scrambler 0.981 0.209 Inf 1 -0.088 0.9299
##
## Tests are performed on the log odds ratio scaleMorph ratios between father morphs
Morph:
H2: Fighters = more fighters; scramblers = more scramblers
#for plot
morph_prop <- prop_off %>%
group_by(father_morph) %>%
mutate(prop_fighter=fighter/male) %>%
mutate(prop_scram=scrambler/male)
fighter <- ggplot(morph_prop, aes(father_morph,prop_fighter, fill=father_morph)) + geom_boxplot() + scale_fill_uchicago() + theme_classic() + ylim(0,1)
scrambler <- ggplot(morph_prop, aes(father_morph, prop_scram, fill=father_morph))+ geom_boxplot() + scale_fill_uchicago() + theme_classic()+ ylim(0,1)
ggarrange(fighter, scrambler, labels=c("A", "B"))
#Did fighter and scrambler males differ due to father morph?
male_ratio_mod <- glmer(cbind(scrambler,fighter)~father_morph + trial + (1|fatherID), family="binomial", data=prop_off)
summary(male_ratio_mod)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cbind(scrambler, fighter) ~ father_morph + trial + (1 | fatherID)
## Data: prop_off
##
## AIC BIC logLik deviance df.resid
## 165.5 172.9 -78.7 157.5 43
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2535 -0.3459 0.1226 0.3739 1.4726
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 0.9726 0.9862
## Number of obs: 47, groups: fatherID, 47
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8279 0.6100 -1.357 0.175
## father_morphscrambler 0.2369 0.4214 0.562 0.574
## trial 0.1725 0.2117 0.815 0.415
##
## Correlation of Fixed Effects:
## (Intr) fthr_m
## fthr_mrphsc -0.350
## trial -0.867 -0.009emmeans(male_ratio_mod, ~father_morph, type="response")## father_morph prob SE df asymp.LCL asymp.UCL
## fighter 0.402 0.0731 Inf 0.270 0.549
## scrambler 0.460 0.0729 Inf 0.324 0.602
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scalemale_ratio_mod_fight <- glmer(cbind(fighter,scrambler)~father_morph + trial + (1|fatherID), family="binomial", data=prop_off)
emmeans(male_ratio_mod_fight, ~father_morph, type="response")## father_morph prob SE df asymp.LCL asymp.UCL
## fighter 0.598 0.0731 Inf 0.451 0.730
## scrambler 0.540 0.0729 Inf 0.398 0.676
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scaleQ2: Longevity of Fathers and Offspring
H1: fighter father > scrambler fathers
H2: females > males
Is father longevity different between morphs?
ggplot(father_fitness, aes(morph, longevity, fill=morph)) + geom_boxplot() + ylab("Father Longevity") + theme_classic2() + scale_fill_uchicago() + xlab("Father Morph") + theme(legend.position = "none")
#is longevity different between fighters and scramblers fathers?
fat.long <- lmer(longevity~morph + (1|trial), data=father_fitness)
summary(fat.long)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ morph + (1 | trial)
## Data: father_fitness
##
## REML criterion at convergence: 639.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2865 -0.6564 -0.3783 0.5766 3.3706
##
## Random effects:
## Groups Name Variance Std.Dev.
## trial (Intercept) 462.4 21.50
## Residual 1223.4 34.98
## Number of obs: 65, groups: trial, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.756 12.409 3.527 4.010 0.0205 *
## morphscrambler -2.727 8.682 59.768 -0.314 0.7545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mrphscrmblr -0.342emmeans(fat.long, pairwise~morph)## $emmeans
## morph emmean SE df lower.CL upper.CL
## fighter 49.8 12.4 3.83 14.7 84.8
## scrambler 47.0 12.5 3.91 12.0 82.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## fighter - scrambler 2.73 8.68 60 0.314 0.7545
##
## Degrees-of-freedom method: kenward-rogerDoes offspring longevity (between sexes) differ?
#between sexes
#singular error using trial as factor
offspring_long_sex <- lmer(longevity~sex*father_morph + (1|fatherID) + trial, contrast=list(sex=contr.sum, father_morph=contr.sum), data=full_long)
summary(offspring_long_sex)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ sex * father_morph + (1 | fatherID) + trial
## Data: full_long
##
## REML criterion at convergence: 2880.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5124 -0.6763 -0.2311 0.4653 3.9477
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 51.57 7.181
## Residual 493.98 22.226
## Number of obs: 318, groups: fatherID, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.2092 4.6455 48.1564 10.162 1.44e-13 ***
## sex1 -0.9320 1.4130 309.8701 -0.660 0.5100
## father_morph1 0.7714 1.7376 52.1625 0.444 0.6589
## trial -2.7543 1.7138 47.4292 -1.607 0.1146
## sex1:father_morph1 3.3143 1.3952 312.0760 2.375 0.0181 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sex1 fthr_1 trial
## sex1 -0.046
## fathr_mrph1 -0.012 -0.058
## trial -0.927 0.166 0.002
## sx1:fthr_m1 -0.069 -0.051 0.291 0.050Anova(offspring_long_sex, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: longevity
## Chisq Df Pr(>Chisq)
## (Intercept) 103.2713 1 < 2e-16 ***
## sex 0.4351 1 0.50950
## father_morph 0.1971 1 0.65707
## trial 2.5831 1 0.10801
## sex:father_morph 5.6428 1 0.01753 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(offspring_long_sex, pairwise~sex|father_morph)## $emmeans
## father_morph = fighter:
## sex emmean SE df lower.CL upper.CL
## female 43.2 3.48 130 36.4 50.1
## male 38.5 2.72 64 33.0 43.9
##
## father_morph = scrambler:
## sex emmean SE df lower.CL upper.CL
## female 35.1 3.73 160 27.7 42.4
## male 43.5 2.64 64 38.3 48.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## father_morph = fighter:
## contrast estimate SE df t.ratio p.value
## female - male 4.76 3.89 311 1.225 0.2214
##
## father_morph = scrambler:
## contrast estimate SE df t.ratio p.value
## female - male -8.49 4.09 311 -2.075 0.0388
##
## Degrees-of-freedom method: kenward-rogergraph_sex <- emmip(offspring_long_sex, sex~father_morph, CIs=TRUE,type="response", plotit=FALSE)
emmeans(offspring_long_sex, pairwise~father_morph|sex)## $emmeans
## sex = female:
## father_morph emmean SE df lower.CL upper.CL
## fighter 43.2 3.48 130 36.4 50.1
## scrambler 35.1 3.73 160 27.7 42.4
##
## sex = male:
## father_morph emmean SE df lower.CL upper.CL
## fighter 38.5 2.72 64 33.0 43.9
## scrambler 43.5 2.64 64 38.3 48.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## sex = female:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler 8.17 5.07 143.8 1.611 0.1094
##
## sex = male:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler -5.09 3.79 63.4 -1.343 0.1839
##
## Degrees-of-freedom method: kenward-rogersex_longevity <- ggplot(graph_sex, aes(x=father_morph, y=yvar, col=sex)) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position=position_dodge(width=0.5)) + ylab("Offspring Longevity") + xlab("Father Morph") + scale_color_uchicago() + theme(legend.position = "bottom", legend.title=element_blank()) + ylim(30,60)#longevity between sexes
ggplot(full_long, aes(father_morph, longevity, fill=sex)) + geom_boxplot()+ scale_fill_uchicago() + ylab("Offspring Longevity (days)") + xlab("Father Morph") + theme_classic() 
#removing females
full_long_males <- full_long %>%
filter(sex!="female")Does longevity differ between scrambler vs fighter sons
#longevity between fighters and scramblers
ggplot(full_long_males, aes(father_morph, longevity, fill=offspring_morph)) + geom_boxplot()+ scale_fill_uchicago() + ylab("Offspring Longevity (days)") + xlab("Father Morph") + theme_classic()
#does longevity differ between sons (fighters vs scramblers) of different father morphs?
#singularity error, putting trial as factor
offspring_long_males <- lmer(longevity~offspring_morph*father_morph + (1|fatherID) + trial, contrast=list(offspring_morph=contr.sum, father_morph=contr.sum), data=full_long_males)
summary(offspring_long_males)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ offspring_morph * father_morph + (1 | fatherID) +
## trial
## Data: full_long_males
##
## REML criterion at convergence: 1958.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6847 -0.6256 -0.2049 0.3963 3.8381
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 71.47 8.454
## Residual 501.98 22.405
## Number of obs: 216, groups: fatherID, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.592 6.117 52.689 7.781 2.6e-10 ***
## offspring_morph1 -2.712 1.660 209.463 -1.634 0.1038
## father_morph1 -2.770 2.043 42.890 -1.356 0.1822
## trial -2.487 2.162 47.188 -1.150 0.2558
## offspring_morph1:father_morph1 4.157 1.660 209.674 2.503 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) offs_1 fthr_1 trial
## offsprng_m1 -0.028
## fathr_mrph1 0.069 -0.050
## trial -0.943 -0.011 -0.058
## offspr_1:_1 -0.045 0.043 -0.116 0.030anova(offspring_long_males)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## offspring_morph 1340.30 1340.30 1 209.463 2.6700 0.10376
## father_morph 923.01 923.01 1 42.890 1.8388 0.18219
## trial 664.30 664.30 1 47.188 1.3234 0.25578
## offspring_morph:father_morph 3145.58 3145.58 1 209.674 6.2664 0.01307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(offspring_long_males, pairwise~offspring_morph|father_morph)## $emmeans
## father_morph = fighter:
## offspring_morph emmean SE df lower.CL upper.CL
## fighter 39.5 3.52 65.3 32.5 46.5
## scrambler 36.6 4.13 86.0 28.4 44.8
##
## father_morph = scrambler:
## offspring_morph emmean SE df lower.CL upper.CL
## fighter 36.7 3.54 73.7 29.7 43.8
## scrambler 50.5 3.79 85.8 42.9 58.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## father_morph = fighter:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler 2.89 4.86 207 0.595 0.5527
##
## father_morph = scrambler:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler -13.74 4.64 211 -2.962 0.0034
##
## Degrees-of-freedom method: kenward-rogergraph_morph <- emmip(offspring_long_males, offspring_morph~father_morph, CIs=TRUE,type="response", plotit=FALSE)
long_morph <- ggplot(graph_morph, aes(x=father_morph, y=yvar, col=offspring_morph)) + theme_classic() + geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position=position_dodge(width=0.5)) + ylab("Offspring Longevity") + xlab("Father Morph") + scale_color_aaas() + theme(legend.position = "bottom", legend.title=element_blank()) + ylim(30,60)
ggarrange(sex_longevity, long_morph, labels=c("A", "B"), nrow=2, ncol=1)
full_surv <- lmer(longevity~offspring_morph*father_morph + (1|fatherID) + trial, data=full_long)
Anova(full_surv)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: longevity
## Chisq Df Pr(>Chisq)
## offspring_morph 3.3576 2 0.186599
## father_morph 0.0263 1 0.871187
## trial 2.5995 1 0.106895
## offspring_morph:father_morph 12.9189 2 0.001566 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1graph_full_surv <- emmip(full_surv, offspring_morph~father_morph, CIs=TRUE, type="response", plotit=FALSE)
ggplot(graph_full_surv, aes(x=father_morph, y=yvar, col=offspring_morph)) + theme_classic()+ geom_pointrange(aes(ymin=yvar-SE, ymax=yvar+SE), position=position_dodge(width=0.5)) + scale_color_aaas() + xlab("Father Morph") + ylab("Offspring Longevity") + theme(legend.position = "bottom", legend.title=element_blank())
emmeans(full_surv, pairwise~offspring_morph|father_morph)## $emmeans
## father_morph = fighter:
## offspring_morph emmean SE df lower.CL upper.CL
## females 43.2 3.40 129 36.5 50.0
## fighter 39.9 3.24 111 33.5 46.3
## scrambler 36.5 3.81 151 29.0 44.0
##
## father_morph = scrambler:
## offspring_morph emmean SE df lower.CL upper.CL
## females 35.0 3.66 161 27.8 42.2
## fighter 37.2 3.28 125 30.7 43.7
## scrambler 50.9 3.52 146 44.0 57.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## father_morph = fighter:
## contrast estimate SE df t.ratio p.value
## females - fighter 3.32 4.33 309 0.767 0.7234
## females - scrambler 6.74 4.65 306 1.451 0.3164
## fighter - scrambler 3.42 4.61 307 0.741 0.7392
##
## father_morph = scrambler:
## contrast estimate SE df t.ratio p.value
## females - fighter -2.21 4.53 309 -0.488 0.8771
## females - scrambler -15.94 4.71 311 -3.383 0.0023
## fighter - scrambler -13.73 4.45 311 -3.086 0.0062
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans(full_surv, pairwise~father_morph|offspring_morph)## $emmeans
## offspring_morph = females:
## father_morph emmean SE df lower.CL upper.CL
## fighter 43.2 3.40 129 36.5 50.0
## scrambler 35.0 3.66 161 27.8 42.2
##
## offspring_morph = fighter:
## father_morph emmean SE df lower.CL upper.CL
## fighter 39.9 3.24 111 33.5 46.3
## scrambler 37.2 3.28 125 30.7 43.7
##
## offspring_morph = scrambler:
## father_morph emmean SE df lower.CL upper.CL
## fighter 36.5 3.81 151 29.0 44.0
## scrambler 50.9 3.52 146 44.0 57.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## offspring_morph = females:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler 8.23 4.97 143 1.655 0.1001
##
## offspring_morph = fighter:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler 2.70 4.61 117 0.585 0.5595
##
## offspring_morph = scrambler:
## contrast estimate SE df t.ratio p.value
## fighter - scrambler -14.45 5.18 148 -2.788 0.0060
##
## Degrees-of-freedom method: kenward-rogerQ3: Correlations
Between father longevity and offspring sex longevity
H2: sons = positive correlation with fathers; daughters = negative correlation with fathers
H3: fighters = positive correlation with fighters, neg with scramblers; no difference with scrambler fathers
pooled_mod <- lmer(rescale(longevity)~rescale(father_long)*father_morph + (1|trial) + (1|fatherID) -1, data=full_long)
summary(pooled_mod)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rescale(longevity) ~ rescale(father_long) * father_morph + (1 |
## trial) + (1 | fatherID) - 1
## Data: full_long
##
## REML criterion at convergence: -95.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6185 -0.6993 -0.2441 0.3566 3.8338
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 0.0021101 0.04594
## trial (Intercept) 0.0008287 0.02879
## Residual 0.0392838 0.19820
## Number of obs: 318, groups: fatherID, 48; trial, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## rescale(father_long) 0.21025 0.07971 29.24910 2.638
## father_morphfighter 0.14159 0.03671 18.68237 3.857
## father_morphscrambler 0.26308 0.03205 10.09549 8.209
## rescale(father_long):father_morphscrambler -0.35014 0.10084 27.59873 -3.472
## Pr(>|t|)
## rescale(father_long) 0.01323 *
## father_morphfighter 0.00109 **
## father_morphscrambler 8.84e-06 ***
## rescale(father_long):father_morphscrambler 0.00172 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## rsc(_) fthr_mrphf fthr_mrphs
## fthr_mrphfg -0.764
## fthr_mrphsc -0.052 0.215
## rscl(ft_):_ -0.745 0.573 -0.402Anova(pooled_mod)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: rescale(longevity)
## Chisq Df Pr(>Chisq)
## rescale(father_long) 0.0059 1 0.938676
## father_morph 66.7839 2 3.148e-15 ***
## rescale(father_long):father_morph 12.0571 1 0.000516 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1fighter fathers and daughters correlation
long_sex <- full_long %>%
select(-offspring_morph)
long_fightfather <- long_sex %>%
filter(sex == "female") %>%
filter(father_morph == "fighter")
long_female_fight_corr <- lmer(longevity ~father_long + (1|fatherID) + (1|trial)-1, dat=long_fightfather)
summary(long_female_fight_corr) #-ve corr not sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + (1 | trial) - 1
## Data: long_fightfather
##
## REML criterion at convergence: 530.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.30742 -0.77595 -0.01675 0.51580 2.34952
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 13.15 3.626
## trial (Intercept) 2026.06 45.012
## Residual 548.51 23.420
## Number of obs: 56, groups: fatherID, 19; trial, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long -0.004541 0.094430 10.851486 -0.048 0.963scrambler fathers and daughters correlation
long_scramfather <- long_sex %>%
filter(sex == "female") %>%
filter(father_morph == "scrambler")
long_female_scram_corr <- lmer(longevity ~father_long + (1|fatherID) + (1|trial) -1, dat=long_scramfather)
summary(long_female_scram_corr) #-ve corr not sig## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + (1 | trial) - 1
## Data: long_scramfather
##
## REML criterion at convergence: 392
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7537 -0.6805 -0.1623 0.5307 3.0034
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 20.53 4.531
## trial (Intercept) 1290.93 35.930
## Residual 175.49 13.247
## Number of obs: 46, groups: fatherID, 20; trial, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long -0.01896 0.05774 12.89455 -0.328 0.748long_morph <- full_long %>%
filter(sex == "male")Fighter father, scrambler son correlation
long_fightf_scramo <- long_morph %>%
filter(father_morph == "fighter") %>%
filter(offspring_morph == "scrambler")
long_fs_corr <- lmer(longevity ~father_long+ (1|fatherID) + (1|trial) -1, dat=long_fightf_scramo)
summary(long_fs_corr) # sig +ve## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + (1 | trial) - 1
## Data: long_fightf_scramo
##
## REML criterion at convergence: 400.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.00063 -0.34238 0.00766 0.37938 2.88147
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 182.9 13.52
## trial (Intercept) 270.5 16.45
## Residual 324.9 18.03
## Number of obs: 44, groups: fatherID, 18; trial, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long 0.33827 0.09622 7.27172 3.516 0.0092 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fighter father, fighter son correlation
long_fightf_fighto <- long_morph %>%
filter(father_morph == "fighter") %>%
filter(offspring_morph == "fighter")
long_ff_corr <- lmer(longevity ~father_long+ (1|fatherID) + trial-1, dat=long_fightf_fighto)
summary(long_ff_corr) # sig +ve## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + trial - 1
## Data: long_fightf_fighto
##
## REML criterion at convergence: 558
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.62658 -0.44373 0.05595 0.41511 2.53578
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 39.12 6.254
## Residual 378.70 19.460
## Number of obs: 63, groups: fatherID, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long 0.39862 0.06416 7.65313 6.213 0.000307 ***
## trial 6.21580 1.40519 8.69626 4.423 0.001808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## fthr_l
## trial -0.690Scrambler father, fighter son correlation
long_scramf_fighto <- long_morph %>%
filter(father_morph == "scrambler") %>%
filter(offspring_morph == "fighter")
long_sf_corr <- lmer(longevity ~father_long+ (1|fatherID) + trial -1, dat=long_scramf_fighto)
summary(long_sf_corr) # not sig +ve## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + trial - 1
## Data: long_scramf_fighto
##
## REML criterion at convergence: 535
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3749 -0.3462 -0.2599 0.3634 3.9605
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 11.86 3.444
## Residual 498.80 22.334
## Number of obs: 59, groups: fatherID, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long 0.001621 0.063838 13.297286 0.025 0.98
## trial 12.922057 1.389042 11.038999 9.303 1.47e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## fthr_l
## trial -0.632Scrambler father, scrambler son correlation
long_scramf_scrambo <- long_morph %>%
filter(father_morph == "scrambler") %>%
filter(offspring_morph == "scrambler")
long_ss_corr <- lmer(longevity ~father_long+ (1|fatherID) + trial -1, dat=long_scramf_scrambo)
summary(long_ss_corr) # not sig +ve## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: longevity ~ father_long + (1 | fatherID) + trial - 1
## Data: long_scramf_scrambo
##
## REML criterion at convergence: 486.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.24014 -0.59517 -0.07654 0.51305 2.41395
##
## Random effects:
## Groups Name Variance Std.Dev.
## fatherID (Intercept) 94.1 9.7
## Residual 942.6 30.7
## Number of obs: 50, groups: fatherID, 18
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## father_long 0.18142 0.07894 9.58998 2.298 0.0454 *
## trial 14.59634 2.12119 10.52683 6.881 3.32e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## fthr_l
## trial -0.535Correlations between father and offspring longevity - FULL GRAPH
ggplot(full_long, aes(father_long,longevity , col=offspring_morph)) + geom_point() + facet_wrap(~father_morph) + geom_smooth(method="lm", se=FALSE) + theme_classic() + ylab("Offspring longevity (days)") + xlab("Father Longevity (days)") + scale_color_uchicago() ## `geom_smooth()` using formula 'y ~ x'
full_mod <- lmer(longevity~father_long*father_morph*offspring_morph + (1|trial) + (1|fatherID), data=full_long)
Anova(full_mod)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: longevity
## Chisq Df Pr(>Chisq)
## father_long 0.0240 1 0.8769937
## father_morph 0.0001 1 0.9922356
## offspring_morph 3.1671 2 0.2052410
## father_long:father_morph 14.8642 1 0.0001155 ***
## father_long:offspring_morph 1.8414 2 0.3982318
## father_morph:offspring_morph 16.8609 2 0.0002181 ***
## father_long:father_morph:offspring_morph 5.7061 2 0.0576682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emtrends(full_mod, pairwise~offspring_morph|father_morph, var="father_long")## $emtrends
## father_morph = fighter:
## offspring_morph father_long.trend SE df lower.CL upper.CL
## females 0.000397 0.0873 118.6 -0.1725 0.1733
## fighter 0.230143 0.0868 85.7 0.0575 0.4028
## scrambler 0.232122 0.0867 108.6 0.0603 0.4040
##
## father_morph = scrambler:
## offspring_morph father_long.trend SE df lower.CL upper.CL
## females -0.069214 0.0712 114.8 -0.2102 0.0718
## fighter -0.186422 0.0813 156.4 -0.3470 -0.0258
## scrambler -0.064638 0.0674 116.2 -0.1981 0.0688
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## father_morph = fighter:
## contrast estimate SE df t.ratio p.value
## females - fighter -0.22975 0.1147 289 -2.004 0.1131
## females - scrambler -0.23172 0.1135 304 -2.042 0.1041
## fighter - scrambler -0.00198 0.1134 275 -0.017 0.9998
##
## father_morph = scrambler:
## contrast estimate SE df t.ratio p.value
## females - fighter 0.11721 0.0983 300 1.193 0.4585
## females - scrambler -0.00458 0.0897 305 -0.051 0.9986
## fighter - scrambler -0.12178 0.0965 304 -1.261 0.4182
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesmylist_long <- list(father_long=seq(0,200, by=20), father_morph=c("fighter", "scrambler"), offspring_morph=c("females", "fighter", "scrambler"))
emmip(full_mod, offspring_morph~father_long|father_morph, at=mylist_long, CI=TRUE) + facet_grid(father_morph~.) + theme_classic() + xlab("Father Longevity")+ scale_color_aaas() + geom_line(size=1) + theme(legend.position="bottom", legend.title=element_blank())
Full Corellation Model
ggplot(full_long, aes(father_long,longevity , col=father_morph)) + geom_point() + geom_smooth(method="lm", se=FALSE) + theme_classic() + ylab("Offspring longevity (days)") + xlab("Father Longevity (days)") + scale_color_uchicago() ## `geom_smooth()` using formula 'y ~ x'
Q4: Daughter Fecundity - Mating with father-like/ father unlike
Total fecundity
#total fecundity
ggplot(daught_fecund, aes(father_morph, egg_count, fill=mate_morph)) + geom_boxplot() + scale_fill_uchicago() + theme_classic() + ylab("Daughter Fecundity") + xlab("Father Morph")## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
Does total fecundity differ between daughters of differently morphed fathers when the daughters are mated with a father-like or father-unlike morph?
#boundary error
#made trial factor
daught_tot_fecund_mod <- glmer(total~father_morph*mate_morph + trial + (1|fatherID2) + (1|daught_ID), family="poisson", data=daught_tot_fecund)
Anova(daught_tot_fecund_mod)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: total
## Chisq Df Pr(>Chisq)
## father_morph 0.3902 1 0.53219
## mate_morph 0.5848 1 0.44442
## trial 3.1759 1 0.07473 .
## father_morph:mate_morph 0.5411 1 0.46197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1daught_tot_fecund_mod.grid <- ref_grid(daught_tot_fecund_mod)
emmeans(regrid(daught_tot_fecund_mod.grid), pairwise~father_morph|mate_morph)## $emmeans
## mate_morph = fighter:
## father_morph rate SE df asymp.LCL asymp.UCL
## fighter 73.5 31.6 Inf 11.57 135.4
## scrambler 42.3 16.8 Inf 9.34 75.3
##
## mate_morph = scrambler:
## father_morph rate SE df asymp.LCL asymp.UCL
## fighter 41.3 18.3 Inf 5.41 77.2
## scrambler 41.0 16.8 Inf 8.01 73.9
##
## Confidence level used: 0.95
##
## $contrasts
## mate_morph = fighter:
## contrast estimate SE df z.ratio p.value
## fighter - scrambler 31.186 35.8 Inf 0.871 0.3836
##
## mate_morph = scrambler:
## contrast estimate SE df z.ratio p.value
## fighter - scrambler 0.323 24.8 Inf 0.013 0.9896Fecundity Rate
ggplot(daught_fecund, aes(week, egg_count, col=mate_morph)) + facet_grid(~father_morph) + geom_smooth(method="lm", formula = y~I(x^2)) + ylab("Daughter Fecundity") + xlab("Week") + scale_color_uchicago() + theme_classic()## Warning: Removed 1 rows containing non-finite values (stat_smooth).
# Does rate of fecundity change between daughters of different morphed fathers when the daughters are mated with father-like and unlike mates?
#sing error, trial as factor
#used optimx to converge model
daught_rate_fecund_mod <- glmer(egg_count~rescale(week)+I(rescale(week)^2)*mate_morph*father_morph+ + trial + (1|obs_effect) + (1|fatherID2) + (1|daught_ID), family="poisson", glmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')), data=daught_fecund)
Anova(daught_rate_fecund_mod)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: egg_count
## Chisq Df Pr(>Chisq)
## rescale(week) 1.6062 1 0.205031
## I(rescale(week)^2) 8.4598 1 0.003631 **
## mate_morph 0.0228 1 0.880089
## father_morph 1.4840 1 0.223152
## trial 3.0003 1 0.083247 .
## I(rescale(week)^2):mate_morph 0.2562 1 0.612761
## I(rescale(week)^2):father_morph 5.9731 1 0.014526 *
## mate_morph:father_morph 0.2151 1 0.642797
## I(rescale(week)^2):mate_morph:father_morph 2.5407 1 0.110943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emtrends(daught_rate_fecund_mod, pairwise ~ father_morph, var="(week)^2")## NOTE: Results may be misleading due to involvement in interactions## $emtrends
## father_morph (week)^2.trend SE df asymp.LCL asymp.UCL
## fighter -171 25.3 Inf -221 -122
## scrambler -287 52.7 Inf -390 -184
##
## Results are averaged over the levels of: mate_morph
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## fighter - scrambler 116 49.7 Inf 2.328 0.0199
##
## Results are averaged over the levels of: mate_morphmylist_fecund <- list(week=seq(0,15, by=2), father_morph=c("fighter", "scrambler"))
emmip(daught_rate_fecund_mod, father_morph~week^2, at=mylist_fecund, CI=TRUE) + theme_classic() + ylab("Linear Prediction") + xlab("Week of Egg Count") + theme(legend.position="bottom", legend.title=element_blank()) + scale_color_uchicago()## NOTE: Results may be misleading due to involvement in interactions
Longevity
longevity_fert_daught <- daught_fecund %>%
group_by(trial, daught_ID, father_morph, mate_morph) %>%
top_n(1, week) %>%
unique()
fit_daught <- survfit(Surv(week)~father_morph+mate_morph + (1|daught_ID), data=longevity_fert_daught)
ggsurvplot(fit_daught, data=longevity_fert_daught) 
mod.surv.daught <- coxme(Surv(week)~father_morph*mate_morph + egg_count +(1|daught_ID), data=longevity_fert_daught)
Anova(mod.surv.daught)## Analysis of Deviance Table (Type II tests)
##
## Response: Surv(week)
## Df Chisq Pr(>Chisq)
## father_morph 1 1.3503 0.245228
## mate_morph 1 0.0300 0.862441
## egg_count 1 6.7307 0.009477 **
## father_morph:mate_morph 1 0.0446 0.832747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1