Changes in Fv/Fm in elevated nutrients and heat stress (Only A.cer)
Ana Palacio
2022-06-21
General project set-up
# Load libraries and sources required to run the script
library(tidyverse)
library(ggthemes)
library(lmerTest)
library(emmeans)
library(multcomp)
library(effects)
library(gridExtra)
library(rstatix)
# Default ggplot settings
Fill.colour<-scale_colour_manual(values = c ("#4A6CAA", "#469B53", "#AA4A74"))
ggthe_bw<-theme_bw() + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
#panel.background = element_blank(),
axis.line = element_line(colour = "black"),
plot.background=element_blank(),
legend.title = element_blank(),
legend.box.background = element_rect(),
panel.background =element_rect(fill = NA, color = "white"),
legend.position="bottom",
strip.background =element_rect(fill=NA))Data exploration
1. Get the files with all the YII by species
Metadata
YII.data<-read.csv("YII_Data/All_YII_data.csv", header = T)
YII.Acer<-subset(YII.data, Spp=="Ac")
YII.Acer<-droplevels(YII.Acer)
summary(YII.Acer)## Sample Date Spp Fragment Treatment
## Ac_288_T21: 2 2017-07-26: 120 Ac:2281 Ac_108 : 24 A :856
## Ac_101_T0 : 1 2017-08-30: 120 Ac_116 : 24 N :705
## Ac_101_T1 : 1 2017-10-10: 120 Ac_119 : 24 N+P:720
## Ac_101_T10: 1 2017-10-19: 120 Ac_122 : 24
## Ac_101_T11: 1 2017-11-06: 120 Ac_143 : 24
## Ac_101_T12: 1 2017-11-16: 120 Ac_152 : 24
## (Other) :2274 (Other) :1561 (Other):2137
## Replicate YII Genotype Days Time_Point
## R1:1202 Min. :0.1540 G_07:462 Min. :-112.00 T0 : 120
## R2:1079 1st Qu.:0.5330 G_08:145 1st Qu.: -9.00 T1 : 120
## Median :0.5920 G_31:304 Median : 28.00 T10 : 120
## Mean :0.5719 G_48:570 Mean : 29.67 T2 : 120
## 3rd Qu.:0.6240 G_50:223 3rd Qu.: 84.00 T3 : 120
## Max. :0.6870 G_62:577 Max. : 130.00 T4 : 120
## (Other):1561
## Phase TotalSH logSH D.Prp Community
## Baseline :720 Min. :0.0012 Min. :-2.9313 Min. :0 A:2281
## Heat :408 1st Qu.:0.0485 1st Qu.:-1.3141 1st Qu.:0
## Nutrients:950 Median :0.1133 Median :-0.9459 Median :0
## Ramping :179 Mean :0.1581 Mean :-0.9831 Mean :0
## Recovery : 24 3rd Qu.:0.2259 3rd Qu.:-0.6461 3rd Qu.:0
## Max. :0.8947 Max. :-0.0483 Max. :0
## NA's :1821 NA's :1821
## InitialCommunity
## A:2281
##
##
##
##
##
## Merge/Transform
# Organize data type
YII.Acer$Date<-as.Date(YII.Acer$Date, "%Y-%m-%d")
YII.Acer$Days<-(as.numeric(YII.Acer$Date) -17485)
#Time as a factor, not as int
str(YII.Acer)## 'data.frame': 2281 obs. of 16 variables:
## $ Sample : Factor w/ 2280 levels "Ac_101_T0","Ac_101_T1",..: 1 2 10 11 12 13 14 15 16 17 ...
## $ Date : Date, format: "2017-07-26" "2017-08-30" ...
## $ Spp : Factor w/ 1 level "Ac": 1 1 1 1 1 1 1 1 1 1 ...
## $ Fragment : Factor w/ 120 levels "Ac_101","Ac_102",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Treatment : Factor w/ 3 levels "A","N","N+P": 3 3 3 3 3 3 3 3 3 3 ...
## $ Replicate : Factor w/ 2 levels "R1","R2": 1 1 1 1 1 1 1 1 1 1 ...
## $ YII : num 0.644 0.576 0.563 0.568 0.645 0.589 0.595 0.606 0.605 0.606 ...
## $ Genotype : Factor w/ 6 levels "G_07","G_08",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Days : num -112 -77 -36 -27 -9 1 8 14 21 28 ...
## $ Time_Point : Factor w/ 24 levels "T0","T1","T10",..: 1 2 12 18 19 20 21 22 23 24 ...
## $ Phase : Factor w/ 5 levels "Baseline","Heat",..: 1 1 1 1 1 1 3 3 3 3 ...
## $ TotalSH : num NA NA NA NA NA ...
## $ logSH : num NA NA NA NA NA ...
## $ D.Prp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Community : Factor w/ 1 level "A": 1 1 1 1 1 1 1 1 1 1 ...
## $ InitialCommunity: Factor w/ 1 level "A": 1 1 1 1 1 1 1 1 1 1 ... YII.Acer$DaysF<-as.factor(YII.Acer$Days)
YII.Acer$Treatment <- as.factor(YII.Acer$Treatment)
YII.Acer$Genotype<-factor(as.character(YII.Acer$Genotype),
levels=c("G_48", "G_62","G_31",
"G_08","G_07", "G_50"
)) # Survivorship order
# Differentiate ambient from elevated nutrients (N and N+P)
YII.Acer$Nutrients<-"Nutrients"
YII.Acer$Nutrients[YII.Acer$Treatment=="A"]<-"Ambient"
# Check the data
str(YII.Acer)## 'data.frame': 2281 obs. of 18 variables:
## $ Sample : Factor w/ 2280 levels "Ac_101_T0","Ac_101_T1",..: 1 2 10 11 12 13 14 15 16 17 ...
## $ Date : Date, format: "2017-07-26" "2017-08-30" ...
## $ Spp : Factor w/ 1 level "Ac": 1 1 1 1 1 1 1 1 1 1 ...
## $ Fragment : Factor w/ 120 levels "Ac_101","Ac_102",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Treatment : Factor w/ 3 levels "A","N","N+P": 3 3 3 3 3 3 3 3 3 3 ...
## $ Replicate : Factor w/ 2 levels "R1","R2": 1 1 1 1 1 1 1 1 1 1 ...
## $ YII : num 0.644 0.576 0.563 0.568 0.645 0.589 0.595 0.606 0.605 0.606 ...
## $ Genotype : Factor w/ 6 levels "G_48","G_62",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Days : num -112 -77 -36 -27 -9 1 8 14 21 28 ...
## $ Time_Point : Factor w/ 24 levels "T0","T1","T10",..: 1 2 12 18 19 20 21 22 23 24 ...
## $ Phase : Factor w/ 5 levels "Baseline","Heat",..: 1 1 1 1 1 1 3 3 3 3 ...
## $ TotalSH : num NA NA NA NA NA ...
## $ logSH : num NA NA NA NA NA ...
## $ D.Prp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Community : Factor w/ 1 level "A": 1 1 1 1 1 1 1 1 1 1 ...
## $ InitialCommunity: Factor w/ 1 level "A": 1 1 1 1 1 1 1 1 1 1 ...
## $ DaysF : Factor w/ 24 levels "-112","-77","-36",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Nutrients : chr "Nutrients" "Nutrients" "Nutrients" "Nutrients" ... summary(YII.Acer)## Sample Date Spp Fragment Treatment
## Ac_288_T21: 2 Min. :2017-07-26 Ac:2281 Ac_108 : 24 A :856
## Ac_101_T0 : 1 1st Qu.:2017-11-06 Ac_116 : 24 N :705
## Ac_101_T1 : 1 Median :2017-12-13 Ac_119 : 24 N+P:720
## Ac_101_T10: 1 Mean :2017-12-14 Ac_122 : 24
## Ac_101_T11: 1 3rd Qu.:2018-02-07 Ac_143 : 24
## Ac_101_T12: 1 Max. :2018-03-25 Ac_152 : 24
## (Other) :2274 (Other):2137
## Replicate YII Genotype Days Time_Point
## R1:1202 Min. :0.1540 G_48:570 Min. :-112.00 T0 : 120
## R2:1079 1st Qu.:0.5330 G_62:577 1st Qu.: -9.00 T1 : 120
## Median :0.5920 G_31:304 Median : 28.00 T10 : 120
## Mean :0.5719 G_08:145 Mean : 29.67 T2 : 120
## 3rd Qu.:0.6240 G_07:462 3rd Qu.: 84.00 T3 : 120
## Max. :0.6870 G_50:223 Max. : 130.00 T4 : 120
## (Other):1561
## Phase TotalSH logSH D.Prp Community
## Baseline :720 Min. :0.0012 Min. :-2.9313 Min. :0 A:2281
## Heat :408 1st Qu.:0.0485 1st Qu.:-1.3141 1st Qu.:0
## Nutrients:950 Median :0.1133 Median :-0.9459 Median :0
## Ramping :179 Mean :0.1581 Mean :-0.9831 Mean :0
## Recovery : 24 3rd Qu.:0.2259 3rd Qu.:-0.6461 3rd Qu.:0
## Max. :0.8947 Max. :-0.0483 Max. :0
## NA's :1821 NA's :1821
## InitialCommunity DaysF Nutrients
## A:2281 -112 : 120 Length:2281
## -77 : 120 Class :character
## -36 : 120 Mode :character
## -27 : 120
## -9 : 120
## 1 : 120
## (Other):1561Remove / subset timepoints
# Remove baseline values
YII.Acer<-subset(YII.Acer, Days>-1)
# Remove recovery values
YII.Acer<-subset(YII.Acer, Days<112)
# write.csv(YII.data, "Outputs/Experiment_YII_data.csv", row.names = F)
# YII.Wide<- reshape(YII.data, idvar = "Fragment", timevar = "Days", direction = "wide")
Spp.fragments<-YII.Acer %>%
group_by(Spp, Genotype, Treatment, Replicate) %>% count(Fragment)
Spp.fragments #write.csv(Spp.fragments, "Outputs/Meassurments_perFragments.csv", row.names = F)
# Subset data
YII.nutrients<-subset(YII.Acer, Days<80)
YII.heat<-subset(YII.Acer, Days>75)
Y.II76<-subset(YII.Acer, Days==76)
write.csv(Y.II76, "YII76.csv", row.names = F)
Y.II99<-subset(YII.Acer, Days==99)
write.csv(Y.II99, "YII99.csv", row.names = F)
Y.II110<-subset(YII.Acer, Days==110)
write.csv(Y.II110, "Y.II110.csv", row.names = F)2. Exploratory graphs
All time points (nutrients + heat stress)
- Colony (Genotype) differences
YII_Colony<- ggplot(YII.Acer, aes (Days, YII, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.5)+
stat_summary(fun.y=mean, geom="line", alpha=0.6) + theme_bw()
YII_Colony + ylim(0.0, 0.65) + facet_grid (Spp~Treatment)
YII_Frag_Gen<- ggplot(YII.Acer, aes (Days, YII, colour=Genotype, group=Fragment)) +
stat_summary(fun.y=mean, geom="line", alpha=0.5) +
theme_bw() + theme(legend.position = "bottom",
legend.title = element_blank())
YII_Frag_Gen + ylim(0.0, 0.65) + facet_grid (Spp~Treatment)
3. Treatment effect (A, N and N+P)
Figure S3: Treatments overall and by genotype
Colour.colour<-scale_colour_manual(values = c ("#4A6CAA", "#469B53", "#AA4A74"))
Fill.colour<-scale_fill_manual(values = c ("#4A6CAA", "#469B53", "#AA4A74"))
YII_Treat<- ggplot(data=YII.Acer, aes (Days, YII,fill=Treatment, shape=factor(Treatment))) +
scale_shape_manual(values=c(21, 22, 24), labels=c("Ambient", "NH4", "NH4+PO4"))+
ggthe_bw + Fill.colour + ggtitle("b") +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 1,
position = position_dodge(1) )+
stat_summary(fun.y=mean, geom="line", position = position_dodge(1),
linetype=1, alpha=1) +
stat_summary(fun.y=mean, geom="point", size =2,
position=position_dodge(width=1), alpha=0.8) +
scale_y_continuous(limits = c(0.1, 0.7),
breaks = seq(0.1, 0.6, 0.1),
expand = c(0, 0),
name=expression(~italic("Fv / Fm"))) +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 15),
expand = c(0, 0))+
annotate("segment", x = 2, xend = 91, y = 0.12, yend = 0.12,
colour = "gray35", linetype=2)+
annotate("segment", x = 79, xend = 90, y = 0.12, yend = 0.20,
colour = "gray35", linetype=3)+
annotate("segment", x = 91, xend = 110, y = 0.20, yend = 0.20,
colour = "gray35", linetype=3)+
annotate("text", x = 45, y = 0.15, label = "Nutrients", size=3)+
annotate("text", x = 99, y = 0.15, label = "Heat", size=3)
YII_Treat
FigureS3<-YII_Treat + facet_wrap (Genotype ~.)
FigureS3
#ggsave(file="Outputs/Fig_3b_Acer_YII_Treat.svg", plot=FigureS3, width=5.5, height=5)Model 1: onlyu treatments (A, N and N+P)
# 1. Find the best model
YII.Acer$DaysF<-as.factor(YII.Acer$Days)
Model1<-lmerTest::lmer(YII ~ Treatment * DaysF +
(1|Genotype) + (1|Replicate) + (1|Fragment),
data=YII.Acer, na.action=na.omit)
summary(Model1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: YII ~ Treatment * DaysF + (1 | Genotype) + (1 | Replicate) +
## (1 | Fragment)
## Data: YII.Acer
##
## REML criterion at convergence: -7082.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -14.5801 -0.4034 0.0606 0.4568 5.5508
##
## Random effects:
## Groups Name Variance Std.Dev.
## Fragment (Intercept) 1.213e-04 0.011014
## Genotype (Intercept) 1.969e-04 0.014033
## Replicate (Intercept) 7.484e-06 0.002736
## Residual 5.142e-04 0.022676
## Number of obs: 1620, groups: Fragment, 120; Genotype, 6; Replicate, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.117e-01 7.298e-03 1.099e+01 83.810 < 2e-16 ***
## TreatmentN -1.116e-02 5.641e-03 1.026e+03 -1.978 0.048220 *
## TreatmentN+P -6.811e-03 5.676e-03 1.025e+03 -1.200 0.230412
## DaysF8 -1.641e-03 5.135e-03 1.450e+03 -0.320 0.749342
## DaysF14 6.462e-03 5.135e-03 1.450e+03 1.258 0.208487
## DaysF21 -6.231e-03 5.135e-03 1.450e+03 -1.213 0.225190
## DaysF28 -1.144e-02 5.135e-03 1.450e+03 -2.227 0.026101 *
## DaysF49 -3.320e-02 5.135e-03 1.450e+03 -6.466 1.37e-10 ***
## DaysF65 -4.377e-02 5.135e-03 1.450e+03 -8.523 < 2e-16 ***
## DaysF71 -4.256e-02 5.135e-03 1.450e+03 -8.289 2.58e-16 ***
## DaysF76 -5.182e-02 5.135e-03 1.450e+03 -10.091 < 2e-16 ***
## DaysF84 -4.601e-02 5.429e-03 1.460e+03 -8.474 < 2e-16 ***
## DaysF89 -6.429e-02 5.429e-03 1.460e+03 -11.842 < 2e-16 ***
## DaysF92 -5.326e-02 5.429e-03 1.460e+03 -9.810 < 2e-16 ***
## DaysF96 -5.304e-02 5.429e-03 1.460e+03 -9.770 < 2e-16 ***
## DaysF99 -7.876e-02 5.429e-03 1.460e+03 -14.507 < 2e-16 ***
## DaysF103 -9.810e-02 5.429e-03 1.460e+03 -18.070 < 2e-16 ***
## DaysF106 -1.150e-01 5.429e-03 1.460e+03 -21.184 < 2e-16 ***
## DaysF110 -2.122e-01 5.429e-03 1.460e+03 -39.092 < 2e-16 ***
## TreatmentN:DaysF8 2.749e-02 7.173e-03 1.450e+03 3.833 0.000132 ***
## TreatmentN+P:DaysF8 2.182e-02 7.217e-03 1.450e+03 3.023 0.002547 **
## TreatmentN:DaysF14 8.197e-03 7.173e-03 1.450e+03 1.143 0.253333
## TreatmentN+P:DaysF14 1.586e-02 7.217e-03 1.450e+03 2.198 0.028094 *
## TreatmentN:DaysF21 4.433e-02 7.173e-03 1.450e+03 6.180 8.32e-10 ***
## TreatmentN+P:DaysF21 2.751e-02 7.217e-03 1.450e+03 3.811 0.000144 ***
## TreatmentN:DaysF28 4.173e-02 7.173e-03 1.450e+03 5.817 7.34e-09 ***
## TreatmentN+P:DaysF28 4.296e-02 7.217e-03 1.450e+03 5.953 3.30e-09 ***
## TreatmentN:DaysF49 6.755e-02 7.173e-03 1.450e+03 9.417 < 2e-16 ***
## TreatmentN+P:DaysF49 6.270e-02 7.217e-03 1.450e+03 8.689 < 2e-16 ***
## TreatmentN:DaysF65 4.211e-02 7.173e-03 1.450e+03 5.871 5.37e-09 ***
## TreatmentN+P:DaysF65 5.629e-02 7.242e-03 1.451e+03 7.773 1.44e-14 ***
## TreatmentN:DaysF71 5.269e-02 7.248e-03 1.453e+03 7.269 5.87e-13 ***
## TreatmentN+P:DaysF71 2.824e-02 7.242e-03 1.451e+03 3.899 0.000101 ***
## TreatmentN:DaysF76 5.121e-02 7.276e-03 1.453e+03 7.038 3.00e-12 ***
## TreatmentN+P:DaysF76 3.903e-02 7.242e-03 1.451e+03 5.390 8.21e-08 ***
## TreatmentN:DaysF84 3.552e-02 7.801e-03 1.464e+03 4.554 5.71e-06 ***
## TreatmentN+P:DaysF84 4.127e-02 7.733e-03 1.462e+03 5.337 1.09e-07 ***
## TreatmentN:DaysF89 3.831e-02 7.847e-03 1.465e+03 4.882 1.16e-06 ***
## TreatmentN+P:DaysF89 3.065e-02 7.733e-03 1.462e+03 3.964 7.73e-05 ***
## TreatmentN:DaysF92 -5.450e-03 8.071e-03 1.466e+03 -0.675 0.499588
## TreatmentN+P:DaysF92 2.439e-02 7.863e-03 1.464e+03 3.101 0.001965 **
## TreatmentN:DaysF96 -4.801e-02 8.377e-03 1.468e+03 -5.731 1.21e-08 ***
## TreatmentN+P:DaysF96 -1.997e-02 8.390e-03 1.467e+03 -2.381 0.017409 *
## TreatmentN:DaysF99 -1.157e-01 8.576e-03 1.469e+03 -13.487 < 2e-16 ***
## TreatmentN+P:DaysF99 -4.192e-02 8.590e-03 1.468e+03 -4.880 1.18e-06 ***
## TreatmentN:DaysF103 -1.113e-01 8.967e-03 1.470e+03 -12.408 < 2e-16 ***
## TreatmentN+P:DaysF103 -9.823e-02 8.705e-03 1.468e+03 -11.284 < 2e-16 ***
## TreatmentN:DaysF106 -1.785e-01 9.819e-03 1.477e+03 -18.178 < 2e-16 ***
## TreatmentN+P:DaysF106 -1.426e-01 9.331e-03 1.471e+03 -15.278 < 2e-16 ***
## TreatmentN+P:DaysF110 -1.298e-01 1.225e-02 1.477e+03 -10.599 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient Step.LME_Acer<-step (Model1) # Replicate is not significant
anova(Model1) ranova(Model1)# Replicate is not significant summary(Model1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: YII ~ Treatment * DaysF + (1 | Genotype) + (1 | Replicate) +
## (1 | Fragment)
## Data: YII.Acer
##
## REML criterion at convergence: -7082.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -14.5801 -0.4034 0.0606 0.4568 5.5508
##
## Random effects:
## Groups Name Variance Std.Dev.
## Fragment (Intercept) 1.213e-04 0.011014
## Genotype (Intercept) 1.969e-04 0.014033
## Replicate (Intercept) 7.484e-06 0.002736
## Residual 5.142e-04 0.022676
## Number of obs: 1620, groups: Fragment, 120; Genotype, 6; Replicate, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.117e-01 7.298e-03 1.099e+01 83.810 < 2e-16 ***
## TreatmentN -1.116e-02 5.641e-03 1.026e+03 -1.978 0.048220 *
## TreatmentN+P -6.811e-03 5.676e-03 1.025e+03 -1.200 0.230412
## DaysF8 -1.641e-03 5.135e-03 1.450e+03 -0.320 0.749342
## DaysF14 6.462e-03 5.135e-03 1.450e+03 1.258 0.208487
## DaysF21 -6.231e-03 5.135e-03 1.450e+03 -1.213 0.225190
## DaysF28 -1.144e-02 5.135e-03 1.450e+03 -2.227 0.026101 *
## DaysF49 -3.320e-02 5.135e-03 1.450e+03 -6.466 1.37e-10 ***
## DaysF65 -4.377e-02 5.135e-03 1.450e+03 -8.523 < 2e-16 ***
## DaysF71 -4.256e-02 5.135e-03 1.450e+03 -8.289 2.58e-16 ***
## DaysF76 -5.182e-02 5.135e-03 1.450e+03 -10.091 < 2e-16 ***
## DaysF84 -4.601e-02 5.429e-03 1.460e+03 -8.474 < 2e-16 ***
## DaysF89 -6.429e-02 5.429e-03 1.460e+03 -11.842 < 2e-16 ***
## DaysF92 -5.326e-02 5.429e-03 1.460e+03 -9.810 < 2e-16 ***
## DaysF96 -5.304e-02 5.429e-03 1.460e+03 -9.770 < 2e-16 ***
## DaysF99 -7.876e-02 5.429e-03 1.460e+03 -14.507 < 2e-16 ***
## DaysF103 -9.810e-02 5.429e-03 1.460e+03 -18.070 < 2e-16 ***
## DaysF106 -1.150e-01 5.429e-03 1.460e+03 -21.184 < 2e-16 ***
## DaysF110 -2.122e-01 5.429e-03 1.460e+03 -39.092 < 2e-16 ***
## TreatmentN:DaysF8 2.749e-02 7.173e-03 1.450e+03 3.833 0.000132 ***
## TreatmentN+P:DaysF8 2.182e-02 7.217e-03 1.450e+03 3.023 0.002547 **
## TreatmentN:DaysF14 8.197e-03 7.173e-03 1.450e+03 1.143 0.253333
## TreatmentN+P:DaysF14 1.586e-02 7.217e-03 1.450e+03 2.198 0.028094 *
## TreatmentN:DaysF21 4.433e-02 7.173e-03 1.450e+03 6.180 8.32e-10 ***
## TreatmentN+P:DaysF21 2.751e-02 7.217e-03 1.450e+03 3.811 0.000144 ***
## TreatmentN:DaysF28 4.173e-02 7.173e-03 1.450e+03 5.817 7.34e-09 ***
## TreatmentN+P:DaysF28 4.296e-02 7.217e-03 1.450e+03 5.953 3.30e-09 ***
## TreatmentN:DaysF49 6.755e-02 7.173e-03 1.450e+03 9.417 < 2e-16 ***
## TreatmentN+P:DaysF49 6.270e-02 7.217e-03 1.450e+03 8.689 < 2e-16 ***
## TreatmentN:DaysF65 4.211e-02 7.173e-03 1.450e+03 5.871 5.37e-09 ***
## TreatmentN+P:DaysF65 5.629e-02 7.242e-03 1.451e+03 7.773 1.44e-14 ***
## TreatmentN:DaysF71 5.269e-02 7.248e-03 1.453e+03 7.269 5.87e-13 ***
## TreatmentN+P:DaysF71 2.824e-02 7.242e-03 1.451e+03 3.899 0.000101 ***
## TreatmentN:DaysF76 5.121e-02 7.276e-03 1.453e+03 7.038 3.00e-12 ***
## TreatmentN+P:DaysF76 3.903e-02 7.242e-03 1.451e+03 5.390 8.21e-08 ***
## TreatmentN:DaysF84 3.552e-02 7.801e-03 1.464e+03 4.554 5.71e-06 ***
## TreatmentN+P:DaysF84 4.127e-02 7.733e-03 1.462e+03 5.337 1.09e-07 ***
## TreatmentN:DaysF89 3.831e-02 7.847e-03 1.465e+03 4.882 1.16e-06 ***
## TreatmentN+P:DaysF89 3.065e-02 7.733e-03 1.462e+03 3.964 7.73e-05 ***
## TreatmentN:DaysF92 -5.450e-03 8.071e-03 1.466e+03 -0.675 0.499588
## TreatmentN+P:DaysF92 2.439e-02 7.863e-03 1.464e+03 3.101 0.001965 **
## TreatmentN:DaysF96 -4.801e-02 8.377e-03 1.468e+03 -5.731 1.21e-08 ***
## TreatmentN+P:DaysF96 -1.997e-02 8.390e-03 1.467e+03 -2.381 0.017409 *
## TreatmentN:DaysF99 -1.157e-01 8.576e-03 1.469e+03 -13.487 < 2e-16 ***
## TreatmentN+P:DaysF99 -4.192e-02 8.590e-03 1.468e+03 -4.880 1.18e-06 ***
## TreatmentN:DaysF103 -1.113e-01 8.967e-03 1.470e+03 -12.408 < 2e-16 ***
## TreatmentN+P:DaysF103 -9.823e-02 8.705e-03 1.468e+03 -11.284 < 2e-16 ***
## TreatmentN:DaysF106 -1.785e-01 9.819e-03 1.477e+03 -18.178 < 2e-16 ***
## TreatmentN+P:DaysF106 -1.426e-01 9.331e-03 1.471e+03 -15.278 < 2e-16 ***
## TreatmentN+P:DaysF110 -1.298e-01 1.225e-02 1.477e+03 -10.599 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient#2. Extract EMMs
Acer.YII.emm<-emmeans(Model1, ~Treatment | DaysF)
contrast(Acer.YII.emm, "tukey")## DaysF = 1:
## contrast estimate SE df t.ratio p.value
## A - N 0.011157 0.00564 1035 1.978 0.1182
## A - (N+P) 0.006811 0.00568 1034 1.200 0.4534
## N - (N+P) -0.004346 0.00560 1035 -0.775 0.7182
##
## DaysF = 8:
## contrast estimate SE df t.ratio p.value
## A - N -0.016338 0.00564 1035 -2.896 0.0108
## A - (N+P) -0.015005 0.00568 1034 -2.644 0.0226
## N - (N+P) 0.001333 0.00560 1035 0.238 0.9693
##
## DaysF = 14:
## contrast estimate SE df t.ratio p.value
## A - N 0.002960 0.00564 1035 0.525 0.8593
## A - (N+P) -0.009053 0.00568 1034 -1.595 0.2483
## N - (N+P) -0.012012 0.00560 1035 -2.143 0.0818
##
## DaysF = 21:
## contrast estimate SE df t.ratio p.value
## A - N -0.033172 0.00564 1035 -5.880 <.0001
## A - (N+P) -0.020695 0.00568 1034 -3.646 0.0008
## N - (N+P) 0.012477 0.00560 1035 2.226 0.0673
##
## DaysF = 28:
## contrast estimate SE df t.ratio p.value
## A - N -0.030572 0.00564 1035 -5.419 <.0001
## A - (N+P) -0.036150 0.00568 1034 -6.369 <.0001
## N - (N+P) -0.005578 0.00560 1035 -0.995 0.5800
##
## DaysF = 49:
## contrast estimate SE df t.ratio p.value
## A - N -0.056390 0.00564 1035 -9.996 <.0001
## A - (N+P) -0.055894 0.00568 1034 -9.848 <.0001
## N - (N+P) 0.000496 0.00560 1035 0.088 0.9957
##
## DaysF = 65:
## contrast estimate SE df t.ratio p.value
## A - N -0.030954 0.00564 1035 -5.487 <.0001
## A - (N+P) -0.049479 0.00571 1043 -8.669 <.0001
## N - (N+P) -0.018525 0.00564 1044 -3.287 0.0030
##
## DaysF = 71:
## contrast estimate SE df t.ratio p.value
## A - N -0.041534 0.00574 1062 -7.240 <.0001
## A - (N+P) -0.021428 0.00571 1043 -3.754 0.0005
## N - (N+P) 0.020107 0.00573 1071 3.508 0.0014
##
## DaysF = 76:
## contrast estimate SE df t.ratio p.value
## A - N -0.040049 0.00577 1073 -6.939 <.0001
## A - (N+P) -0.032223 0.00571 1043 -5.646 <.0001
## N - (N+P) 0.007827 0.00577 1082 1.357 0.3640
##
## DaysF = 84:
## contrast estimate SE df t.ratio p.value
## A - N -0.024364 0.00642 1235 -3.795 0.0005
## A - (N+P) -0.034460 0.00632 1204 -5.453 <.0001
## N - (N+P) -0.010096 0.00651 1261 -1.552 0.2672
##
## DaysF = 89:
## contrast estimate SE df t.ratio p.value
## A - N -0.027157 0.00648 1247 -4.193 0.0001
## A - (N+P) -0.023842 0.00632 1204 -3.773 0.0005
## N - (N+P) 0.003315 0.00656 1272 0.505 0.8688
##
## DaysF = 92:
## contrast estimate SE df t.ratio p.value
## A - N 0.016607 0.00675 1303 2.462 0.0371
## A - (N+P) -0.017575 0.00648 1240 -2.713 0.0185
## N - (N+P) -0.034181 0.00698 1347 -4.900 <.0001
##
## DaysF = 96:
## contrast estimate SE df t.ratio p.value
## A - N 0.059167 0.00711 1366 8.323 <.0001
## A - (N+P) 0.026784 0.00711 1359 3.769 0.0005
## N - (N+P) -0.032383 0.00788 1456 -4.108 0.0001
##
## DaysF = 99:
## contrast estimate SE df t.ratio p.value
## A - N 0.126823 0.00734 1399 17.272 <.0001
## A - (N+P) 0.048727 0.00734 1394 6.637 <.0001
## N - (N+P) -0.078096 0.00830 1488 -9.411 <.0001
##
## DaysF = 103:
## contrast estimate SE df t.ratio p.value
## A - N 0.122428 0.00780 1452 15.706 <.0001
## A - (N+P) 0.105041 0.00748 1412 14.049 <.0001
## N - (N+P) -0.017387 0.00881 1517 -1.972 0.1194
##
## DaysF = 106:
## contrast estimate SE df t.ratio p.value
## A - N 0.189641 0.00876 1515 21.645 <.0001
## A - (N+P) 0.149366 0.00820 1482 18.221 <.0001
## N - (N+P) -0.040275 0.01025 1555 -3.930 0.0003
##
## DaysF = 110:
## contrast estimate SE df t.ratio p.value
## A - N nonEst NA NA NA NA
## A - (N+P) 0.136642 0.01141 1564 11.971 <.0001
## N - (N+P) nonEst NA NA NA NA
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates Acer.YII.emm<-emmeans(Model1, ~Treatment * DaysF)
# Effect plot options
#emmip(Model1, ~DaysF|Treatment, CIs = TRUE) + theme_bw() # interaction plot of predictions
Acer.YII_groups<-cld(Acer.YII.emm, by=NULL) # compact-letter display
Acer.YII_groups<-Acer.YII_groups[order(Acer.YII_groups$Treatment, Acer.YII_groups$Day),]
Acer.YII_groups #write.csv(Acer.YII_groups, "Outputs/Multicomp_AcerYII.csv", row.names = F)4. Pooled N and N+P versus A.
Figure 4
YII.AcerB<-YII.Acer
YII.AcerB<-subset(YII.AcerB, Sample!="Ac_103_T11")
YII_Genotype<- ggplot(data=YII.AcerB, aes (Days, YII, colour=factor(Genotype))) +
ggthe_bw +
annotate("segment", x = 2, xend = 91, y = 0.05, yend = 0.05,
colour = "gray35", linetype=2)+
annotate("segment", x = 79, xend = 91, y = 0.06, yend = 0.65,
colour = "gray35", linetype=3)+
annotate("segment", x = 91, xend = 110, y = 0.65, yend = 0.65,
colour = "gray35", linetype=3)+
annotate("text", x = 45, y = 0.02, label = "Nutrients", size=3)+
annotate("text", x = 100, y = 0.61, label = "Heat", size=3)+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 1,
position = position_dodge(1), alpha=0.5 )+
#stat_summary(fun.y=mean, geom="line", position = position_dodge(1), linetype=1, alpha=1) +
stat_summary(fun.y=mean, geom="point", size =2,
position=position_dodge(width=1), alpha=0.5) +
theme(legend.position="bottom",
legend.title = element_blank(),
strip.background = element_rect(fill="white"))+
scale_y_continuous(limits = c(0.0, 0.7),
breaks = seq(0.0, 0.6, 0.1),
expand = c(0, 0),
name=expression(~italic("Fv / Fm"))) +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 15),
expand = c(0, 0))+
facet_grid (~Nutrients)
Figure4 <- YII_Genotype + geom_smooth(method = "loess", span=0.4, alpha=0.1, size=0.5)
Figure4
#ggsave(file="Outputs/Figure4.svg", plot=Figure4, width=6.5, height=5.5)Model 2: Nutrient Treatments (A versus elevated nutrients)
# 1. Find the best model
Model2<-lmerTest::lmer(YII ~ Genotype* Nutrients * DaysF +
(1|Replicate) + (1|Fragment),
data=YII.Acer, na.action=na.omit)
#step (Model2) # Replicate is not significant
anova(Model2) ranova(Model2)# Replicate is not significant summary(Model2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: YII ~ Genotype * Nutrients * DaysF + (1 | Replicate) + (1 | Fragment)
## Data: YII.Acer
##
## REML criterion at convergence: -6268.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.4269 -0.3670 0.0344 0.4112 5.4935
##
## Random effects:
## Groups Name Variance Std.Dev.
## Fragment (Intercept) 7.873e-05 0.008873
## Replicate (Intercept) 8.487e-06 0.002913
## Residual 5.185e-04 0.022771
## Number of obs: 1620, groups: Fragment, 120; Replicate, 2
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 6.260e-01 7.998e-03 1.346e+02
## GenotypeG_62 -7.761e-03 1.123e-02 1.156e+03
## GenotypeG_31 -7.571e-03 1.339e-02 1.156e+03
## GenotypeG_08 -2.150e-02 1.893e-02 1.156e+03
## GenotypeG_07 -2.921e-02 1.123e-02 1.156e+03
## GenotypeG_50 -1.625e-02 1.446e-02 1.156e+03
## NutrientsNutrients -9.261e-03 9.639e-03 1.156e+03
## DaysF8 -4.600e-03 1.018e-02 1.318e+03
## DaysF14 7.600e-03 1.018e-02 1.318e+03
## DaysF21 -6.000e-04 1.018e-02 1.318e+03
## DaysF28 -4.600e-03 1.018e-02 1.318e+03
## DaysF49 -2.950e-02 1.018e-02 1.318e+03
## DaysF65 -2.440e-02 1.018e-02 1.318e+03
## DaysF71 -2.740e-02 1.018e-02 1.318e+03
## DaysF76 -3.870e-02 1.018e-02 1.318e+03
## DaysF84 -3.325e-02 1.084e-02 1.330e+03
## DaysF89 -5.663e-02 1.084e-02 1.330e+03
## DaysF92 -4.188e-02 1.084e-02 1.330e+03
## DaysF96 -5.063e-02 1.084e-02 1.330e+03
## DaysF99 -7.125e-02 1.084e-02 1.330e+03
## DaysF103 -9.425e-02 1.084e-02 1.330e+03
## DaysF106 -1.008e-01 1.084e-02 1.330e+03
## DaysF110 -2.053e-01 1.084e-02 1.330e+03
## GenotypeG_62:NutrientsNutrients -1.327e-03 1.375e-02 1.156e+03
## GenotypeG_31:NutrientsNutrients -1.155e-03 1.633e-02 1.156e+03
## GenotypeG_08:NutrientsNutrients -2.121e-03 2.216e-02 1.156e+03
## GenotypeG_07:NutrientsNutrients 7.711e-03 1.394e-02 1.156e+03
## GenotypeG_50:NutrientsNutrients -6.583e-03 1.757e-02 1.156e+03
## GenotypeG_62:DaysF8 3.822e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF8 9.800e-03 1.764e-02 1.318e+03
## GenotypeG_08:DaysF8 1.860e-02 2.494e-02 1.318e+03
## GenotypeG_07:DaysF8 2.667e-04 1.480e-02 1.318e+03
## GenotypeG_50:DaysF8 -1.900e-03 1.905e-02 1.318e+03
## GenotypeG_62:DaysF14 4.178e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF14 8.000e-04 1.764e-02 1.318e+03
## GenotypeG_08:DaysF14 -8.100e-03 2.494e-02 1.318e+03
## GenotypeG_07:DaysF14 -8.822e-03 1.480e-02 1.318e+03
## GenotypeG_50:DaysF14 2.400e-03 1.905e-02 1.318e+03
## GenotypeG_62:DaysF21 2.267e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF21 -1.080e-02 1.764e-02 1.318e+03
## GenotypeG_08:DaysF21 1.100e-03 2.494e-02 1.318e+03
## GenotypeG_07:DaysF21 -2.384e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF21 6.600e-03 1.905e-02 1.318e+03
## GenotypeG_62:DaysF28 -7.333e-04 1.480e-02 1.318e+03
## GenotypeG_31:DaysF28 -1.000e-03 1.764e-02 1.318e+03
## GenotypeG_08:DaysF28 1.160e-02 2.494e-02 1.318e+03
## GenotypeG_07:DaysF28 -3.029e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF28 -1.400e-03 1.905e-02 1.318e+03
## GenotypeG_62:DaysF49 7.500e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF49 -1.000e-04 1.764e-02 1.318e+03
## GenotypeG_08:DaysF49 1.800e-02 2.494e-02 1.318e+03
## GenotypeG_07:DaysF49 -3.294e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF49 1.225e-02 1.905e-02 1.318e+03
## GenotypeG_62:DaysF65 -2.227e-02 1.480e-02 1.318e+03
## GenotypeG_31:DaysF65 -3.860e-02 1.764e-02 1.318e+03
## GenotypeG_08:DaysF65 -1.060e-02 2.494e-02 1.318e+03
## GenotypeG_07:DaysF65 -2.838e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF65 -2.135e-02 1.905e-02 1.318e+03
## GenotypeG_62:DaysF71 -2.156e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF71 -7.000e-03 1.764e-02 1.318e+03
## GenotypeG_08:DaysF71 -6.100e-03 2.494e-02 1.318e+03
## GenotypeG_07:DaysF71 -5.227e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF71 -1.360e-02 1.905e-02 1.318e+03
## GenotypeG_62:DaysF76 -3.856e-03 1.480e-02 1.318e+03
## GenotypeG_31:DaysF76 -1.500e-03 1.764e-02 1.318e+03
## GenotypeG_08:DaysF76 1.370e-02 2.494e-02 1.318e+03
## GenotypeG_07:DaysF76 -5.497e-02 1.480e-02 1.318e+03
## GenotypeG_50:DaysF76 -5.500e-04 1.905e-02 1.318e+03
## GenotypeG_62:DaysF84 -2.371e-04 1.582e-02 1.331e+03
## GenotypeG_31:DaysF84 3.147e-04 1.877e-02 1.330e+03
## GenotypeG_08:DaysF84 -5.749e-03 2.522e-02 1.320e+03
## GenotypeG_07:DaysF84 -5.780e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF84 3.001e-03 1.941e-02 1.322e+03
## GenotypeG_62:DaysF89 -1.015e-02 1.582e-02 1.331e+03
## GenotypeG_31:DaysF89 8.190e-03 1.877e-02 1.330e+03
## GenotypeG_08:DaysF89 7.626e-03 2.522e-02 1.320e+03
## GenotypeG_07:DaysF89 -3.685e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF89 9.876e-03 1.941e-02 1.322e+03
## GenotypeG_62:DaysF92 4.102e-03 1.582e-02 1.331e+03
## GenotypeG_31:DaysF92 -7.560e-03 1.877e-02 1.330e+03
## GenotypeG_08:DaysF92 -1.112e-02 2.522e-02 1.320e+03
## GenotypeG_07:DaysF92 -4.646e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF92 -2.874e-03 1.941e-02 1.322e+03
## GenotypeG_62:DaysF96 1.424e-03 1.582e-02 1.331e+03
## GenotypeG_31:DaysF96 6.190e-03 1.877e-02 1.330e+03
## GenotypeG_08:DaysF96 4.626e-03 2.522e-02 1.320e+03
## GenotypeG_07:DaysF96 -1.657e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF96 -3.741e-04 1.941e-02 1.322e+03
## GenotypeG_62:DaysF99 -1.452e-02 1.582e-02 1.331e+03
## GenotypeG_31:DaysF99 -2.444e-02 1.877e-02 1.330e+03
## GenotypeG_08:DaysF99 1.251e-03 2.522e-02 1.320e+03
## GenotypeG_07:DaysF99 -1.566e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF99 1.750e-02 1.941e-02 1.322e+03
## GenotypeG_62:DaysF103 -1.438e-02 1.582e-02 1.331e+03
## GenotypeG_31:DaysF103 -9.435e-03 1.877e-02 1.330e+03
## GenotypeG_08:DaysF103 1.825e-02 2.522e-02 1.320e+03
## GenotypeG_07:DaysF103 -1.323e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF103 1.875e-02 1.941e-02 1.322e+03
## GenotypeG_62:DaysF106 7.120e-03 1.582e-02 1.331e+03
## GenotypeG_31:DaysF106 -4.794e-02 1.877e-02 1.330e+03
## GenotypeG_08:DaysF106 1.775e-02 2.522e-02 1.320e+03
## GenotypeG_07:DaysF106 -4.158e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF106 -1.375e-02 1.941e-02 1.322e+03
## GenotypeG_62:DaysF110 1.262e-02 1.582e-02 1.331e+03
## GenotypeG_31:DaysF110 -1.869e-02 1.877e-02 1.330e+03
## GenotypeG_08:DaysF110 -3.249e-03 2.522e-02 1.320e+03
## GenotypeG_07:DaysF110 -4.066e-02 1.582e-02 1.331e+03
## GenotypeG_50:DaysF110 1.450e-02 1.941e-02 1.322e+03
## NutrientsNutrients:DaysF8 2.321e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF14 1.268e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF21 3.277e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF28 3.532e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF49 5.500e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF65 4.084e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF71 2.746e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF76 3.953e-02 1.270e-02 1.318e+03
## NutrientsNutrients:DaysF84 3.048e-02 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF89 2.328e-02 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF92 -6.470e-03 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF96 -3.679e-02 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF99 -8.431e-02 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF103 -1.011e-01 1.356e-02 1.331e+03
## NutrientsNutrients:DaysF106 -1.580e-01 1.396e-02 1.335e+03
## NutrientsNutrients:DaysF110 -1.238e-01 2.040e-02 1.350e+03
## GenotypeG_62:NutrientsNutrients:DaysF8 -8.333e-05 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF8 7.134e-03 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF8 -1.688e-02 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF8 6.593e-03 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF8 -1.267e-03 2.315e-02 1.318e+03
## GenotypeG_62:NutrientsNutrients:DaysF14 -2.556e-03 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF14 -1.441e-03 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF14 7.989e-03 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF14 -4.279e-03 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF14 3.100e-03 2.315e-02 1.318e+03
## GenotypeG_62:NutrientsNutrients:DaysF21 2.617e-03 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF21 1.163e-02 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF21 -1.767e-03 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF21 1.297e-02 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF21 -1.888e-02 2.315e-02 1.318e+03
## GenotypeG_62:NutrientsNutrients:DaysF28 6.961e-03 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF28 8.914e-03 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF28 -6.156e-03 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF28 2.463e-02 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF28 -1.343e-02 2.315e-02 1.318e+03
## GenotypeG_62:NutrientsNutrients:DaysF49 -4.350e-03 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF49 8.418e-03 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF49 2.333e-03 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF49 4.344e-02 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF49 -4.750e-03 2.315e-02 1.318e+03
## GenotypeG_62:NutrientsNutrients:DaysF65 2.187e-02 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF65 3.788e-02 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF65 -1.184e-02 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF65 2.240e-02 1.837e-02 1.318e+03
## GenotypeG_50:NutrientsNutrients:DaysF65 -5.862e-02 2.331e-02 1.320e+03
## GenotypeG_62:NutrientsNutrients:DaysF71 8.800e-03 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF71 6.581e-03 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF71 -4.556e-04 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF71 4.746e-02 1.843e-02 1.319e+03
## GenotypeG_50:NutrientsNutrients:DaysF71 -1.240e-02 2.381e-02 1.326e+03
## GenotypeG_62:NutrientsNutrients:DaysF76 6.222e-04 1.812e-02 1.318e+03
## GenotypeG_31:NutrientsNutrients:DaysF76 -1.024e-02 2.152e-02 1.318e+03
## GenotypeG_08:NutrientsNutrients:DaysF76 -1.553e-02 2.920e-02 1.318e+03
## GenotypeG_07:NutrientsNutrients:DaysF76 3.888e-02 1.843e-02 1.319e+03
## GenotypeG_50:NutrientsNutrients:DaysF76 -1.463e-02 2.419e-02 1.329e+03
## GenotypeG_62:NutrientsNutrients:DaysF84 3.583e-03 1.943e-02 1.331e+03
## GenotypeG_31:NutrientsNutrients:DaysF84 -6.755e-03 2.306e-02 1.331e+03
## GenotypeG_08:NutrientsNutrients:DaysF84 -2.114e-03 2.989e-02 1.323e+03
## GenotypeG_07:NutrientsNutrients:DaysF84 4.452e-02 1.978e-02 1.332e+03
## GenotypeG_50:NutrientsNutrients:DaysF84 -1.429e-02 2.521e-02 1.336e+03
## GenotypeG_62:NutrientsNutrients:DaysF89 2.046e-02 1.943e-02 1.331e+03
## GenotypeG_31:NutrientsNutrients:DaysF89 3.566e-03 2.306e-02 1.331e+03
## GenotypeG_08:NutrientsNutrients:DaysF89 -2.472e-02 2.989e-02 1.323e+03
## GenotypeG_07:NutrientsNutrients:DaysF89 4.272e-02 1.988e-02 1.333e+03
## GenotypeG_50:NutrientsNutrients:DaysF89 -1.010e-02 2.521e-02 1.336e+03
## GenotypeG_62:NutrientsNutrients:DaysF92 5.281e-03 1.943e-02 1.331e+03
## GenotypeG_31:NutrientsNutrients:DaysF92 5.441e-03 2.306e-02 1.331e+03
## GenotypeG_08:NutrientsNutrients:DaysF92 3.427e-02 3.253e-02 1.335e+03
## GenotypeG_07:NutrientsNutrients:DaysF92 5.899e-02 2.035e-02 1.336e+03
## GenotypeG_50:NutrientsNutrients:DaysF92 2.908e-02 2.610e-02 1.340e+03
## GenotypeG_62:NutrientsNutrients:DaysF96 2.298e-03 1.943e-02 1.331e+03
## GenotypeG_31:NutrientsNutrients:DaysF96 -1.071e-02 2.327e-02 1.332e+03
## GenotypeG_08:NutrientsNutrients:DaysF96 6.148e-02 3.649e-02 1.345e+03
## GenotypeG_07:NutrientsNutrients:DaysF96 4.923e-02 2.987e-02 1.362e+03
## GenotypeG_62:NutrientsNutrients:DaysF99 1.785e-02 1.943e-02 1.331e+03
## GenotypeG_31:NutrientsNutrients:DaysF99 2.103e-02 2.393e-02 1.335e+03
## GenotypeG_62:NutrientsNutrients:DaysF103 6.911e-03 1.950e-02 1.332e+03
## GenotypeG_31:NutrientsNutrients:DaysF103 1.748e-04 2.713e-02 1.344e+03
## GenotypeG_62:NutrientsNutrients:DaysF106 -3.397e-02 2.017e-02 1.335e+03
## GenotypeG_31:NutrientsNutrients:DaysF106 5.189e-02 3.191e-02 1.351e+03
## GenotypeG_62:NutrientsNutrients:DaysF110 2.256e-02 2.906e-02 1.350e+03
## GenotypeG_31:NutrientsNutrients:DaysF110 -1.120e-01 3.520e-02 1.353e+03
## t value Pr(>|t|)
## (Intercept) 78.270 < 2e-16 ***
## GenotypeG_62 -0.691 0.489590
## GenotypeG_31 -0.566 0.571827
## GenotypeG_08 -1.136 0.256291
## GenotypeG_07 -2.601 0.009417 **
## GenotypeG_50 -1.124 0.261270
## NutrientsNutrients -0.961 0.336851
## DaysF8 -0.452 0.651550
## DaysF14 0.746 0.455614
## DaysF21 -0.059 0.953025
## DaysF28 -0.452 0.651550
## DaysF49 -2.897 0.003831 **
## DaysF65 -2.396 0.016712 *
## DaysF71 -2.691 0.007222 **
## DaysF76 -3.800 0.000151 ***
## DaysF84 -3.068 0.002202 **
## DaysF89 -5.224 2.03e-07 ***
## DaysF92 -3.863 0.000117 ***
## DaysF96 -4.670 3.31e-06 ***
## DaysF99 -6.573 7.05e-11 ***
## DaysF103 -8.695 < 2e-16 ***
## DaysF106 -9.295 < 2e-16 ***
## DaysF110 -18.935 < 2e-16 ***
## GenotypeG_62:NutrientsNutrients -0.096 0.923142
## GenotypeG_31:NutrientsNutrients -0.071 0.943643
## GenotypeG_08:NutrientsNutrients -0.096 0.923775
## GenotypeG_07:NutrientsNutrients 0.553 0.580345
## GenotypeG_50:NutrientsNutrients -0.375 0.707900
## GenotypeG_62:DaysF8 0.258 0.796197
## GenotypeG_31:DaysF8 0.556 0.578571
## GenotypeG_08:DaysF8 0.746 0.456004
## GenotypeG_07:DaysF8 0.018 0.985624
## GenotypeG_50:DaysF8 -0.100 0.920574
## GenotypeG_62:DaysF14 0.282 0.777716
## GenotypeG_31:DaysF14 0.045 0.963830
## GenotypeG_08:DaysF14 -0.325 0.745441
## GenotypeG_07:DaysF14 -0.596 0.551111
## GenotypeG_50:DaysF14 0.126 0.899771
## GenotypeG_62:DaysF21 0.153 0.878270
## GenotypeG_31:DaysF21 -0.612 0.540441
## GenotypeG_08:DaysF21 0.044 0.964833
## GenotypeG_07:DaysF21 -1.612 0.107305
## GenotypeG_50:DaysF21 0.346 0.729075
## GenotypeG_62:DaysF28 -0.050 0.960479
## GenotypeG_31:DaysF28 -0.057 0.954797
## GenotypeG_08:DaysF28 0.465 0.641982
## GenotypeG_07:DaysF28 -2.047 0.040849 *
## GenotypeG_50:DaysF28 -0.073 0.941431
## GenotypeG_62:DaysF49 0.507 0.612319
## GenotypeG_31:DaysF49 -0.006 0.995477
## GenotypeG_08:DaysF49 0.722 0.470663
## GenotypeG_07:DaysF49 -2.227 0.026146 *
## GenotypeG_50:DaysF49 0.643 0.520340
## GenotypeG_62:DaysF65 -1.505 0.132592
## GenotypeG_31:DaysF65 -2.188 0.028814 *
## GenotypeG_08:DaysF65 -0.425 0.670944
## GenotypeG_07:DaysF65 -1.918 0.055338 .
## GenotypeG_50:DaysF65 -1.121 0.262642
## GenotypeG_62:DaysF71 -0.146 0.884194
## GenotypeG_31:DaysF71 -0.397 0.691531
## GenotypeG_08:DaysF71 -0.245 0.806847
## GenotypeG_07:DaysF71 -3.532 0.000426 ***
## GenotypeG_50:DaysF71 -0.714 0.475443
## GenotypeG_62:DaysF76 -0.261 0.794459
## GenotypeG_31:DaysF76 -0.085 0.932241
## GenotypeG_08:DaysF76 0.549 0.582944
## GenotypeG_07:DaysF76 -3.715 0.000212 ***
## GenotypeG_50:DaysF76 -0.029 0.976973
## GenotypeG_62:DaysF84 -0.015 0.988045
## GenotypeG_31:DaysF84 0.017 0.986630
## GenotypeG_08:DaysF84 -0.228 0.819710
## GenotypeG_07:DaysF84 -3.654 0.000269 ***
## GenotypeG_50:DaysF84 0.155 0.877155
## GenotypeG_62:DaysF89 -0.641 0.521311
## GenotypeG_31:DaysF89 0.436 0.662760
## GenotypeG_08:DaysF89 0.302 0.762406
## GenotypeG_07:DaysF89 -2.330 0.019977 *
## GenotypeG_50:DaysF89 0.509 0.610975
## GenotypeG_62:DaysF92 0.259 0.795426
## GenotypeG_31:DaysF92 -0.403 0.687247
## GenotypeG_08:DaysF92 -0.441 0.659217
## GenotypeG_07:DaysF92 -2.937 0.003372 **
## GenotypeG_50:DaysF92 -0.148 0.882310
## GenotypeG_62:DaysF96 0.090 0.928304
## GenotypeG_31:DaysF96 0.330 0.741695
## GenotypeG_08:DaysF96 0.183 0.854490
## GenotypeG_07:DaysF96 -1.047 0.295190
## GenotypeG_50:DaysF96 -0.019 0.984627
## GenotypeG_62:DaysF99 -0.918 0.358753
## GenotypeG_31:DaysF99 -1.301 0.193317
## GenotypeG_08:DaysF99 0.050 0.960447
## GenotypeG_07:DaysF99 -0.990 0.322528
## GenotypeG_50:DaysF99 0.902 0.367413
## GenotypeG_62:DaysF103 -0.909 0.363498
## GenotypeG_31:DaysF103 -0.503 0.615365
## GenotypeG_08:DaysF103 0.724 0.469384
## GenotypeG_07:DaysF103 -0.836 0.403236
## GenotypeG_50:DaysF103 0.966 0.334202
## GenotypeG_62:DaysF106 0.450 0.652714
## GenotypeG_31:DaysF106 -2.553 0.010786 *
## GenotypeG_08:DaysF106 0.704 0.481641
## GenotypeG_07:DaysF106 -2.629 0.008669 **
## GenotypeG_50:DaysF106 -0.708 0.478856
## GenotypeG_62:DaysF110 0.798 0.425140
## GenotypeG_31:DaysF110 -0.995 0.319807
## GenotypeG_08:DaysF110 -0.129 0.897510
## GenotypeG_07:DaysF110 -2.570 0.010277 *
## GenotypeG_50:DaysF110 0.747 0.455148
## NutrientsNutrients:DaysF8 1.828 0.067850 .
## NutrientsNutrients:DaysF14 0.998 0.318379
## NutrientsNutrients:DaysF21 2.580 0.009992 **
## NutrientsNutrients:DaysF28 2.781 0.005495 **
## NutrientsNutrients:DaysF49 4.330 1.60e-05 ***
## NutrientsNutrients:DaysF65 3.216 0.001332 **
## NutrientsNutrients:DaysF71 2.162 0.030822 *
## NutrientsNutrients:DaysF76 3.113 0.001894 **
## NutrientsNutrients:DaysF84 2.248 0.024767 *
## NutrientsNutrients:DaysF89 1.717 0.086238 .
## NutrientsNutrients:DaysF92 -0.477 0.633349
## NutrientsNutrients:DaysF96 -2.713 0.006749 **
## NutrientsNutrients:DaysF99 -6.218 6.75e-10 ***
## NutrientsNutrients:DaysF103 -7.455 1.61e-13 ***
## NutrientsNutrients:DaysF106 -11.321 < 2e-16 ***
## NutrientsNutrients:DaysF110 -6.071 1.64e-09 ***
## GenotypeG_62:NutrientsNutrients:DaysF8 -0.005 0.996332
## GenotypeG_31:NutrientsNutrients:DaysF8 0.332 0.740272
## GenotypeG_08:NutrientsNutrients:DaysF8 -0.578 0.563365
## GenotypeG_07:NutrientsNutrients:DaysF8 0.359 0.719769
## GenotypeG_50:NutrientsNutrients:DaysF8 -0.055 0.956368
## GenotypeG_62:NutrientsNutrients:DaysF14 -0.141 0.887874
## GenotypeG_31:NutrientsNutrients:DaysF14 -0.067 0.946601
## GenotypeG_08:NutrientsNutrients:DaysF14 0.274 0.784444
## GenotypeG_07:NutrientsNutrients:DaysF14 -0.233 0.815868
## GenotypeG_50:NutrientsNutrients:DaysF14 0.134 0.893482
## GenotypeG_62:NutrientsNutrients:DaysF21 0.144 0.885211
## GenotypeG_31:NutrientsNutrients:DaysF21 0.541 0.588841
## GenotypeG_08:NutrientsNutrients:DaysF21 -0.061 0.951766
## GenotypeG_07:NutrientsNutrients:DaysF21 0.706 0.480278
## GenotypeG_50:NutrientsNutrients:DaysF21 -0.816 0.414905
## GenotypeG_62:NutrientsNutrients:DaysF28 0.384 0.700941
## GenotypeG_31:NutrientsNutrients:DaysF28 0.414 0.678738
## GenotypeG_08:NutrientsNutrients:DaysF28 -0.211 0.833073
## GenotypeG_07:NutrientsNutrients:DaysF28 1.340 0.180360
## GenotypeG_50:NutrientsNutrients:DaysF28 -0.580 0.561784
## GenotypeG_62:NutrientsNutrients:DaysF49 -0.240 0.810332
## GenotypeG_31:NutrientsNutrients:DaysF49 0.391 0.695692
## GenotypeG_08:NutrientsNutrients:DaysF49 0.080 0.936323
## GenotypeG_07:NutrientsNutrients:DaysF49 2.365 0.018190 *
## GenotypeG_50:NutrientsNutrients:DaysF49 -0.205 0.837442
## GenotypeG_62:NutrientsNutrients:DaysF65 1.207 0.227660
## GenotypeG_31:NutrientsNutrients:DaysF65 1.761 0.078543 .
## GenotypeG_08:NutrientsNutrients:DaysF65 -0.406 0.685084
## GenotypeG_07:NutrientsNutrients:DaysF65 1.219 0.222896
## GenotypeG_50:NutrientsNutrients:DaysF65 -2.514 0.012043 *
## GenotypeG_62:NutrientsNutrients:DaysF71 0.486 0.627325
## GenotypeG_31:NutrientsNutrients:DaysF71 0.306 0.759777
## GenotypeG_08:NutrientsNutrients:DaysF71 -0.016 0.987555
## GenotypeG_07:NutrientsNutrients:DaysF71 2.575 0.010124 *
## GenotypeG_50:NutrientsNutrients:DaysF71 -0.521 0.602465
## GenotypeG_62:NutrientsNutrients:DaysF76 0.034 0.972614
## GenotypeG_31:NutrientsNutrients:DaysF76 -0.476 0.634148
## GenotypeG_08:NutrientsNutrients:DaysF76 -0.532 0.594847
## GenotypeG_07:NutrientsNutrients:DaysF76 2.110 0.035064 *
## GenotypeG_50:NutrientsNutrients:DaysF76 -0.605 0.545575
## GenotypeG_62:NutrientsNutrients:DaysF84 0.184 0.853768
## GenotypeG_31:NutrientsNutrients:DaysF84 -0.293 0.769655
## GenotypeG_08:NutrientsNutrients:DaysF84 -0.071 0.943627
## GenotypeG_07:NutrientsNutrients:DaysF84 2.251 0.024561 *
## GenotypeG_50:NutrientsNutrients:DaysF84 -0.567 0.570806
## GenotypeG_62:NutrientsNutrients:DaysF89 1.053 0.292502
## GenotypeG_31:NutrientsNutrients:DaysF89 0.155 0.877139
## GenotypeG_08:NutrientsNutrients:DaysF89 -0.827 0.408462
## GenotypeG_07:NutrientsNutrients:DaysF89 2.148 0.031860 *
## GenotypeG_50:NutrientsNutrients:DaysF89 -0.401 0.688833
## GenotypeG_62:NutrientsNutrients:DaysF92 0.272 0.785844
## GenotypeG_31:NutrientsNutrients:DaysF92 0.236 0.813530
## GenotypeG_08:NutrientsNutrients:DaysF92 1.054 0.292274
## GenotypeG_07:NutrientsNutrients:DaysF92 2.899 0.003803 **
## GenotypeG_50:NutrientsNutrients:DaysF92 1.114 0.265270
## GenotypeG_62:NutrientsNutrients:DaysF96 0.118 0.905887
## GenotypeG_31:NutrientsNutrients:DaysF96 -0.460 0.645565
## GenotypeG_08:NutrientsNutrients:DaysF96 1.685 0.092213 .
## GenotypeG_07:NutrientsNutrients:DaysF96 1.648 0.099530 .
## GenotypeG_62:NutrientsNutrients:DaysF99 0.919 0.358406
## GenotypeG_31:NutrientsNutrients:DaysF99 0.879 0.379792
## GenotypeG_62:NutrientsNutrients:DaysF103 0.354 0.723070
## GenotypeG_31:NutrientsNutrients:DaysF103 0.006 0.994859
## GenotypeG_62:NutrientsNutrients:DaysF106 -1.684 0.092324 .
## GenotypeG_31:NutrientsNutrients:DaysF106 1.626 0.104140
## GenotypeG_62:NutrientsNutrients:DaysF110 0.776 0.437594
## GenotypeG_31:NutrientsNutrients:DaysF110 -3.183 0.001492 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 13 columns / coefficients#2. Extract EMMs
Acer.YII.emm2<-emmeans(Model2, ~ Genotype| Nutrients | DaysF)
contrast(Acer.YII.emm2, "tukey")## Nutrients = Ambient, DaysF = 1:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 7.76e-03 0.01123 1165 0.691 0.9830
## G_48 - G_31 7.57e-03 0.01339 1164 0.565 0.9932
## G_48 - G_08 2.15e-02 0.01893 1165 1.136 0.8664
## G_48 - G_07 2.92e-02 0.01123 1165 2.601 0.0978
## G_48 - G_50 1.63e-02 0.01446 1165 1.124 0.8715
## G_62 - G_31 -1.91e-04 0.01363 1165 -0.014 1.0000
## G_62 - G_08 1.37e-02 0.01910 1165 0.719 0.9796
## G_62 - G_07 2.14e-02 0.01152 1165 1.861 0.4267
## G_62 - G_50 8.49e-03 0.01469 1165 0.578 0.9925
## G_31 - G_08 1.39e-02 0.02045 1165 0.681 0.9840
## G_31 - G_07 2.16e-02 0.01363 1165 1.587 0.6072
## G_31 - G_50 8.68e-03 0.01640 1164 0.529 0.9950
## G_08 - G_07 7.71e-03 0.01910 1165 0.403 0.9986
## G_08 - G_50 -5.25e-03 0.02116 1165 -0.248 0.9999
## G_07 - G_50 -1.30e-02 0.01469 1165 -0.882 0.9508
##
## Nutrients = Nutrients, DaysF = 1:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 9.09e-03 0.00794 1164 1.144 0.8626
## G_48 - G_31 8.73e-03 0.00935 1165 0.933 0.9380
## G_48 - G_08 2.36e-02 0.01153 1163 2.048 0.3159
## G_48 - G_07 2.15e-02 0.00827 1165 2.601 0.0979
## G_48 - G_50 2.28e-02 0.00998 1165 2.289 0.1994
## G_62 - G_31 -3.63e-04 0.00917 1165 -0.040 1.0000
## G_62 - G_08 1.45e-02 0.01138 1164 1.277 0.7978
## G_62 - G_07 1.24e-02 0.00806 1165 1.539 0.6391
## G_62 - G_50 1.37e-02 0.00981 1164 1.401 0.7265
## G_31 - G_08 1.49e-02 0.01241 1163 1.200 0.8370
## G_31 - G_07 1.28e-02 0.00946 1165 1.350 0.7566
## G_31 - G_50 1.41e-02 0.01098 1165 1.284 0.7937
## G_08 - G_07 -2.13e-03 0.01162 1163 -0.183 1.0000
## G_08 - G_50 -7.88e-04 0.01289 1163 -0.061 1.0000
## G_07 - G_50 1.34e-03 0.01007 1165 0.133 1.0000
##
## Nutrients = Ambient, DaysF = 8:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 3.94e-03 0.01123 1165 0.351 0.9993
## G_48 - G_31 -2.23e-03 0.01339 1164 -0.167 1.0000
## G_48 - G_08 2.90e-03 0.01893 1165 0.153 1.0000
## G_48 - G_07 2.89e-02 0.01123 1165 2.577 0.1037
## G_48 - G_50 1.81e-02 0.01446 1165 1.255 0.8091
## G_62 - G_31 -6.17e-03 0.01363 1165 -0.453 0.9976
## G_62 - G_08 -1.04e-03 0.01910 1165 -0.054 1.0000
## G_62 - G_07 2.50e-02 0.01152 1165 2.170 0.2526
## G_62 - G_50 1.42e-02 0.01469 1165 0.968 0.9281
## G_31 - G_08 5.13e-03 0.02045 1165 0.251 0.9999
## G_31 - G_07 3.12e-02 0.01363 1165 2.286 0.2003
## G_31 - G_50 2.04e-02 0.01640 1164 1.243 0.8155
## G_08 - G_07 2.60e-02 0.01910 1165 1.363 0.7492
## G_08 - G_50 1.52e-02 0.02116 1165 0.721 0.9795
## G_07 - G_50 -1.08e-02 0.01469 1165 -0.735 0.9776
##
## Nutrients = Nutrients, DaysF = 8:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.35e-03 0.00794 1164 0.674 0.9848
## G_48 - G_31 -8.21e-03 0.00935 1165 -0.878 0.9518
## G_48 - G_08 2.19e-02 0.01153 1163 1.899 0.4034
## G_48 - G_07 1.46e-02 0.00827 1165 1.771 0.4853
## G_48 - G_50 2.60e-02 0.00998 1165 2.606 0.0966
## G_62 - G_31 -1.36e-02 0.00917 1165 -1.478 0.6785
## G_62 - G_08 1.65e-02 0.01138 1164 1.454 0.6938
## G_62 - G_07 9.29e-03 0.00806 1165 1.152 0.8594
## G_62 - G_50 2.07e-02 0.00981 1164 2.105 0.2853
## G_31 - G_08 3.01e-02 0.01241 1163 2.425 0.1484
## G_31 - G_07 2.28e-02 0.00946 1165 2.416 0.1516
## G_31 - G_50 3.42e-02 0.01098 1165 3.114 0.0232
## G_08 - G_07 -7.26e-03 0.01162 1163 -0.625 0.9892
## G_08 - G_50 4.10e-03 0.01289 1163 0.318 0.9996
## G_07 - G_50 1.14e-02 0.01007 1165 1.128 0.8698
##
## Nutrients = Ambient, DaysF = 14:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 3.58e-03 0.01123 1165 0.319 0.9996
## G_48 - G_31 6.77e-03 0.01339 1164 0.506 0.9960
## G_48 - G_08 2.96e-02 0.01893 1165 1.564 0.6228
## G_48 - G_07 3.80e-02 0.01123 1165 3.386 0.0095
## G_48 - G_50 1.38e-02 0.01446 1165 0.958 0.9310
## G_62 - G_31 3.19e-03 0.01363 1165 0.234 0.9999
## G_62 - G_08 2.60e-02 0.01910 1165 1.362 0.7499
## G_62 - G_07 3.44e-02 0.01152 1165 2.990 0.0338
## G_62 - G_50 1.03e-02 0.01469 1165 0.699 0.9821
## G_31 - G_08 2.28e-02 0.02045 1165 1.116 0.8747
## G_31 - G_07 3.13e-02 0.01363 1165 2.293 0.1976
## G_31 - G_50 7.08e-03 0.01640 1164 0.432 0.9981
## G_08 - G_07 8.43e-03 0.01910 1165 0.441 0.9979
## G_08 - G_50 -1.58e-02 0.02116 1165 -0.744 0.9763
## G_07 - G_50 -2.42e-02 0.01469 1165 -1.646 0.5679
##
## Nutrients = Nutrients, DaysF = 14:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 7.47e-03 0.00794 1164 0.940 0.9360
## G_48 - G_31 9.37e-03 0.00935 1165 1.001 0.9175
## G_48 - G_08 2.37e-02 0.01153 1163 2.058 0.3106
## G_48 - G_07 3.46e-02 0.00827 1165 4.186 0.0004
## G_48 - G_50 1.73e-02 0.00998 1165 1.737 0.5072
## G_62 - G_31 1.90e-03 0.00917 1165 0.207 0.9999
## G_62 - G_08 1.63e-02 0.01138 1164 1.429 0.7094
## G_62 - G_07 2.71e-02 0.00806 1165 3.365 0.0102
## G_62 - G_50 9.87e-03 0.00981 1164 1.006 0.9161
## G_31 - G_08 1.44e-02 0.01241 1163 1.157 0.8570
## G_31 - G_07 2.52e-02 0.00946 1165 2.668 0.0826
## G_31 - G_50 7.97e-03 0.01098 1165 0.725 0.9789
## G_08 - G_07 1.09e-02 0.01162 1163 0.935 0.9374
## G_08 - G_50 -6.40e-03 0.01289 1163 -0.496 0.9963
## G_07 - G_50 -1.73e-02 0.01007 1165 -1.713 0.5230
##
## Nutrients = Ambient, DaysF = 21:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.49e-03 0.01123 1165 0.489 0.9965
## G_48 - G_31 1.84e-02 0.01339 1164 1.372 0.7438
## G_48 - G_08 2.04e-02 0.01893 1165 1.078 0.8903
## G_48 - G_07 5.31e-02 0.01123 1165 4.724 <.0001
## G_48 - G_50 9.65e-03 0.01446 1165 0.667 0.9854
## G_62 - G_31 1.29e-02 0.01363 1165 0.945 0.9348
## G_62 - G_08 1.49e-02 0.01910 1165 0.780 0.9709
## G_62 - G_07 4.76e-02 0.01152 1165 4.128 0.0006
## G_62 - G_50 4.16e-03 0.01469 1165 0.283 0.9998
## G_31 - G_08 2.03e-03 0.02045 1165 0.099 1.0000
## G_31 - G_07 3.47e-02 0.01363 1165 2.544 0.1124
## G_31 - G_50 -8.72e-03 0.01640 1164 -0.532 0.9949
## G_08 - G_07 3.27e-02 0.01910 1165 1.709 0.5260
## G_08 - G_50 -1.07e-02 0.02116 1165 -0.508 0.9959
## G_07 - G_50 -4.34e-02 0.01469 1165 -2.955 0.0375
##
## Nutrients = Nutrients, DaysF = 21:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 4.21e-03 0.00794 1164 0.530 0.9950
## G_48 - G_31 7.89e-03 0.00935 1165 0.844 0.9592
## G_48 - G_08 2.43e-02 0.01153 1163 2.106 0.2849
## G_48 - G_07 3.24e-02 0.00827 1165 3.916 0.0013
## G_48 - G_50 3.51e-02 0.00998 1165 3.519 0.0060
## G_62 - G_31 3.69e-03 0.00917 1165 0.402 0.9987
## G_62 - G_08 2.01e-02 0.01138 1164 1.764 0.4895
## G_62 - G_07 2.82e-02 0.00806 1165 3.493 0.0066
## G_62 - G_50 3.09e-02 0.00981 1164 3.150 0.0207
## G_31 - G_08 1.64e-02 0.01241 1163 1.321 0.7736
## G_31 - G_07 2.45e-02 0.00946 1165 2.588 0.1009
## G_31 - G_50 2.72e-02 0.01098 1165 2.478 0.1314
## G_08 - G_07 8.08e-03 0.01162 1163 0.696 0.9825
## G_08 - G_50 1.08e-02 0.01289 1163 0.840 0.9601
## G_07 - G_50 2.74e-03 0.01007 1165 0.272 0.9998
##
## Nutrients = Ambient, DaysF = 28:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 8.49e-03 0.01123 1165 0.756 0.9746
## G_48 - G_31 8.57e-03 0.01339 1164 0.640 0.9879
## G_48 - G_08 9.90e-03 0.01893 1165 0.523 0.9953
## G_48 - G_07 5.95e-02 0.01123 1165 5.298 <.0001
## G_48 - G_50 1.76e-02 0.01446 1165 1.221 0.8268
## G_62 - G_31 7.58e-05 0.01363 1165 0.006 1.0000
## G_62 - G_08 1.41e-03 0.01910 1165 0.074 1.0000
## G_62 - G_07 5.10e-02 0.01152 1165 4.427 0.0002
## G_62 - G_50 9.16e-03 0.01469 1165 0.623 0.9893
## G_31 - G_08 1.33e-03 0.02045 1165 0.065 1.0000
## G_31 - G_07 5.09e-02 0.01363 1165 3.736 0.0027
## G_31 - G_50 9.08e-03 0.01640 1164 0.554 0.9938
## G_08 - G_07 4.96e-02 0.01910 1165 2.596 0.0990
## G_08 - G_50 7.75e-03 0.02116 1165 0.366 0.9991
## G_07 - G_50 -4.18e-02 0.01469 1165 -2.849 0.0507
##
## Nutrients = Nutrients, DaysF = 28:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 2.86e-03 0.00794 1164 0.360 0.9992
## G_48 - G_31 8.11e-04 0.00935 1165 0.087 1.0000
## G_48 - G_08 1.82e-02 0.01153 1163 1.576 0.6147
## G_48 - G_07 2.72e-02 0.00827 1165 3.286 0.0134
## G_48 - G_50 3.77e-02 0.00998 1165 3.775 0.0023
## G_62 - G_31 -2.05e-03 0.00917 1165 -0.223 0.9999
## G_62 - G_08 1.53e-02 0.01138 1164 1.345 0.7594
## G_62 - G_07 2.43e-02 0.00806 1165 3.014 0.0315
## G_62 - G_50 3.48e-02 0.00981 1164 3.548 0.0054
## G_31 - G_08 1.74e-02 0.01241 1163 1.399 0.7278
## G_31 - G_07 2.63e-02 0.00946 1165 2.786 0.0604
## G_31 - G_50 3.69e-02 0.01098 1165 3.355 0.0106
## G_08 - G_07 8.98e-03 0.01162 1163 0.773 0.9720
## G_08 - G_50 1.95e-02 0.01289 1163 1.512 0.6568
## G_07 - G_50 1.05e-02 0.01007 1165 1.043 0.9032
##
## Nutrients = Ambient, DaysF = 49:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 2.61e-04 0.01123 1165 0.023 1.0000
## G_48 - G_31 7.67e-03 0.01339 1164 0.573 0.9928
## G_48 - G_08 3.50e-03 0.01893 1165 0.185 1.0000
## G_48 - G_07 6.22e-02 0.01123 1165 5.535 <.0001
## G_48 - G_50 4.00e-03 0.01446 1165 0.277 0.9998
## G_62 - G_31 7.41e-03 0.01363 1165 0.544 0.9943
## G_62 - G_08 3.24e-03 0.01910 1165 0.170 1.0000
## G_62 - G_07 6.19e-02 0.01152 1165 5.372 <.0001
## G_62 - G_50 3.74e-03 0.01469 1165 0.255 0.9999
## G_31 - G_08 -4.17e-03 0.02045 1165 -0.204 1.0000
## G_31 - G_07 5.45e-02 0.01363 1165 3.997 0.0010
## G_31 - G_50 -3.67e-03 0.01640 1164 -0.224 0.9999
## G_08 - G_07 5.87e-02 0.01910 1165 3.070 0.0266
## G_08 - G_50 5.00e-04 0.02116 1165 0.024 1.0000
## G_07 - G_50 -5.82e-02 0.01469 1165 -3.959 0.0011
##
## Nutrients = Nutrients, DaysF = 49:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.94e-03 0.00794 1164 0.748 0.9758
## G_48 - G_31 4.07e-04 0.00935 1165 0.044 1.0000
## G_48 - G_08 3.29e-03 0.01153 1163 0.285 0.9997
## G_48 - G_07 1.10e-02 0.00827 1165 1.330 0.7682
## G_48 - G_50 1.53e-02 0.00998 1165 1.537 0.6404
## G_62 - G_31 -5.53e-03 0.00917 1165 -0.603 0.9908
## G_62 - G_08 -2.65e-03 0.01138 1164 -0.233 0.9999
## G_62 - G_07 5.06e-03 0.00806 1165 0.627 0.9890
## G_62 - G_50 9.39e-03 0.00981 1164 0.958 0.9310
## G_31 - G_08 2.88e-03 0.01241 1163 0.232 0.9999
## G_31 - G_07 1.06e-02 0.00946 1165 1.120 0.8734
## G_31 - G_50 1.49e-02 0.01098 1165 1.359 0.7516
## G_08 - G_07 7.71e-03 0.01162 1163 0.664 0.9858
## G_08 - G_50 1.20e-02 0.01289 1163 0.934 0.9376
## G_07 - G_50 4.34e-03 0.01007 1165 0.431 0.9981
##
## Nutrients = Ambient, DaysF = 65:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 3.00e-02 0.01123 1165 2.674 0.0813
## G_48 - G_31 4.62e-02 0.01339 1164 3.449 0.0077
## G_48 - G_08 3.21e-02 0.01893 1165 1.696 0.5349
## G_48 - G_07 5.76e-02 0.01123 1165 5.128 <.0001
## G_48 - G_50 3.76e-02 0.01446 1165 2.601 0.0979
## G_62 - G_31 1.61e-02 0.01363 1165 1.184 0.8445
## G_62 - G_08 2.07e-03 0.01910 1165 0.108 1.0000
## G_62 - G_07 2.76e-02 0.01152 1165 2.392 0.1598
## G_62 - G_50 7.57e-03 0.01469 1165 0.516 0.9956
## G_31 - G_08 -1.41e-02 0.02045 1165 -0.688 0.9833
## G_31 - G_07 1.14e-02 0.01363 1165 0.837 0.9605
## G_31 - G_50 -8.57e-03 0.01640 1164 -0.523 0.9953
## G_08 - G_07 2.55e-02 0.01910 1165 1.334 0.7661
## G_08 - G_50 5.50e-03 0.02116 1165 0.260 0.9998
## G_07 - G_50 -2.00e-02 0.01469 1165 -1.361 0.7506
##
## Nutrients = Nutrients, DaysF = 65:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 9.48e-03 0.00794 1164 1.194 0.8398
## G_48 - G_31 9.44e-03 0.00935 1165 1.010 0.9148
## G_48 - G_08 4.61e-02 0.01153 1163 3.994 0.0010
## G_48 - G_07 2.75e-02 0.00827 1165 3.323 0.0118
## G_48 - G_50 1.03e-01 0.01036 1199 9.923 <.0001
## G_62 - G_31 -4.07e-05 0.00917 1165 -0.004 1.0000
## G_62 - G_08 3.66e-02 0.01138 1164 3.214 0.0169
## G_62 - G_07 1.80e-02 0.00806 1165 2.231 0.2243
## G_62 - G_50 9.33e-02 0.01020 1200 9.150 <.0001
## G_31 - G_08 3.66e-02 0.01241 1163 2.950 0.0380
## G_31 - G_07 1.80e-02 0.00946 1165 1.906 0.3988
## G_31 - G_50 9.34e-02 0.01133 1194 8.238 <.0001
## G_08 - G_07 -1.86e-02 0.01162 1163 -1.601 0.5980
## G_08 - G_50 5.67e-02 0.01319 1185 4.302 0.0003
## G_07 - G_50 7.53e-02 0.01045 1199 7.207 <.0001
##
## Nutrients = Ambient, DaysF = 71:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 9.92e-03 0.01123 1165 0.883 0.9506
## G_48 - G_31 1.46e-02 0.01339 1164 1.088 0.8861
## G_48 - G_08 2.76e-02 0.01893 1165 1.458 0.6912
## G_48 - G_07 8.15e-02 0.01123 1165 7.255 <.0001
## G_48 - G_50 2.99e-02 0.01446 1165 2.065 0.3068
## G_62 - G_31 4.65e-03 0.01363 1165 0.341 0.9994
## G_62 - G_08 1.77e-02 0.01910 1165 0.926 0.9400
## G_62 - G_07 7.16e-02 0.01152 1165 6.211 <.0001
## G_62 - G_50 1.99e-02 0.01469 1165 1.357 0.7526
## G_31 - G_08 1.30e-02 0.02045 1165 0.637 0.9882
## G_31 - G_07 6.69e-02 0.01363 1165 4.908 <.0001
## G_31 - G_50 1.53e-02 0.01640 1164 0.932 0.9383
## G_08 - G_07 5.39e-02 0.01910 1165 2.820 0.0551
## G_08 - G_50 2.25e-03 0.02116 1165 0.106 1.0000
## G_07 - G_50 -5.16e-02 0.01469 1165 -3.515 0.0061
##
## Nutrients = Nutrients, DaysF = 71:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 2.44e-03 0.00794 1164 0.308 0.9996
## G_48 - G_31 9.14e-03 0.00935 1165 0.978 0.9250
## G_48 - G_08 3.02e-02 0.01153 1163 2.616 0.0941
## G_48 - G_07 2.63e-02 0.00839 1179 3.136 0.0217
## G_48 - G_50 4.88e-02 0.01143 1278 4.274 0.0003
## G_62 - G_31 6.70e-03 0.00917 1165 0.730 0.9782
## G_62 - G_08 2.77e-02 0.01138 1164 2.436 0.1447
## G_62 - G_07 2.39e-02 0.00819 1180 2.914 0.0422
## G_62 - G_50 4.64e-02 0.01128 1281 4.112 0.0006
## G_31 - G_08 2.10e-02 0.01241 1163 1.694 0.5359
## G_31 - G_07 1.72e-02 0.00956 1176 1.794 0.4699
## G_31 - G_50 3.97e-02 0.01232 1263 3.223 0.0164
## G_08 - G_07 -3.87e-03 0.01171 1171 -0.331 0.9995
## G_08 - G_50 1.87e-02 0.01405 1241 1.328 0.7694
## G_07 - G_50 2.25e-02 0.01160 1282 1.942 0.3768
##
## Nutrients = Ambient, DaysF = 76:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 1.16e-02 0.01123 1165 1.034 0.9063
## G_48 - G_31 9.07e-03 0.01339 1164 0.678 0.9844
## G_48 - G_08 7.80e-03 0.01893 1165 0.412 0.9985
## G_48 - G_07 8.42e-02 0.01123 1165 7.496 <.0001
## G_48 - G_50 1.68e-02 0.01446 1165 1.162 0.8548
## G_62 - G_31 -2.55e-03 0.01363 1165 -0.187 1.0000
## G_62 - G_08 -3.82e-03 0.01910 1165 -0.200 1.0000
## G_62 - G_07 7.26e-02 0.01152 1165 6.298 <.0001
## G_62 - G_50 5.18e-03 0.01469 1165 0.353 0.9993
## G_31 - G_08 -1.27e-03 0.02045 1165 -0.062 1.0000
## G_31 - G_07 7.51e-02 0.01363 1165 5.509 <.0001
## G_31 - G_50 7.73e-03 0.01640 1164 0.471 0.9971
## G_08 - G_07 7.64e-02 0.01910 1165 3.998 0.0010
## G_08 - G_50 9.00e-03 0.02116 1165 0.425 0.9982
## G_07 - G_50 -6.74e-02 0.01469 1165 -4.587 0.0001
##
## Nutrients = Nutrients, DaysF = 76:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 1.23e-02 0.00794 1164 1.552 0.6307
## G_48 - G_31 2.05e-02 0.00935 1165 2.188 0.2439
## G_48 - G_08 2.55e-02 0.01153 1163 2.207 0.2352
## G_48 - G_07 3.76e-02 0.00839 1179 4.480 0.0001
## G_48 - G_50 3.80e-02 0.01221 1320 3.113 0.0232
## G_62 - G_31 8.15e-03 0.00917 1165 0.888 0.9495
## G_62 - G_08 1.31e-02 0.01138 1164 1.154 0.8585
## G_62 - G_07 2.53e-02 0.00819 1180 3.085 0.0254
## G_62 - G_50 2.57e-02 0.01207 1323 2.127 0.2736
## G_31 - G_08 4.99e-03 0.01241 1163 0.402 0.9987
## G_31 - G_07 1.71e-02 0.00956 1176 1.789 0.4730
## G_31 - G_50 1.75e-02 0.01305 1303 1.345 0.7599
## G_08 - G_07 1.21e-02 0.01171 1171 1.036 0.9057
## G_08 - G_50 1.26e-02 0.01469 1276 0.855 0.9570
## G_07 - G_50 4.27e-04 0.01237 1322 0.034 1.0000
##
## Nutrients = Ambient, DaysF = 84:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 8.00e-03 0.01255 1269 0.637 0.9882
## G_48 - G_31 7.26e-03 0.01485 1263 0.488 0.9966
## G_48 - G_08 2.72e-02 0.01929 1185 1.412 0.7195
## G_48 - G_07 8.70e-02 0.01255 1269 6.933 <.0001
## G_48 - G_50 1.32e-02 0.01493 1198 0.888 0.9495
## G_62 - G_31 -7.43e-04 0.01520 1267 -0.049 1.0000
## G_62 - G_08 1.93e-02 0.01956 1190 0.984 0.9230
## G_62 - G_07 7.90e-02 0.01295 1274 6.099 <.0001
## G_62 - G_50 5.25e-03 0.01527 1206 0.344 0.9994
## G_31 - G_08 2.00e-02 0.02111 1198 0.947 0.9341
## G_31 - G_07 7.97e-02 0.01520 1267 5.247 <.0001
## G_31 - G_50 5.99e-03 0.01722 1215 0.348 0.9993
## G_08 - G_07 5.98e-02 0.01956 1190 3.055 0.0278
## G_08 - G_50 -1.40e-02 0.02116 1165 -0.661 0.9860
## G_07 - G_50 -7.38e-02 0.01527 1206 -4.829 <.0001
##
## Nutrients = Nutrients, DaysF = 84:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.74e-03 0.00900 1280 0.638 0.9881
## G_48 - G_31 1.52e-02 0.01073 1289 1.414 0.7188
## G_48 - G_08 3.15e-02 0.01266 1253 2.488 0.1284
## G_48 - G_07 3.48e-02 0.00952 1291 3.653 0.0037
## G_48 - G_50 3.41e-02 0.01362 1372 2.506 0.1231
## G_62 - G_31 9.42e-03 0.01059 1293 0.890 0.9491
## G_62 - G_08 2.57e-02 0.01254 1257 2.053 0.3129
## G_62 - G_07 2.90e-02 0.00937 1297 3.099 0.0243
## G_62 - G_50 2.84e-02 0.01352 1375 2.100 0.2878
## G_31 - G_08 1.63e-02 0.01383 1266 1.180 0.8465
## G_31 - G_07 1.96e-02 0.01104 1300 1.777 0.4812
## G_31 - G_50 1.90e-02 0.01472 1366 1.288 0.7918
## G_08 - G_07 3.29e-03 0.01292 1264 0.255 0.9999
## G_08 - G_50 2.64e-03 0.01619 1338 0.163 1.0000
## G_07 - G_50 -6.52e-04 0.01387 1374 -0.047 1.0000
##
## Nutrients = Ambient, DaysF = 89:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 1.79e-02 0.01255 1269 1.427 0.7104
## G_48 - G_31 -6.19e-04 0.01485 1263 -0.042 1.0000
## G_48 - G_08 1.39e-02 0.01929 1185 0.719 0.9796
## G_48 - G_07 6.61e-02 0.01255 1269 5.264 <.0001
## G_48 - G_50 6.37e-03 0.01493 1198 0.427 0.9982
## G_62 - G_31 -1.85e-02 0.01520 1267 -1.219 0.8276
## G_62 - G_08 -4.04e-03 0.01956 1190 -0.206 0.9999
## G_62 - G_07 4.81e-02 0.01295 1274 3.717 0.0029
## G_62 - G_50 -1.15e-02 0.01527 1206 -0.755 0.9747
## G_31 - G_08 1.45e-02 0.02111 1198 0.686 0.9835
## G_31 - G_07 6.67e-02 0.01520 1267 4.387 0.0002
## G_31 - G_50 6.99e-03 0.01722 1215 0.406 0.9986
## G_08 - G_07 5.22e-02 0.01956 1190 2.668 0.0825
## G_08 - G_50 -7.50e-03 0.02116 1165 -0.354 0.9993
## G_07 - G_50 -5.97e-02 0.01527 1206 -3.908 0.0014
##
## Nutrients = Nutrients, DaysF = 89:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 -1.23e-03 0.00900 1280 -0.136 1.0000
## G_48 - G_31 -3.03e-03 0.01073 1289 -0.283 0.9998
## G_48 - G_08 4.07e-02 0.01266 1253 3.217 0.0167
## G_48 - G_07 1.56e-02 0.00974 1307 1.605 0.5953
## G_48 - G_50 2.31e-02 0.01362 1372 1.693 0.5368
## G_62 - G_31 -1.80e-03 0.01059 1293 -0.170 1.0000
## G_62 - G_08 4.19e-02 0.01254 1257 3.345 0.0109
## G_62 - G_07 1.69e-02 0.00959 1312 1.758 0.4934
## G_62 - G_50 2.43e-02 0.01352 1375 1.797 0.4683
## G_31 - G_08 4.37e-02 0.01383 1266 3.163 0.0199
## G_31 - G_07 1.87e-02 0.01122 1311 1.662 0.5570
## G_31 - G_50 2.61e-02 0.01472 1366 1.772 0.4844
## G_08 - G_07 -2.51e-02 0.01308 1274 -1.918 0.3915
## G_08 - G_50 -1.77e-02 0.01619 1338 -1.091 0.8851
## G_07 - G_50 7.42e-03 0.01402 1378 0.530 0.9950
##
## Nutrients = Ambient, DaysF = 92:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 3.66e-03 0.01255 1269 0.292 0.9997
## G_48 - G_31 1.51e-02 0.01485 1263 1.019 0.9118
## G_48 - G_08 3.26e-02 0.01929 1185 1.691 0.5379
## G_48 - G_07 7.57e-02 0.01255 1269 6.030 <.0001
## G_48 - G_50 1.91e-02 0.01493 1198 1.281 0.7955
## G_62 - G_31 1.15e-02 0.01520 1267 0.755 0.9748
## G_62 - G_08 2.90e-02 0.01956 1190 1.481 0.6766
## G_62 - G_07 7.20e-02 0.01295 1274 5.559 <.0001
## G_62 - G_50 1.55e-02 0.01527 1206 1.013 0.9138
## G_31 - G_08 1.75e-02 0.02111 1198 0.829 0.9622
## G_31 - G_07 6.05e-02 0.01520 1267 3.983 0.0010
## G_31 - G_50 3.99e-03 0.01722 1215 0.232 0.9999
## G_08 - G_07 4.30e-02 0.01956 1190 2.201 0.2381
## G_08 - G_50 -1.35e-02 0.02116 1165 -0.638 0.9881
## G_07 - G_50 -5.65e-02 0.01527 1206 -3.702 0.0031
##
## Nutrients = Nutrients, DaysF = 92:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 -2.95e-04 0.00900 1280 -0.033 1.0000
## G_48 - G_31 1.08e-02 0.01073 1289 1.011 0.9144
## G_48 - G_08 4.76e-04 0.01802 1414 0.026 1.0000
## G_48 - G_07 8.96e-03 0.01065 1358 0.841 0.9597
## G_48 - G_50 -3.38e-03 0.01520 1404 -0.222 0.9999
## G_62 - G_31 1.11e-02 0.01059 1293 1.052 0.9002
## G_62 - G_08 7.71e-04 0.01794 1415 0.043 1.0000
## G_62 - G_07 9.26e-03 0.01052 1363 0.880 0.9513
## G_62 - G_50 -3.08e-03 0.01511 1406 -0.204 1.0000
## G_31 - G_08 -1.04e-02 0.01886 1409 -0.550 0.9940
## G_31 - G_07 -1.88e-03 0.01203 1352 -0.156 1.0000
## G_31 - G_50 -1.42e-02 0.01620 1398 -0.878 0.9518
## G_08 - G_07 8.49e-03 0.01882 1419 0.451 0.9977
## G_08 - G_50 -3.85e-03 0.02173 1421 -0.177 1.0000
## G_07 - G_50 -1.23e-02 0.01615 1413 -0.764 0.9734
##
## Nutrients = Ambient, DaysF = 96:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 6.34e-03 0.01255 1269 0.505 0.9960
## G_48 - G_31 1.38e-03 0.01485 1263 0.093 1.0000
## G_48 - G_08 1.69e-02 0.01929 1185 0.875 0.9525
## G_48 - G_07 4.58e-02 0.01255 1269 3.648 0.0037
## G_48 - G_50 1.66e-02 0.01493 1198 1.114 0.8759
## G_62 - G_31 -4.96e-03 0.01520 1267 -0.326 0.9995
## G_62 - G_08 1.05e-02 0.01956 1190 0.539 0.9946
## G_62 - G_07 3.94e-02 0.01295 1274 3.044 0.0287
## G_62 - G_50 1.03e-02 0.01527 1206 0.674 0.9848
## G_31 - G_08 1.55e-02 0.02111 1198 0.734 0.9777
## G_31 - G_07 4.44e-02 0.01520 1267 2.921 0.0413
## G_31 - G_50 1.52e-02 0.01722 1215 0.885 0.9500
## G_08 - G_07 2.89e-02 0.01956 1190 1.477 0.6788
## G_08 - G_50 -2.50e-04 0.02116 1165 -0.012 1.0000
## G_07 - G_50 -2.91e-02 0.01527 1206 -1.909 0.3973
##
## Nutrients = Nutrients, DaysF = 96:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.37e-03 0.00900 1280 0.596 0.9913
## G_48 - G_31 1.32e-02 0.01117 1318 1.185 0.8441
## G_48 - G_08 -4.25e-02 0.02447 1427 -1.736 0.5078
## G_48 - G_07 -1.12e-02 0.02434 1413 -0.459 0.9975
## G_48 - G_50 nonEst NA NA NA NA
## G_62 - G_31 7.88e-03 0.01104 1323 0.713 0.9804
## G_62 - G_08 -4.79e-02 0.02441 1427 -1.961 0.3657
## G_62 - G_07 -1.65e-02 0.02428 1413 -0.681 0.9840
## G_62 - G_50 nonEst NA NA NA NA
## G_31 - G_08 -5.57e-02 0.02529 1428 -2.204 0.2365
## G_31 - G_07 -2.44e-02 0.02517 1418 -0.970 0.9274
## G_31 - G_50 nonEst NA NA NA NA
## G_08 - G_07 3.13e-02 0.03328 1415 0.941 0.9358
## G_08 - G_50 nonEst NA NA NA NA
## G_07 - G_50 nonEst NA NA NA NA
##
## Nutrients = Ambient, DaysF = 99:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 2.23e-02 0.01255 1269 1.776 0.4818
## G_48 - G_31 3.20e-02 0.01485 1263 2.155 0.2601
## G_48 - G_08 2.02e-02 0.01929 1185 1.050 0.9009
## G_48 - G_07 4.49e-02 0.01255 1269 3.575 0.0049
## G_48 - G_50 -1.25e-03 0.01493 1198 -0.084 1.0000
## G_62 - G_31 9.72e-03 0.01520 1267 0.640 0.9880
## G_62 - G_08 -2.04e-03 0.01956 1190 -0.104 1.0000
## G_62 - G_07 2.26e-02 0.01295 1274 1.743 0.5035
## G_62 - G_50 -2.35e-02 0.01527 1206 -1.541 0.6377
## G_31 - G_08 -1.18e-02 0.02111 1198 -0.557 0.9937
## G_31 - G_07 1.29e-02 0.01520 1267 0.846 0.9588
## G_31 - G_50 -3.33e-02 0.01722 1215 -1.932 0.3832
## G_08 - G_07 2.46e-02 0.01956 1190 1.258 0.8076
## G_08 - G_50 -2.15e-02 0.02116 1165 -1.016 0.9127
## G_07 - G_50 -4.61e-02 0.01527 1206 -3.019 0.0310
##
## Nutrients = Nutrients, DaysF = 99:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 5.76e-03 0.00900 1280 0.640 0.9880
## G_48 - G_31 1.21e-02 0.01249 1377 0.971 0.9270
## G_48 - G_08 nonEst NA NA NA NA
## G_48 - G_07 nonEst NA NA NA NA
## G_48 - G_50 nonEst NA NA NA NA
## G_62 - G_31 6.38e-03 0.01238 1380 0.515 0.9956
## G_62 - G_08 nonEst NA NA NA NA
## G_62 - G_07 nonEst NA NA NA NA
## G_62 - G_50 nonEst NA NA NA NA
## G_31 - G_08 nonEst NA NA NA NA
## G_31 - G_07 nonEst NA NA NA NA
## G_31 - G_50 nonEst NA NA NA NA
## G_08 - G_07 nonEst NA NA NA NA
## G_08 - G_50 nonEst NA NA NA NA
## G_07 - G_50 nonEst NA NA NA NA
##
## Nutrients = Ambient, DaysF = 103:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 2.21e-02 0.01255 1269 1.764 0.4893
## G_48 - G_31 1.70e-02 0.01485 1263 1.145 0.8625
## G_48 - G_08 3.25e-03 0.01929 1185 0.168 1.0000
## G_48 - G_07 4.24e-02 0.01255 1269 3.381 0.0097
## G_48 - G_50 -2.50e-03 0.01493 1198 -0.168 1.0000
## G_62 - G_31 -5.14e-03 0.01520 1267 -0.338 0.9994
## G_62 - G_08 -1.89e-02 0.01956 1190 -0.966 0.9286
## G_62 - G_07 2.03e-02 0.01295 1274 1.566 0.6210
## G_62 - G_50 -2.46e-02 0.01527 1206 -1.614 0.5897
## G_31 - G_08 -1.38e-02 0.02111 1198 -0.652 0.9869
## G_31 - G_07 2.54e-02 0.01520 1267 1.673 0.5500
## G_31 - G_50 -1.95e-02 0.01722 1215 -1.133 0.8676
## G_08 - G_07 3.92e-02 0.01956 1190 2.003 0.3409
## G_08 - G_50 -5.75e-03 0.02116 1165 -0.272 0.9998
## G_07 - G_50 -4.49e-02 0.01527 1206 -2.942 0.0389
##
## Nutrients = Nutrients, DaysF = 103:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 1.66e-02 0.00914 1293 1.811 0.4592
## G_48 - G_31 1.80e-02 0.01787 1428 1.007 0.9159
## G_48 - G_08 nonEst NA NA NA NA
## G_48 - G_07 nonEst NA NA NA NA
## G_48 - G_50 nonEst NA NA NA NA
## G_62 - G_31 1.43e-03 0.01786 1428 0.080 1.0000
## G_62 - G_08 nonEst NA NA NA NA
## G_62 - G_07 nonEst NA NA NA NA
## G_62 - G_50 nonEst NA NA NA NA
## G_31 - G_08 nonEst NA NA NA NA
## G_31 - G_07 nonEst NA NA NA NA
## G_31 - G_50 nonEst NA NA NA NA
## G_08 - G_07 nonEst NA NA NA NA
## G_08 - G_50 nonEst NA NA NA NA
## G_07 - G_50 nonEst NA NA NA NA
##
## Nutrients = Ambient, DaysF = 106:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 6.41e-04 0.01255 1269 0.051 1.0000
## G_48 - G_31 5.55e-02 0.01485 1263 3.737 0.0027
## G_48 - G_08 3.75e-03 0.01929 1185 0.194 1.0000
## G_48 - G_07 7.08e-02 0.01255 1269 5.641 <.0001
## G_48 - G_50 3.00e-02 0.01493 1198 2.010 0.3373
## G_62 - G_31 5.49e-02 0.01520 1267 3.610 0.0043
## G_62 - G_08 3.11e-03 0.01956 1190 0.159 1.0000
## G_62 - G_07 7.01e-02 0.01295 1274 5.415 <.0001
## G_62 - G_50 2.94e-02 0.01527 1206 1.922 0.3889
## G_31 - G_08 -5.18e-02 0.02111 1198 -2.452 0.1397
## G_31 - G_07 1.53e-02 0.01520 1267 1.006 0.9162
## G_31 - G_50 -2.55e-02 0.01722 1215 -1.482 0.6762
## G_08 - G_07 6.70e-02 0.01956 1190 3.428 0.0083
## G_08 - G_50 2.62e-02 0.02116 1165 1.240 0.8169
## G_07 - G_50 -4.08e-02 0.01527 1206 -2.671 0.0819
##
## Nutrients = Nutrients, DaysF = 106:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 3.59e-02 0.01049 1372 3.425 0.0083
## G_48 - G_31 4.77e-03 0.02454 1414 0.194 1.0000
## G_48 - G_08 nonEst NA NA NA NA
## G_48 - G_07 nonEst NA NA NA NA
## G_48 - G_50 nonEst NA NA NA NA
## G_62 - G_31 -3.12e-02 0.02462 1412 -1.266 0.8035
## G_62 - G_08 nonEst NA NA NA NA
## G_62 - G_07 nonEst NA NA NA NA
## G_62 - G_50 nonEst NA NA NA NA
## G_31 - G_08 nonEst NA NA NA NA
## G_31 - G_07 nonEst NA NA NA NA
## G_31 - G_50 nonEst NA NA NA NA
## G_08 - G_07 nonEst NA NA NA NA
## G_08 - G_50 nonEst NA NA NA NA
## G_07 - G_50 nonEst NA NA NA NA
##
## Nutrients = Ambient, DaysF = 110:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 -4.86e-03 0.01255 1269 -0.387 0.9989
## G_48 - G_31 2.63e-02 0.01485 1263 1.768 0.4872
## G_48 - G_08 2.47e-02 0.01929 1185 1.283 0.7945
## G_48 - G_07 6.99e-02 0.01255 1269 5.567 <.0001
## G_48 - G_50 1.75e-03 0.01493 1198 0.117 1.0000
## G_62 - G_31 3.11e-02 0.01520 1267 2.047 0.3162
## G_62 - G_08 2.96e-02 0.01956 1190 1.514 0.6554
## G_62 - G_07 7.47e-02 0.01295 1274 5.768 <.0001
## G_62 - G_50 6.61e-03 0.01527 1206 0.433 0.9981
## G_31 - G_08 -1.51e-03 0.02111 1198 -0.071 1.0000
## G_31 - G_07 4.36e-02 0.01520 1267 2.869 0.0479
## G_31 - G_50 -2.45e-02 0.01722 1215 -1.423 0.7127
## G_08 - G_07 4.51e-02 0.01956 1190 2.306 0.1921
## G_08 - G_50 -2.30e-02 0.02116 1165 -1.087 0.8868
## G_07 - G_50 -6.81e-02 0.01527 1206 -4.460 0.0001
##
## Nutrients = Nutrients, DaysF = 110:
## contrast estimate SE df t.ratio p.value
## G_48 - G_62 -2.61e-02 0.02341 1404 -1.115 0.8755
## G_48 - G_31 1.39e-01 0.02869 1404 4.861 <.0001
## G_48 - G_08 nonEst NA NA NA NA
## G_48 - G_07 nonEst NA NA NA NA
## G_48 - G_50 nonEst NA NA NA NA
## G_62 - G_31 1.66e-01 0.02868 1403 5.771 <.0001
## G_62 - G_08 nonEst NA NA NA NA
## G_62 - G_07 nonEst NA NA NA NA
## G_62 - G_50 nonEst NA NA NA NA
## G_31 - G_08 nonEst NA NA NA NA
## G_31 - G_07 nonEst NA NA NA NA
## G_31 - G_50 nonEst NA NA NA NA
## G_08 - G_07 nonEst NA NA NA NA
## G_08 - G_50 nonEst NA NA NA NA
## G_07 - G_50 nonEst NA NA NA NA
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 6 estimates Acer.YII.emm2<-emmeans(Model2, ~ Genotype*Nutrients * DaysF)
Acer.YII_groups<-cld(Acer.YII.emm2, by=NULL) # compact-letter display
Acer.YII_groups<-Acer.YII_groups[order(
Acer.YII_groups$Day,
Acer.YII_groups$Nutrients,
Acer.YII_groups$Genotype),]
Acer.YII_groups #write.csv(Acer.YII_groups, "Outputs/Multicomp_AcerYII.csv", row.names = F)Supporting plots
- Treatment effect by genotype
FigureS3_genotype<-YII_Treat_BW<- ggplot(data=YII.Acer, aes (Days, YII, colour=factor(Nutrients), shape=factor(Nutrients))) +
ggthe_bw + Fill.colour+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 5,
position = position_dodge(1) )+
stat_summary(fun.y=mean, geom="line", position = position_dodge(1),
linetype=1, alpha=1) +
stat_summary(fun.y=mean, geom="point", size =1,
position=position_dodge(width=1), alpha=0.5) +
theme(axis.title.y=element_text(size=12),
legend.position="bottom",
legend.title = element_blank()) + ggtitle("b") +
scale_y_continuous(limits = c(0.0, 0.7),
breaks = seq(0.0, 0.6, 0.1),
expand = c(0, 0),
name=expression(~italic("Fv / Fm"))) +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 30),
expand = c(0, 0))+
annotate("segment", x = 2, xend = 91, y = 0.12, yend = 0.12,
colour = "gray90", linetype=1)+
annotate("segment", x = 79, xend = 91, y = 0.12, yend = 0.20,
colour = "gray90", linetype=1)+
annotate("segment", x = 91, xend = 110, y = 0.20, yend = 0.20,
colour = "gray90", linetype=1)+
annotate("text", x = 45, y = 0.05, label = "Nutrients", size=3)+
annotate("text", x = 99, y = 0.05, label = "H", size=3) +
facet_wrap(~Genotype)
FigureS3_genotype
#ggsave(file="Outputs/S3b_YII_Treat_Colo.svg", plot=Figure3_genotype, width=6.0, height=3.5)- Figure S: Treatment difference by genotype
YII.Acer$Nutrients2<-YII.Acer$Nutrients
YII.Acer$Nutrients2[YII.Acer$DaysF=="1"]<-"Ambient"
YII.Summary <-YII.Acer %>%
group_by(Genotype, Nutrients2, DaysF) %>%
get_summary_stats(YII, type = "mean_sd")
YII.Summary YII.Summary<-subset(YII.Summary, Nutrients2=="Ambient" )
YII.Summary<-YII.Summary[, -3]
YII.Summary<-YII.Summary[, -3]
YII.Summary<-YII.Summary[, -3]
YII.Acer<-merge(YII.Acer, YII.Summary, by=c("Genotype", "DaysF"), all.x = T)
YII.Acer$Difference<-YII.Acer$YII-YII.Acer$mean
FigureS4_genotype<-YII_Treat_BW<- ggplot(data=YII.Acer, aes (Days, Difference, colour=factor(Nutrients), shape=factor(Nutrients))) +
geom_smooth()+
ggthe_bw + Fill.colour+
stat_summary(fun.y=mean, geom="point", size =2,
position=position_dodge(width=1), alpha=0.5) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 5,
position = position_dodge(1) )+
theme(axis.title.y=element_text(size=12),
legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA)) +
ggtitle("b)") +
scale_y_continuous(limits = c(-0.3, 0.1),
breaks = seq(-0.4, 0.1, 0.1),
expand = c(0, 0),
name=expression(~italic("Fv / Fm"))) +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 30),
expand = c(0, 0))+
annotate("segment", x = 2, xend = 91, y = 0.12, yend = 0.12,
colour = "gray90", linetype=1)+
annotate("segment", x = 79, xend = 91, y = 0.12, yend = 0.65,
colour = "gray90", linetype=1)+
annotate("segment", x = 91, xend = 110, y = 0.65, yend = 0.65,
colour = "gray90", linetype=1)+
annotate("text", x = 45, y = 0.05, label = "Nutrients", size=3)+
annotate("text", x = 99, y = 0.05, label = "H", size=3) +
facet_wrap(~Genotype)
FigureS4_genotype
#ggsave(file="Outputs/S3b_YII_Treat_Colo.svg", plot=Figure3_genotype, width=6.0, height=3.5)- Figure S4: Treatment difference by genotype N and N+P pooled
YII.Summary <-YII.Acer %>%
group_by(Genotype, Treatment, DaysF) %>%
get_summary_stats(YII, type = "mean_sd")
YII.Summary YII.Summary<-subset(YII.Summary, Treatment=="Ambient" )
YII.Summary<-YII.Summary[, -3]
YII.Summary<-YII.Summary[, -3]
YII.Summary<-YII.Summary[, -3]
#YII.Acer<-merge(YII.Acer, YII.Summary, by=c("Genotype", "DaysF"), all.x = T)
YII.Acer$Difference2<-YII.Acer$YII-YII.Acer$mean
YII.Acer$Percentaje2 <- (YII.Acer$Difference2/YII.Acer$mean)*100
#Colour.colour<-scale_colour_manual(values = c("black", "gray70"))
#Fill.colour<-scale_fill_manual(values = c("black", "gray70"))
FigureS4b_genotype<-YII_Treat_BW<- ggplot(data=YII.Acer, aes (Days, Percentaje2,
colour=factor(Treatment), fill=factor(Treatment), shape=factor(Treatment))) +
annotate("segment", x = 2, xend = 91, y = -35, yend = -35,
colour = "gray90", linetype=1)+
annotate("segment", x = 79, xend = 91, y = -34, yend = 10,
colour = "gray90", linetype=1)+
annotate("segment", x = 91, xend = 110, y = 10, yend = 10,
colour = "gray90", linetype=1)+
annotate("text", x = 45, y = -40, label = "Nutrients", size=3)+
annotate("text", x = 101, y = 13, label = "H", size=3) +
geom_smooth()+
#stat_summary(fun.y=mean, geom="point", size =2,
# position=position_dodge(width=1), alpha=0.5) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 5,
position = position_dodge(1) )+
#ggtitle("b)") +
#scale_shape_manual(values=c(21, 14),
# labels=c("A", "N and N+P"))+
ggthe_bw + #Fill.colour+ Colour.colour +
scale_y_continuous(limits = c(-45, 25),
breaks = seq(-40, 25, 10),
expand = c(0, 0),
name="Fv/Fm change respect to A (%)") +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 30),
expand = c(0, 0))+
theme(axis.title.y=element_text(size=12),
legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA)) +
facet_wrap(~Genotype)
FigureS4b_genotype
#ggsave(file="Outputs/S4_YII_Treat_Colo.svg", plot=FigureS4_genotype, width=5.0, height=5.5)- Figure S4: Treatment difference by genotype N and N+P separated
YII.Acer$Percentaje<- (YII.Acer$Difference/YII.Acer$mean)*100
Colour.colour<-scale_colour_manual(values = c("black", "gray70"))
Fill.colour<-scale_fill_manual(values = c("black", "gray70"))
YII.Acer$Nutrients2<factor(YII.Acer$Nutrients2, levels = c("Nutrients", "Ambient"))## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [25] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [49] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [73] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [97] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [121] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [145] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [169] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [193] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [217] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [241] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [265] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [289] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
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## [1609] NA NA NA NA NA NA NA NA NA NA NA NAFigureS4_genotype<-YII_Treat_BW<- ggplot(data=YII.Acer, aes (Days, Percentaje,
colour=factor(Nutrients2), fill=factor(Nutrients2), shape=factor(Nutrients2))) +
annotate("segment", x = 2, xend = 91, y = -35, yend = -35,
colour = "gray90", linetype=1)+
annotate("segment", x = 79, xend = 91, y = -34, yend = 10,
colour = "gray90", linetype=1)+
annotate("segment", x = 91, xend = 110, y = 10, yend = 10,
colour = "gray90", linetype=1)+
annotate("text", x = 45, y = -40, label = "Nutrients", size=3)+
annotate("text", x = 101, y = 13, label = "H", size=3) +
geom_smooth()+
#stat_summary(fun.y=mean, geom="point", size =2,
# position=position_dodge(width=1), alpha=0.5) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 5,
position = position_dodge(1) )+
#ggtitle("b)") +
scale_shape_manual(values=c(21, 14),
labels=c("A", "N and N+P"))+
ggthe_bw + Fill.colour+ Colour.colour +
scale_y_continuous(limits = c(-45, 25),
breaks = seq(-40, 25, 10),
expand = c(0, 0),
name="Fv/Fm change respect to A (%)") +
scale_x_continuous(name="Days in the experiment",
limits = c(-1,113),
breaks = seq(0, 113, 30),
expand = c(0, 0))+
theme(axis.title.y=element_text(size=12),
legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA)) +
facet_wrap(~Genotype)
FigureS4_genotype
#ggsave(file="Outputs/S4_YII_Treat_Colo.svg", plot=FigureS4_genotype, width=5.0, height=5.5)Subset timepoints
summary(YII.Acer$DaysF)## 1 8 14 21 28 49 65 71 76 84 89 92 96 99 103 106 110
## 120 120 120 120 120 120 119 116 115 90 89 82 70 66 62 54 37YII.Acer1<-subset(YII.Acer, DaysF=="1")
YII.Acer76<-subset(YII.Acer, DaysF=="76")
YII.Acer92<-subset(YII.Acer, DaysF=="92")
YII.Acer110<-subset(YII.Acer, DaysF=="110")
YII.AcerTimePoints<-rbind(YII.Acer1, YII.Acer76, YII.Acer92, YII.Acer110)
YII_Timepoints<- ggplot(YII.AcerTimePoints, aes (DaysF, YII, fill=Genotype, shape=Nutrients2)) +
#ggtitle("(a) Baseline")+
ggthe_bw +
scale_shape_manual(values=c(21, 14),
labels=c("A", "N and N+P"))+
stat_summary(fun.y=mean, geom="point", size =2, alpha=0.9) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2) +
scale_y_continuous(name=("Fv/Fm"),
limits = c(0.1, 0.65),
breaks = seq(0, 0.7, by=0.1))+
theme(legend.position="right",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))+
facet_grid(~Genotype)
YII_Timepoints
YII_Timepoints<- ggplot(YII.AcerTimePoints, aes (Genotype, YII, fill=Genotype, shape=Nutrients2)) +
#ggtitle("(a) Baseline")+
scale_shape_manual(values=c(21, 14),
labels=c("A", "N and N+P"))+
stat_summary(fun.y=mean, geom="point", size =2, alpha=1) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2) +
ggthe_bw +
scale_y_continuous(name=("Fv/Fm"),
limits = c(0.1, 0.65),
breaks = seq(0, 0.7, by=0.1))+
theme(legend.position="right",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))+
facet_grid(~DaysF)
YII_Timepoints
#ggsave(file="Outputs/S4_YII_Treat_Colo.svg", plot=YII_Timepoints, width=5.5, height=3.0)Supporting GLMs
Genotypic differences with subset
## Data subsets
summary(YII.Acer)## Genotype DaysF Sample Date Spp
## G_48:413 1 :120 Ac_288_T21: 2 Min. :2017-11-16 Ac:1620
## G_62:418 8 :120 Ac_101_T10: 1 1st Qu.:2017-12-06
## G_31:216 14 :120 Ac_101_T11: 1 Median :2018-01-19
## G_08:101 21 :120 Ac_101_T12: 1 Mean :2018-01-10
## G_07:320 28 :120 Ac_101_T13: 1 3rd Qu.:2018-02-12
## G_50:152 49 :120 Ac_101_T15: 1 Max. :2018-03-05
## (Other):900 (Other) :1613
## Fragment Treatment Replicate YII Days
## Ac_102 : 17 A :607 R1:858 Min. :0.1540 Min. : 1.0
## Ac_105 : 17 N :500 R2:762 1st Qu.:0.5640 1st Qu.: 21.0
## Ac_108 : 17 N+P:513 Median :0.5990 Median : 65.0
## Ac_116 : 17 Mean :0.5805 Mean : 56.6
## Ac_119 : 17 3rd Qu.:0.6242 3rd Qu.: 89.0
## Ac_122 : 17 Max. :0.6810 Max. :110.0
## (Other):1518
## Time_Point Phase TotalSH logSH D.Prp
## T10 :120 Baseline :120 Min. :0.0012 Min. :-2.9313 Min. :0
## T5 :120 Heat :371 1st Qu.:0.0485 1st Qu.:-1.3141 1st Qu.:0
## T6 :120 Nutrients:950 Median :0.1133 Median :-0.9459 Median :0
## T7 :120 Ramping :179 Mean :0.1581 Mean :-0.9831 Mean :0
## T8 :120 Recovery : 0 3rd Qu.:0.2259 3rd Qu.:-0.6461 3rd Qu.:0
## T9 :120 Max. :0.8947 Max. :-0.0483 Max. :0
## (Other):900 NA's :1160 NA's :1160
## Community InitialCommunity Nutrients Nutrients2
## A:1620 A:1620 Length:1620 Length:1620
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## mean sd Difference Difference2
## Min. :0.3500 Min. :0.00100 Min. :-0.340000 Min. :-0.340000
## 1st Qu.:0.5560 1st Qu.:0.00900 1st Qu.:-0.006000 1st Qu.:-0.006000
## Median :0.5840 Median :0.01200 Median : 0.008000 Median : 0.008000
## Mean :0.5744 Mean :0.01479 Mean : 0.006111 Mean : 0.006111
## 3rd Qu.:0.6070 3rd Qu.:0.01900 3rd Qu.: 0.028000 3rd Qu.: 0.028000
## Max. :0.6340 Max. :0.06900 Max. : 0.178000 Max. : 0.178000
##
## Percentaje2 Percentaje
## Min. :-61.2595 Min. :-61.2595
## 1st Qu.: -1.1124 1st Qu.: -1.1124
## Median : 1.3913 Median : 1.3913
## Mean : 0.9771 Mean : 0.9771
## 3rd Qu.: 4.8719 3rd Qu.: 4.8719
## Max. : 35.3877 Max. : 35.3877
## YII.0<-subset(YII.Acer, DaysF=="1") # Only baseline
YII.0<-droplevels(YII.0)
YII.Control<-subset(YII.Acer, Treatment=="A") # Only Ambient
YII.Control<-subset(YII.Control, Days<77)
YII.Control<-droplevels(YII.Control)
summary(YII.Control)## Genotype DaysF Sample Date Spp
## G_48:90 1 : 39 Ac_102_T10: 1 Min. :2017-11-16 Ac:351
## G_62:81 8 : 39 Ac_102_T11: 1 1st Qu.:2017-11-29
## G_31:45 14 : 39 Ac_102_T12: 1 Median :2017-12-13
## G_08:18 21 : 39 Ac_102_T13: 1 Mean :2017-12-22
## G_07:81 28 : 39 Ac_102_T5 : 1 3rd Qu.:2018-01-19
## G_50:36 49 : 39 Ac_102_T6 : 1 Max. :2018-01-30
## (Other):117 (Other) :345
## Fragment Treatment Replicate YII Days Time_Point
## Ac_102 : 9 A:351 R1:189 Min. :0.4420 Min. : 1 T10 : 39
## Ac_105 : 9 R2:162 1st Qu.:0.5780 1st Qu.:14 T11 : 39
## Ac_108 : 9 Median :0.6000 Median :28 T12 : 39
## Ac_116 : 9 Mean :0.5934 Mean :37 T13 : 39
## Ac_119 : 9 3rd Qu.:0.6170 3rd Qu.:65 T5 : 39
## Ac_122 : 9 Max. :0.6570 Max. :76 T6 : 39
## (Other):297 (Other):117
## Phase TotalSH logSH D.Prp Community
## Baseline : 39 Min. :0.00117 Min. :-2.9313 Min. :0 A:351
## Nutrients:312 1st Qu.:0.08806 1st Qu.:-1.0553 1st Qu.:0
## Median :0.17393 Median :-0.7596 Median :0
## Mean :0.20915 Mean :-0.8027 Mean :0
## 3rd Qu.:0.31554 3rd Qu.:-0.5010 3rd Qu.:0
## Max. :0.71229 Max. :-0.1473 Max. :0
## NA's :236 NA's :236
## InitialCommunity Nutrients Nutrients2 mean
## A:351 Length:351 Length:351 Min. :0.5030
## Class :character Class :character 1st Qu.:0.5760
## Mode :character Mode :character Median :0.5960
## Mean :0.5927
## 3rd Qu.:0.6180
## Max. :0.6340
##
## sd Difference Difference2 Percentaje2
## Min. :0.00100 Min. :-0.1020000 Min. :-0.1020000 Min. :-18.7500
## 1st Qu.:0.00900 1st Qu.:-0.0080000 1st Qu.:-0.0080000 1st Qu.: -1.3356
## Median :0.01200 Median : 0.0010000 Median : 0.0010000 Median : 0.1613
## Mean :0.01465 Mean : 0.0007151 Mean : 0.0007151 Mean : 0.1177
## 3rd Qu.:0.01600 3rd Qu.: 0.0100000 3rd Qu.: 0.0100000 3rd Qu.: 1.6779
## Max. :0.06900 Max. : 0.0640000 Max. : 0.0640000 Max. : 11.7647
##
## Percentaje
## Min. :-18.7500
## 1st Qu.: -1.3356
## Median : 0.1613
## Mean : 0.1177
## 3rd Qu.: 1.6779
## Max. : 11.7647
## YII.FinalAC<-subset(YII.Control, DaysF=="76")
YII.FinalAC<-droplevels(YII.FinalAC)
#BW.nutrients<-subset(BW.Tall, Days>-10) # Remove baseline
#BW.nutrients<-subset(BW.nutrients, Days<76)
YII.nutrients<-subset(YII.Acer, Days<76) # with baseline
YII.nutrients<-droplevels(YII.nutrients)
summary(YII.nutrients)## Genotype DaysF Sample Date Spp
## G_48:224 1 :120 Ac_101_T10: 1 Min. :2017-11-16 Ac:955
## G_62:232 8 :120 Ac_101_T11: 1 1st Qu.:2017-11-23
## G_31:128 14 :120 Ac_101_T12: 1 Median :2017-12-06
## G_08: 64 21 :120 Ac_101_T5 : 1 Mean :2017-12-16
## G_07:207 28 :120 Ac_101_T6 : 1 3rd Qu.:2018-01-03
## G_50:100 49 :120 Ac_101_T7 : 1 Max. :2018-01-25
## (Other):235 (Other) :949
## Fragment Treatment Replicate YII Days
## Ac_101 : 8 A :312 R1:502 Min. :0.224 Min. : 1.00
## Ac_102 : 8 N :325 R2:453 1st Qu.:0.599 1st Qu.: 8.00
## Ac_104 : 8 N+P:318 Median :0.619 Median :21.00
## Ac_105 : 8 Mean :0.614 Mean :31.93
## Ac_106 : 8 3rd Qu.:0.634 3rd Qu.:49.00
## Ac_107 : 8 Max. :0.670 Max. :71.00
## (Other):907
## Time_Point Phase TotalSH logSH D.Prp
## T10 :120 Baseline :120 Min. :0.0042 Min. :-2.3816 Min. :0
## T5 :120 Nutrients:835 1st Qu.:0.0449 1st Qu.:-1.3475 1st Qu.:0
## T6 :120 Median :0.1028 Median :-0.9881 Median :0
## T7 :120 Mean :0.1639 Mean :-0.9907 Mean :0
## T8 :120 3rd Qu.:0.2397 3rd Qu.:-0.6203 3rd Qu.:0
## T9 :120 Max. :0.8947 Max. :-0.0483 Max. :0
## (Other):235 NA's :722 NA's :722
## Community InitialCommunity Nutrients Nutrients2
## A:955 A:955 Length:955 Length:955
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## mean sd Difference Difference2
## Min. :0.5170 Min. :0.00100 Min. :-0.3400 Min. :-0.3400
## 1st Qu.:0.5890 1st Qu.:0.00900 1st Qu.:-0.0020 1st Qu.:-0.0020
## Median :0.5990 Median :0.01200 Median : 0.0110 Median : 0.0110
## Mean :0.5969 Mean :0.01448 Mean : 0.0171 Mean : 0.0171
## 3rd Qu.:0.6200 3rd Qu.:0.01500 3rd Qu.: 0.0335 3rd Qu.: 0.0335
## Max. :0.6340 Max. :0.06900 Max. : 0.1180 Max. : 0.1180
##
## Percentaje2 Percentaje
## Min. :-60.2837 Min. :-60.2837
## 1st Qu.: -0.3223 1st Qu.: -0.3223
## Median : 1.9324 Median : 1.9324
## Mean : 2.9629 Mean : 2.9629
## 3rd Qu.: 5.5986 3rd Qu.: 5.5986
## Max. : 22.8240 Max. : 22.8240
## Baseline
YII_Init<- ggplot(YII.0, aes (Genotype, YII, colour=Genotype)) +
ggtitle("(a) Baseline")+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2) +
stat_summary(fun.y=mean, geom="point", size =3, alpha=0.5) + ggthe_bw +
scale_y_continuous(name=("Fv/Fm"),
limits = c(0.46, 0.62),
breaks = seq(0.46, 0.62, by=0.02))+
theme(legend.position="none",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))
YII_Init
# ANOVA
LM_bl <- lm(YII ~ Genotype, data= YII.0)
anova(LM_bl)# Pairwise comparisons
BW_bl.emmc<-emmeans(LM_bl, ~ Genotype)
contrast(BW_bl.emmc, "tukey")## contrast estimate SE df t.ratio p.value
## G_48 - G_62 0.009179 0.00330 114 2.777 0.0685
## G_48 - G_31 0.008679 0.00391 114 2.220 0.2367
## G_48 - G_08 0.024679 0.00500 114 4.935 <.0001
## G_48 - G_07 0.024255 0.00340 114 7.140 <.0001
## G_48 - G_50 0.021255 0.00419 114 5.077 <.0001
## G_62 - G_31 -0.000500 0.00388 114 -0.129 1.0000
## G_62 - G_08 0.015500 0.00498 114 3.112 0.0278
## G_62 - G_07 0.015077 0.00337 114 4.475 0.0003
## G_62 - G_50 0.012077 0.00416 114 2.901 0.0497
## G_31 - G_08 0.016000 0.00540 114 2.962 0.0421
## G_31 - G_07 0.015577 0.00396 114 3.930 0.0020
## G_31 - G_50 0.012577 0.00466 114 2.700 0.0830
## G_08 - G_07 -0.000423 0.00504 114 -0.084 1.0000
## G_08 - G_50 -0.003423 0.00561 114 -0.611 0.9901
## G_07 - G_50 -0.003000 0.00424 114 -0.708 0.9806
##
## P value adjustment: tukey method for comparing a family of 6 estimates#Tukey groups
BW_bl_groups<-cld(BW_bl.emmc)
BW_bl_groups #write.csv(BW_bl_groups, "Outputs/BW_baseline_groups.csv", row.names = F)
# N fragments
N0.fragments<-YII.0 %>%
group_by(Genotype) %>% count(Genotype)
N0.fragmentsAmbient final day
YII_76<- ggplot(YII.FinalAC, aes (Genotype, YII, colour=Genotype)) +
ggtitle("(b) Ambient (day 76)")+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2) +
stat_summary(fun.y=mean, geom="point", size =3, alpha=0.5) + ggthe_bw +
scale_y_continuous(name=("Fv/Fm"),
limits = c(0.46, 0.62),
breaks = seq(0.46, 0.62, by=0.02))+
theme(legend.position="none",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))
YII_76
summary(YII.FinalAC)## Genotype DaysF Sample Date Spp Fragment
## G_48:10 76:39 Ac_102_T13: 1 Min. :2018-01-30 Ac:39 Ac_102 : 1
## G_62: 9 Ac_105_T13: 1 1st Qu.:2018-01-30 Ac_105 : 1
## G_31: 5 Ac_108_T13: 1 Median :2018-01-30 Ac_108 : 1
## G_08: 2 Ac_116_T13: 1 Mean :2018-01-30 Ac_116 : 1
## G_07: 9 Ac_119_T13: 1 3rd Qu.:2018-01-30 Ac_119 : 1
## G_50: 4 Ac_122_T13: 1 Max. :2018-01-30 Ac_122 : 1
## (Other) :33 (Other):33
## Treatment Replicate YII Days Time_Point Phase
## A:39 R1:21 Min. :0.4670 Min. :76 T13:39 Nutrients:39
## R2:18 1st Qu.:0.5545 1st Qu.:76
## Median :0.5770 Median :76
## Mean :0.5621 Mean :76
## 3rd Qu.:0.5880 3rd Qu.:76
## Max. :0.6030 Max. :76
##
## TotalSH logSH D.Prp Community InitialCommunity
## Min. :0.001172 Min. :-2.9313 Min. :0 A:39 A:39
## 1st Qu.:0.161137 1st Qu.:-0.7929 1st Qu.:0
## Median :0.257041 Median :-0.5902 Median :0
## Mean :0.251061 Mean :-0.7231 Mean :0
## 3rd Qu.:0.358505 3rd Qu.:-0.4455 3rd Qu.:0
## Max. :0.465400 Max. :-0.3322 Max. :0
## NA's :1 NA's :1
## Nutrients Nutrients2 mean sd
## Length:39 Length:39 Min. :0.5030 Min. :0.0040
## Class :character Class :character 1st Qu.:0.5700 1st Qu.:0.0110
## Mode :character Mode :character Median :0.5760 Median :0.0150
## Mean :0.5619 Mean :0.0149
## 3rd Qu.:0.5830 3rd Qu.:0.0160
## Max. :0.5870 Max. :0.0240
##
## Difference Difference2 Percentaje2
## Min. :-0.0360000 Min. :-0.0360000 Min. :-7.15706
## 1st Qu.:-0.0090000 1st Qu.:-0.0090000 1st Qu.:-1.53322
## Median :-0.0010000 Median :-0.0010000 Median :-0.17036
## Mean : 0.0001538 Mean : 0.0001538 Mean : 0.02851
## 3rd Qu.: 0.0135000 3rd Qu.: 0.0135000 3rd Qu.: 2.31513
## Max. : 0.0280000 Max. : 0.0280000 Max. : 5.56660
##
## Percentaje
## Min. :-7.15706
## 1st Qu.:-1.53322
## Median :-0.17036
## Mean : 0.02851
## 3rd Qu.: 2.31513
## Max. : 5.56660
## # Model
LM_A_C.75 <- lmer(YII ~ Genotype + (1|Replicate), data= YII.FinalAC)
isSingular(LM_A_C.75)## [1] TRUE anova(LM_A_C.75) # ranova(LM_A_C.75) # Replicate is not significant# Pairwise comparisons
YII_A_C75.emmc<-emmeans(LM_A_C.75, ~Genotype)
#contrast(BW_A_C.emmc, "tukey")
#Tukey groups
YII_A_C75_groups<-cld(YII_A_C75.emmc)
YII_A_C75_groups #write.csv(YII_A_C75_groups, "Outputs/YII_A_C75_groups.csv", row.names = F)
# N fragments
N.fragments_A_C75<-YII.FinalAC %>%
count(Genotype)
N.fragments_A_C75Captivityeffect<-grid.arrange(YII_Init,YII_76, nrow=1)
#ggsave(file="Outputs/S_Genotypes.svg", plot=Captivityeffect, width=6, height=3.5)- Ambient and Control evolution
summary(YII.Control)## Genotype DaysF Sample Date Spp
## G_48:90 1 : 39 Ac_102_T10: 1 Min. :2017-11-16 Ac:351
## G_62:81 8 : 39 Ac_102_T11: 1 1st Qu.:2017-11-29
## G_31:45 14 : 39 Ac_102_T12: 1 Median :2017-12-13
## G_08:18 21 : 39 Ac_102_T13: 1 Mean :2017-12-22
## G_07:81 28 : 39 Ac_102_T5 : 1 3rd Qu.:2018-01-19
## G_50:36 49 : 39 Ac_102_T6 : 1 Max. :2018-01-30
## (Other):117 (Other) :345
## Fragment Treatment Replicate YII Days Time_Point
## Ac_102 : 9 A:351 R1:189 Min. :0.4420 Min. : 1 T10 : 39
## Ac_105 : 9 R2:162 1st Qu.:0.5780 1st Qu.:14 T11 : 39
## Ac_108 : 9 Median :0.6000 Median :28 T12 : 39
## Ac_116 : 9 Mean :0.5934 Mean :37 T13 : 39
## Ac_119 : 9 3rd Qu.:0.6170 3rd Qu.:65 T5 : 39
## Ac_122 : 9 Max. :0.6570 Max. :76 T6 : 39
## (Other):297 (Other):117
## Phase TotalSH logSH D.Prp Community
## Baseline : 39 Min. :0.00117 Min. :-2.9313 Min. :0 A:351
## Nutrients:312 1st Qu.:0.08806 1st Qu.:-1.0553 1st Qu.:0
## Median :0.17393 Median :-0.7596 Median :0
## Mean :0.20915 Mean :-0.8027 Mean :0
## 3rd Qu.:0.31554 3rd Qu.:-0.5010 3rd Qu.:0
## Max. :0.71229 Max. :-0.1473 Max. :0
## NA's :236 NA's :236
## InitialCommunity Nutrients Nutrients2 mean
## A:351 Length:351 Length:351 Min. :0.5030
## Class :character Class :character 1st Qu.:0.5760
## Mode :character Mode :character Median :0.5960
## Mean :0.5927
## 3rd Qu.:0.6180
## Max. :0.6340
##
## sd Difference Difference2 Percentaje2
## Min. :0.00100 Min. :-0.1020000 Min. :-0.1020000 Min. :-18.7500
## 1st Qu.:0.00900 1st Qu.:-0.0080000 1st Qu.:-0.0080000 1st Qu.: -1.3356
## Median :0.01200 Median : 0.0010000 Median : 0.0010000 Median : 0.1613
## Mean :0.01465 Mean : 0.0007151 Mean : 0.0007151 Mean : 0.1177
## 3rd Qu.:0.01600 3rd Qu.: 0.0100000 3rd Qu.: 0.0100000 3rd Qu.: 1.6779
## Max. :0.06900 Max. : 0.0640000 Max. : 0.0640000 Max. : 11.7647
##
## Percentaje
## Min. :-18.7500
## 1st Qu.: -1.3356
## Median : 0.1613
## Mean : 0.1177
## 3rd Qu.: 1.6779
## Max. : 11.7647
## # Model
LM_A_C <- lmer(YII ~ Genotype * DaysF +
(1|Fragment)+ (1|Replicate), data=YII.Control)
anova(LM_A_C) # Day alone is not significant, but Day:Genet is ranova(LM_A_C) # Replicate is not significant step(LM_A_C)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 57 744.87 -1375.7
## (1 | Fragment) 0 56 731.71 -1351.4 26.3292 1 2.879e-07 ***
## (1 | Replicate) 0 56 742.13 -1372.3 5.4873 1 0.01916 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Genotype:DaysF 0 0.029929 0.00074823 40 264 3.0299 5.744e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## YII ~ Genotype * DaysF + (1 | Fragment) + (1 | Replicate)# Pairwise comparisons
BW_A_C.emmc<-emmeans(LM_A_C, ~Genotype * DaysF)
#contrast(BW_A_C.emmc, "tukey")
#Tukey groups
BW_A_C_groups<-cld(BW_A_C.emmc)
BW_A_C_groups<-BW_A_C_groups[order(BW_A_C_groups$Genotype,BW_A_C_groups$Day),]
BW_A_C_groups #write.csv(BW_A_C_groups, "Outputs/BW_A_C_groups.csv", row.names = F)
# N fragments
N.fragments_A_C<-YII.Control %>%
group_by(Genotype) %>% count(DaysF)
N.fragments_A_C# 1. Model
LM_A_C <- lmer(YII ~ Genotype * Days +
(1|Fragment)+ (1|Replicate), data=YII.Control)
anova(LM_A_C) # Day alone is not significant, but Day:Genet is ranova(LM_A_C) # Replicate is not significant step(LM_A_C)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 15 840.16 -1650.3
## (1 | Fragment) 0 14 830.23 -1632.5 19.8590 1 8.337e-06 ***
## (1 | Replicate) 0 14 837.41 -1646.8 5.4873 1 0.01916 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Genotype:Days 0 0.014761 0.0029521 5 306 10.291 3.921e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## YII ~ Genotype * Days + (1 | Fragment) + (1 | Replicate)# 2. Predict values:
pred_ambient <- predict(LM_A_C,re.form = NA)
#3. Bootstrap CI:
ambient.boot1 <- bootMer(LM_A_C, predict, nsim = 1000, re.form = NULL) # include random effects, reduce CI lot!
std.err <- apply(ambient.boot1$t, 2, sd)
CI.lo_1 <- pred_ambient - std.err*1.96
CI.hi_1 <- pred_ambient + std.err*1.96
#Plot
Model_ambients_plot<- ggplot(
YII.Control, aes(x = Days, y = YII, colour =Genotype)) +
geom_line(aes(y = pred_ambient),size=2) +
#geom_point(aes(fill=factor(Treatment)),
# shape = 21, colour = "black", size = 2, stroke = 0.3, alpha=0.3) +
geom_ribbon(aes(ymin = CI.lo_1, ymax = CI.hi_1),
size=2, alpha = 0.1, linetype = 0) +
#scale_color_manual(values=my_colours) +
#scale_fill_manual(values=my_colours) +
scale_y_continuous(name=expression(~italic("YII")),
limits = c(0.45,0.65),
breaks = seq(0.45, 0.65, by=0.02), expand = c(0,0))+
scale_x_continuous("Days in the experiment", limits = c(-0, 78),
breaks = seq(-30, 76, by=15), expand = c(0,0))+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 1,
position = position_dodge(1) )+
stat_summary(fun.y=mean, geom="line", position = position_dodge(1),
linetype=1, alpha=0.5) + ggthe_bw
Model_ambients_plot
Arnold, Jeffrey B. 2019. Ggthemes: Extra Themes, Scales and Geoms for ’Ggplot2’. https://CRAN.R-project.org/package=ggthemes.
Bates, Douglas, and Martin Maechler. 2019. Matrix: Sparse and Dense Matrix Classes and Methods. https://CRAN.R-project.org/package=Matrix.
Bates, Douglas, Martin Maechler, Ben Bolker, and Steven Walker. 2019. Lme4: Linear Mixed-Effects Models Using ’Eigen’ and S4. https://CRAN.R-project.org/package=lme4.
Fox, John, Sanford Weisberg, and Brad Price. 2018. CarData: Companion to Applied Regression Data Sets. https://CRAN.R-project.org/package=carData.
Fox, John, Sanford Weisberg, Brad Price, Michael Friendly, and Jangman Hong. 2019. Effects: Effect Displays for Linear, Generalized Linear, and Other Models. https://CRAN.R-project.org/package=effects.
Genz, Alan, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, and Torsten Hothorn. 2019. Mvtnorm: Multivariate Normal and T Distributions. https://CRAN.R-project.org/package=mvtnorm.
Gohel, David, Hadley Wickham, Lionel Henry, and Jeroen Ooms. 2019. Gdtools: Utilities for Graphical Rendering. https://CRAN.R-project.org/package=gdtools.
Henry, Lionel, and Hadley Wickham. 2019. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr.
Hothorn, Torsten. 2019. TH.data: TH’s Data Archive. https://CRAN.R-project.org/package=TH.data.
Hothorn, Torsten, Frank Bretz, and Peter Westfall. 2019. Multcomp: Simultaneous Inference in General Parametric Models. https://CRAN.R-project.org/package=multcomp.
Kuznetsova, Alexandra, Per Bruun Brockhoff, and Rune Haubo Bojesen Christensen. 2019. LmerTest: Tests in Linear Mixed Effects Models. https://CRAN.R-project.org/package=lmerTest.
Lenth, Russell. 2019. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans.
Müller, Kirill, and Hadley Wickham. 2019. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Ripley, Brian. 2019. MASS: Support Functions and Datasets for Venables and Ripley’s Mass. https://CRAN.R-project.org/package=MASS.
Therneau, Terry M. 2019. Survival: Survival Analysis. https://CRAN.R-project.org/package=survival.
Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.
———. 2019a. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.
———. 2019b. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, and Hiroaki Yutani. 2019. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Lionel Henry. 2020. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.