Yogi Honours Reproductive data
J. Donelson
March 2022
R Markdown
library(ggplot2)
library(lme4)## Loading required package: Matrixlibrary(MuMIn)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-7library(emmeans)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodelibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepsetwd("C:/Users/jc130714/OneDrive - James Cook University/Documents/Yogi Yasutake/Honours/Post-honours/Statistics and data/Final data")
getwd()## [1] "C:/Users/jc130714/OneDrive - James Cook University/Documents/Yogi Yasutake/Honours/Post-honours/Statistics and data/Final data"#PCA
ReproPCA = read.csv('Repro PCA.csv', strip.white=TRUE)
head(ReproPCA)## Number.of.clutches.per.pair Total.number.of.eggs Clutch.size..1st.
## 1 3 947 320
## 2 3 778 216
## 3 2 459 239
## 4 1 233 233
## 5 3 913 323
## 6 3 883 364
## Average.egg.area
## 1 5.0277
## 2 4.3568
## 3 4.8173
## 4 4.3846
## 5 4.5416
## 6 3.8513#standardising
Repro.std = stdize(ReproPCA, centre=TRUE, scale = TRUE)
head(Repro.std)## z.Number.of.clutches.per.pair z.Total.number.of.eggs z.Clutch.size..1st.
## 1 0.8735644 1.2749315 0.9286975
## 2 0.8735644 0.6374657 -0.6303750
## 3 -0.3743847 -0.5657980 -0.2855801
## 4 -1.6223339 -1.4182670 -0.3755266
## 5 0.8735644 1.1466839 0.9736707
## 6 0.8735644 1.0335243 1.5883051
## z.Average.egg.area
## 1 0.6478583
## 2 -0.7253050
## 3 0.2172225
## 4 -0.6684054
## 5 -0.3470659
## 6 -1.7599361Repro.rda = rda(Repro.std)
summary(Repro.rda, display=NULL)##
## Call:
## rda(X = Repro.std)
##
## Partitioning of variance:
## Inertia Proportion
## Total 4 1
## Unconstrained 4 1
##
## Eigenvalues, and their contribution to the variance
##
## Importance of components:
## PC1 PC2 PC3 PC4
## Eigenvalue 2.256 1.1078 0.6232 0.013192
## Proportion Explained 0.564 0.2769 0.1558 0.003298
## Cumulative Proportion 0.564 0.8409 0.9967 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores:screeplot(Repro.rda)
Repro.sites.score = (scores(Repro.rda, display='sites'))
head(Repro.sites.score)## PC1 PC2
## sit1 -0.8437966 0.11506770
## sit2 -0.2378545 -0.07765641
## sit3 0.3076685 0.17791421
## sit4 0.9733689 -0.41061116
## sit5 -0.7399997 -0.42432679
## sit6 -0.7237659 -1.37428992Repro.species.score = as.data.frame(scores(Repro.rda, display='species'))
Repro.species.score$Species = rownames(Repro.species.score)
Repro.sites.score = data.frame(Repro.sites.score, ReproPCA)#getting and adding groupings
ReproGroup = read.csv('Repro groups.csv')
head(ReproGroup)## Treatment Dev.treatment Repro.treatment Family...Female SL...Female..cm.
## 1 HH-H HH H A 9.074
## 2 CC-H CC H A 8.548
## 3 HH-C HH C A 8.500
## 4 HH-H HH H E 8.280
## 5 CC-H CC H A 9.096
## 6 CC-H CC H C 9.338
## Weight...Female..g. Condition...Female Family...Male SL...Male..cm.
## 1 33.20 4.443670 C 9.478
## 2 27.50 4.402903 E 8.766
## 3 27.59 4.492571 D 8.948
## 4 21.74 3.829733 A 8.894
## 5 29.60 3.933149 D 9.184
## 6 30.10 3.696621 E 8.500
## Weight...Male..g. Condition...Male
## 1 31.90 3.746628
## 2 28.75 4.268089
## 3 29.68 4.142724
## 4 30.37 4.316715
## 5 32.40 4.182629
## 6 23.80 3.875433Repro.sites.score2 = cbind(Repro.sites.score, ReproGroup)
head(Repro.sites.score2)## PC1 PC2 Number.of.clutches.per.pair Total.number.of.eggs
## sit1 -0.8437966 0.11506770 3 947
## sit2 -0.2378545 -0.07765641 3 778
## sit3 0.3076685 0.17791421 2 459
## sit4 0.9733689 -0.41061116 1 233
## sit5 -0.7399997 -0.42432679 3 913
## sit6 -0.7237659 -1.37428992 3 883
## Clutch.size..1st. Average.egg.area Treatment Dev.treatment Repro.treatment
## sit1 320 5.0277 HH-H HH H
## sit2 216 4.3568 CC-H CC H
## sit3 239 4.8173 HH-C HH C
## sit4 233 4.3846 HH-H HH H
## sit5 323 4.5416 CC-H CC H
## sit6 364 3.8513 CC-H CC H
## Family...Female SL...Female..cm. Weight...Female..g. Condition...Female
## sit1 A 9.074 33.20 4.443670
## sit2 A 8.548 27.50 4.402903
## sit3 A 8.500 27.59 4.492571
## sit4 E 8.280 21.74 3.829733
## sit5 A 9.096 29.60 3.933149
## sit6 C 9.338 30.10 3.696621
## Family...Male SL...Male..cm. Weight...Male..g. Condition...Male
## sit1 C 9.478 31.90 3.746628
## sit2 E 8.766 28.75 4.268089
## sit3 D 8.948 29.68 4.142724
## sit4 A 8.894 30.37 4.316715
## sit5 D 9.184 32.40 4.182629
## sit6 E 8.500 23.80 3.875433#making a PCA figure
#putting the sites on the graph
g = ggplot() + geom_point(data=Repro.sites.score2, aes(y=PC2, x=PC1, shape=Treatment, colour= Treatment), show.legend = TRUE) + theme_classic()
#adding the arrows
g = g + geom_segment(data=Repro.species.score, aes(y=0, x=0, yend=PC2, xend=PC1), arrow = arrow(length=unit(0.3, 'lines')), show.legend = FALSE)
hjust = ifelse(Repro.species.score$PC1>0,0,1)
vjust = ifelse(Repro.species.score$PC2>0,0,1)
g = g + geom_text(data=Repro.species.score, aes(y=PC2, x=PC1, label = Species))
#now adding cross hairs and axis lines
g = g + geom_segment(data=NULL, aes(y=-Inf, x=-0, yend=Inf, xend=0))
g = g + geom_segment(data=NULL, aes(y=0, x=-Inf, yend=0, xend=Inf))
g
#PCA linear models
#Linear regression to investigate potential maternal effects
lm.1 = lm(PC1 ~ SL...Female..cm., data=Repro.sites.score2)
summary(lm.1)##
## Call:
## lm(formula = PC1 ~ SL...Female..cm., data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97104 -0.46093 -0.05538 0.38673 1.28677
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5458 2.9995 0.849 0.407
## SL...Female..cm. -0.2865 0.3371 -0.850 0.407
##
## Residual standard error: 0.6824 on 18 degrees of freedom
## Multiple R-squared: 0.03858, Adjusted R-squared: -0.01483
## F-statistic: 0.7223 on 1 and 18 DF, p-value: 0.4066lm.2 = lm(PC1 ~ Weight...Female..g., data=Repro.sites.score2)
summary(lm.2)##
## Call:
## lm(formula = PC1 ~ Weight...Female..g., data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7702 -0.5989 0.0166 0.2897 1.5242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.59691 0.95918 1.665 0.113
## Weight...Female..g. -0.05396 0.03204 -1.684 0.109
##
## Residual standard error: 0.6468 on 18 degrees of freedom
## Multiple R-squared: 0.1361, Adjusted R-squared: 0.08813
## F-statistic: 2.836 on 1 and 18 DF, p-value: 0.1094lm.3 = lm(PC1 ~ Condition...Female, data=Repro.sites.score2)
summary(lm.3)##
## Call:
## lm(formula = PC1 ~ Condition...Female, data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9086 -0.6730 -0.1668 0.6766 0.9823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5074 1.2484 1.207 0.243
## Condition...Female -0.3578 0.2942 -1.216 0.240
##
## Residual standard error: 0.669 on 18 degrees of freedom
## Multiple R-squared: 0.07594, Adjusted R-squared: 0.0246
## F-statistic: 1.479 on 1 and 18 DF, p-value: 0.2396lm.1 = lm(PC2 ~ SL...Female..cm., data=Repro.sites.score2)
summary(lm.1)##
## Call:
## lm(formula = PC2 ~ SL...Female..cm., data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.05281 -0.35207 -0.00926 0.45363 1.04081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3186 2.6710 2.366 0.0294 *
## SL...Female..cm. -0.7111 0.3002 -2.369 0.0292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6076 on 18 degrees of freedom
## Multiple R-squared: 0.2376, Adjusted R-squared: 0.1953
## F-statistic: 5.611 on 1 and 18 DF, p-value: 0.02924lm.2 = lm(PC2 ~ Weight...Female..g., data=Repro.sites.score2)
summary(lm.2)##
## Call:
## lm(formula = PC2 ~ Weight...Female..g., data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3649 -0.4158 -0.0770 0.3899 1.3537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55181 1.02357 0.539 0.596
## Weight...Female..g. -0.01865 0.03419 -0.545 0.592
##
## Residual standard error: 0.6903 on 18 degrees of freedom
## Multiple R-squared: 0.01625, Adjusted R-squared: -0.0384
## F-statistic: 0.2974 on 1 and 18 DF, p-value: 0.5922lm.3 = lm(PC2 ~ Condition...Female, data=Repro.sites.score2)
summary(lm.3)##
## Call:
## lm(formula = PC2 ~ Condition...Female, data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.36762 -0.21304 0.08111 0.38227 0.85624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.8449 1.1092 -2.565 0.0195 *
## Condition...Female 0.6752 0.2614 2.583 0.0187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5944 on 18 degrees of freedom
## Multiple R-squared: 0.2705, Adjusted R-squared: 0.23
## F-statistic: 6.674 on 1 and 18 DF, p-value: 0.01874lm.PC1a = lm(PC1 ~ Dev.treatment* Repro.treatment, data = Repro.sites.score2)
summary(lm.PC1a)##
## Call:
## lm(formula = PC1 ~ Dev.treatment * Repro.treatment, data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2970 -0.5162 0.0708 0.3363 1.2164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33895 0.25796 -1.314 0.2074
## Dev.treatmentHH 0.72768 0.38261 1.902 0.0753 .
## Repro.treatmentH 0.12734 0.36480 0.349 0.7316
## Dev.treatmentHH:Repro.treatmentH -0.06283 0.58823 -0.107 0.9163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6319 on 16 degrees of freedom
## Multiple R-squared: 0.2673, Adjusted R-squared: 0.1299
## F-statistic: 1.945 on 3 and 16 DF, p-value: 0.163anova(lm.PC1a)## Analysis of Variance Table
##
## Response: PC1
## Df Sum Sq Mean Sq F value Pr(>F)
## Dev.treatment 1 2.2734 2.27342 5.6943 0.02972 *
## Repro.treatment 1 0.0519 0.05189 0.1300 0.72317
## Dev.treatment:Repro.treatment 1 0.0046 0.00455 0.0114 0.91627
## Residuals 16 6.3879 0.39925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lm.PC1a), col="darkgray")
plot(fitted(lm.PC1a), residuals(lm.PC1a))
PC1EMM = (emmeans(lm.PC1a, ~ Dev.treatment*Repro.treatment) %>% as.data.frame)
PC1EMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C -0.3389519 0.2579553 16 -0.8857927 0.2078889
## 2 HH C 0.3887327 0.2825759 16 -0.2103015 0.9877668
## 3 CC H -0.2116138 0.2579553 16 -0.7584547 0.3352270
## 4 HH H 0.4532437 0.3648039 16 -0.3201060 1.2265934ggplot(PC1EMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 
lm.PC2a = lm(PC2 ~ Dev.treatment* Repro.treatment + Condition...Female, data = Repro.sites.score2)
summary(lm.PC2a)##
## Call:
## lm(formula = PC2 ~ Dev.treatment * Repro.treatment + Condition...Female,
## data = Repro.sites.score2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.94030 -0.22429 0.06101 0.35086 0.76228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.71610 1.15359 -1.488 0.1576
## Dev.treatmentHH 0.07091 0.31892 0.222 0.8271
## Repro.treatmentH -0.81471 0.31577 -2.580 0.0209 *
## Condition...Female 0.47245 0.24907 1.897 0.0773 .
## Dev.treatmentHH:Repro.treatmentH 0.42520 0.47153 0.902 0.3814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5011 on 15 degrees of freedom
## Multiple R-squared: 0.5679, Adjusted R-squared: 0.4527
## F-statistic: 4.928 on 4 and 15 DF, p-value: 0.009724anova(lm.PC2a)## Analysis of Variance Table
##
## Response: PC2
## Df Sum Sq Mean Sq F value Pr(>F)
## Dev.treatment 1 0.2425 0.2425 0.9655 0.341383
## Repro.treatment 1 3.4492 3.4492 13.7345 0.002113 **
## Condition...Female 1 1.0549 1.0549 4.2007 0.058316 .
## Dev.treatment:Repro.treatment 1 0.2042 0.2042 0.8131 0.381447
## Residuals 15 3.7670 0.2511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PC2EMM = (emmeans(lm.PC2a, ~ Dev.treatment*Repro.treatment) %>% as.data.frame)
PC2EMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C 0.27447940 0.2218879 15 -0.1984634 0.74742224
## 2 HH C 0.34538451 0.2244466 15 -0.1330121 0.82378111
## 3 CC H -0.54023329 0.2085737 15 -0.9847977 -0.09566891
## 4 HH H -0.04413309 0.2977320 15 -0.6787338 0.59046762ggplot(PC2EMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 
#Regressions of maternal traits to offspring and reproduction
Parental= read.csv('Correlations parental to offspring_FINAL.csv', strip.white=TRUE)
head(Parental)## Tank.ID Treatment Developmental.treatment Reproductive.treatment
## 1 9 HH-H HH H
## 2 25 CC-H CC H
## 3 42 HH-C HH C
## 4 75 HH-H HH H
## 5 76 CC-H CC H
## 6 77 CC-H CC H
## Pair.replicate Female.ID Family...Female Family.Number SL...Female..cm.
## 1 1 AHH A 1 9.074
## 2 1 ACH2 A 1 8.548
## 3 1 AHC6 A 1 8.500
## 4 2 EHHa E 5 8.280
## 5 2 ACH5 A 1 9.096
## 6 3 CCH1 C 3 9.338
## Weight...Female..g. Condition...Female Male.ID Family...Male Family.Number.1
## 1 33.20 4.443670 CHH9 C 3
## 2 27.50 4.402903 ECHf E 5
## 3 27.59 4.492571 DHCh D 4
## 4 21.74 3.829733 AHH2 A 1
## 5 29.60 3.933149 DCHc D 4
## 6 30.10 3.696621 ECHa E 5
## SL...Male..cm. Weight...Male..g. Condition...Male Total.number.of.eggs
## 1 9.478 31.90 3.746628 947
## 2 8.766 28.75 4.268089 778
## 3 8.948 29.68 4.142724 459
## 4 8.894 30.37 4.316715 233
## 5 9.184 32.40 4.182629 913
## 6 8.500 23.80 3.875433 883
## Number.of.clutches.per.pair Clutch.size..1st. Average.of.Egg.area..mm2.
## 1 3 320 5.0277
## 2 3 216 4.3568
## 3 2 239 4.8173
## 4 1 233 4.3846
## 5 3 323 4.5416
## 6 3 364 3.8513
## Average.of.Juvenile.weight..mg. Average.of.Juvenile.SL..mm.
## 1 3.665000 5.242800
## 2 3.725000 5.213400
## 3 4.695000 5.263000
## 4 4.514286 4.838714
## 5 3.820000 5.355600
## 6 3.355000 5.075150
## Average.of.Juvenile.yolk.area..mm.2. Average.of.Fulton.s.K
## 1 1.701300 2.547800
## 2 1.418950 2.632600
## 3 1.210650 3.238250
## 4 1.132143 4.028429
## 5 1.434800 2.489750
## 6 1.276400 2.569600#Linear regression to investigate potential maternal effects
lm.1 = lm(Clutch.size..1st. ~ SL...Female..cm., data=Parental)
summary(lm.1)##
## Call:
## lm(formula = Clutch.size..1st. ~ SL...Female..cm., data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.216 -36.695 4.832 42.940 99.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -456.78 249.58 -1.830 0.0838 .
## SL...Female..cm. 80.45 28.05 2.868 0.0102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.78 on 18 degrees of freedom
## Multiple R-squared: 0.3136, Adjusted R-squared: 0.2755
## F-statistic: 8.224 on 1 and 18 DF, p-value: 0.01023lm.2 = lm(Clutch.size..1st. ~ Weight...Female..g., data=Parental)
summary(lm.2)##
## Call:
## lm(formula = Clutch.size..1st. ~ Weight...Female..g., data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114.44 -27.55 11.96 34.57 102.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.976 89.093 0.595 0.5595
## Weight...Female..g. 6.929 2.976 2.328 0.0317 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 60.08 on 18 degrees of freedom
## Multiple R-squared: 0.2315, Adjusted R-squared: 0.1888
## F-statistic: 5.422 on 1 and 18 DF, p-value: 0.03174lm.3 = lm(Clutch.size..1st. ~ Condition...Female, data=Parental)
summary(lm.3)##
## Call:
## lm(formula = Clutch.size..1st. ~ Condition...Female, data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114.47 -47.60 -10.67 61.95 99.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320.16 127.04 2.520 0.0214 *
## Condition...Female -14.74 29.93 -0.492 0.6284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 68.08 on 18 degrees of freedom
## Multiple R-squared: 0.01329, Adjusted R-squared: -0.04152
## F-statistic: 0.2425 on 1 and 18 DF, p-value: 0.6284lm.1 = lm(Average.of.Egg.area..mm2. ~ SL...Female..cm., data=Parental)
summary(lm.1)##
## Call:
## lm(formula = Average.of.Egg.area..mm2. ~ SL...Female..cm., data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.74644 -0.32307 -0.01939 0.28466 1.17278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.9405 2.1428 3.239 0.00455 **
## SL...Female..cm. -0.2509 0.2408 -1.042 0.31133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4875 on 18 degrees of freedom
## Multiple R-squared: 0.05686, Adjusted R-squared: 0.004465
## F-statistic: 1.085 on 1 and 18 DF, p-value: 0.3113lm.2 = lm(Average.of.Egg.area..mm2. ~ Weight...Female..g., data=Parental)
summary(lm.2)##
## Call:
## lm(formula = Average.of.Egg.area..mm2. ~ Weight...Female..g.,
## data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86639 -0.25074 -0.04564 0.27997 1.08511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.32925 0.73877 5.860 1.5e-05 ***
## Weight...Female..g. 0.01291 0.02468 0.523 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4982 on 18 degrees of freedom
## Multiple R-squared: 0.01497, Adjusted R-squared: -0.03976
## F-statistic: 0.2735 on 1 and 18 DF, p-value: 0.6074lm.3 = lm(Average.of.Egg.area..mm2. ~ Condition...Female, data=Parental)
summary(lm.3)##
## Call:
## lm(formula = Average.of.Egg.area..mm2. ~ Condition...Female,
## data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8494 -0.1910 -0.0315 0.2445 0.7583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7027 0.8063 3.352 0.00355 **
## Condition...Female 0.4767 0.1900 2.509 0.02189 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4321 on 18 degrees of freedom
## Multiple R-squared: 0.2591, Adjusted R-squared: 0.218
## F-statistic: 6.296 on 1 and 18 DF, p-value: 0.02189lm.1 = lm(Total.number.of.eggs ~ SL...Female..cm., data=Parental)
summary(lm.1)##
## Call:
## lm(formula = Total.number.of.eggs ~ SL...Female..cm., data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486.81 -149.02 30.63 211.06 395.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -391.6 1173.8 -0.334 0.743
## SL...Female..cm. 112.6 131.9 0.854 0.405
##
## Residual standard error: 267 on 18 degrees of freedom
## Multiple R-squared: 0.0389, Adjusted R-squared: -0.01449
## F-statistic: 0.7285 on 1 and 18 DF, p-value: 0.4046lm.2 = lm(Total.number.of.eggs ~ Weight...Female..g., data=Parental)
summary(lm.2)##
## Call:
## lm(formula = Total.number.of.eggs ~ Weight...Female..g., data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -572.3 -135.8 -11.7 212.2 321.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.319 377.387 0.014 0.989
## Weight...Female..g. 20.398 12.606 1.618 0.123
##
## Residual standard error: 254.5 on 18 degrees of freedom
## Multiple R-squared: 0.127, Adjusted R-squared: 0.07849
## F-statistic: 2.618 on 1 and 18 DF, p-value: 0.123lm.3 = lm(Total.number.of.eggs ~ Condition...Female, data=Parental)
summary(lm.3)##
## Call:
## lm(formula = Total.number.of.eggs ~ Condition...Female, data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -358.83 -269.77 61.99 227.05 340.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 70.38 491.94 0.143 0.888
## Condition...Female 127.84 115.92 1.103 0.285
##
## Residual standard error: 263.6 on 18 degrees of freedom
## Multiple R-squared: 0.06329, Adjusted R-squared: 0.01125
## F-statistic: 1.216 on 1 and 18 DF, p-value: 0.2846lm.1 = lm(Number.of.clutches.per.pair ~ SL...Female..cm., data=Parental)
summary(lm.1)##
## Call:
## lm(formula = Number.of.clutches.per.pair ~ SL...Female..cm.,
## data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3807 -0.3522 0.1583 0.7089 0.7602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4829 3.6080 0.965 0.347
## SL...Female..cm. -0.1331 0.4055 -0.328 0.746
##
## Residual standard error: 0.8208 on 18 degrees of freedom
## Multiple R-squared: 0.005951, Adjusted R-squared: -0.04927
## F-statistic: 0.1078 on 1 and 18 DF, p-value: 0.7465lm.2 = lm(Number.of.clutches.per.pair ~ Weight...Female..g., data=Parental)
summary(lm.2)##
## Call:
## lm(formula = Number.of.clutches.per.pair ~ Weight...Female..g.,
## data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5414 -0.3987 0.1905 0.6905 0.8218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.55768 1.20792 1.290 0.214
## Weight...Female..g. 0.02508 0.04035 0.622 0.542
##
## Residual standard error: 0.8146 on 18 degrees of freedom
## Multiple R-squared: 0.02102, Adjusted R-squared: -0.03337
## F-statistic: 0.3865 on 1 and 18 DF, p-value: 0.542lm.3 = lm(Number.of.clutches.per.pair ~ Condition...Female, data=Parental)
summary(lm.3)##
## Call:
## lm(formula = Number.of.clutches.per.pair ~ Condition...Female,
## data = Parental)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.22395 -0.46314 -0.02657 0.63694 1.01189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08599 1.42808 -0.060 0.953
## Condition...Female 0.56630 0.33650 1.683 0.110
##
## Residual standard error: 0.7653 on 18 degrees of freedom
## Multiple R-squared: 0.136, Adjusted R-squared: 0.08795
## F-statistic: 2.832 on 1 and 18 DF, p-value: 0.1097##Individuals trait models #Egg area
EggArea = read.csv('Egg area_FINAL.csv', strip.white=TRUE)
head(EggArea)## Tank.ID Treatment Dev.treatment Repro.treatment Pair.replicate Female.ID
## 1 139 CC-C CC C 1 ACC4
## 2 139 CC-C CC C 1 ACC4
## 3 139 CC-C CC C 1 ACC4
## 4 139 CC-C CC C 1 ACC4
## 5 139 CC-C CC C 1 ACC4
## 6 139 CC-C CC C 1 ACC4
## Male.ID Male.family Male.SL..mm. Male.weight..g. Male.condition Female.family
## 1 FCC F 8.648 22.63 3.498948 A
## 2 FCC F 8.648 22.63 3.498948 A
## 3 FCC F 8.648 22.63 3.498948 A
## 4 FCC F 8.648 22.63 3.498948 A
## 5 FCC F 8.648 22.63 3.498948 A
## 6 FCC F 8.648 22.63 3.498948 A
## Female.SL Female.weight Condition...Female Egg.replicate Egg.area..mm2.
## 1 9.144 31.27 4.089962 1 4.659
## 2 9.144 31.27 4.089962 2 5.380
## 3 9.144 31.27 4.089962 3 5.026
## 4 9.144 31.27 4.089962 4 5.122
## 5 9.144 31.27 4.089962 5 4.631
## 6 9.144 31.27 4.089962 6 4.708str(EggArea)## 'data.frame': 199 obs. of 17 variables:
## $ Tank.ID : int 139 139 139 139 139 139 139 139 139 139 ...
## $ Treatment : chr "CC-C" "CC-C" "CC-C" "CC-C" ...
## $ Dev.treatment : chr "CC" "CC" "CC" "CC" ...
## $ Repro.treatment : chr "C" "C" "C" "C" ...
## $ Pair.replicate : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Female.ID : chr "ACC4" "ACC4" "ACC4" "ACC4" ...
## $ Male.ID : chr "FCC" "FCC" "FCC" "FCC" ...
## $ Male.family : chr "F" "F" "F" "F" ...
## $ Male.SL..mm. : num 8.65 8.65 8.65 8.65 8.65 ...
## $ Male.weight..g. : num 22.6 22.6 22.6 22.6 22.6 ...
## $ Male.condition : num 3.5 3.5 3.5 3.5 3.5 ...
## $ Female.family : chr "A" "A" "A" "A" ...
## $ Female.SL : num 9.14 9.14 9.14 9.14 9.14 ...
## $ Female.weight : num 31.3 31.3 31.3 31.3 31.3 ...
## $ Condition...Female: num 4.09 4.09 4.09 4.09 4.09 ...
## $ Egg.replicate : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Egg.area..mm2. : num 4.66 5.38 5.03 5.12 4.63 ...EggArea$Male.family = factor(EggArea$Male.family)
EggArea$Dev.treatment = factor(EggArea$Dev.treatment)
EggArea$Repro.treatment = factor(EggArea$Repro.treatment)
str(EggArea)## 'data.frame': 199 obs. of 17 variables:
## $ Tank.ID : int 139 139 139 139 139 139 139 139 139 139 ...
## $ Treatment : chr "CC-C" "CC-C" "CC-C" "CC-C" ...
## $ Dev.treatment : Factor w/ 2 levels "CC","HH": 1 1 1 1 1 1 1 1 1 1 ...
## $ Repro.treatment : Factor w/ 2 levels "C","H": 1 1 1 1 1 1 1 1 1 1 ...
## $ Pair.replicate : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Female.ID : chr "ACC4" "ACC4" "ACC4" "ACC4" ...
## $ Male.ID : chr "FCC" "FCC" "FCC" "FCC" ...
## $ Male.family : Factor w/ 5 levels "A","C","D","E",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Male.SL..mm. : num 8.65 8.65 8.65 8.65 8.65 ...
## $ Male.weight..g. : num 22.6 22.6 22.6 22.6 22.6 ...
## $ Male.condition : num 3.5 3.5 3.5 3.5 3.5 ...
## $ Female.family : chr "A" "A" "A" "A" ...
## $ Female.SL : num 9.14 9.14 9.14 9.14 9.14 ...
## $ Female.weight : num 31.3 31.3 31.3 31.3 31.3 ...
## $ Condition...Female: num 4.09 4.09 4.09 4.09 4.09 ...
## $ Egg.replicate : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Egg.area..mm2. : num 4.66 5.38 5.03 5.12 4.63 ...lmer.EAr = lmer(Egg.area..mm2. ~ Dev.treatment * Repro.treatment + Condition...Female + (1|Male.family) + (1|Female.family) + (1|Egg.replicate) , data = EggArea)
summary(lmer.EAr)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Egg.area..mm2. ~ Dev.treatment * Repro.treatment + Condition...Female +
## (1 | Male.family) + (1 | Female.family) + (1 | Egg.replicate)
## Data: EggArea
##
## REML criterion at convergence: 181.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.10972 -0.75441 -0.07345 0.65499 2.74648
##
## Random effects:
## Groups Name Variance Std.Dev.
## Egg.replicate (Intercept) 0.02215 0.1488
## Female.family (Intercept) 0.02363 0.1537
## Male.family (Intercept) 0.03056 0.1748
## Residual 0.11649 0.3413
## Number of obs: 199, groups:
## Egg.replicate, 10; Female.family, 5; Male.family, 5
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.90156 0.31996 93.47974 12.194
## Dev.treatmentHH -0.19642 0.08149 134.69238 -2.410
## Repro.treatmentH -0.75511 0.08800 95.78531 -8.581
## Condition...Female 0.24802 0.06591 156.70832 3.763
## Dev.treatmentHH:Repro.treatmentH 0.49278 0.12448 145.93574 3.959
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Dev.treatmentHH 0.017290 *
## Repro.treatmentH 1.68e-13 ***
## Condition...Female 0.000237 ***
## Dev.treatmentHH:Repro.treatmentH 0.000117 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Dv.tHH Rpr.tH Cn...F
## Dv.trtmntHH -0.331
## Rpr.trtmntH -0.336 0.647
## Cndtn...Fml -0.916 0.253 0.242
## Dv.trHH:R.H 0.086 -0.680 -0.731 -0.008anova(lmer.EAr)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Dev.treatment 0.0803 0.0803 1 170.16 0.6896 0.4074750
## Repro.treatment 8.3405 8.3405 1 156.68 71.5963 1.759e-14 ***
## Condition...Female 1.6493 1.6493 1 156.71 14.1582 0.0002371 ***
## Dev.treatment:Repro.treatment 1.8256 1.8256 1 145.94 15.6708 0.0001174 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lmer.EAr), col="darkgray")
plot(fitted(lmer.EAr), residuals(lmer.EAr))
EaEMM = (emmeans(lmer.EAr, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
EaEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C 4.946704 0.1311469 10.85502 4.657581 5.235828
## 2 HH C 4.750280 0.1358089 11.27185 4.452244 5.048316
## 3 CC H 4.191595 0.1350207 11.03086 3.894518 4.488673
## 4 HH H 4.487956 0.1424562 13.99942 4.182416 4.793495ggplot(EaEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 
##Clutch size/ number of eggs in clutch 1
ClutchSz = read.csv('Clutch size_FINAL.csv', strip.white=TRUE)
head(ClutchSz)## Tank.ID Treatment Dev.treatment Repro.treatment Pair.replicate Female.ID
## 1 139 CC-C CC C 1 ACC4
## 2 185 CC-C CC C 2 ACC3
## 3 190 CC-C CC C 3 ECC
## 4 192 CC-C CC C 4 ECC
## 5 197 CC-C CC C 5 ACCb
## 6 221 CC-C CC C 6 DCC4
## Family...Female Family.Number SL...Female..cm. Weight...Female..g.
## 1 A 1 9.144 31.27
## 2 A 1 8.912 35.88
## 3 E 5 9.274 33.00
## 4 E 5 7.800 26.62
## 5 A 1 8.776 32.62
## 6 D 4 9.346 29.53
## Condition...Female Male.ID Family...Male Family.Number.1 SL...Male..cm.
## 1 4.089962 FCC F 6 8.648
## 2 5.069054 DCC D 4 9.650
## 3 4.137259 ACC2 A 1 9.450
## 4 5.609501 FCC F 6 7.738
## 5 4.826076 CCC3 C 3 8.598
## 6 3.617313 ACC5 A 1 8.814
## Weight...Male..g. Condition...Male Clutch.size..1st.
## 1 22.63 3.498948 198
## 2 41.12 4.575844 324
## 3 36.87 4.368956 309
## 4 19.68 4.247554 123
## 5 23.61 3.714528 349
## 6 29.09 4.248392 302ClutchSz$Tank.ID = factor(ClutchSz$Tank.ID)
str(ClutchSz)## 'data.frame': 20 obs. of 18 variables:
## $ Tank.ID : Factor w/ 20 levels "9","25","42",..: 11 14 15 16 18 20 2 5 6 7 ...
## $ Treatment : chr "CC-C" "CC-C" "CC-C" "CC-C" ...
## $ Dev.treatment : chr "CC" "CC" "CC" "CC" ...
## $ Repro.treatment : chr "C" "C" "C" "C" ...
## $ Pair.replicate : int 1 2 3 4 5 6 1 2 3 4 ...
## $ Female.ID : chr "ACC4" "ACC3" "ECC" "ECC" ...
## $ Family...Female : chr "A" "A" "E" "E" ...
## $ Family.Number : int 1 1 5 5 1 4 1 1 3 3 ...
## $ SL...Female..cm. : num 9.14 8.91 9.27 7.8 8.78 ...
## $ Weight...Female..g.: num 31.3 35.9 33 26.6 32.6 ...
## $ Condition...Female : num 4.09 5.07 4.14 5.61 4.83 ...
## $ Male.ID : chr "FCC" "DCC" "ACC2" "FCC" ...
## $ Family...Male : chr "F" "D" "A" "F" ...
## $ Family.Number.1 : int 6 4 1 6 3 1 5 4 5 4 ...
## $ SL...Male..cm. : num 8.65 9.65 9.45 7.74 8.6 ...
## $ Weight...Male..g. : num 22.6 41.1 36.9 19.7 23.6 ...
## $ Condition...Male : num 3.5 4.58 4.37 4.25 3.71 ...
## $ Clutch.size..1st. : int 198 324 309 123 349 302 216 323 364 278 ...lm.Csz = lm(Clutch.size..1st. ~ Dev.treatment * Repro.treatment + SL...Female..cm., data = ClutchSz)
summary(lm.Csz)##
## Call:
## lm(formula = Clutch.size..1st. ~ Dev.treatment * Repro.treatment +
## SL...Female..cm., data = ClutchSz)
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.69 -36.03 13.73 36.08 88.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -359.23316 300.32125 -1.196 0.2502
## Dev.treatmentHH -36.77985 36.70738 -1.002 0.3322
## Repro.treatmentH -0.05357 36.11106 -0.001 0.9988
## SL...Female..cm. 70.61517 33.72581 2.094 0.0537 .
## Dev.treatmentHH:Repro.treatmentH 30.26588 57.21414 0.529 0.6046
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 59.78 on 15 degrees of freedom
## Multiple R-squared: 0.3659, Adjusted R-squared: 0.1968
## F-statistic: 2.164 on 4 and 15 DF, p-value: 0.1229anova(lm.Csz)## Analysis of Variance Table
##
## Response: Clutch.size..1st.
## Df Sum Sq Mean Sq F value Pr(>F)
## Dev.treatment 1 12649 12648.5 3.5389 0.07950 .
## Repro.treatment 1 2608 2607.7 0.7296 0.40645
## SL...Female..cm. 1 14676 14676.2 4.1062 0.06089 .
## Dev.treatment:Repro.treatment 1 1000 1000.2 0.2798 0.60455
## Residuals 15 53612 3574.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lm.Csz), col="darkgray")
plot(fitted(lm.Csz), residuals(lm.Csz ))
CszEMM = (emmeans(lm.Csz, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
CszEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C 268.2462 24.40942 15 216.2187 320.2736
## 2 HH C 231.4663 27.49902 15 172.8535 290.0791
## 3 CC H 268.1926 26.47412 15 211.7643 324.6208
## 4 HH H 261.6786 35.69082 15 185.6054 337.7518ggplot(CszEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 
##Total eggs and number of clutches per pair
Totals = read.csv('Clutches per pair_FINAL.csv', strip.white=TRUE)
head(Totals)## Tank.ID Treatment Dev.treatment Repro.treatment Pair.replicate Female.ID
## 1 9 HH-H HH H 1 AHH
## 2 25 CC-H CC H 1 ACH2
## 3 42 HH-C HH C 1 AHC6
## 4 75 HH-H HH H 2 EHHa
## 5 76 CC-H CC H 2 ACH5
## 6 77 CC-H CC H 3 CCH1
## Family...Female Family.Number SL...Female..cm. Weight...Female..g.
## 1 A 1 9.074 33.20
## 2 A 1 8.548 27.50
## 3 A 1 8.500 27.59
## 4 E 5 8.280 21.74
## 5 A 1 9.096 29.60
## 6 C 3 9.338 30.10
## Condition...Female Male.ID Family...Male Family.Number.1 SL...Male..cm.
## 1 4.443670 CHH9 C 3 9.478
## 2 4.402903 ECHf E 5 8.766
## 3 4.492571 DHCh D 4 8.948
## 4 3.829733 AHH2 A 1 8.894
## 5 3.933149 DCHc D 4 9.184
## 6 3.696621 ECHa E 5 8.500
## Weight...Male..g. Condition...Male Number.of.clutches.per.pair
## 1 31.90 3.746628 3
## 2 28.75 4.268089 3
## 3 29.68 4.142724 2
## 4 30.37 4.316715 1
## 5 32.40 4.182629 3
## 6 23.80 3.875433 3
## Total.number.of.eggs
## 1 947
## 2 778
## 3 459
## 4 233
## 5 913
## 6 883Totals$Tank.ID = factor(Totals$Tank.ID)
str(Totals)## 'data.frame': 20 obs. of 19 variables:
## $ Tank.ID : Factor w/ 20 levels "9","25","42",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Treatment : chr "HH-H" "CC-H" "HH-C" "HH-H" ...
## $ Dev.treatment : chr "HH" "CC" "HH" "HH" ...
## $ Repro.treatment : chr "H" "H" "C" "H" ...
## $ Pair.replicate : int 1 1 1 2 2 3 4 5 3 2 ...
## $ Female.ID : chr "AHH" "ACH2" "AHC6" "EHHa" ...
## $ Family...Female : chr "A" "A" "A" "E" ...
## $ Family.Number : int 1 1 1 5 1 3 3 3 3 1 ...
## $ SL...Female..cm. : num 9.07 8.55 8.5 8.28 9.1 ...
## $ Weight...Female..g. : num 33.2 27.5 27.6 21.7 29.6 ...
## $ Condition...Female : num 4.44 4.4 4.49 3.83 3.93 ...
## $ Male.ID : chr "CHH9" "ECHf" "DHCh" "AHH2" ...
## $ Family...Male : chr "C" "E" "D" "A" ...
## $ Family.Number.1 : int 3 5 4 1 4 5 4 1 4 3 ...
## $ SL...Male..cm. : num 9.48 8.77 8.95 8.89 9.18 ...
## $ Weight...Male..g. : num 31.9 28.8 29.7 30.4 32.4 ...
## $ Condition...Male : num 3.75 4.27 4.14 4.32 4.18 ...
## $ Number.of.clutches.per.pair: int 3 3 2 1 3 3 3 2 1 2 ...
## $ Total.number.of.eggs : int 947 778 459 233 913 883 782 700 175 367 ...lm.TotEgg1 = lm(Total.number.of.eggs ~ Dev.treatment * Repro.treatment , data = Totals)
summary(lm.TotEgg1)##
## Call:
## lm(formula = Total.number.of.eggs ~ Dev.treatment * Repro.treatment,
## data = Totals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -481.8 -136.1 -0.7 175.7 495.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 724.000 100.471 7.206 2.09e-06 ***
## Dev.treatmentHH -285.600 149.023 -1.916 0.0733 .
## Repro.treatmentH -9.167 142.088 -0.065 0.9494
## Dev.treatmentHH:Repro.treatmentH 22.433 229.110 0.098 0.9232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 246.1 on 16 degrees of freedom
## Multiple R-squared: 0.2743, Adjusted R-squared: 0.1383
## F-statistic: 2.016 on 3 and 16 DF, p-value: 0.1522anova(lm.TotEgg1)## Analysis of Variance Table
##
## Response: Total.number.of.eggs
## Df Sum Sq Mean Sq F value Pr(>F)
## Dev.treatment 1 365755 365755 6.0389 0.02579 *
## Repro.treatment 1 1 1 0.0000 0.99621
## Dev.treatment:Repro.treatment 1 581 581 0.0096 0.92322
## Residuals 16 969069 60567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lm.TotEgg1), col="darkgray")
plot(fitted(lm.TotEgg1), residuals(lm.TotEgg1))
TEggEMM = (emmeans(lm.TotEgg1, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
TEggEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C 724.0000 100.4712 16 511.0105 936.9895
## 2 HH C 438.4000 110.0607 16 205.0817 671.7183
## 3 CC H 714.8333 100.4712 16 501.8439 927.8228
## 4 HH H 451.6667 142.0878 16 150.4541 752.8793ggplot(TEggEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 
#Number of clutches produced over the season
lm.Clutch = lm(Number.of.clutches.per.pair ~ Dev.treatment * Repro.treatment , data = Totals)
summary(lm.Clutch)##
## Call:
## lm(formula = Number.of.clutches.per.pair ~ Dev.treatment * Repro.treatment,
## data = Totals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5000 -0.6667 0.1667 0.5000 1.3333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6667 0.3146 8.477 2.6e-07 ***
## Dev.treatmentHH -0.6667 0.4666 -1.429 0.172
## Repro.treatmentH -0.1667 0.4449 -0.375 0.713
## Dev.treatmentHH:Repro.treatmentH -0.1667 0.7173 -0.232 0.819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7706 on 16 degrees of freedom
## Multiple R-squared: 0.2213, Adjusted R-squared: 0.07531
## F-statistic: 1.516 on 3 and 16 DF, p-value: 0.2486anova(lm.Clutch)## Analysis of Variance Table
##
## Response: Number.of.clutches.per.pair
## Df Sum Sq Mean Sq F value Pr(>F)
## Dev.treatment 1 2.4083 2.40833 4.0561 0.06114 .
## Repro.treatment 1 0.2596 0.25962 0.4372 0.51786
## Dev.treatment:Repro.treatment 1 0.0321 0.03205 0.0540 0.81922
## Residuals 16 9.5000 0.59375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1hist(residuals(lm.Clutch), col="darkgray")
plot(fitted(lm.Clutch), residuals(lm.Clutch))
ClutchEMM = (emmeans(lm.Clutch, ~ Dev.treatment * Repro.treatment) %>% as.data.frame)
ClutchEMM## Dev.treatment Repro.treatment emmean SE df lower.CL upper.CL
## 1 CC C 2.666667 0.3145764 16 1.9997944 3.333539
## 2 HH C 2.000000 0.3446012 16 1.2694781 2.730522
## 3 CC H 2.500000 0.3145764 16 1.8331277 3.166872
## 4 HH H 1.666667 0.4448783 16 0.7235669 2.609766ggplot(ClutchEMM, aes(x=Repro.treatment, y=emmean, colour=Dev.treatment))+ geom_point() + geom_linerange(aes (ymin = emmean - SE, ymax = emmean + SE)) + theme_classic() 