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7.Statistical analyses.R
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# Data statistical analyses & figure plots
# setwd('E:/Shangri-La_2024/network')
library(readxl)
library(tidyverse)
library(vegan)
library(ggplot2)
library(glmmTMB)
library(MuMIn)
library(DHARMa)
library(effects)
library(gridExtra)
library(cowplot)
library(car)
rm(list=ls())
######################
# Fig. 3 Plant_AMF.top10
data <- read_excel("source_data.xlsx", sheet="Plant_AMF.top10")
head(data)
data$Plant_Species <- as.factor(data$Plant_Species)
data$Plant_Species_abbreviation <- as.factor(data$Plant_Species_abbreviation)
data$Plot <- as.factor(data$Plot)
data$Type <- as.factor(data$Type)
head(data)
#####
data.plant <- subset(data, Type=="Plant_relative_abundance")
data.plant.Euadjacent <- subset(data.plant, Plot=="Eu-adjacent")
data.plant.Eudistant <- subset(data.plant, Plot=="Eu-distant")
p1 <- ggplot(data.plant.Euadjacent,aes(Plant_Species_abbreviation,Value))+
geom_bar(aes(fill=Plot),stat = "identity",width=0.8)+
facet_grid(Plot~.)+
theme_classic()+
scale_color_manual(values=c("gray50"))+
scale_fill_manual(values=c("gray50"))+
scale_y_continuous(labels=scales::percent,limits=c(0,0.08))+
ylab("Plant relative abundance")+# 设置Y轴标题
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.text.x=element_blank(),#修改x轴刻度标签文本
# axis.text.x=element_text(angle=30, size=6.5, hjust=0.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_blank(),#修改图例标题大小
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.95,0.95),#修改图例位置
legend.margin=margin(0,0,0,0),#修改图例位置
axis.line.x=element_line(),
strip.text = element_blank(), strip.background = element_blank()) # 取消分面标签)
p1
ggsave(filename="Fig3a_plant_top10_Euadjacent.png",plot=p1,width=89*1.3,height=59*1.3/2,units="mm",dpi=900)
ggsave(filename="Fig3a_plant_top10_Euadjacent.pdf",plot=p1,width=89*1.3,height=59*1.3/2,units="mm")
p2 <- ggplot(data.plant.Eudistant,aes(Plant_Species_abbreviation,Value))+
geom_bar(aes(fill=Plot),stat = "identity",width=0.8)+
facet_grid(Plot~.)+
theme_classic()+
scale_color_manual(values=c("gray50"))+
scale_fill_manual(values=c("gray50"))+
scale_y_continuous(labels=scales::percent)+
ylab("Plant relative abundance")+# 设置Y轴标题
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.text.x=element_text(angle=30, size=6.5, hjust=0.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_blank(),#修改图例标题大小
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.95,0.95),#修改图例位置
legend.margin=margin(0,0,0,0),#修改图例位置
axis.line.x=element_line(),
strip.text = element_blank(), strip.background = element_blank()) # 取消分面标签)
p2
ggsave(filename="Fig3b_plant_top10_Eudistant.png",plot=p2,width=89*1.3,height=59*1.3/2,units="mm",dpi=900)
ggsave(filename="Fig3b_plant_top10_Eudistant.pdf",plot=p2,width=89*1.3,height=59*1.3/2,units="mm")
#####
data.fungi <- subset(data, Type=="AMF_richness")
data.fungi$AMF_richness_Estimator <- as.numeric(data.fungi$AMF_richness_Estimator)
data.fungi.Euadjacent <- subset(data.fungi, Plot=="Eu-adjacent")
data.fungi.Eudistant <- subset(data.fungi, Plot=="Eu-distant")
p3 <- ggplot(data.fungi.Euadjacent, aes(x = Plant_Species_abbreviation, y = Value)) +
geom_bar(aes(fill=Plot),stat = "identity", position = position_dodge(width = 0.8)) +
geom_errorbar(aes(y=Value,ymin=Value*0.95,ymax=AMF_richness_Estimator),width=0.8,linewidth=0.4,color="gray50",alpha=0.8)+
geom_text(aes(label=paste0("n=",Sample_count)),size=1.5,vjust=0)+
facet_grid(Plot ~ .) +
theme_classic()+
scale_color_manual(values=c("gray50"))+
scale_fill_manual(values=c("gray50"))+
ylab("AMF richness")+# 设置Y轴标题
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.text.x=element_blank(),#修改x轴刻度标签文本
# axis.text.x=element_text(angle=30, size=6.5, hjust=0.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_blank(),#修改图例标题大小
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.95,0.95),#修改图例位置
legend.margin=margin(0,0,0,0),#修改图例位置
axis.line.x=element_line(),
strip.text = element_blank(), strip.background = element_blank()) # 取消分面标签)
p3
ggsave(filename="Fig3c_AMF_top10_Euadjacent.png",plot=p3,width=89*1.3,height=59*1.3/2,units="mm",dpi=900)
ggsave(filename="Fig3c_AMF_top10_Euadjacent.pdf",plot=p3,width=89*1.3,height=59*1.3/2,units="mm")
p4 <- ggplot(data.fungi.Eudistant, aes(x = Plant_Species_abbreviation, y = Value)) +
geom_bar(aes(fill=Plot),stat = "identity", position = position_dodge(width = 0.8)) +
geom_errorbar(aes(y=Value,ymin=Value*0.95,ymax=AMF_richness_Estimator),width=0.8,linewidth=0.4,color="gray50",alpha=0.8)+
geom_text(aes(label=paste0("n=",Sample_count)),size=1.5,vjust=0)+
facet_grid(Plot ~ .) +
theme_classic()+
scale_color_manual(values=c("gray50"))+
scale_fill_manual(values=c("gray50"))+
ylab("AMF richness")+# 设置Y轴标题
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.text.x=element_text(angle=30, size=6.5, hjust=0.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_blank(),#修改图例标题大小
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.95,0.95),#修改图例位置
legend.margin=margin(0,0,0,0),#修改图例位置
axis.line.x=element_line(),
strip.text = element_blank(), strip.background = element_blank()) # 取消分面标签)
p4
ggsave(filename="Fig3d_AMF_top10_Eudistant.png",plot=p4,width=89*1.3,height=59*1.3/2,units="mm",dpi=900)
ggsave(filename="Fig3d_AMF_top10_Eudistant.pdf",plot=p4,width=89*1.3,height=59*1.3/2,units="mm")
?plot_grid
p_all <- plot_grid(p1,p2,p3,p4,labels=c("a","","b",""),label_size=10,label_x=0,label_y=1,ncol=1,nrow=4)
p_all
ggsave(filename="Fig3.png",plot=p_all,width=89*1.2,height=59*2.4,units="mm",dpi=900)
ggsave(filename="Fig3.pdf",plot=p_all,width=89*1.2,height=59*2.4,units="mm")
################################################################################
# Fig. 4 Species_richness_interactions
rm(list=ls())
richness <- read_excel("source_data.xlsx", sheet="Species_richness_interactions")
head(richness)
richness$Site <- as.factor(richness$Site)
richness$Plot <- as.factor(richness$Plot)
richness$Plot <- as.factor(richness$Plot)
summary(richness)
# 如果Shapiro-Wilk检验的p值小于0.05，说明数据不符合正态分布。以下结果大于0.05，符合正态分布。
shapiro.test(richness$Plant_richness)
# Levene检验的p值如果大于0.05，说明各组之间方差齐性，可以进行方差分析，否则变换数据或选择非参数检验。
leveneTest(Plant_richness~Plot, data = richness)
# 方差分析
anova_result <- aov(Plant_richness~Plot, data = richness)
summary(anova_result)
# 非参数检验（Kruskal-Wallis检验）
kruskal_test <- kruskal.test(Plant_richness~Plot, data = richness)
# 输出检验结果
kruskal_test
# Plant_richness 比较Plot间植物richness是在统计上否有显著性差异
{
Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
Eudistant.plot <- subset(richness,Plot=="Eu-distant")
Euadjacent.plot$PlotID==Eudistant.plot$PlotID
x <- Euadjacent.plot$Plant_richness
y <- Eudistant.plot$Plant_richness
t.test(x,y,paired=T)
mean(x);mean(y)
sd(x);sd(y)
var(x);var(y)
shapiro.test(x)
shapiro.test(y)
}
# library(lme4)
# mod1 <- glm(Plant_richness ~ Plot, family=poisson, data=richness)
# mod2 <- glmer(Plant_richness ~ Plot + (1|Site), family=poisson, data=richness)
# (msAICc <- model.sel(mod1,mod2))
# summary(mod1)
# performance::r2(mod1)
# allEffects(mod1)
# plot(allEffects(mod1))
modlmer1 <- glmmTMB(Plant_richness ~ Plot, family=poisson, data=richness)
modlmer2 <- glmmTMB(Plant_richness ~ Plot + (1|Site), family=poisson, data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
# modlmer3 <- glmmTMB(Plant_richness ~ Plot + (1|Site), family=nbinom2, data=richness)
# modlmer4 <- glmmTMB(Plant_richness ~ Plot + (1|Site), family=nbinom1, data=richness)
# (msAICc <- model.sel(modlmer1,modlmer2,modlmer3,modlmer4))
plot(simulateResiduals(modlmer1))
summary(modlmer1)
performance::r2(modlmer1)
allEffects(modlmer1)
plot(allEffects(modlmer1))
Anova(modlmer1,type=2)
Anova(modlmer2,type=2)
##
modlmer1 <- glmmTMB(log(Plant_richness) ~ Plot, data=richness)
modlmer2 <- glmmTMB(log(Plant_richness) ~ Plot + (1|Site), data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
plot(simulateResiduals(modlmer2))
summary(modlmer2)
performance::r2(modlmer2)
allEffects(modlmer2)
plot(allEffects(modlmer2))
p1 <- ggplot(richness,aes(x=Plot,y=Plant_richness,color=Plot))+
geom_boxplot(width=0.15,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.15,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0.25,jitter.width=0.45,dodge.width=0.5),alpha=0.4,show.legend=T)+
# geom_point(aes(x=Plot,y=mean(Plant_richness)),shape=16,size=0.8,color="black",position=position_dodge(0.5),alpha=1)+
# geom_errorbar(aes(x=Plot,y=mean(Plant_richness),ymin=mean(Plant_richness)-sd(Plant_richness), ymax=mean(Plant_richness)+sd(Plant_richness)),width=0,linewidth=0.4,color="black",position=position_dodge(0.5),alpha=1)+
# scale_color_manual(values=c("#bc40bf","#bc40bf","black"))+
# scale_fill_manual(values=c("#bc40bf","#bc40bf"))+
scale_color_manual(values=c("gray40","gray40","gray40"))+
scale_fill_manual(values=c("gray40","gray40"))+
theme_classic()+
ylab("Plant richness")+
# scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
# scale_y_continuous(labels = function(x) sprintf("%03d", x))+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
axis.text.x=element_blank(),#修改x轴刻度标签文本
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_text(size=8),#修改图例标题大小
legend.text=element_text(size=6.5),#修改图例文本大小
legend.position="none")+#修改图例位置
# annotate("text",x=1.7,y=max(richness$Plant_richness),hjust=1,vjust=1,label=paste0("p > ",0.05),size=2)+
annotate("text",x=1.5,y=(max(richness$Plant_richness)+(max(richness$Plant_richness)-min(richness$Plant_richness))*0.1),hjust=1,vjust=1,label=paste0("NS"),size=2)
p1
ggsave(filename="Fig4a_plant_richness_glmm.png",plot=p1,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig4a_plant_richness_glmm.pdf",plot=p1,width=89/2,height=59/2,units="mm")
#####
# Biomass of Plant
# Aboveground_biomass
{
Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
Eudistant.plot <- subset(richness,Plot=="Eu-distant")
Euadjacent.plot$PlotID==Eudistant.plot$PlotID
x <- Euadjacent.plot$`Aboveground_biomass (kg/m2)`
y <- Eudistant.plot$`Aboveground_biomass (kg/m2)`
t.test(x,y,paired=T)
mean(x);mean(y)
sd(x);sd(y)
var(x);var(y)
shapiro.test(x)
shapiro.test(y)
}
leveneTest(`Aboveground_biomass (kg/m2)`~Plot, data = richness)
# mod1 <- glm(`Aboveground_biomass (kg/m2)` ~ Plot, family=Gamma(link="log"), data=richness)
# mod2 <- glmer(`Aboveground_biomass (kg/m2)` ~ Plot + (1|Site), family=Gamma(link="log"), data=richness)
# (msAICc <- model.sel(mod1,mod2))
# summary(mod2)
# performance::r2(mod2)
# allEffects(mod2)
# plot(allEffects(mod2))
modlmer1 <- glmmTMB(`Aboveground_biomass (kg/m2)`~Plot,family=Gamma(link="log"),data=richness)
modlmer2 <- glmmTMB(`Aboveground_biomass (kg/m2)`~Plot+(1|Site),family=Gamma(link="log"),data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
modlmer3 <- glmmTMB(`Aboveground_biomass (kg/m2)`~Plot*Plant_richness+(1|Site),family=Gamma(link="log"),data=richness)
modlmer4 <- glmmTMB(`Aboveground_biomass (kg/m2)`~Plot*AMF_richness+(1|Site),family=Gamma(link="log"),data=richness)
modlmer5 <- glmmTMB(`Aboveground_biomass (kg/m2)`~Plot*Interaction_number+(1|Site),family=Gamma(link="log"),data=richness)
(msAICc <- model.sel(modlmer2,modlmer3,modlmer4,modlmer5))
plot(simulateResiduals(modlmer2))
summary(modlmer2)
performance::r2(modlmer2)
allEffects(modlmer2)
plot(allEffects(modlmer2))
Anova(modlmer2,type=2)
# Belowground_biomass
{
Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
Eudistant.plot <- subset(richness,Plot=="Eu-distant")
Euadjacent.plot$PlotID==Eudistant.plot$PlotID
x <- Euadjacent.plot$`Belowground_biomass (kg/m2)`
y <- Eudistant.plot$`Belowground_biomass (kg/m2)`
t.test(x,y,paired=T)
mean(x);mean(y)
sd(x);sd(y)
var(x);var(y)
shapiro.test(x)
shapiro.test(y)
}
leveneTest(`Belowground_biomass (kg/m2)`~Plot, data = richness)
modlmer1 <- glmmTMB(`Belowground_biomass (kg/m2)`~Plot,family=Gamma(link="log"),data=richness)
modlmer2 <- glmmTMB(`Belowground_biomass (kg/m2)`~Plot+(1|Site),family=Gamma(link="log"),data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
modlmer3 <- glmmTMB(`Belowground_biomass (kg/m2)`~Plot*Plant_richness+(1|Site),family=Gamma(link="log"),data=richness)
modlmer4 <- glmmTMB(`Belowground_biomass (kg/m2)`~Plot*AMF_richness+(1|Site),family=Gamma(link="log"),data=richness)
modlmer5 <- glmmTMB(`Belowground_biomass (kg/m2)`~Plot*Interaction_number+(1|Site),family=Gamma(link="log"),data=richness)
(msAICc <- model.sel(modlmer2,modlmer3,modlmer4,modlmer5))
plot(simulateResiduals(modlmer2))
summary(modlmer2)
performance::r2(modlmer2)
allEffects(modlmer2)
plot(allEffects(modlmer2))
Anova(modlmer2,type=2)
biomass <- richness[,c("Site","Plot","PlotID","Aboveground_biomass (kg/m2)","Belowground_biomass (kg/m2)")]
colnames(biomass) <- c("Site","Plot","PlotID","Aboveground_biomass","Belowground_biomass")
biomass_long <- pivot_longer(biomass, cols = c(Aboveground_biomass, Belowground_biomass), names_to="Type", values_to="biomass")
biomass_long[which(biomass_long$Type=="Aboveground_biomass"),]$Type <- "Above"
biomass_long[which(biomass_long$Type=="Belowground_biomass"),]$Type <- "Below"
biomass_long$Plot <- as.factor(biomass_long$Plot)
biomass_long$Type <- as.factor(biomass_long$Type)
# 计算均值和标准差
biomass_long.summary <- biomass_long %>%
group_by(Plot, Type) %>%
summarise(biomass.mean = mean(biomass),biomass.sd = sd(biomass))
p2 <- ggplot(biomass_long,aes(x=Plot,y=biomass,color=Type))+
geom_boxplot(width=0.25,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.25,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0,jitter.width=0.45,dodge.width=0.5),alpha=0.4,show.legend=T)+
theme_classic()+
scale_color_manual(values=c("#bc40bf","#4BA465","black"))+
scale_fill_manual(values=c("#bc40bf","#4BA465","black"))+
ylab("Biomass of plant")+
# scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
scale_y_continuous(labels = function(x) sprintf("%03d", x))+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.text.x=element_blank(),#修改x轴刻度标签文本
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.title=element_blank(),#修改图例标题大小
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.9,0.9),#修改图例位置
legend.margin=margin(0,0,0,0))+#修改图例位置
annotate("text",x=0.8,y=max(biomass_long$biomass)*0.95,hjust=1,vjust=1,label=expression(paste("kg/","m"^2)),size=2,color="black")+
# annotate("text",x=1.9,y=max(biomass_long$biomass)*0.85,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
annotate("text",x=1.5,y=max(biomass_long$biomass)*0.9,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
annotate("text",x=1.5+0.2,y=max(biomass_long$biomass)*1,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
p2
ggsave(filename="Fig4b_biomass_glmm.png",plot=p2,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig4b_biomass_glmm.pdf",plot=p2,width=89/2,height=59/2,units="mm")
#####
# AMF_richness 比较Plot间AMF richness是在统计上否有显著性差异
{
Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
Eudistant.plot <- subset(richness,Plot=="Eu-distant")
Euadjacent.plot$PlotID==Eudistant.plot$PlotID
x <- Euadjacent.plot$AMF_richness
y <- Eudistant.plot$AMF_richness
t.test(x,y,paired=T)
mean(x);mean(y)
sd(x);sd(y)
var(x);var(y)
shapiro.test(x)
shapiro.test(y)
}
leveneTest(AMF_richness~Plot, data = richness)
modlmer1 <- glmmTMB(AMF_richness ~ Plot, family=poisson, data=richness)
modlmer2 <- glmmTMB(AMF_richness ~ Plot + (1|Site), family=poisson, data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
modlmer3 <- glmmTMB(AMF_richness ~ Plot + (1|Site), family=nbinom2, data=richness)
modlmer4 <- glmmTMB(AMF_richness ~ Plot + (1|Site), family=nbinom1, data=richness)
(msAICc <- model.sel(modlmer2,modlmer3,modlmer4))
plot(simulateResiduals(modlmer4))
summary(modlmer4)
performance::r2(modlmer4)
allEffects(modlmer4)
plot(allEffects(modlmer4))
Anova(modlmer4,type=2)
# {
# Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
# Eudistant.plot <- subset(richness,Plot=="Eu-distant")
# Euadjacent.plot$PlotID==Eudistant.plot$PlotID
# x <- Euadjacent.plot$AMF_richness_Euphorbia_excluded
# y <- Eudistant.plot$AMF_richness_Euphorbia_excluded
# t.test(x,y,paired=T)
# mean(x);mean(y)
# sd(x);sd(y)
# var(x);var(y)
# }
#
# modlmer1 <- glmmTMB(AMF_richness_Euphorbia_excluded ~ Plot, family=poisson, data=richness)
# modlmer2 <- glmmTMB(AMF_richness_Euphorbia_excluded ~ Plot + (1|Site), family=poisson, data=richness)
# (msAICc <- model.sel(modlmer1,modlmer2))
#
# modlmer3 <- glmmTMB(AMF_richness_Euphorbia_excluded ~ Plot + (1|Site), family=nbinom2, data=richness)
# modlmer4 <- glmmTMB(AMF_richness_Euphorbia_excluded ~ Plot + (1|Site), family=nbinom1, data=richness)
# (msAICc <- model.sel(modlmer2,modlmer3,modlmer4))
#
# plot(simulateResiduals(modlmer4))
#
# summary(modlmer4)
# performance::r2(modlmer4)
# allEffects(modlmer4)
# plot(allEffects(modlmer4))
#
# richness.AMF <- richness[,c("Site","Plot","PlotID","AMF_richness","AMF_richness_Euphorbia_excluded")]
# richness.AMF_long <- pivot_longer(richness.AMF, cols = c(AMF_richness, AMF_richness_Euphorbia_excluded), names_to = "Type", values_to = "AMF_richness")
#
# richness.AMF_long[which(richness.AMF_long$Type=="AMF_richness"),]$Type <- "included"
# richness.AMF_long[which(richness.AMF_long$Type=="AMF_richness_Euphorbia_excluded"),]$Type <- "excluded"
# richness.AMF_long$Type <- factor(richness.AMF_long$Type,levels=c("included","excluded"))
#
# # 计算均值和标准差
# richness.AMF_long.summary <- richness.AMF_long %>%
# group_by(Plot, Type) %>%
# summarise(AMF_richness.mean = mean(AMF_richness),AMF_richness.sd = sd(AMF_richness))
#
# p3 <- ggplot(richness.AMF_long,aes(x=Plot,y=AMF_richness,,color=Type))+
# geom_boxplot(width=0.25,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
# stat_boxplot(geom="errorbar",width=0.25,linewidth=0.25,position=position_dodge(0.5))+
# geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0,jitter.width=0.4,dodge.width=0.5),alpha=0.4,show.legend=T)+
# theme_classic()+
# scale_color_manual(values=c("#bc40bf","#4BA465","black"))+
# scale_fill_manual(values=c("#bc40bf","#4BA465","black"))+
# ylab("AMF richness")+
# # scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
# theme(axis.title.x=element_blank(),#修改X轴标题文本
# # axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
# axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# # axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
# panel.grid=element_blank(),#隐藏网格线
# # legend.title=element_text(size=6.5),#修改图例标题大小
# legend.title=element_blank(),#修改图例标题大小
# legend.text=element_text(size=5.5),#修改图例文本大小
# legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
# legend.background=element_blank(),#设置图例背景为空白
# legend.position="inside",#修改图例位置
# # legend.justification=c("right","top"),#修改图例位置
# legend.position.inside=c(0.9,0.9),#修改图例位置
# legend.margin=margin(0,0,0,0))+#修改图例位置
# # annotate("text",x=1.9,y=max(richness.AMF_long$AMF_richness)*0.85,hjust=1,vjust=1,label=paste0("p > ",0.05),size=2,color="black")+
# annotate("text",x=1.5,y=(max(richness.AMF_long$AMF_richness)+(max(richness.AMF_long$AMF_richness)-min(richness.AMF_long$AMF_richness))*0.2),hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")+
# annotate("text",x=1.5+0.2,y=(max(richness.AMF_long$AMF_richness)+(max(richness.AMF_long$AMF_richness)-min(richness.AMF_long$AMF_richness))*0.1),hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")
# p3
p3 <- ggplot(richness,aes(x=Plot,y=AMF_richness,color=Plot))+
geom_boxplot(width=0.15,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.15,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0.25,jitter.width=0.45,dodge.width=0.5),alpha=0.4,show.legend=T)+
# geom_point(aes(x=Plot,y=mean(Plant_richness)),shape=16,size=0.8,color="black",position=position_dodge(0.5),alpha=1)+
# geom_errorbar(aes(x=Plot,y=mean(Plant_richness),ymin=mean(Plant_richness)-sd(Plant_richness), ymax=mean(Plant_richness)+sd(Plant_richness)),width=0,linewidth=0.4,color="black",position=position_dodge(0.5),alpha=1)+
# scale_color_manual(values=c("#bc40bf","#bc40bf","black"))+
# scale_fill_manual(values=c("#bc40bf","#bc40bf"))+
scale_color_manual(values=c("gray40","gray40","gray40"))+
scale_fill_manual(values=c("gray40","gray40"))+
theme_classic()+
ylab("AMF richness")+
# scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
# scale_y_continuous(labels = function(x) sprintf("%03d", x))+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.text.x=element_blank(),#修改x轴刻度标签文本
axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_text(size=8),#修改图例标题大小
legend.text=element_text(size=6.5),#修改图例文本大小
legend.position="none")+#修改图例位置
# annotate("text",x=1.7,y=max(richness$AMF_richness),hjust=1,vjust=1,label=paste0("p > ",0.05),size=2)+
annotate("text",x=1.5,y=(max(richness$AMF_richness)+(max(richness$AMF_richness)-min(richness$AMF_richness))*0.1),hjust=1,vjust=1,label=paste0("NS"),size=2)
p3
ggsave(filename="Fig4c_AMF_richness_glmm.png",plot=p3,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig4c_AMF_richness_glmm.pdf",plot=p3,width=89/2,height=59/2,units="mm")
#####
# Interaction_number 比较Plot间Interaction number是在统计上否有显著性差异
{
Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
Eudistant.plot <- subset(richness,Plot=="Eu-distant")
Euadjacent.plot$PlotID==Eudistant.plot$PlotID
x <- Euadjacent.plot$Interaction_number
y <- Eudistant.plot$Interaction_number
t.test(x,y,paired=T)
mean(x);mean(y)
sd(x);sd(y)
var(x);var(y)
shapiro.test(x)
shapiro.test(y)
}
leveneTest(Interaction_number~Plot, data = richness)
modlmer1 <- glmmTMB(Interaction_number ~ Plot, family=poisson, data=richness)
modlmer2 <- glmmTMB(Interaction_number ~ Plot + (1|Site), family=poisson, data=richness)
(msAICc <- model.sel(modlmer1,modlmer2))
modlmer3 <- glmmTMB(Interaction_number ~ Plot + (1|Site), family=nbinom2, data=richness)
modlmer4 <- glmmTMB(Interaction_number ~ Plot + (1|Site), family=nbinom1, data=richness)
(msAICc <- model.sel(modlmer2,modlmer3,modlmer4))
plot(simulateResiduals(modlmer4))
summary(modlmer4)
performance::r2(modlmer4)
allEffects(modlmer4)
plot(allEffects(modlmer4))
Anova(modlmer4,type=2)
# {
# Euadjacent.plot <- subset(richness,Plot=="Eu-adjacent")
# Eudistant.plot <- subset(richness,Plot=="Eu-distant")
# Euadjacent.plot$PlotID==Eudistant.plot$PlotID
# x <- Euadjacent.plot$Interaction_number_Euphorbia_excluded
# y <- Eudistant.plot$Interaction_number_Euphorbia_excluded
# t.test(x,y,paired=T)
# mean(x);mean(y)
# sd(x);sd(y)
# var(x);var(y)
# }
#
# modlmer1 <- glmmTMB(Interaction_number_Euphorbia_excluded ~ Plot, family=poisson, data=richness)
# modlmer2 <- glmmTMB(Interaction_number_Euphorbia_excluded ~ Plot + (1|Site), family=poisson, data=richness)
# (msAICc <- model.sel(modlmer1,modlmer2))
#
# modlmer3 <- glmmTMB(Interaction_number_Euphorbia_excluded ~ Plot + (1|Site), family=nbinom2, data=richness)
# modlmer4 <- glmmTMB(Interaction_number_Euphorbia_excluded ~ Plot + (1|Site), family=nbinom1, data=richness)
# (msAICc <- model.sel(modlmer2,modlmer3,modlmer4))
#
# plot(simulateResiduals(modlmer4))
#
# summary(modlmer4)
# performance::r2(modlmer4)
# allEffects(modlmer4)
# plot(allEffects(modlmer4))
#
# richness.interaction <- richness[,c("Site","Plot","PlotID","Interaction_number","Interaction_number_Euphorbia_excluded")]
# richness.interaction_long <- pivot_longer(richness.interaction, cols = c(Interaction_number, Interaction_number_Euphorbia_excluded), names_to = "Type", values_to = "Interaction_number")
#
# richness.interaction_long[which(richness.interaction_long$Type=="Interaction_number"),]$Type <- "included"
# richness.interaction_long[which(richness.interaction_long$Type=="Interaction_number_Euphorbia_excluded"),]$Type <- "excluded"
# richness.interaction_long$Type <- factor(richness.interaction_long$Type,levels=c("included","excluded"))
#
# # 计算均值和标准差
# richness.interaction_long.summary <- richness.interaction_long %>%
# group_by(Plot, Type) %>%
# summarise(Interaction_number.mean = mean(Interaction_number),Interaction_number.sd = sd(Interaction_number))
#
# p4 <- ggplot(richness.interaction_long,aes(x=Plot,y=Interaction_number,color=Type))+
# geom_boxplot(width=0.25,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
# stat_boxplot(geom="errorbar",width=0.25,linewidth=0.25,position=position_dodge(0.5))+
# geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0,jitter.width=0.4,dodge.width=0.5),alpha=0.4,show.legend=T)+
# theme_classic()+
# scale_color_manual(values=c("#bc40bf","#4BA465","black"))+
# scale_fill_manual(values=c("#bc40bf","#4BA465","black"))+
# ylab("Number of interaction")+
# # scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
# theme(axis.title.x=element_blank(),#修改X轴标题文本
# # axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
# axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# # axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
# panel.grid=element_blank(),#隐藏网格线
# # legend.title=element_text(size=6.5),#修改图例标题大小
# legend.title=element_blank(),#修改图例标题大小
# legend.text=element_text(size=5.5),#修改图例文本大小
# legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
# legend.background=element_blank(),#设置图例背景为空白
# legend.position="inside",#修改图例位置
# # legend.justification=c("right","top"),#修改图例位置
# legend.position.inside=c(0.9,0.9),#修改图例位置
# legend.margin=margin(0,0,0,0))+#修改图例位置
# # annotate("text",x=2,y=max(richness.interaction_long$Interaction_number)*0.85,hjust=1,vjust=1,label=paste0("p > 0.05"," / ","p < 0.01"),size=2,color="black")+
# annotate("text",x=1.5,y=(max(richness.interaction_long$Interaction_number)+(max(richness.interaction_long$Interaction_number)-min(richness.interaction_long$Interaction_number))*0.1),hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")+
# annotate("text",x=1.5+0.2,y=(max(richness.interaction_long$Interaction_number)),hjust=1,vjust=1,label=paste0("**"),size=2,color="black")
# p4
p4 <- ggplot(richness,aes(x=Plot,y=Interaction_number,color=Plot))+
geom_boxplot(width=0.15,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.15,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.4,position=position_jitterdodge(jitter.height=0.25,jitter.width=0.45,dodge.width=0.5),alpha=0.4,show.legend=T)+
# geom_point(aes(x=Plot,y=mean(Plant_richness)),shape=16,size=0.8,color="black",position=position_dodge(0.5),alpha=1)+
# geom_errorbar(aes(x=Plot,y=mean(Plant_richness),ymin=mean(Plant_richness)-sd(Plant_richness), ymax=mean(Plant_richness)+sd(Plant_richness)),width=0,linewidth=0.4,color="black",position=position_dodge(0.5),alpha=1)+
# scale_color_manual(values=c("#bc40bf","#bc40bf","black"))+
# scale_fill_manual(values=c("#bc40bf","#bc40bf"))+
scale_color_manual(values=c("gray40","gray40","gray40"))+
scale_fill_manual(values=c("gray40","gray40"))+
theme_classic()+
ylab("Number of interaction")+
# scale_x_discrete(breaks=c("Eu-adjacent","Eu-distant"),labels=c("Eu-adjacent", "Eu-distant"))+
# scale_y_continuous(labels = function(x) sprintf("%03d", x))+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.line.x = element_line(arrow = arrow(length = unit(0.2,"lines"))),
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.text.x=element_blank(),#修改x轴刻度标签文本
axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_text(size=8),#修改图例标题大小
legend.text=element_text(size=6.5),#修改图例文本大小
legend.position="none")+#修改图例位置
# annotate("text",x=1.7,y=max(richness$Interaction_number),hjust=1,vjust=1,label=paste0("p > ",0.05),size=2)+
annotate("text",x=1.5,y=(max(richness$Interaction_number)+(max(richness$Interaction_number)-min(richness$Interaction_number))*0.1),hjust=1,vjust=1,label=paste0("NS"),size=2)
p4
ggsave(filename="Fig4d_interaction_number_glmm.png",plot=p4,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig4d_interaction_number_glmm.pdf",plot=p4,width=89/2,height=59/2,units="mm")
p_all <- plot_grid(p1,p2,p3,p4,labels=c("a","b","c","d"),label_size=8,label_x=0.25,label_y=1,ncol=2,nrow=2)
p_all
ggsave(filename="Fig4_0127.png",plot=p_all,width=89,height=59,units="mm",dpi=900)
ggsave(filename="Fig4_0127.pdf",plot=p_all,width=89,height=59,units="mm")
################################################################################
# Fig. 5 Beta_diversity
rm(list=ls())
Beta_diversity <- read_excel("source_data.xlsx", sheet="Beta_diversity")
head(Beta_diversity)
Beta_diversity$Site <- as.factor(Beta_diversity$Site)
Beta_diversity$Between_plot <- as.factor(Beta_diversity$Between_plot)
Beta_diversity$Beta_diversity_type <- as.factor(Beta_diversity$Beta_diversity_type)
Beta_diversity$Components <- as.factor(Beta_diversity$Components)
Beta_diversity$Components <- factor(Beta_diversity$Components,levels=c("Total","Turnover","Nestedness","βWN","βOS","βST","βST.h","βST.l","βST.lh"))
summary(Beta_diversity)
plant_beta <- subset(Beta_diversity,Beta_diversity_type=="Plant_beta_diversity")
x <- subset(plant_beta,Components=="Turnover")$Observe
y <- subset(plant_beta,Components=="Nestedness")$Observe
shapiro.test(x)
shapiro.test(y)
levene_test <- leveneTest(Observe ~ Components, data = plant_beta)
levene_test
t.test(x,y,paired=T)
d <- x-y
# 进行Welch's t检验
result_welch <- t.test(d, mu = 0, alternative = "two.sided", var.equal = FALSE)
result_welch
# 计算均值和标准差
plant_beta_combined <- plant_beta %>%
group_by(Beta_diversity_type, Components) %>%
summarise(Observe.mean = mean(Observe),Observe.sd = sd(Observe))
p1 <- ggplot(plant_beta,aes(x=Components,y=Observe,color=Components))+
geom_boxplot(width=0.225,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.225,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.6,position=position_jitterdodge(jitter.height=0,jitter.width=0.8,dodge.width=0.5),alpha=0.6,show.legend=T)+
scale_color_manual(values=c("gray40","gray40","gray40"))+
scale_fill_manual(values=c("gray40","gray40","gray40"))+
theme_classic()+
ylab("Plant beta diversity")+# 设置Y轴标题
geom_vline(xintercept=1.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_text(size=8),#修改图例标题大小
legend.text=element_text(size=6.5),#修改图例文本大小
legend.position="none")+#修改图例位置
# annotate("text",x=3,y=max(plant_beta$Observe)*0.85,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
annotate("text",x=2.5,y=(max(plant_beta$Observe)+(max(plant_beta$Observe)-min(plant_beta$Observe))*0.1),hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
p1
ggsave(filename="Fig5a_plant_beta_diversity.png",plot=p1,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig5a_plant_beta_diversity.pdf",plot=p1,width=89/2,height=59/2,units="mm")
#####
AMF_beta <- subset(Beta_diversity,Beta_diversity_type=="AMF_beta_diversity")
x <- subset(AMF_beta,Components=="Turnover")$Observe_Euphorbia_excluded
y <- subset(AMF_beta,Components=="Nestedness")$Observe_Euphorbia_excluded
shapiro.test(x)
shapiro.test(y)
levene_test <- leveneTest(Observe ~ Components, data = AMF_beta)
levene_test
t.test(x,y,paired=T)
# AMF_beta_long <- pivot_longer(AMF_beta, cols = c(Observe, Observe_Euphorbia_excluded), names_to = "Type", values_to = "Value")
# AMF_beta_long[which(AMF_beta_long$Type=="Observe"),]$Type <- "included"
# AMF_beta_long[which(AMF_beta_long$Type=="Observe_Euphorbia_excluded"),]$Type <- "excluded"
# AMF_beta_long$Type <- factor(AMF_beta_long$Type,levels=c("included","excluded"))
#
# # 计算均值和标准差
# AMF_beta_long.summary <- AMF_beta_long %>%
# group_by(Components, Type) %>%
# summarise(Value.mean = mean(Value),Value.sd = sd(Value))
#
# p2 <- ggplot(AMF_beta_long,aes(x=Components,y=Value,color=Type))+
# geom_boxplot(width=0.4,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
# stat_boxplot(geom="errorbar",width=0.4,linewidth=0.25,position=position_dodge(0.5))+
# geom_jitter(shape=16,size=0.6,position=position_jitterdodge(jitter.height=0,jitter.width=0.45,dodge.width=0.5),alpha=0.6,show.legend=T)+
# scale_color_manual(values=c("#bc40bf","#4BA465","black"))+
# scale_fill_manual(values=c("#bc40bf","#4BA465","black"))+
# theme_classic()+
# ylab("AMF beta diversity")+# 设置Y轴标题
# geom_vline(xintercept=1.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
# theme(axis.title.x=element_blank(),#修改X轴标题文本
# # axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
# axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
# panel.grid=element_blank(),#隐藏网格线
# # legend.title=element_text(size=6.5),#修改图例标题大小
# legend.title=element_blank(),
# legend.text=element_text(size=5.5),#修改图例文本大小
# legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
# legend.background=element_blank(),#设置图例背景为空白
# legend.position="inside",#修改图例位置
# # legend.justification=c("right","top"),#修改图例位置
# legend.position.inside=c(0.9,0.9),#修改图例位置
# legend.margin=margin(0,0,0,0))+#修改图例位置
# # annotate("text",x=3,y=max(AMF_beta_long$Value)*0.85,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
# annotate("text",x=2.5,y=(max(AMF_beta_long$Value)+(max(AMF_beta_long$Value)-min(AMF_beta_long$Value))*0.1),hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=2.5+0.2,y=(max(AMF_beta_long$Value)),hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
# p2
p2 <- ggplot(AMF_beta,aes(x=Components,y=Observe,color=Components))+
geom_boxplot(width=0.225,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.225,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.6,position=position_jitterdodge(jitter.height=0,jitter.width=0.8,dodge.width=0.5),alpha=0.6,show.legend=T)+
scale_color_manual(values=c("gray40","gray40","gray40"))+
scale_fill_manual(values=c("gray40","gray40","gray40"))+
theme_classic()+
ylab("AMF beta diversity")+# 设置Y轴标题
geom_vline(xintercept=1.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
legend.title=element_text(size=8),#修改图例标题大小
legend.text=element_text(size=6.5),#修改图例文本大小
legend.position="none")+#修改图例位置
# annotate("text",x=3,y=max(plant_beta$Observe)*0.85,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
annotate("text",x=2.5,y=(max(plant_beta$Observe)+(max(plant_beta$Observe)-min(plant_beta$Observe))*0.1),hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
p2
ggsave(filename="Fig5b_AMF_beta_diversity.png",plot=p2,width=89/2,height=59/2,units="mm",dpi=900)
ggsave(filename="Fig5b_AMF_beta_diversity.pdf",plot=p2,width=89/2,height=59/2,units="mm")
#####
interaction_beta <- subset(Beta_diversity,Beta_diversity_type=="Interaction_beta_diversity")
x <- subset(interaction_beta,Components=="βOS")$Observe
y <- subset(interaction_beta,Components=="βST")$Observe
shapiro.test(x)
shapiro.test(y)
levene_test <- leveneTest(Observe ~ Components, data = interaction_beta)
levene_test
t.test(x,y,paired=T)
d <- x-y
# 进行Welch's t检验
result_welch <- t.test(d, mu = 0, alternative = "two.sided", var.equal = FALSE)
result_welch
x <- subset(interaction_beta,Components=="βOS")$Observe_Euphorbia_excluded
y <- subset(interaction_beta,Components=="βST")$Observe_Euphorbia_excluded
shapiro.test(x)
shapiro.test(y)
levene_test <- leveneTest(Observe ~ Components, data = interaction_beta)
levene_test
t.test(x,y,paired=T)
d <- x-y
# 进行Welch's t检验
result_welch <- t.test(d, mu = 0, alternative = "two.sided", var.equal = FALSE)
result_welch
x <- subset(interaction_beta,Components=="βST.h")$Observe
y <- subset(interaction_beta,Components=="βST.l")$Observe
z <- subset(interaction_beta,Components=="βST.lh")$Observe
shapiro.test(x)
shapiro.test(y)
shapiro.test(z)
levene_test <- leveneTest(Observe ~ Components, data = interaction_beta)
levene_test
t.test(x,y,paired=T)
aov(x,y,z)
d <- x-y
# 进行Welch's t检验
result_welch <- t.test(d, mu = 0, alternative = "two.sided", var.equal = FALSE)
result_welch
interaction_beta2 <- subset(interaction_beta,Components %in% c("βST.h","βST.l","βST.lh"))[,c(1,4,5)]
interaction_beta2 <- as.data.frame(interaction_beta2)
# # 进行Friedman检验
friedman_result <- friedman.test(Observe~Components|Site,data=interaction_beta2)
friedman_result <- friedman.test(Observe~Site|Components,data=interaction_beta2)
# 使用aov函数
anova_result <- aov(Observe~Components, data = interaction_beta2)
summary(anova_result)
# x:"βST.h";y:"βST.l";z:"βST.lh"
wilcox.test(x, y, paired = TRUE);t.test(x,y,paired=T)
# V = 55, p-value = 0.001953;t = 5.6994, df = 9, p-value = 0.0002945
wilcox.test(x, z, paired = TRUE);t.test(x,z,paired=T)
# V = 55, p-value = 0.001953;t = 9.175, df = 9, p-value = 7.292e-06
wilcox.test(y, z, paired = TRUE);t.test(y,z,paired=T)
# V = 51, p-value = 0.01367;t = 3.5067, df = 9, p-value = 0.006653
x <- subset(interaction_beta,Components=="βST.h")$Observe_Euphorbia_excluded
y <- subset(interaction_beta,Components=="βST.lh")$Observe_Euphorbia_excluded
z <- subset(interaction_beta,Components=="βST.lh")$Observe_Euphorbia_excluded
shapiro.test(x)
shapiro.test(y)
shapiro.test(z)
levene_test <- leveneTest(Observe ~ Components, data = interaction_beta)
levene_test
t.test(x,y,paired=T)
d <- x-y
# 进行Welch's t检验
result_welch <- t.test(d, mu = 0, alternative = "two.sided", var.equal = FALSE)
result_welch
# interaction_beta_long <- pivot_longer(interaction_beta, cols = c(Observe, Observe_Euphorbia_excluded), names_to = "Type", values_to = "Value")
# interaction_beta_long[which(interaction_beta_long$Type=="Observe"),]$Type <- "included"
# interaction_beta_long[which(interaction_beta_long$Type=="Observe_Euphorbia_excluded"),]$Type <- "excluded"
# interaction_beta_long$Type <- factor(interaction_beta_long$Type,levels=c("included","excluded"))
#
# # 计算均值和标准差
# interaction_beta_long.summary <- interaction_beta_long %>%
# group_by(Components, Type) %>%
# summarise(Value.mean = mean(Value),Value.sd = sd(Value))
#
# p3 <- ggplot(interaction_beta_long,aes(x=Components,y=Value,color=Type))+
# geom_boxplot(width=0.4,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
# stat_boxplot(geom="errorbar",width=0.4,linewidth=0.25,position=position_dodge(0.5))+
# geom_jitter(shape=16,size=0.6,position=position_jitterdodge(jitter.height=0,jitter.width=0.45,dodge.width=0.5),alpha=0.6,show.legend=T)+
# scale_color_manual(values=c("#bc40bf","#4BA465","black"))+
# scale_fill_manual(values=c("#bc40bf","#4BA465","black"))+
# theme_classic()+
# ylab("Interaction beta diversity")+# 设置Y轴标题
# geom_vline(xintercept=1.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
# geom_vline(xintercept=3.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
# theme(axis.title.x=element_blank(),#修改X轴标题文本
# # axis.title.x=element_text(size=8),#修改X轴标题文本
# axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
# axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
# axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
# panel.grid=element_blank(),#隐藏网格线
# # legend.title=element_text(size=6.5),#修改图例标题大小
# legend.title=element_blank(),
# legend.text=element_text(size=5.5),#修改图例文本大小
# legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
# legend.background=element_blank(),#设置图例背景为空白
# legend.position="inside",#修改图例位置
# # legend.justification=c("right","top"),#修改图例位置
# legend.position.inside=c(0.92,0.9),#修改图例位置
# legend.margin=margin(0,0,0,0))+#修改图例位置
# # annotate("text",x=2.6,y=max(interaction_beta_long$Value)*0.85,hjust=1,vjust=1,label=paste0("p > ",0.05),size=2,color="black")+
# # annotate("text",x=5.3,y=max(interaction_beta_long$Value)*0.85/1.2,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
# # annotate("text",x=4.8,y=max(interaction_beta_long$Value)*0.85/1.5,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")+
# # annotate("text",x=5.8,y=max(interaction_beta_long$Value)*0.85/4,hjust=1,vjust=1,label=paste0("p < ",0.001),size=2,color="black")
# annotate("text",x=2.5,y=max(interaction_beta_long$Value)*1,hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")+
# annotate("text",x=2.5+0.2,y=max(interaction_beta_long$Value)*0.9,hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")+
# annotate("text",x=5,y=max(interaction_beta_long$Value)*1,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=5+0.2,y=max(interaction_beta_long$Value)*0.9,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=4.5,y=max(interaction_beta_long$Value)*0.8,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=4.5+0.2,y=max(interaction_beta_long$Value)*0.7,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=5.5,y=max(interaction_beta_long$Value)*0.4,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
# annotate("text",x=5.5+0.2,y=max(interaction_beta_long$Value)*0.3,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
# p3
# ggsave(filename="Fig5c_interaction_beta_diversity.png",plot=p3,width=89,height=59/2,units="mm",dpi=900)
# ggsave(filename="Fig5c_interaction_beta_diversity.pdf",plot=p3,width=89,height=59/2,units="mm")
p3 <- ggplot(interaction_beta,aes(x=Components,y=Observe,color=Components))+
geom_boxplot(width=0.225,linewidth=0.25,outliers=F,alpha=1,show.legend=T,position=position_dodge(0.5))+
stat_boxplot(geom="errorbar",width=0.225,linewidth=0.25,position=position_dodge(0.5))+
geom_jitter(shape=16,size=0.6,position=position_jitterdodge(jitter.height=0,jitter.width=0.8,dodge.width=0.5),alpha=0.6,show.legend=T)+
scale_color_manual(values=c("gray40","gray40","gray40","gray40","gray40","gray40","black"))+
scale_fill_manual(values=c("gray40","gray40","gray40","gray40","gray40","gray40","black"))+
theme_classic()+
ylab("Interaction beta diversity")+# 设置Y轴标题
geom_vline(xintercept=1.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
geom_vline(xintercept=3.5,linetype=2,linewidth=0.2,color="gray50",alpha=0.8)+
theme(axis.title.x=element_blank(),#修改X轴标题文本
# axis.title.x=element_text(size=8),#修改X轴标题文本
axis.title.y=element_text(size=8,angle=90),#修改y轴标题文本
axis.text.y=element_text(size=6.5),#修改y轴刻度标签文本
axis.text.x=element_text(size=6.5),#修改x轴刻度标签文本
panel.grid=element_blank(),#隐藏网格线
# legend.title=element_text(size=6.5),#修改图例标题大小
legend.title=element_blank(),
legend.text=element_text(size=5.5),#修改图例文本大小
legend.key.size=unit(0.1,"lines"),#缩小图例符号的大小
legend.background=element_blank(),#设置图例背景为空白
legend.position="inside",#修改图例位置
# legend.justification=c("right","top"),#修改图例位置
legend.position.inside=c(0.92,0.9),#修改图例位置
legend.margin=margin(0,0,0,0))+#修改图例位置
annotate("text",x=2.5,y=max(interaction_beta$Observe)*0.9,hjust=1,vjust=1,label=paste0("NS"),size=2,color="black")+
annotate("text",x=5,y=max(interaction_beta$Observe)*0.82,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
annotate("text",x=4.5,y=max(interaction_beta$Observe)*0.7,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")+
annotate("text",x=5.5,y=max(interaction_beta$Observe)*0.3,hjust=1,vjust=1,label=paste0("***"),size=2,color="black")
p3
p4 <- plot_grid(p1,p2,labels=c("a","b"),label_size=8,label_x=0.25,label_y=1,ncol=2,nrow=1)
p4
p_all <- plot_grid(p4,p3,labels=c("","c"),label_size=8,label_x=0.125,label_y=1,ncol=1,nrow=2)
p_all
ggsave(filename="Fig5_0127.png",plot=p_all,width=89,height=59,units="mm",dpi=900)
ggsave(filename="Fig5_0127.pdf",plot=p_all,width=89,height=59,units="mm")