Stomatal Conductance

Stomatal conductance analysis

1. Prepare data

yp_data <- read.csv("SDlluteus_rev.csv")
yp_data$eco <- as.factor(yp_data$eco)
yp_data$temp <-as.factor(yp_data$temp)
levels(yp_data$temp) <- c("20°C","27.5°C","35°C")
yp_data$treatment <- as.factor(yp_data$treatment)

2. Visualize

Stomatal conductance mean +/- SE

library(Rmisc)

Loading required package: lattice

Loading required package: plyr

library(plyr)
library(lattice)
# compute mean and standard error of the mean by subgroup
summary_stat <- summarySE(yp_data,
                          measurevar = "stomcond",
                          groupvars = c("temp", "eco")
)
library(ggplot2)
ggplot(
  data = summary_stat,
  aes(x = temp, y = stomcond, colour = eco)
) +
  geom_errorbar(aes(ymin = stomcond - se, ymax = stomcond + se), # add error bars
                width = 0.1 # width of error bars
  ) +
  geom_line(aes(group=eco)) +
  geom_point() +
  labs(y = "Stom Cond")+
  scale_color_manual(values=c("deepskyblue3", "darkgoldenrod"))

Boxplots

library(ggplot2)
ggplot(data=yp_data, aes(x=temp,y=stomcond,fill=eco))+
  geom_boxplot(outlier.shape = NA)+
  geom_point(position=position_jitterdodge(),size=3,alpha=0.5)+
  theme(
    plot.tag = element_text(size = 16),
    panel.border = element_rect(colour = "black", fill=NA, size=1),
    panel.background = element_rect(fill = "white"),
    axis.title.x = element_blank(), 
    axis.text.x = element_text(colour = "black", size=12),
    axis.title.y = element_text(colour = "black", size=14),
    axis.text.y = element_text(colour = "black", size=12)
  )+
  labs(y=expression("Stom Cond"))+
  scale_fill_manual(values=c("deepskyblue3", "darkgoldenrod"))

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

3. “Interaction” v. “No interaction” model

   • no interaction: stomcond ~ temp + eco

   • interaction: stomcond ~ temp * eco

-> Identify best model using AIC

nointeract <- aov(stomcond ~ temp + eco, data = yp_data)
interaction <- aov(stomcond ~ temp * eco, data = yp_data)
#find best model (best listed first via aictab)
library(AICcmodavg)
model.set <- list(nointeract, interaction)
model.names <- c("no interaction", "interaction")
aictab(model.set, modnames = model.names)

Model selection based on AICc:

               K   AICc Delta_AICc AICcWt Cum.Wt    LL
no interaction 5 -28.53       0.00   0.97   0.97 20.93
interaction    7 -21.57       6.96   0.03   1.00 21.28

-> “No interaction” model is the best fit.

4. Run ANOVA model

nointeract <- aov(stomcond ~ temp + eco, data = yp_data)
summary(nointeract)

            Df  Sum Sq Mean Sq F value Pr(>F)   
temp         2 0.06152 0.03076   2.505 0.1069   
eco          1 0.14661 0.14661  11.941 0.0025 **
Residuals   20 0.24555 0.01228                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• The ecotype has a statistically significant effect on stomatal conductance (p=0.0025).