frontier_chemical_ecology_yoneya2022 v3 (final)
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Loading libraries
library(vegan)
library(ggplot2)
library(ggsci)
library(gplots)
library(iNEXT)
library(nlme)
library(rcompanion)
library(car)
set.seed(4321)Loading datasets
library(vegan) 要求されたパッケージ permute をロード中です
要求されたパッケージ lattice をロード中です
This is vegan 2.5-7library(ggplot2)
library(ggsci)
library(gplots)
次のパッケージを付け加えます: ‘gplots’
以下のオブジェクトは ‘package:stats’ からマスクされています:
lowess library(iNEXT)
library(nlme)
library(rcompanion)Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'DescTools':
method from
reorder.factor gplotslibrary(car) 要求されたパッケージ carData をロード中です set.seed(4321)Note for symbols to represent plant initial conditions
- “U” and “N” were interchangeably used to represent “Uninfested plants” in chamber C1
- “E”, “S”, and “T” were interchangeably used to represent “Exposed plants” in chamber C2
- “D” and “W” were interchangeably used to represent “Damaged plants” in chamber C2
Basic Analysis of the time evolution (succession) of the abundant species (for supporting information)
Data pretreatment
uds_data5.temp <- read.csv("monthly-for-R3.csv",header=T) #for 2 or 3 continuous sampling at day 10, 32, and 60
uds_data_all_day <-uds_data5.temp[,c(-1)]
uds_data_all_day2 <-uds_data5.temp
#converting treatment to chamber
chamber <- uds_data_all_day2$treatment
chamber[chamber == "N"] <- "C1"
chamber[chamber == "W"] <- "C2"
chamber[chamber == "T"] <- "C2"
uds_data_all_day2 <- cbind.data.frame(uds_data_all_day2, chamber)
uds_data_all_LBa <- read.csv("leaf_beetle_adult_summary.csv", header=T) #for leaf beetle adult data
uds_dataLB <- uds_data_all_day[, c(3)]
head(uds_data_all_LBa)head(uds_data_all_day2)Time evolution of the rank 1-10 taxa
SAD_total <- data.frame(abundance=apply(uds_data_all_day2[,4:76], 2, sum))
SAD_rank <- order(SAD_total[,1], decreasing=T)
SAD_total2 <- data.frame(rank=c(1:73), ID=rownames(SAD_total)[SAD_rank], abundance=SAD_total[SAD_rank,1])
#how many species?
sum(SAD_total2$abundance>0)[1] 48plot(SAD_total2$rank, log10(SAD_total2$abundance)) #SAD, including the rank 1 speices (wl) but with log10 scale
plot(SAD_total2$rank[-1], SAD_total2$abundance[-1]) #SAD, excluding the rank 1 speices (wl)
plot(SAD_total2$rank[2:10], SAD_total2$abundance[2:10]) #SAD, from rank 2 to rank 10
#text(SAD_total2$rank[2:10], SAD_total2$abundance[2:10], labels=SAD_total2$ID[2:10])
ggplot(SAD_total2, aes(x=rank, y=log10(abundance))) + geom_point()
SAD_total2_sub <- SAD_total2[2:10,]
sad_plot <- ggplot(SAD_total2_sub, aes(x=rank, y=abundance)) + geom_point()
sad_plot <- sad_plot + geom_text(aes(label=ID), size=3,vjust=-1)
plot(sad_plot)
Leaf beetle population dynamics analysis (Fig.1)
Incorporating nested structure
#Taxon (species) identity
SAD_total2_sub$ID[1] "spider3" "lep9" "lep7" "lep10" "aphi5" "spider4" "parasitoid3"
[8] "lep12" "lep13" uds_data_all_day_SI <- uds_data_all_day2 #copy for SI only
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="N"] <- "U"
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="W"] <- "D"
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="T"] <- "E"
treatment_day <- as.factor(paste(uds_data_all_day_SI$treatment,uds_data_all_day_SI$day))
uds_data_all_day_SI <- cbind.data.frame(uds_data_all_day_SI, treatment_day)
plot(uds_data_all_day_SI$treatment_day ,uds_data_all_day_SI$wl)
#rank 1
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=wl, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.2) + ggtitle("Plagiodera versicolora")
#rank 2
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=spider3, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("spider group 3 (< 5mm with spiderweb)")
#rank 3
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep9, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Caloptilia sp.2")
#rank 4
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep7, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Saliciphaga caesia")
#rank 5
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep10, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Phyllocolpa sp")
#rank 6
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=aphi5, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Cavariella salicicola")
#rank 7
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep12, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("spider group 4 (< 5mm without spiderweb)")
#rank 8
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=parasitoid3, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Tachinidae sp.1 (host: P. versicolora)")
#rank 9
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep12, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Microleon longipalpis")
#rank 10
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep13, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Monema flavescens")
#rank 11
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=grasshopper1, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Orthoptera sp.1")
NA
NARepeated ANOVA with autoregression
Ref: http://rcompanion.org/handbook/I_09.html
chamber <- append(rep("C1",16),rep("C2",32))
uds_data_all_LBa <- cbind.data.frame(uds_data_all_LBa, chamber)
uds_data_all_LBahour scale (Fig.1a)
#Data decomposition
uds_data_LBa24h <- subset(uds_data_all_LBa, uds_data_all_LBa$day==1)
uds_data_LBa2_7d <- subset(uds_data_all_LBa, (uds_data_all_LBa$day >= 2 & uds_data_all_LBa$day <= 7))
uds_data_LBa10_60d <- subset(uds_data_all_LBa, (uds_data_all_LBa$day >= 10 & uds_data_all_LBa$day <= 60))Summary of hour scale results (Fig.1a)
- There were statistically significant differences bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
- there were differences bet. U vs (S + D) at 12 h and 24 h
- but no differences bet. S vs D either.
a week scale (Fig.1b)
#short (Fig.1a)
model24h_0 = gls(adult ~ chamber + hour + chamber*hour,data=uds_data_LBa24h)
#Anova(model24h_0)
ACF_s <- ACF(model24h_0, form = ~ hour|id)
head(ACF_s)model24h = gls(adult ~ chamber + hour + chamber*hour, correlation = corAR1(form = ~ hour|id, value = ACF_s$ACF[2]),data=uds_data_LBa24h)
Anova(model24h)Analysis of Deviance Table (Type II tests)
Response: adult
Df Chisq Pr(>Chisq)
chamber 1 4.7975 0.02850 *
hour 1 5.9301 0.01488 *
chamber:hour 1 5.8661 0.01544 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#Post-hoc test
data12h<-subset(uds_data_LBa24h, hour==12)
pairwise.t.test(data12h$adult, data12h$chamber,p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data12h$adult and data12h$chamber
C1
C2 0.015
P value adjustment method: holm pairwise.t.test(data12h$adult[-c(1:16)],data12h$treatment[-c(1:16)],p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data12h$adult[-c(1:16)] and data12h$treatment[-c(1:16)]
D
S 0.33
P value adjustment method: holm data24h<-subset(uds_data_LBa24h, hour==24)
pairwise.t.test(data24h$adult, data24h$chamber,p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data24h$adult and data24h$chamber
C1
C2 0.027
P value adjustment method: holm pairwise.t.test(data12h$adult[-c(1:16)], data12h$treatment[-c(1:16)],p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data12h$adult[-c(1:16)] and data12h$treatment[-c(1:16)]
D
S 0.33
P value adjustment method: holm #Summary statistics for graph
sum24h = groupwiseMean(adult ~ treatment + hour, data = uds_data_LBa24h, conf = 0.95, digits = 3,traditional = FALSE, percentile = TRUE)
sum24h
#shortest graph (Fig.1a)
pd = position_dodge(1)
g_line<-ggplot(sum24h, aes(y=Mean, x=hour)) + xlim(0,25) + ylim(0,1.1) + coord_fixed(25/1.1)
g_line<-g_line+geom_line(aes(colour=treatment),size=1,position=pd)
g_line<- g_line+ geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd)
print(g_line)
Summary of a week scale results (Fig.1b)
- There was no statistically significant difference bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
- there was no difference bet. U vs (S + D) at 7d
- but no differences bet. S vs D either.
monthly scale (Fig.1c)
#middle (Fig.1b)
model2_7d0 = gls(adult ~ chamber + day + chamber*day,data=uds_data_LBa2_7d)
#Anova(model2_7d0)
ACF_m <- ACF(model2_7d0, form = ~ day|id)
head(ACF_m)model2_7d = gls(adult ~ chamber + day + chamber*day, correlation = corAR1(form = ~ day|id, value = ACF_m$ACF[2]),data=uds_data_LBa2_7d)
Anova(model2_7d)Analysis of Deviance Table (Type II tests)
Response: adult
Df Chisq Pr(>Chisq)
chamber 1 0.1239 0.724869
day 1 16.9347 3.869e-05 ***
chamber:day 1 6.6496 0.009918 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#Post-hoc test
data7d<-subset(uds_data_LBa2_7d, day==7)
pairwise.t.test(data7d$adult, data7d$chamber,p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data7d$adult and data7d$chamber
C1
C2 0.21
P value adjustment method: holm pairwise.t.test(data7d$adult[-c(1:16)], data7d$treatment[-c(1:16)],p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data7d$adult[-c(1:16)] and data7d$treatment[-c(1:16)]
D
S 0.94
P value adjustment method: holm #Summary statistics for graph
sum2_7d = groupwiseMean(adult ~ treatment + day, data = uds_data_LBa2_7d, conf = 0.95, digits = 3,traditional = FALSE, percentile = TRUE)
#a week scale graph (Fig.1b)
pd = position_dodge(0.3)
g_line<-ggplot(sum2_7d, aes(y=Mean, x=day)) + xlim(1,8) + ylim(0,1.1) + coord_fixed(7/2.0)
g_line<-g_line+geom_line(aes(colour=treatment),size=1, position=pd)
g_line<- g_line+ geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd)
print(g_line)
Summary of monthly scale results (Fig.1c)
- There was no statistically significant difference bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
- there was no difference bet. U vs (S + D) at 10d
- but a difference bet. S vs D at 10d.
Ordinations for community composition
Preparing the function for Fig.2 & Fig.S1
model10_60d0 = gls(adult ~ chamber + day + chamber*day, data=uds_data_LBa10_60d)
#Anova(model10_60d0)
ACF_l <- ACF(model10_60d0, form = ~ day|id)
head(ACF_l)model10_60d = gls(adult ~ chamber + day + chamber*day, correlation = corAR1(form = ~ day|id, value = ACF_l$ACF[2]),data=uds_data_LBa10_60d)
Anova(model10_60d)Analysis of Deviance Table (Type II tests)
Response: adult
Df Chisq Pr(>Chisq)
chamber 1 2.2242 0.13586
day 1 41.4459 1.212e-10 ***
chamber:day 1 2.8322 0.09239 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#Post-hoc test
data10d<-subset(uds_data_LBa10_60d, day==10)
pairwise.t.test(data10d$adult, data10d$chamber,p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data10d$adult and data10d$chamber
C1
C2 0.14
P value adjustment method: holm pairwise.t.test(data10d$adult[-c(1:16)], data10d$treatment[-c(1:16)],p.adjust.method="holm")
Pairwise comparisons using t tests with pooled SD
data: data10d$adult[-c(1:16)] and data10d$treatment[-c(1:16)]
D
S 0.041
P value adjustment method: holm #Summary statistics for graph
sum10_60d = groupwiseMean(adult ~ treatment + day, data = uds_data_LBa10_60d, conf = 0.95, digits = 3,traditional = FALSE, percentile = TRUE)
#graphics
pd = position_dodge(3)
g_line<-ggplot(sum10_60d, aes(y=Mean, x=day)) + xlim(5,65) + ylim(0,4) + coord_fixed(ratio = 60/4)
g_line<-g_line+geom_line(aes(colour=treatment),size=1, position=pd)
g_line<- g_line+geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd)
print(g_line)
Figure 2a & 2b
- With excluding leaf beetle and using chao distance
disper_uds_centroid_graph <- function(data_name, c_LB, meth, adj=NULL, type="violin", lim_min=-0.6, lim_max=0.4) {
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-uds_data1[,c(-1,-2,-3, -77,-78, -79, -80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-uds_data1[,c(-1,-2,-3,-4,-77,-78,-79, -80)]
} #When leaf beele is NOT included
if(c_LB == "LBonly") {
uds_data1 <- subset(data_name, abundance2 > 0)
uds_each <- subset(uds_data1, day==date)[, c(4)] #picking up LB only
}
sample_each <- uds_data1$sample
#meth="chao"
uds_bray_each0.d<-vegdist(uds_each, method=meth)
if(c_LB == "LBonly") tp <- "centroid"
else tp <- "median"
uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
uds_median <- data.frame(uds_dist_each0$centroids) #calculate the centroid (spatial median) for repeated measures
sample <- row.names(uds_median)
#sample <- sample_each
#Replace the sample name into the treatment
sample[grep("1n", sample)]<- "U10"
sample[grep("2n", sample)]<- "U32"
sample[grep("3n", sample)]<- "U60"
sample[grep("1t", sample)]<- "S10"
sample[grep("2t", sample)]<- "S32"
sample[grep("3t", sample)]<- "S60"
sample[grep("1w", sample)]<- "D10"
sample[grep("2w", sample)]<- "D32"
sample[grep("3w", sample)]<- "D60"
treatment_each <- as.factor(sample) #just conversion
uds_each.d<-vegdist(uds_median, method="euclidean")
uds_dist_each <-betadisper(uds_each.d, treatment_each, type="centroid", bias.adjust = TRUE) #Choose centroid because of euclidean distance
#Plot
if(c_LB == "LBonly") boxplot(uds_dist_each)
else {
#boxplot(uds_dist_each)
#ordiplot(uds_dist_each$centroids, type="text", choices=c(1,2), cex=1, xlim=c(-0.6,0.4), ylim=c(-0.6, 0.5))
#par(new=T)
if(type == "violin") {
#boxplot(uds_dist_each)
##from ggplot2
#convert to data.frame (which is necessary for ggplot2)
uds_dist_each2 <- cbind.data.frame(uds_dist_each$distances, as.character(uds_dist_each$group))
treatment<-as.character(uds_dist_each$group)
#Replace the sample name into the treatment
treatment[grep("U", treatment)]<- "U"
treatment[grep("D", treatment)]<- "D"
treatment[grep("S", treatment)]<- "S"
uds_dist_each2<-cbind.data.frame(uds_dist_each2, treatment)
colnames(uds_dist_each2) <- c("distances", "group", "treatment")
uds_dist_each2$distances <- as.numeric(as.character(uds_dist_each2$distances))
g_box<-ggplot(uds_dist_each2, aes(y=distances, x=group,colour=treatment))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(fill="black", width=0.1)
g_box<-g_box + coord_fixed(ratio = 15/1)
print(g_box)
}
else if(type == "PCoA") {
#PCoA
test0<-capscale(uds_each.d~1)
test<-cmdscale(uds_each.d, eig=FALSE, k = 2)
#prepare dataframe for each point
test2<-summary(test)
#uds_dist_each_test<-data.frame(PCoA1=-test2$sites[,1], PCoA2=-test2$sites[,2])
uds_dist_each_test<-as.data.frame(test)
uds_dist_each_test<-cbind.data.frame(sample, uds_dist_each_test)
treatment<-as.character(sample)
day<-as.character(sample)
#Replace the sample name into the treatment & day
treatment[grep("U", treatment)]<- "U"
treatment[grep("D", treatment)]<- "D"
treatment[grep("S", treatment)]<- "S"
day[grep("10", day)]<-"Day10"
day[grep("32", day)]<-"Day32"
day[grep("60", day)]<-"Day60"
uds_dist_each_test<-cbind.data.frame(cbind.data.frame(treatment, day), uds_dist_each_test)
colnames(uds_dist_each_test)<-c("treatment", "day", "group", "PCoA1", "PCoA2")
class(uds_dist_each_test$day)
#prepare dataframe for centroid
uds_dist_each_centr<-as.data.frame(uds_dist_each$centroids)
treatment<-as.character(rownames(uds_dist_each_centr))
day<-as.character(rownames(uds_dist_each_centr))
#Replace the sample name into the treatment & day
treatment[grep("U", treatment)]<- "U"
treatment[grep("D", treatment)]<- "D"
treatment[grep("S", treatment)]<- "S"
day[grep("10", day)]<-"Day10"
day[grep("32", day)]<-"Day32"
day[grep("60", day)]<-"Day60"
uds_dist_each_centr<-cbind.data.frame(uds_dist_each_centr, data.frame(treatment=treatment, day=day, group=rownames(uds_dist_each_centr)))
g_PCoA <- ggplot(uds_dist_each_test, aes(x=PCoA1,y=PCoA2))
g_PCoA <- g_PCoA + geom_point(aes(colour=treatment, shape=day), size=3, alpha=0.5)
g_PCoA <- g_PCoA + xlim(lim_min, lim_max) + ylim(lim_min,lim_max) + coord_fixed(ratio = 1/1)
#Add the centroid information
g_PCoA <- g_PCoA + layer(
data=uds_dist_each_centr,
mapping=aes(x=PCoA1, y=PCoA2, colour=treatment, shape=day),
params=list(size=5),
geom="point",
stat="identity",
position="identity"
)
print(g_PCoA)
#Performance of PCoA http://d.hatena.ne.jp/hoxo_m/20120313/p1
summary(test0)
}
}
#Performance of PCoA http://d.hatena.ne.jp/hoxo_m/20120313/p1
#summary(test0)
if(type == "NMDS") {
uds_each.mds<-metaMDS(uds_median, distance="euclidean")
rownames(uds_each.mds$points)<-treatment_each
ordiplot(uds_each.mds$points, type='points', xlim=c(-1.0, 1.0))
text(uds_each.mds$points,labels=treatment_each, col=unclass(treatment_each),cex=0.8)
}
}Figure S1
- Using bray-curtis dissimilarity w/o LB
- Using Chao dissimilarity w/ LB
#Figure 2a
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="chao", type="violin") 警告: some squared distances are negative and changed to zero
#Figure 2b
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="chao", type="PCoA") 警告: some squared distances are negative and changed to zero
Statistical tests corresponding to the results shown in Fig.2
Definitions of functions fot statistical test
####For Figure S1a
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="bray", type="PCoA", lim_min=-0.75, lim_max=0.5) 警告: some squared distances are negative and changed to zero
####For Figure S1b
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="wLB", meth="chao", type="PCoA", lim_min=-0.75, lim_max=0.5) 警告: some squared distances are negative and changed to zero
Statistical tests for Fig.2
For PERMDISP: comparison between dates (Fig.2A)
#///////////////////////Pre-Evaluation of effectiveness of PERMANOVA (or can be used for assessing beta diversity) through PERMDISP for each of 10d, 32d, 60d/////////////////////
disper_uds_centroid <- function(data_name, date, c_LB, meth, adj=NULL, plot="box", dist_out=FALSE) {
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3, -77,-78, -79, -80)]
} #When leaf beetle is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-4,-77,-78,-79, -80)]
} #When leaf beetle is NOT included
if(c_LB == "LBonly") { #For leaf beetle only but consider the case with other insects only
uds_data1 <- subset(data_name, abundance2 > 0)
#uds_data1 <-data_name
uds_each <- subset(uds_data1, day==date)[, c(4)] #picking up LB only
}
sample_each <- subset(uds_data1, day==date)$sample
#length(uds_each)
#length(sample_each)
##In case of leaf beetle only, different calculation
if(c_LB == "LBonly") {
sample_ID<-as.factor(sample_each)
LB_average<-tapply(uds_each, sample_ID, mean) #calculate the average of short-term repeated measurement
LB_average2<-LB_average #just copy
sample <- row.names(LB_average2)
sample2 <- row.names(LB_average2)
#Replace the sample name into the treatment
sample[grep("n", sample)]<- "U" #undamaged
sample[grep("t", sample)]<- "S" #Signaled
sample[grep("w", sample)]<- "D" #Damaged
treatment_each <- as.factor(sample) #just conversion
#print(treatment_each)
#Replace the sample name into the chamber
sample2[grep("n", sample2)]<- "C1" #undamaged
sample2[grep("t", sample2)]<- "C2" #Signaled
sample2[grep("w", sample2)]<- "C2" #Damaged
chamber_each <- as.factor(sample2) #just conversion
average_treatment<-tapply(LB_average, treatment_each, mean) #calculate the average of U, D, S, respectively
average_chamber<-tapply(LB_average2, chamber_each, mean) #calculate the average of C1 amd C2
#print(average_treatment)
for(i in 1: length(LB_average)) { #normalizing the original data to exclude the effect of average on the distance to centroid
if(treatment_each[i] == "D" && average_treatment[1] > 0.0) LB_average[i] = LB_average[i]/average_treatment[1]
if(treatment_each[i] == "S" && average_treatment[2] > 0.0) LB_average[i] = LB_average[i]/average_treatment[2]
if(treatment_each[i] == "U" && average_treatment[3] > 0.0) LB_average[i] = LB_average[i]/average_treatment[3]
}
for(i in 1: length(LB_average)) { #normalizing the original data to exclude the effect of average on the distance to centroid
if(chamber_each[i] == "C1" && average_chamber[1] > 0.0) LB_average2[i] = LB_average2[i]/average_chamber[1]
if(chamber_each[i] == "C2" && average_chamber[2] > 0.0) LB_average2[i] = LB_average2[i]/average_chamber[2]
}
uds_median<-LB_average #send the data for further analysis, distinguishing D,S,and U
uds_median2<-LB_average2 #send the data for further analysis, distinguishing C1 and C2
#print(uds_median)
if(dist_out == "C1") uds_median <- subset(uds_median, chamber_each=="C1")
else if(dist_out == "C2") uds_median <- subset(uds_median, chamber_each=="C2")
#print(uds_median)
}
else {
uds_bray_each0.d<-vegdist(uds_each, method=meth)
tp <- "median"
uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
uds_median <- data.frame(uds_dist_each0$centroids) #calculate the centroid (spatial median) for repeated measures
sample <- row.names(uds_median)
sample2 <- sample #just copy
#Replace the sample name into the treatment
sample[grep("n", sample)]<- "U" #undamaged
sample[grep("t", sample)]<- "S" #Signaled
sample[grep("w", sample)]<- "D" #Damaged
treatment_each <- as.factor(sample) #just conversion
#Replace the sample name into the chamber
sample2[grep("n", sample2)]<- "C1" #undamaged
sample2[grep("t", sample2)]<- "C2" #Signaled
sample2[grep("w", sample2)]<- "C2" #Damaged
chamber_each <- as.factor(sample2) #just conversion
if(dist_out == "C1") uds_median <- uds_median[chamber_each=="C1",]
else if(dist_out == "C2") uds_median <- uds_median[chamber_each=="C2",]
#print(uds_median)
}
uds_each.d<-vegdist(uds_median, method="euclidean")
if(dist_out!= FALSE) return(uds_each.d)
#treatment_each
uds_dist_each <-betadisper(uds_each.d, treatment_each, type="centroid", bias.adjust = TRUE) #Choose centroid because of euclidean distance
#chamber_each
uds_dist_each_C <-betadisper(uds_each.d, chamber_each, type="centroid", bias.adjust = TRUE)
#Plot
if(c_LB == "LBonly") boxplot(uds_dist_each)
else {
if(plot=="box") {
boxplot(uds_dist_each_C)
boxplot(uds_dist_each)
}
else{
plot(uds_dist_each)
ordilabel(scores(uds_dist_each, "centroids"))
plot(uds_dist_each_C)
ordilabel(scores(uds_dist_each_C, "centroids"))
}
}
#PERDISP & permutation
perm_result_C <- permutest(uds_dist_each_C, pairwise=FALSE, permutations=9999)
perm_result <- permutest(uds_dist_each, pairwise=TRUE, permutations=9999)
cat("Sampling day was\n")
print(subset(uds_data1, day==date)$day[1])
cat("Comparison between chambers\n")
print(perm_result_C)
cat("Comparison between treatments\n")
print(perm_result)
#In fact, the adjustment is not necessary because permutest.betadisper does not repeat permutation for each pair
if(!is.null(adj)) {
pairwise_adj<-p.adjust(perm_result$pairwise$permuted, method = adj , n = length(perm_result$pairwise$permuted))
cat("Pairwise comparision by permutation, adjusted by")
print(as.character(adj))
print(pairwise_adj[1])
print(pairwise_adj[2])
print(pairwise_adj[3])
}
}
#///PERMANOVA for each initial conditions, based on the spatial median for repeated measures
com_div_uds_centroid <- function(data_name, date, c_LB, meth, adj)
{
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-77,-78, -79, -80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79, -80)]
uds_each_LB<-subset(uds_data1, day==date)[,c(4)] #extract leaf beetle abundance
} #When leaf beele is NOTincluded
sample_each <- subset(uds_data1, day==date)$sample
uds_each.d<-vegdist(uds_each, method=meth)
uds_dist_each <-betadisper(uds_each.d, sample_each, type="median", bias.adjust = TRUE)
uds_median <- data.frame(uds_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
sample <- row.names(uds_median)
sample2 <- row.names(uds_median)
#Replace the sample name into the treatment
sample[grep("n", sample)]<- "U"
sample[grep("t", sample)]<- "S"
sample[grep("w", sample)]<- "D"
treatment_each <- as.factor(sample) #just conversion
#Replace the sample name into the chamber
sample2[grep("n", sample2)]<- "C1" #undamaged
sample2[grep("t", sample2)]<- "C2" #Signaled
sample2[grep("w", sample2)]<- "C2" #Damaged
chamber_each <- as.factor(sample2) #just conversion
adonis_result_C<-adonis(uds_median ~ chamber_each, method="euclidean", permutations=9999)
adonis_result_T<-adonis(uds_median ~ treatment_each, method="euclidean", permutations=9999)
print(adonis_result_C) #Chamber effect
print(adonis_result_T) #Treatment effect
#For pairwise comparison (only between D and S)
cat("The pairwise PERMANOVA (adonis) analysis:\n")
uds_dataDS0<-subset(data_name, treatment != "N") #We still need the symbols N, W, and T because it is used in the raw data files
uds_dataSU0<-subset(data_name, treatment != "W")
uds_dataUD0<-subset(data_name, treatment != "T")
if(c_LB == "wLB") {
uds_dataDS1 <-subset(uds_dataDS0, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
DS_each<-subset(uds_dataDS1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
uds_dataSU1 <-subset(uds_dataSU0, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
SU_each<-subset(uds_dataSU1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
uds_dataUD1 <-subset(uds_dataUD0, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
UD_each<-subset(uds_dataUD1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_dataDS1 <-subset(uds_dataDS0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
DS_each<-subset(uds_dataDS1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79, -80)]
uds_dataSU1 <-subset(uds_dataSU0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
SU_each<-subset(uds_dataSU1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79,-80)]
uds_dataUD1 <-subset(uds_dataUD0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
UD_each<-subset(uds_dataUD1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79,-80)]
} #When leaf beetle is NO Tincluded
sample_DS <- subset(uds_dataDS1, day==date)$sample
sample_SU <- subset(uds_dataSU1, day==date)$sample
sample_UD <- subset(uds_dataUD1, day==date)$sample
DS_bray_each.d<-vegdist(DS_each, method=meth)
SU_bray_each.d<-vegdist(SU_each, method=meth)
UD_bray_each.d<-vegdist(UD_each, method=meth)
DS_dist_each <-betadisper(DS_bray_each.d, sample_DS, type="median", bias.adjust = TRUE)
SU_dist_each <-betadisper(SU_bray_each.d, sample_SU, type="median", bias.adjust = TRUE)
UD_dist_each <-betadisper(UD_bray_each.d, sample_UD, type="median", bias.adjust = TRUE)
DS_median <- data.frame(DS_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
SU_median <- data.frame(SU_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
UD_median <- data.frame(UD_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
DS_sample <- row.names(DS_median)
SU_sample <- row.names(SU_median)
UD_sample <- row.names(UD_median)
#Replace the sample name into the treatment
DS_sample[grep("n", DS_sample)]<- "U"
DS_sample[grep("t", DS_sample)]<- "S"
DS_sample[grep("w", DS_sample)]<- "D"
treatment_DS <- as.factor(DS_sample) #just conversion
SU_sample[grep("n", SU_sample)]<- "U"
SU_sample[grep("t", SU_sample)]<- "S"
SU_sample[grep("w", SU_sample)]<- "D"
treatment_SU <- as.factor(SU_sample) #just conversion
UD_sample[grep("n", UD_sample)]<- "U"
UD_sample[grep("t", UD_sample)]<- "S"
UD_sample[grep("w", UD_sample)]<- "D"
treatment_UD <- as.factor(UD_sample) #just conversion
adonis_resultDS<-adonis(DS_median ~ treatment_DS, method="euclidean", permutations=9999)
adonis_resultSU<-adonis(SU_median ~ treatment_SU, method="euclidean", permutations=9999)
adonis_resultUD<-adonis(UD_median ~ treatment_UD, method="euclidean", permutations=9999)
p_list <- c(adonis_resultDS$aov$Pr[1], adonis_resultSU$aov$Pr[1], adonis_resultUD$aov$Pr[1])
cat("The comparision between D and S is:\n")
print(adonis_resultDS)
pairwise_adj<-p.adjust(p_list, method = adj , n = length(p_list))
#cat("Pairwise comparision by permutation, adjusted by")
#print(adj)
#cat("The adjusted P-value for S vs D was\n")
#print(pairwise_adj[1])
#cat("The adjusted P-value for U vs S was\n")
#print(pairwise_adj[2])
#cat("The adjusted P-value for D vs U was\n")
#print(pairwise_adj[3])
}
#///PERMANOVA for each date, based on the spatial median for repeated measures
com_div_DAY_centroid <- function(data_name, treat, c_LB, meth, adj=NULL) {
#data_name <-uds_data_all_day2
#treat <- "T"
#meth="chao"
#date <- "10d","32d", or "60d", or any
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3, -77,-78, -79,-80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
} #When leaf beele is NOTincluded
if(c_LB == "LBonly") {
uds_data1 <- subset(data_name, abundance2 > 0)
uds_each <- subset(uds_data1, treatment==treat)[, c(4)] #picking up LB only
}
sample_each <- subset(uds_data1, treatment==treat)$sample
day_each <- subset(uds_data1, treatment==treat)$day
uds_bray_each0.d<-vegdist(uds_each, method=meth)
if(c_LB == "LBonly") tp <- "centroid"
else tp <- "median"
uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
uds_median <- data.frame(uds_dist_each0$centroids) #calculate the centroid (spatial median) for repeasted measures
sample <- row.names(uds_median)
#Replace the sample name into the date
sample[grep("^1", sample)]<- "day10"
sample[grep("^2", sample)]<- "day32"
sample[grep("^3", sample)]<- "day60"
day_each <- as.factor(sample) #just conversion
adonis_result<-adonis(uds_median ~ day_each, method="euclidean", permutations=9999)
print(adonis_result)
#For pairwise comarison (with Bonferroni adjustment)
cat("The pairwise PERMANOVA (adonis) analysis:\n")
uds_dataDay1032_0<-subset(data_name, day != "60d")
uds_dataDay1060_0<-subset(data_name, day != "32d")
uds_dataDay3260_0<-subset(data_name, day != "10d")
if(c_LB == "wLB") {
uds_dataDay1032_1 <-subset(uds_dataDay1032_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day1032_each<-subset(uds_dataDay1032_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
uds_dataDay1060_1 <-subset(uds_dataDay1060_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day1060_each<-subset(uds_dataDay1060_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
uds_dataDay3260_1 <-subset(uds_dataDay3260_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day3260_each<-subset(uds_dataDay3260_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_dataDay1032_1 <-subset(uds_dataDay1032_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day1032_each<-subset(uds_dataDay1032_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
uds_dataDay1060_1 <-subset(uds_dataDay1060_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day1060_each<-subset(uds_dataDay1060_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
uds_dataDay3260_1 <-subset(uds_dataDay3260_0, abundance2 > 0) #delete the sampling data with 0 total abundance when leaf beetle
Day3260_each<-subset(uds_dataDay3260_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
} #When leaf beele is NOTincluded
sample_1032 <- subset(uds_dataDay1032_1, treatment==treat)$sample
sample_1060 <- subset(uds_dataDay1060_1, treatment==treat)$sample
sample_3260 <- subset(uds_dataDay3260_1, treatment==treat)$sample
Day1032_each.d<-vegdist(Day1032_each, method=meth)
Day1060_each.d<-vegdist(Day1060_each, method=meth)
Day3260_each.d<-vegdist(Day3260_each, method=meth)
Day1032_dist_each <-betadisper(Day1032_each.d, sample_1032, type="median", bias.adjust = TRUE)
Day1060_dist_each <-betadisper(Day1060_each.d, sample_1060, type="median", bias.adjust = TRUE)
Day3260_dist_each <-betadisper(Day3260_each.d, sample_3260, type="median", bias.adjust = TRUE)
Day1032_median <- data.frame(Day1032_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
Day1060_median <- data.frame(Day1060_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
Day3260_median <- data.frame(Day3260_dist_each$centroids) #calculate the centroid (spatial median) for repeasted measures
Day1032_sample <- row.names(Day1032_median)
Day1060_sample <- row.names(Day1060_median)
Day3260_sample <- row.names(Day3260_median)
#Replace the sample name into the day
Day1032_sample[grep("^1", Day1032_sample)]<- "day10"
Day1032_sample[grep("^2", Day1032_sample)]<- "day32"
day_Day1032 <- as.factor(Day1032_sample) #just conversion
Day1060_sample[grep("^1", Day1060_sample)]<- "day10"
Day1060_sample[grep("^3", Day1060_sample)]<- "day60"
day_Day1060 <- as.factor(Day1060_sample) #just conversion
Day3260_sample[grep("^2", Day3260_sample)]<- "day32"
Day3260_sample[grep("^3", Day3260_sample)]<- "day60"
day_Day3260 <- as.factor(Day3260_sample) #just conversion
adonis_resultDay1032<-adonis(Day1032_median ~ day_Day1032, method="euclidian", permutations=9999)
adonis_resultDay1060<-adonis(Day1060_median ~ day_Day1060, method="euclidian", permutations=9999)
adonis_resultDay3260<-adonis(Day3260_median ~ day_Day3260, method="euclidian", permutations=9999)
p_list <- c(adonis_resultDay1032$aov$Pr[1], adonis_resultDay1060$aov$Pr[1], adonis_resultDay3260$aov$Pr[1])
pairwise_adj<-p.adjust(p_list, method = adj , n = length(p_list))
cat("Pairwise comparision by permutation, adjusted by")
print(adj)
cat("The adjusted P-value for day10 vs day32 was\n")
print(pairwise_adj[1])
cat("The adjusted P-value for day10 vs day60 was\n")
print(pairwise_adj[2])
cat("The adjusted P-value for day32 vs day60 was\n")
print(pairwise_adj[3])
}
#///PERMDISP or each date, based on the spatial median for repeated measures
disper_DAY_centroid <- function(data_name, treat, c_LB, meth, adj=NULL) {
#resolving inconsistency of the symbols to represent the treatments (and chamber)
if(treat == "U") treat <- "N"
else if(treat == "E") treat <- "T"
else if(treat == "D") treat <- "W"
else if(treat == "S") treat <- "T"
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3, -77,-78, -79,-80)]
} #When leaf beele is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
} #When leaf beele is NOTincluded
if(c_LB == "LBonly") {
uds_data1 <- subset(data_name, abundance2 > 0)
uds_each <- subset(uds_data1, treatment==treat)[, c(4)] #picking up LB only
}
sample_each <- subset(uds_data1, treatment==treat)$sample
day_each <- subset(uds_data1, treatment==treat)$day
uds_bray_each0.d<-vegdist(uds_each, method=meth)
if(c_LB == "LBonly") tp <- "centroid"
else tp <- "median"
uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
uds_median <- data.frame(uds_dist_each0$centroids) #calculate the centroid (spatial median) for repeasted measures
sample <- row.names(uds_median)
#Replace the sample name into the date
sample[grep("^1", sample)]<- "day10"
sample[grep("^2", sample)]<- "day32"
sample[grep("^3", sample)]<- "day60"
day_each <- as.factor(sample) #just conversion
#treatment_each
uds_each.d<-vegdist(uds_median, method="euclidean")
uds_dist_each <-betadisper(uds_each.d, day_each, type="centroid", bias.adjust = TRUE) #Choose centroid becasue of euclidean distance
#Plot
if(c_LB == "LBonly") boxplot(uds_dist_each)
else {
boxplot(uds_dist_each)
ordilabel(scores(uds_dist_each, "centroids"))
}
#PERDISP & ANOVA & MULTIPLE COMPARISON
#anova_dist <- anova(uds_dist_each)
#TukeyHSD(uds_dist_each, which = "group", ordered = FALSE, conf.levels = 0.95)
#PERDIPST & permutation
perm_result <- permutest(uds_dist_each, pairwise=TRUE, permutations=9999)
#perm_result
#print (anova_dist$Pr[1])
cat("Target treatment was\n")
print(treat)
print(uds_dist_each)
print(perm_result)
#In fact, the adjustment is not necessary because permutest.betadisper does not repeat permutation for each pair
if(!is.null(adj)) {
pairwise_adj<-p.adjust(perm_result$pairwise$permuted, method = adj, n = length(perm_result$pairwise$permuted))
cat("Pairwise comparision by permutation, adjusted by")
print(as.character(adj))
print(pairwise_adj[1])
print(pairwise_adj[2])
print(pairwise_adj[3])
}
#TukeyHSD(uds_dist_each)
}
#####For PERMDISP: comparison between chambers and treatments
(Fig.2a)
#Temporal variability in beta diversity for manuscript
disper_DAY_centroid(data_name=uds_data_all_day2, treat="U", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zeroTarget treatment was
[1] "N"
Homogeneity of multivariate dispersions
Call: betadisper(d = uds_each.d, group = day_each, type = "centroid", bias.adjust = TRUE)
No. of Positive Eigenvalues: 32
No. of Negative Eigenvalues: 0
Average distance to centroid:
day10 day32 day60
0.7089 0.6904 0.5292
Eigenvalues for PCoA axes:
(Showing 8 of 32 eigenvalues)
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
2.881 2.155 1.684 1.528 1.387 1.347 1.037 1.003
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.23186 0.115929 7.1717 9999 0.0024 **
Residuals 36 0.58193 0.016165
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
day10 day32 day60
day10 0.4429000 0.0065
day32 0.4385478 0.0163
day60 0.0068012 0.0161265 
disper_DAY_centroid(data_name=uds_data_all_day2, treat="S", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zeroTarget treatment was
[1] "T"
Homogeneity of multivariate dispersions
Call: betadisper(d = uds_each.d, group = day_each, type = "centroid", bias.adjust = TRUE)
No. of Positive Eigenvalues: 35
No. of Negative Eigenvalues: 0
Average distance to centroid:
day10 day32 day60
0.6462 0.6452 0.6937
Eigenvalues for PCoA axes:
(Showing 8 of 35 eigenvalues)
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
2.9445 2.0693 1.7712 1.4165 1.3933 1.1933 0.9916 0.8363
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.02078 0.010389 1.1044 9999 0.3316
Residuals 36 0.33865 0.009407
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
day10 day32 day60
day10 0.98260 0.2935
day32 0.98237 0.1310
day60 0.30142 0.13155 
disper_DAY_centroid(data_name=uds_data_all_day2, treat="D", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zeroTarget treatment was
[1] "W"
Homogeneity of multivariate dispersions
Call: betadisper(d = uds_each.d, group = day_each, type = "centroid", bias.adjust = TRUE)
No. of Positive Eigenvalues: 38
No. of Negative Eigenvalues: 0
Average distance to centroid:
day10 day32 day60
0.6880 0.6697 0.6604
Eigenvalues for PCoA axes:
(Showing 8 of 38 eigenvalues)
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
2.8219 2.1875 2.0501 1.6154 1.2362 1.0316 0.9482 0.8815
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00529 0.0026448 0.3821 9999 0.6883
Residuals 40 0.27685 0.0069213
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
day10 day32 day60
day10 0.51180 0.3856
day32 0.50939 0.7932
day60 0.38878 0.78578 
For PERMANOVA (Fig.2b)
#Results from 2-3 times measurement in 10d, 32d, and 60d without leaf beetle through spatial median
#With the recommended measure (Chao index) (for Fig.2b)
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao")
Sampling day was
[1] "10d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.001891 0.0018907 0.6307 9999 0.4249
Residuals 34 0.101927 0.0029979
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.015571 0.0077853 1.599 9999 0.2128
Residuals 33 0.160670 0.0048688
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.17600 0.8996
S 0.17609 0.1955
U 0.90043 0.19124 
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "32d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.002424 0.0024237 0.4006 9999 0.5329
Residuals 44 0.266220 0.0060504
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01282 0.0064087 0.7447 9999 0.4736
Residuals 43 0.37004 0.0086056
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.64410 0.4333
S 0.64677 0.2309
U 0.43762 0.22977 
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "60d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.15801 0.158009 7.9115 9999 0.0079 **
Residuals 37 0.73897 0.019972
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.15157 0.075784 3.6986 9999 0.0324 *
Residuals 36 0.73764 0.020490
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.434700 0.0852
S 0.432947 0.0289
U 0.084784 0.027949 
PERMANOVA (for Supplementary Material)
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao", adj="holm")
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.2694 0.26937 0.55504 0.01606 0.9468
Residuals 34 16.5006 0.48531 0.98394
Total 35 16.7699 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 0.7546 0.37732 0.77748 0.045 0.8075
Residuals 33 16.0153 0.48531 0.955
Total 35 16.7699 1.000
The pairwise PERMANOVA (adonis) analysis:
The comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.4758 0.47578 1.0316 0.04905 0.3875
Residuals 20 9.2242 0.46121 0.95095
Total 21 9.7000 1.00000 com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.4108 0.41082 0.80329 0.01793 0.6873
Residuals 44 22.5024 0.51142 0.98207
Total 45 22.9132 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.5462 0.77311 1.5558 0.06748 0.031 *
Residuals 43 21.3670 0.49691 0.93252
Total 45 22.9132 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 1.1096 1.10957 2.4132 0.07445 0.0052 **
Residuals 30 13.7936 0.45979 0.92555
Total 31 14.9031 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.8018 0.80177 1.8611 0.04789 0.0397 *
Residuals 37 15.9400 0.43081 0.95211
Total 38 16.7418 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.468 0.73398 1.73 0.08768 0.0218 *
Residuals 36 15.274 0.42427 0.91232
Total 38 16.742 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.6575 0.65750 1.4895 0.05418 0.1079
Residuals 26 11.4773 0.44144 0.94582
Total 27 12.1348 1.00000 Summary for the statistical tests on Figure 2
- Dispersion with time
- Dispersion of U (Chamber 1) decreased with time (day10=day32, but day10 > day60)
- No differences in dispersion bet. dates in S(E) and D treatments
- Dispersion between chambers and treatment
- Dispersion of U (Chamber 1) was smaller than chamber 2 (D+S) at 60d (P=0.0075).
- No differences in dispersion bet. S(E) and D treatments at 60d
- PERMANOVA (with PERMDISP) results tell that:
- the centroid community composition was not different between U vs (D + S) (no chamber effects).
- the difference between D and E at 32d (F = 2.4132, df=2, P = 0.0036)
- Temporal dynamics of centroid within each treatment
- For all U, D, and E, all pairwise PERMANOVA resulted in P < 0.001
- But due to heterogeneous dispersion day10 day32 > day60 for U, we cannot conclude the centroid of U at day 60 is difference from day 10 not day 32.
Statistical tests for Figure S1
For PERMDISP: comparision between chambers and treatments with bray-curtis measure
com_div_DAY_centroid(data_name=uds_data_all_day2, treat="N", c_LB="woLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ day_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
day_each 2 3.028 1.51401 3.3933 0.15862 1e-04 ***
Residuals 36 16.062 0.44617 0.84138
Total 38 19.090 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroPairwise comparision by permutation, adjusted by[1] "holm"
The adjusted P-value for day10 vs day32 was
[1] 0.0019
The adjusted P-value for day10 vs day60 was
[1] 3e-04
The adjusted P-value for day32 vs day60 was
[1] 3e-04com_div_DAY_centroid(data_name=uds_data_all_day2, treat="T", c_LB="woLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ day_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
day_each 2 2.8635 1.43177 3.1913 0.15059 1e-04 ***
Residuals 36 16.1514 0.44865 0.84941
Total 38 19.0149 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroPairwise comparision by permutation, adjusted by[1] "holm"
The adjusted P-value for day10 vs day32 was
[1] 3e-04
The adjusted P-value for day10 vs day60 was
[1] 0.0016
The adjusted P-value for day32 vs day60 was
[1] 3e-04com_div_DAY_centroid(data_name=uds_data_all_day2, treat="W", c_LB="woLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ day_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
day_each 2 2.3439 1.17194 2.5569 0.11335 1e-04 ***
Residuals 40 18.3339 0.45835 0.88665
Total 42 20.6778 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroPairwise comparision by permutation, adjusted by[1] "holm"
The adjusted P-value for day10 vs day32 was
[1] 9e-04
The adjusted P-value for day10 vs day60 was
[1] 9e-04
The adjusted P-value for day32 vs day60 was
[1] 0.0035For PERMANOVA: comparision between chambers and treatments with bray-curtis measure
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="bray")
Sampling day was
[1] "10d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.006804 0.0068039 1.9548 9999 0.1736
Residuals 34 0.118340 0.0034806
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.017357 0.0086784 1.9915 9999 0.1473
Residuals 33 0.143806 0.0043578
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.19600 0.5771
S 0.19320 0.1024
U 0.56666 0.10418 
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="bray") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "32d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.006626 0.0066260 1.0004 9999 0.3207
Residuals 44 0.291426 0.0066233
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.03321 0.0166063 1.9505 9999 0.1571
Residuals 43 0.36609 0.0085137
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.224300 0.4531
S 0.223032 0.0832
U 0.453786 0.080459 
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="bray") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "60d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.15997 0.159968 6.6985 9999 0.0139 *
Residuals 37 0.88360 0.023881
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.15006 0.075028 3.1041 9999 0.0558 .
Residuals 36 0.87015 0.024171
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.662300 0.0904
S 0.665325 0.0440
U 0.089952 0.043258 
For PERMDISP: comparision between chambers and treatments with Chao measure but with leaf beetle
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="bray", adj="holm")
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.3591 0.35914 0.70982 0.02045 0.8327
Residuals 34 17.2023 0.50595 0.97955
Total 35 17.5615 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 0.8157 0.40785 0.80373 0.04645 0.81
Residuals 33 16.7458 0.50745 0.95355
Total 35 17.5615 1.00000
The pairwise PERMANOVA (adonis) analysis:
The comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.4289 0.42887 0.91093 0.04356 0.5466
Residuals 20 9.4161 0.47080 0.95644
Total 21 9.8449 1.00000 com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="bray", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.4391 0.43915 0.83573 0.01864 0.6478
Residuals 44 23.1206 0.52547 0.98136
Total 45 23.5597 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.6115 0.80577 1.5786 0.0684 0.0244 *
Residuals 43 21.9482 0.51042 0.9316
Total 45 23.5597 1.0000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 1.1362 1.13619 2.4501 0.0755 0.0033 **
Residuals 30 13.9117 0.46372 0.9245
Total 31 15.0479 1.0000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="bray", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.8212 0.82119 1.7852 0.04603 0.0472 *
Residuals 37 17.0200 0.46000 0.95397
Total 38 17.8412 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.5345 0.76726 1.6939 0.08601 0.0255 *
Residuals 36 16.3066 0.45296 0.91399
Total 38 17.8412 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.7021 0.70206 1.5148 0.05506 0.0958 .
Residuals 26 12.0498 0.46345 0.94494
Total 27 12.7519 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1For PERMANOVA: comparision between chambers and treatments with bray-curtis measure
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="wLB", meth="chao")
Sampling day was
[1] "10d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00423 0.004225 0.0875 9999 0.7699
Residuals 42 2.02866 0.048301
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.52667 0.263337 6.4684 9999 0.0042 **
Residuals 41 1.66917 0.040712
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.0026000 0.1386
S 0.0014055 0.0463
U 0.1382685 0.0443417 
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="wLB", meth="chao") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "32d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.04387 0.043871 1.8568 9999 0.176
Residuals 46 1.08683 0.023627
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.03582 0.017910 0.7201 9999 0.4965
Residuals 45 1.11917 0.024871
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.62540 0.5226
S 0.62872 0.2060
U 0.52614 0.20338 
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="wLB", meth="chao") 警告: some squared distances are negative and changed to zero
Sampling day was
[1] "60d"
Comparison between chambers
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09148 0.091485 6.4648 9999 0.0169 *
Residuals 41 0.58020 0.014151
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Comparison between treatments
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 9999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.08619 0.043094 2.8724 9999 0.0737 .
Residuals 40 0.60010 0.015003
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
D S U
D 0.606000 0.1055
S 0.606209 0.0581
U 0.107893 0.050774 
Temporal divergence on each willow tree (Decided not to present)
Preparation of functions to calculate/visualize temporal divergence
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="wLB", meth="chao", adj="holm")
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.1711 0.17113 0.45554 0.01073 0.9935
Residuals 42 15.7776 0.37566 0.98927
Total 43 15.9487 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 0.7672 0.38361 1.036 0.04811 0.3699
Residuals 41 15.1815 0.37028 0.95189
Total 43 15.9487 1.00000
The pairwise PERMANOVA (adonis) analysis:
The comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.5947 0.59473 1.7606 0.06342 0.0572 .
Residuals 26 8.7825 0.33779 0.93658
Total 27 9.3772 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="wLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.3734 0.37342 0.91069 0.01941 0.4693
Residuals 46 18.8616 0.41003 0.98059
Total 47 19.2350 1.00000
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.1599 0.57994 1.4438 0.0603 0.0904 .
Residuals 45 18.0751 0.40167 0.9397
Total 47 19.2350 1.0000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.7938 0.79381 1.9693 0.0616 0.0474 *
Residuals 30 12.0926 0.40309 0.9384
Total 31 12.8864 1.0000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="wLB", meth="chao", adj="holm") 警告: some squared distances are negative and changed to zero
Call:
adonis(formula = uds_median ~ chamber_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
chamber_each 1 0.699 0.69904 1.5518 0.03647 0.0877 .
Residuals 41 18.469 0.45047 0.96353
Total 42 19.168 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
adonis(formula = uds_median ~ treatment_each, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_each 2 1.3211 0.66053 1.4804 0.06892 0.0596 .
Residuals 40 17.8472 0.44618 0.93108
Total 42 19.1683 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The pairwise PERMANOVA (adonis) analysis: 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zero 警告: some squared distances are negative and changed to zeroThe comparision between D and S is:
Call:
adonis(formula = DS_median ~ treatment_DS, permutations = 9999, method = "euclidean")
Permutation: free
Number of permutations: 9999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
treatment_DS 1 0.6081 0.60811 1.3234 0.04513 0.1711
Residuals 28 12.8664 0.45952 0.95487
Total 29 13.4745 1.00000 ####Calculate/Visualize within day turnover (which is not used in the manuscript)
turnover_comparison_short <- function(data_name, date, c_LB, meth, output = NULL)
{
#data_name <- uds_data_all_day2
#date <- "10d"
#meth<-"chao"
#date <- "10d","32d", or "60d", or any
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_data2 <-subset(uds_data1, day==date)
uds_each<-uds_data2[,c(-1,-2,-3, -77,-78, -79,-80)]
} #When leaf beetle is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_data2 <-subset(uds_data1, day==date)
uds_each<-uds_data2[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
} #When leaf beetle is NOT included
sample_each <- subset(uds_data1, day==date)$sample
treatment_each <- subset(uds_data1, day==date)$treatment
chamber_each <- subset(uds_data1, day==date)$chamber
#View(chamber_each)
time_each <- subset(uds_data1, day==date)$time
#prepare dummy vectors
sample_n <- NULL
distance_n <-NULL
treatment_n <-NULL
chamber_n <-NULL
timei_n <-NULL
timej_n <-NULL
#calculate distance between 1P, 2P, and 3P
for(i in 1:(length(sample_each) - 1)) {
for(j in (i + 1): length(sample_each)) {
if(sample_each[i] == sample_each[j]) {
sample_n <- append(sample_n, as.character(sample_each[i]))
distance_n <- append(distance_n, as.numeric(vegdist(rbind(uds_each[i,], uds_each[j,])), method=meth))
treatment_n <-append(treatment_n, as.character(treatment_each[i]))
chamber_n <- append(chamber_n, as.character(chamber_each[i]))
timei_n <-append(timei_n, as.character(time_each[i]))
timej_n <-append(timej_n, as.character(time_each[j]))
}
}
}
turnover<- data.frame(sample=sample_n, distance=distance_n, treatment=treatment_n, chamber=chamber_n, timei=timei_n, timej=timej_n)
turnover2 <-subset(turnover, !(timei == "1P" & timej=="3P")) #exclude the combination of 1P & 3P (redundant)
#turnoverN <- subset(turnover2, treatment=="N") #separate data by treatment
turnoverT <- subset(turnover2, treatment=="T") #separate data by treatment
turnoverW <- subset(turnover2, treatment=="W") #separate data by treatment
turnoverC1 <- subset(turnover2, chamber=="C1") #separate data by chamber
turnoverC2 <- subset(turnover2, chamber=="C2") #separate data by chamber
#calculate average of 1P vs 2P & 2P vs 3P if any
#average_turnoverN<-by(as.numeric(turnoverN$distance), turnoverN$sample, mean)
average_turnoverT<-by(as.numeric(turnoverT$distance), turnoverT$sample, mean)
average_turnoverW<-by(as.numeric(turnoverW$distance), turnoverW$sample, mean)
average_turnoverC1<-by(as.numeric(turnoverC1$distance), turnoverC1$sample, mean)
average_turnoverC2<-by(as.numeric(turnoverC2$distance), turnoverC2$sample, mean)
#average_N<-subset(average_turnoverN, average_turnoverN>=0)
average_T<-subset(average_turnoverT, average_turnoverT>=0)
average_W<-subset(average_turnoverW, average_turnoverW>=0)
average_C1<-subset(average_turnoverC1, average_turnoverC1>=0)
average_C2<-subset(average_turnoverC2, average_turnoverC2>=0)
length(average_T)
#trN<-NULL
trT<-NULL
trW<-NULL
trC1<-NULL
trC2<-NULL
#for(i in 1:length(average_N)) trN <-append(trN, "N")
for(i in 1:length(average_T)) trT <-append(trT, "E")
for(i in 1:length(average_W)) trW <-append(trW, "D")
for(i in 1:length(average_C1)) trC1 <-append(trC1, "C1")
for(i in 1:length(average_C2)) trC2 <-append(trC2, "C2")
#av_N<-data.frame(treatment=trN, distance=average_N)
av_T<-data.frame(treatment=trT, distance=average_T)
av_W<-data.frame(treatment=trW, distance=average_W)
av_C1<-data.frame(chamber=trC1, distance=average_C1)
av_C2<-data.frame(chamber=trC2, distance=average_C2)
#summary dataframe
turnover_short <- rbind.data.frame(av_T, av_W)
turnover_shortC <- rbind.data.frame(av_C1, av_C2)
#print(turnover_shortC$distance)
#View(turnover_shortC)
#View(turnover_short)
#print(distance_n)
g_box<-ggplot(turnover_shortC, aes(y=distance, x=chamber))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(width=0.1)
g_box<-g_box + coord_fixed(ratio = 5/1) + coord_cartesian(ylim = c(0, 1))
print(g_box)
g_box2<-ggplot(turnover_short, aes(y=distance, x=treatment))
g_box2<-g_box2 + geom_violin()
g_box2<-g_box2 + geom_boxplot(width=0.1)
g_box2<-g_box2 + coord_fixed(ratio = 5/1) + coord_cartesian(ylim = c(0, 1))
print(g_box2)
#plotmeans(turnover_shortC$distance~turnover_shortC$chamber)
#plotmeans(turnover_short$distance~turnover_short$treatment)
print(bartlett.test(turnover_shortC$distance~turnover_shortC$chamber))
#One-way ANOVA with unequal-variance
print(oneway.test(turnover_shortC$distance~turnover_shortC$chamber,var.equal=FALSE))
print(bartlett.test(turnover_short$distance~turnover_short$treatment))
#One-way ANOVA with unequal-variance
print(oneway.test(turnover_short$distance~turnover_short$treatment,var.equal=FALSE))
#Pairwise comparison with Welch's t test
#print(pairwise.t.test(turnover_short$distance, turnover_short$treatment, p.adj="holm"))
#if(output==TRUE) return(turnover_short)
}
turnover_comparison_long <- function(data_name, from_date, c_LB, meth="chao", output = NULL)
{
if(c_LB == "wLB") {
uds_data1 <-subset(data_name, abundance > 0) #delete the sampling data with 0 total abundance when leaf beetle
uds_each<-uds_data1[,c(-1,-2,-3, -77,-78,-79,-80)]
} #When leaf beetle is included
if(c_LB == "woLB") {
uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_each<-uds_data1[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
} #When leaf beetle is NOT included
sample_each <- uds_data1$sample
chamber_each <- uds_data1$chamber
treatment_each <- uds_data1$treatment
time_each <- uds_data1$time
uds_time.d<-vegdist(uds_each, method=meth)
uds_dist_time <-betadisper(uds_time.d, sample_each, type="median", bias.adjust = TRUE)
uds_day.median <- data.frame(uds_dist_time$centroids) #calculate the centroid (spatial median) for repeated measures
#Replace the sample name into the treatment & day
sample0 <- row.names(uds_day.median)
sample1 <- sample0
sample2 <- sample0
sample3 <- sample0
sample0<-gsub("1n", "n", sample0)
sample0<-gsub("2n", "n", sample0)
sample0<-gsub("3n", "n", sample0)
sample0<-gsub("1t", "t", sample0)
sample0<-gsub("2t", "t", sample0)
sample0<-gsub("3t", "t", sample0)
sample0<-gsub("1w", "w", sample0)
sample0<-gsub("2w", "w", sample0)
sample0<-gsub("3w", "w", sample0)
sample1[grep("n", sample1)]<- "C1"
sample1[grep("t", sample1)]<- "C2"
sample1[grep("w", sample1)]<- "C2"
sample2[grep("n", sample2)]<- "U"
sample2[grep("t", sample2)]<- "E"
sample2[grep("w", sample2)]<- "D"
sample3[grep("1n", sample3)] <-"10d"
sample3[grep("2n", sample3)] <-"32d"
sample3[grep("3n", sample3)] <-"60d"
sample3[grep("1t", sample3)] <-"10d"
sample3[grep("2t", sample3)] <-"32d"
sample3[grep("3t", sample3)] <-"60d"
sample3[grep("1w", sample3)] <-"10d"
sample3[grep("2w", sample3)] <-"32d"
sample3[grep("3w", sample3)] <-"60d"
sample_each <- as.factor(sample0)
chamber_each <- as.factor(sample1)
treatment_each <- as.factor(sample2) #just conversion
day_each <- as.factor(sample3) #just conversion
uds_day.median2<-cbind(chamber=chamber_each, uds_day.median)
uds_day.median2<-cbind(treatment=treatment_each, uds_day.median2)
uds_day.median2<-cbind(day=day_each, uds_day.median2)
uds_day.median2<-cbind(sample_each, uds_day.median2)
uds_day.median2[1,c(-1,-2,-3)]
uds_day.median2$sample_each[2]
#View(uds_day.median2)
#prepare dummy vectors
sample_n <- NULL
distance_n <-NULL
chamber_n <-NULL
treatment_n <-NULL
dayi_n <-NULL
dayj_n <-NULL
#calculate distance between 10d, 32d, and 60d
for(i in 1:(length(sample_each) - 1)) {
for(j in (i + 1): length(sample_each)) {
if(sample_each[i] == sample_each[j]) {
sample_n <- append(sample_n, as.character(sample_each[i]))
distance_n <- append(distance_n, as.numeric(vegdist(rbind.data.frame(uds_day.median2[i,c(-1,-2,-3,-4)], uds_day.median2[j,c(-1,-2,-3,-4)]), method="euclidean")))
chamber_n <-append(chamber_n, as.character(chamber_each[i]))
treatment_n <-append(treatment_n, as.character(treatment_each[i]))
dayi_n <-append(dayi_n, as.character(day_each[i]))
dayj_n <-append(dayj_n, as.character(day_each[j]))
}
}
}
turnover<- data.frame(sample=sample_n, distance=distance_n, chamber=chamber_n, treatment=treatment_n, dayi=dayi_n, dayj=dayj_n)
if(from_date == "10d") turnover_long <-subset(turnover, (dayi == "10d" & dayj=="32d"))
#turnover_10d60d <-subset(turnover, (dayi == "10d" & dayj=="60d")) #not used excluding the combination of 10d & 60d (redundanct)
if(from_date == "32d") turnover_long <-subset(turnover, (dayi == "32d" & dayj=="60d"))
turnover_long_tr <- subset(turnover_long, chamber == "C2")
View(turnover_long)
View(turnover_long_tr)
g_box<-ggplot(turnover_long, aes(y=distance, x=chamber))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(width=0.1)
g_box<-g_box + coord_fixed(ratio = 5/1)
print(g_box)
g_box2<-ggplot(turnover_long_tr, aes(y=distance, x=treatment))
g_box2<-g_box2 + geom_violin()
g_box2<-g_box2 + geom_boxplot(width=0.1)
g_box2<-g_box2 + coord_fixed(ratio = 5/1)
print(g_box2)
#plotmeans(turnover_long$distance~turnover_long$chamber)
#plotmeans(turnover_long_tr$distance~turnover_long_tr$treatment)
print(bartlett.test(turnover_long$distance~turnover_long$chamber))
print(bartlett.test(turnover_long_tr$distance~turnover_long_tr$treatment))
#One-way ANOVA with unequal-variance
print(oneway.test(turnover_long$distance~turnover_long$chamber,var.equal=FALSE))
print(oneway.test(turnover_long_tr$distance~turnover_long_tr$treatment,var.equal=FALSE))
}####Calculate/Visualize between dates turnover (not used)
#(No difference at all @ 10 day between time) ####For Fig. S1
#turnover_comparison_short(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao") #error due to lack of replication within day
turnover_comparison_short(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao")Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Bartlett test of homogeneity of variances
data: turnover_shortC$distance by turnover_shortC$chamber
Bartlett's K-squared = 0.59528, df = 1, p-value = 0.4404
One-way analysis of means (not assuming equal variances)
data: turnover_shortC$distance and turnover_shortC$chamber
F = 0.77285, num df = 1.000, denom df = 21.334, p-value = 0.3891
Bartlett test of homogeneity of variances
data: turnover_short$distance by turnover_short$treatment
Bartlett's K-squared = 1.1809, df = 1, p-value = 0.2772
One-way analysis of means (not assuming equal variances)
data: turnover_short$distance and turnover_short$treatment
F = 0.34211, num df = 1.000, denom df = 27.826, p-value = 0.5633
turnover_comparison_short(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao")Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Bartlett test of homogeneity of variances
data: turnover_shortC$distance by turnover_shortC$chamber
Bartlett's K-squared = 1.396, df = 1, p-value = 0.2374
One-way analysis of means (not assuming equal variances)
data: turnover_shortC$distance and turnover_shortC$chamber
F = 2.5418, num df = 1.000, denom df = 13.773, p-value = 0.1335
Bartlett test of homogeneity of variances
data: turnover_short$distance by turnover_short$treatment
Bartlett's K-squared = 0.38892, df = 1, p-value = 0.5329
One-way analysis of means (not assuming equal variances)
data: turnover_short$distance and turnover_short$treatment
F = 0.024992, num df = 1.000, denom df = 15.321, p-value = 0.8765
Summary of the species turnover analysis (Decided not to use)
- No differences between Chambers
- No differences between E and D
Relationship between leaf beetle population size and community composition
Preparation for Mantel test
#(No difference at all @ 10 day between time) ####For Fig. S1
turnover_comparison_long(data_name=uds_data_all_day2, from_date="10d", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zero
Bartlett test of homogeneity of variances
data: turnover_long$distance by turnover_long$chamber
Bartlett's K-squared = 3.6943, df = 1, p-value = 0.0546
Bartlett test of homogeneity of variances
data: turnover_long_tr$distance by turnover_long_tr$treatment
Bartlett's K-squared = 3.5787, df = 1, p-value = 0.05852
One-way analysis of means (not assuming equal variances)
data: turnover_long$distance and turnover_long$chamber
F = 0.46726, num df = 1.000, denom df = 15.545, p-value = 0.5043
One-way analysis of means (not assuming equal variances)
data: turnover_long_tr$distance and turnover_long_tr$treatment
F = 0.040366, num df = 1.000, denom df = 11.225, p-value = 0.8444
turnover_comparison_long(data_name=uds_data_all_day2, from_date="32d", c_LB="woLB", meth="chao") 警告: some squared distances are negative and changed to zero
Bartlett test of homogeneity of variances
data: turnover_long$distance by turnover_long$chamber
Bartlett's K-squared = 4.5976, df = 1, p-value = 0.03202
Bartlett test of homogeneity of variances
data: turnover_long_tr$distance by turnover_long_tr$treatment
Bartlett's K-squared = 0.0072188, df = 1, p-value = 0.9323
One-way analysis of means (not assuming equal variances)
data: turnover_long$distance and turnover_long$chamber
F = 0.15229, num df = 1.00, denom df = 28.73, p-value = 0.6992
One-way analysis of means (not assuming equal variances)
data: turnover_long_tr$distance and turnover_long_tr$treatment
F = 1.0881, num df = 1.000, denom df = 25.985, p-value = 0.3065
Mantel test and simple plots
LB_diff_C1_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C12")
LB_diff_C1_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C12")
LB_diff_C1_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C12")
comm_diff_C1_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C1")
comm_diff_C2_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C2")
comm_diff_C12_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C12")
comm_diff_C1_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C1") 警告: some squared distances are negative and changed to zerocomm_diff_C2_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C2") 警告: some squared distances are negative and changed to zerocomm_diff_C12_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C12") 警告: some squared distances are negative and changed to zerocomm_diff_C1_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C1") 警告: some squared distances are negative and changed to zerocomm_diff_C2_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C2") 警告: some squared distances are negative and changed to zerocomm_diff_C12_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C12") 警告: some squared distances are negative and changed to zeroggplot for mantel test
Only for day 32
plot(LB_diff_C1_d10.d, comm_diff_C1_d10.d)
mantel(LB_diff_C1_d10.d, comm_diff_C1_d10.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C1_d10.d, ydis = comm_diff_C1_d10.d, method = "pearson", permutations = 9999)
Mantel statistic r: -0.1332
Significance: 0.8255
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.141 0.168 0.190 0.214
Permutation: free
Number of permutations: 9999plot(LB_diff_C2_d10.d, comm_diff_C2_d10.d)
mantel(LB_diff_C2_d10.d, comm_diff_C2_d10.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C2_d10.d, ydis = comm_diff_C2_d10.d, method = "pearson", permutations = 9999)
Mantel statistic r: -0.07778
Significance: 0.8019
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.114 0.150 0.178 0.209
Permutation: free
Number of permutations: 9999plot(LB_diff_C12_d10.d, comm_diff_C12_d10.d)
mantel(LB_diff_C12_d10.d, comm_diff_C12_d10.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C12_d10.d, ydis = comm_diff_C12_d10.d, method = "pearson", permutations = 9999)
Mantel statistic r: -0.09194
Significance: 0.9521
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.0771 0.0999 0.1190 0.1402
Permutation: free
Number of permutations: 9999plot(LB_diff_C1_d32.d, comm_diff_C1_d32.d)
mantel(LB_diff_C1_d32.d, comm_diff_C1_d32.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C1_d32.d, ydis = comm_diff_C1_d32.d, method = "pearson", permutations = 9999)
Mantel statistic r: -0.01861
Significance: 0.5386
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.154 0.199 0.239 0.285
Permutation: free
Number of permutations: 9999plot(LB_diff_C2_d32.d, comm_diff_C2_d32.d)
mantel(LB_diff_C2_d32.d, comm_diff_C2_d32.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C2_d32.d, ydis = comm_diff_C2_d32.d, method = "pearson", permutations = 9999)
Mantel statistic r: 0.1698
Significance: 0.0151
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.117 0.139 0.158 0.179
Permutation: free
Number of permutations: 9999plot(LB_diff_C12_d32.d, comm_diff_C12_d32.d)
mantel(LB_diff_C12_d32.d, comm_diff_C12_d32.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C12_d32.d, ydis = comm_diff_C12_d32.d, method = "pearson", permutations = 9999)
Mantel statistic r: 0.128
Significance: 0.026
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.0936 0.1136 0.1288 0.1465
Permutation: free
Number of permutations: 9999plot(LB_diff_C1_d60.d, comm_diff_C1_d60.d)
#mantel(LB_diff_C1_d60.d, comm_diff_C1_d60.d, method="pearson", permutations=9999)
plot(LB_diff_C2_d60.d, comm_diff_C2_d60.d)
mantel(LB_diff_C2_d60.d, comm_diff_C2_d60.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C2_d60.d, ydis = comm_diff_C2_d60.d, method = "pearson", permutations = 9999)
Mantel statistic r: -0.007227
Significance: 0.5256
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.149 0.183 0.206 0.232
Permutation: free
Number of permutations: 9999plot(LB_diff_C12_d60.d, comm_diff_C12_d60.d)
mantel(LB_diff_C12_d60.d, comm_diff_C12_d60.d, method="pearson", permutations=9999)
Mantel statistic based on Pearson's product-moment correlation
Call:
mantel(xdis = LB_diff_C12_d60.d, ydis = comm_diff_C12_d60.d, method = "pearson", permutations = 9999)
Mantel statistic r: 0.02801
Significance: 0.4126
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.135 0.161 0.180 0.198
Permutation: free
Number of permutations: 9999Summary of mantel test
- Positive relationship between LB population size and community composition at day 32 for the whole data
- Positive relationship between LB population size and community composition at day 32 for C2 chamber
- No relationship between LB population size and community composition at day 32 for C1 chamber
Species richness analysis (Fig.3 & Fig.S3)
Preparation of functions with iNEXT package
library(reshape)
#C1 only
m_LB <- as.matrix(LB_diff_C1_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C1_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)
g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C1 only")
g_points<-g_points+geom_point(size=2,alpha=0.3)
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
summary(result)
Call:
lm(formula = m2_LB_com$distance_com ~ m2_LB_com$distance_LB)
Residuals:
Min 1Q Median 3Q Max
-1.01101 -0.02574 0.06248 0.10782 0.19192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.015442 0.033019 30.754 <2e-16 ***
m2_LB_com$distance_LB -0.004041 0.023008 -0.176 0.861
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1831 on 89 degrees of freedom
Multiple R-squared: 0.0003464, Adjusted R-squared: -0.01089
F-statistic: 0.03084 on 1 and 89 DF, p-value: 0.861result$coefficients[1](Intercept)
1.015442 g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=2,colour="blue") # slope
print(g_points)
#C2 only
m_LB <- as.matrix(LB_diff_C2_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C2_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)
g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C2 only")
g_points<-g_points+geom_point(size=2,alpha=0.3)
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
summary(result)
Call:
lm(formula = m2_LB_com$distance_com ~ m2_LB_com$distance_LB)
Residuals:
Min 1Q Median 3Q Max
-0.98366 -0.06532 0.03618 0.10218 0.32866
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.971440 0.008921 108.892 < 2e-16 ***
m2_LB_com$distance_LB 0.013777 0.003598 3.829 0.000145 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1607 on 494 degrees of freedom
Multiple R-squared: 0.02882, Adjusted R-squared: 0.02685
F-statistic: 14.66 on 1 and 494 DF, p-value: 0.0001454result$coefficients[1](Intercept)
0.9714401 g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=1,colour="blue") # slope
print(g_points)
#C1 + C2
m_LB <- as.matrix(LB_diff_C12_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C12_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,] 警告: 'as.is' should be specified by the caller; using TRUE 警告: 'as.is' should be specified by the caller; using TRUEnames(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)
g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C1 + C2")
g_points<-g_points+geom_point(size=2,alpha=0.3)
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
result$coefficients[1](Intercept)
0.9765874 g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=1,colour="blue") # slope
print(g_points)
Temporal dynamics of species richness
Observed species richness per plant individuals (Fig.S2)
alpha_hill_number <- function(data_name, date, q)
{
#data_name <-uds_data_all_day2
#date <- "32d"
#Including leaf_beetle
uds_data1 <-subset(data_name, abundance2 >= 0) #OR, delete the sampling when species other than leaf beetle does not exist
uds_data2 <-subset(uds_data1, day==date)
#if excluding leaf beetle
#uds_each<-uds_data2[,c(-1,-2,-3,-4,-77,-78,-79)]
#OR inclcding leaf beetle
uds_each<-uds_data2[,c(-1,-2,-3,-77,-78,-79,-80)]
#Hill number q = 0, 1, 2
uds_hill<-renyi(uds_each, scales=c(0,1,2), hill=TRUE)
uds_hill2<-cbind(uds_hill, uds_data2[,c(77,78,79,80)])
colnames(uds_hill2) <-c("H0", "H1", "H2", "day", "treatment", "time", "chamber")
#convert NA to 0
uds_hill2$H0[is.na(uds_hill2$H0)] <- 0
uds_hill3 <- subset(uds_hill2,chamber=="C2")
uds_hill3$treatment[uds_hill3$treatment=="T"] <- "E"
uds_hill3$treatment[uds_hill3$treatment=="W"] <- "D"
#View(uds_hill2)
if(q == 0) {
g_box<-ggplot(uds_hill2, aes(y=H0, x=chamber))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(width=0.1)
g_box<-g_box + coord_fixed(ratio = 1/2)
print(g_box)
g_box2<-ggplot(uds_hill3, aes(y=H0, x=treatment))
g_box2<-g_box2 + geom_violin()
g_box2<-g_box2 + geom_boxplot(width=0.1)
g_box2<-g_box2 + coord_fixed(ratio = 1/2)
print(g_box2)
plotmeans(uds_hill2$H0~uds_hill2$chamber)
View(uds_hill2)
#homogeneity of variance
print(bartlett.test(uds_hill2$H0~uds_hill2$chamber))
print(bartlett.test(uds_hill3$H0~uds_hill3$treatment))
#One-way ANOVA with unequal-variance between chambers
print(oneway.test(uds_hill2$H0~uds_hill2$chamber,var.equal=FALSE))
print(oneway.test(uds_hill3$H0~uds_hill3$treatment,var.equal=FALSE))
#Pairwise comparison with Welch's t test
#print(pairwise.t.test(uds_hill2$H0, uds_hill2$treatment, p.adj="holm"))
}
if(q == 1) {
g_box<-ggplot(uds_hill2, aes(y=H1, x=chamber))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(width=0.1)
g_box<-g_box + coord_fixed(ratio = 1/2)
print(g_box)
g_box2<-ggplot(uds_hill3, aes(y=H1, x=treatment))
g_box2<-g_box2 + geom_violin()
g_box2<-g_box2 + geom_boxplot(width=0.1)
g_box2<-g_box2 + coord_fixed(ratio = 1/2)
print(g_box2)
plotmeans(uds_hill2$H1~uds_hill2$chamber)
View(uds_hill2)
#homogeneity of variance
print(bartlett.test(uds_hill2$H1~uds_hill2$chamber))
print(bartlett.test(uds_hill3$H1~uds_hill3$treatment))
#One-way ANOVA with unequal-variance between chambers
print(oneway.test(uds_hill2$H1~uds_hill2$chamber,var.equal=FALSE))
print(oneway.test(uds_hill3$H1~uds_hill3$treatment,var.equal=FALSE))
}
if(q == 2) {
g_box<-ggplot(uds_hill2, aes(y=H2, x=chamber))
g_box<-g_box + geom_violin()
g_box<-g_box + geom_boxplot(width=0.1)
g_box<-g_box + coord_fixed(ratio = 1/2)
print(g_box)
g_box2<-ggplot(uds_hill3, aes(y=H2, x=treatment))
g_box2<-g_box2 + geom_violin()
g_box2<-g_box2 + geom_boxplot(width=0.1)
g_box2<-g_box2 + coord_fixed(ratio = 1/2)
print(g_box2)
plotmeans(uds_hill2$H1~uds_hill2$chamber)
View(uds_hill2)
#homogeneity of variance
print(bartlett.test(uds_hill2$H2~uds_hill2$chamber))
print(bartlett.test(uds_hill3$H2~uds_hill3$treatment))
#One-way ANOVA with unequal-variance between chambers
print(oneway.test(uds_hill2$H2~uds_hill2$chamber,var.equal=FALSE))
print(oneway.test(uds_hill3$H2~uds_hill3$treatment,var.equal=FALSE))
}
}
Summary of observerd richness
- There were significant differences bet.chambers (C1 < C2) at day 32 and day 60 but the difference is minor
- There were no differences bet. D and E at any days
Species richness estimates analyzed by iNEXT (Fig.3)
Loading data for iNEXT analyses
#all samplings are treated as independent, used in the manuscript
####For Figure S2
alpha_hill_number(uds_data_all_day2, date="10d", q=0)
Bartlett test of homogeneity of variances
data: uds_hill2$H0 by uds_hill2$chamber
Bartlett's K-squared = 1.3053, df = 1, p-value = 0.2532
Bartlett test of homogeneity of variances
data: uds_hill3$H0 by uds_hill3$treatment
Bartlett's K-squared = 0.095202, df = 1, p-value = 0.7577
One-way analysis of means (not assuming equal variances)
data: uds_hill2$H0 and uds_hill2$chamber
F = 0.74438, num df = 1.000, denom df = 38.077, p-value = 0.3937
One-way analysis of means (not assuming equal variances)
data: uds_hill3$H0 and uds_hill3$treatment
F = 0.57447, num df = 1.000, denom df = 29.805, p-value = 0.4544
alpha_hill_number(uds_data_all_day2, date="32d", q=0)
Bartlett test of homogeneity of variances
data: uds_hill2$H0 by uds_hill2$chamber
Bartlett's K-squared = 0.81835, df = 1, p-value = 0.3657
Bartlett test of homogeneity of variances
data: uds_hill3$H0 by uds_hill3$treatment
Bartlett's K-squared = 0.49807, df = 1, p-value = 0.4803
One-way analysis of means (not assuming equal variances)
data: uds_hill2$H0 and uds_hill2$chamber
F = 6.7446, num df = 1.000, denom df = 85.245, p-value = 0.01107
One-way analysis of means (not assuming equal variances)
data: uds_hill3$H0 and uds_hill3$treatment
F = 0.032126, num df = 1.000, denom df = 93.009, p-value = 0.8581
alpha_hill_number(uds_data_all_day2, date="60d", q=0)
Bartlett test of homogeneity of variances
data: uds_hill2$H0 by uds_hill2$chamber
Bartlett's K-squared = 8.5728, df = 1, p-value = 0.003412
Bartlett test of homogeneity of variances
data: uds_hill3$H0 by uds_hill3$treatment
Bartlett's K-squared = 1.1076, df = 1, p-value = 0.2926
One-way analysis of means (not assuming equal variances)
data: uds_hill2$H0 and uds_hill2$chamber
F = 9.2244, num df = 1.00, denom df = 129.39, p-value = 0.002889
One-way analysis of means (not assuming equal variances)
data: uds_hill3$H0 and uds_hill3$treatment
F = 0.21631, num df = 1.000, denom df = 91.839, p-value = 0.643
Estimating the coverage-based standardized richness (Fig.3a) & asymptotic richness (Fig.3b)
For standardized richness (Results for Fig.3a)
incidence_whole <- read.csv("monthly-for-R_iNEXT.csv", header=T) #for incidence data calculated by ExcelFor asymptotic richness (Results for Fig.3b)
####This is code for graphics in the manuscript (Fig.3)
stat_test<-DataInfo(incidence_whole, datatype = "incidence") ##This is necessary to find the minimum value of the coverage
estimateD(incidence_whole, datatype="incidence_freq", base="coverage", level=min(stat_test$SC))Loading data for graphics
The above iNEXT results were manually summarized in excel and
csv.
#When Short-term repeated measurement is assumed to be independent
ChaoRichness(incidence_whole, datatype="incidence") #It is OK to use dataframe, this is from iNEXTFigure 3a
diversity_summary <- read.csv("iNEXT_summary.csv",header=T)
class(diversity_summary)[1] "data.frame"View(diversity_summary)
diversity_summary2<-diversity_summary[1:20,]
class(diversity_summary2$day)[1] "character"Figure 3b
#colours <- c("#F8766D", "#EE55B7", "#00BA38", "#619CFF", "#E76BF3")
g_div_a <- ggplot(data=diversity_summary2, aes(x=ID,y=SR))
g_div_a <- g_div_a + geom_errorbar(aes(ymax = Hconf, ymin = Lconf, colour=treatment), width = 0.0,size=5, alpha=0.5) #+ scale_colour_manual("Group", values = colours)
g_div_a <- g_div_a + geom_point(aes(colour=treatment), size=3) #+ scale_colour_manual("Group", values = colours)
g_div_a <- g_div_a + geom_point(aes(y=Observed), size=3) + coord_fixed(ratio = 1/5) #+ scale_colour_manual("Group", values = colours)
print(g_div_a)
Summary for richness analysis
Observed richness (Fig.3a)
- the whole richness at each day was greater than any of single treatment or single chamber
- the richness in the whole period was greater than any of single date for every treatment and chamber
Standardized richness (Fig.3a)
Asymptotic richness (Fig.3b)
- Chamber 2 (E&D) had a greater richness than Chamber 1 (U)
- Chamber 2 had a greater richness than D
- The whole richness (UED) was greater than Chamber 1 and D
Species richness partitioning (Table S2, Fig. S3, and Fig.3C)
#Plot Asymptotic richness (Chao index)(Fig3b)
diversity_summary3<-diversity_summary[16:20,]
diversity_summary3$ID <- c("1U", "2ED", "3E", "4D", "5UED")
#colours <- c("#F8766D", "#00BA38", "#619CFF", "#E76BF3", "#00B6EB", "#A58AFF", "#FB61D7")
g_div_b <- ggplot(data=diversity_summary3, aes(x=ID,y=AsyEST))
g_div_b <- g_div_b + geom_errorbar(aes(ymax = Hconf_asy, ymin = Lconf_asy, colour=treatment), width = 0.0,size=5, alpha=1.0) #+ scale_colour_manual("Group", values = colours)
g_div_b <- g_div_b + geom_point(size=5)
print(g_div_b)
--- 
title: "frontier_chemical_ecology_yoneya2022 v3 (final)"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

## Loading libraries
```{r}
library(vegan)
library(ggplot2)
library(ggsci)
library(gplots)
library(iNEXT)
library(nlme)
library(rcompanion)
library(car)
set.seed(4321)
```
## Loading datasets
```{r}
uds_data5.temp <- read.csv("monthly-for-R3.csv",header=T)  #for 2 or 3 continuous sampling at day 10, 32, and 60

uds_data_all_day <-uds_data5.temp[,c(-1)]
uds_data_all_day2 <-uds_data5.temp

#converting treatment to chamber
chamber <- uds_data_all_day2$treatment
chamber[chamber == "N"] <- "C1"
chamber[chamber == "W"] <- "C2"
chamber[chamber == "T"] <- "C2"
uds_data_all_day2 <- cbind.data.frame(uds_data_all_day2, chamber)

uds_data_all_LBa <- read.csv("leaf_beetle_adult_summary.csv", header=T) #for leaf beetle adult data
uds_dataLB <- uds_data_all_day[, c(3)]
head(uds_data_all_LBa)
head(uds_data_all_day2)
```
## Note for symbols to represent plant initial conditions
- "U" and "N" were interchangeably used to represent "Uninfested plants" in chamber C1
- "E", "S", and "T" were interchangeably used to represent "Exposed plants" in chamber C2
- "D" and "W" were interchangeably used to represent "Damaged plants" in chamber C2

## Basic Analysis of the time evolution (succession) of the abundant species (for supporting information)
### Data pretreatment
```{r}
SAD_total <- data.frame(abundance=apply(uds_data_all_day2[,4:76], 2, sum))
SAD_rank <- order(SAD_total[,1], decreasing=T)
SAD_total2 <- data.frame(rank=c(1:73), ID=rownames(SAD_total)[SAD_rank], abundance=SAD_total[SAD_rank,1])
#how many species?
sum(SAD_total2$abundance>0)

plot(SAD_total2$rank, log10(SAD_total2$abundance)) #SAD, including the rank 1 speices (wl) but with log10 scale
plot(SAD_total2$rank[-1], SAD_total2$abundance[-1]) #SAD, excluding the rank 1 speices (wl)
plot(SAD_total2$rank[2:10], SAD_total2$abundance[2:10]) #SAD, from rank 2 to rank 10
#text(SAD_total2$rank[2:10], SAD_total2$abundance[2:10], labels=SAD_total2$ID[2:10])
ggplot(SAD_total2, aes(x=rank, y=log10(abundance))) + geom_point()
SAD_total2_sub <- SAD_total2[2:10,]
sad_plot <- ggplot(SAD_total2_sub, aes(x=rank, y=abundance)) + geom_point()
sad_plot <- sad_plot + geom_text(aes(label=ID), size=3,vjust=-1)
plot(sad_plot)
```
### Time evolution of the rank 1-10 taxa
```{r}
#Taxon (species) identity
SAD_total2_sub$ID
uds_data_all_day_SI <- uds_data_all_day2  #copy for SI only
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="N"] <- "U"
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="W"] <- "D"
uds_data_all_day_SI$treatment[uds_data_all_day_SI$treatment=="T"] <- "E"
treatment_day <- as.factor(paste(uds_data_all_day_SI$treatment,uds_data_all_day_SI$day))
uds_data_all_day_SI <- cbind.data.frame(uds_data_all_day_SI, treatment_day)
plot(uds_data_all_day_SI$treatment_day ,uds_data_all_day_SI$wl)

#rank 1
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=wl, fill=treatment)) + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.2) + ggtitle("Plagiodera versicolora")

#rank 2
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=spider3, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("spider group 3 (< 5mm with spiderweb)")

#rank 3
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep9, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Caloptilia sp.2")

#rank 4
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep7, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Saliciphaga caesia")

#rank 5
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep10, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Phyllocolpa sp")

#rank 6
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=aphi5, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Cavariella salicicola")

#rank 7
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep12, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("spider group 4 (< 5mm without spiderweb)")

#rank 8
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=parasitoid3, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Tachinidae sp.1 (host: P. versicolora)")

#rank 9
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep12, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Microleon longipalpis")

#rank 10
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=lep13, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Monema flavescens")

#rank 11
ggplot(uds_data_all_day_SI, aes(x=treatment_day, y=grasshopper1, fill=treatment))  + geom_boxplot(outlier.shape = NA) + geom_point(alpha=0.1, size=2) + ggtitle("Orthoptera sp.1")


```



## Leaf beetle population dynamics analysis (Fig.1)
### Incorporating nested structure
```{r}
chamber <- append(rep("C1",16),rep("C2",32))
uds_data_all_LBa <- cbind.data.frame(uds_data_all_LBa, chamber)
uds_data_all_LBa
```
### Repeated ANOVA with autoregression
Ref: http://rcompanion.org/handbook/I_09.html
```{r}
#Data decomposition
uds_data_LBa24h <- subset(uds_data_all_LBa, uds_data_all_LBa$day==1)
uds_data_LBa2_7d <- subset(uds_data_all_LBa, (uds_data_all_LBa$day >= 2 & uds_data_all_LBa$day <= 7))
uds_data_LBa10_60d <- subset(uds_data_all_LBa, (uds_data_all_LBa$day >= 10 & uds_data_all_LBa$day <= 60))
```
#### hour scale (Fig.1a)
```{r}
#short (Fig.1a)
model24h_0 = gls(adult ~ chamber + hour + chamber*hour,data=uds_data_LBa24h)
#Anova(model24h_0)
ACF_s <- ACF(model24h_0, form = ~ hour|id)
head(ACF_s)
model24h = gls(adult ~ chamber + hour + chamber*hour, correlation = corAR1(form = ~ hour|id, value = ACF_s$ACF[2]),data=uds_data_LBa24h)
Anova(model24h)
#Post-hoc test
data12h<-subset(uds_data_LBa24h, hour==12)
pairwise.t.test(data12h$adult, data12h$chamber,p.adjust.method="holm")
pairwise.t.test(data12h$adult[-c(1:16)],data12h$treatment[-c(1:16)],p.adjust.method="holm")

data24h<-subset(uds_data_LBa24h, hour==24)
pairwise.t.test(data24h$adult, data24h$chamber,p.adjust.method="holm")
pairwise.t.test(data12h$adult[-c(1:16)], data12h$treatment[-c(1:16)],p.adjust.method="holm")

#Summary statistics for graph
sum24h = groupwiseMean(adult ~ treatment + hour, data = uds_data_LBa24h, conf = 0.95, digits = 3,traditional = FALSE, percentile  = TRUE)
sum24h

#shortest graph (Fig.1a)
pd = position_dodge(1)
g_line<-ggplot(sum24h, aes(y=Mean, x=hour)) + xlim(0,25) + ylim(0,1.1) + coord_fixed(25/1.1)
g_line<-g_line+geom_line(aes(colour=treatment),size=1,position=pd) 
g_line<- g_line+ geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd) 
print(g_line)
```
#### Summary of hour scale results (Fig.1a)
- There were statistically significant differences bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
  - there were differences bet. U vs (S + D) at 12 h and 24 h 
  - but no differences bet. S vs D either. 

#### a week scale (Fig.1b)
```{r}
#middle (Fig.1b)
model2_7d0 = gls(adult ~ chamber + day + chamber*day,data=uds_data_LBa2_7d)
#Anova(model2_7d0)
ACF_m <- ACF(model2_7d0, form = ~ day|id)
head(ACF_m)
model2_7d = gls(adult ~ chamber + day + chamber*day, correlation = corAR1(form = ~ day|id, value = ACF_m$ACF[2]),data=uds_data_LBa2_7d)
Anova(model2_7d)
#Post-hoc test
data7d<-subset(uds_data_LBa2_7d, day==7)
pairwise.t.test(data7d$adult, data7d$chamber,p.adjust.method="holm")
pairwise.t.test(data7d$adult[-c(1:16)], data7d$treatment[-c(1:16)],p.adjust.method="holm")

#Summary statistics for graph
sum2_7d = groupwiseMean(adult ~ treatment + day, data = uds_data_LBa2_7d, conf = 0.95, digits = 3,traditional = FALSE, percentile  = TRUE)

#a week scale graph (Fig.1b)
pd = position_dodge(0.3)
g_line<-ggplot(sum2_7d, aes(y=Mean, x=day)) + xlim(1,8) + ylim(0,1.1) + coord_fixed(7/1.1)
g_line<-g_line+geom_line(aes(colour=treatment),size=1, position=pd) 
g_line<- g_line+ geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd) 
print(g_line)
```

#### Summary of a week scale results (Fig.1b)
- There was no statistically significant difference bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
  - there was no difference bet. U vs (S + D) at 7d
  - but no differences bet. S vs D either. 

#### monthly scale (Fig.1c)
```{r}
model10_60d0 = gls(adult ~ chamber + day + chamber*day, data=uds_data_LBa10_60d)
#Anova(model10_60d0)
ACF_l <- ACF(model10_60d0, form = ~ day|id)
head(ACF_l)
model10_60d = gls(adult ~ chamber + day + chamber*day, correlation = corAR1(form = ~ day|id, value = ACF_l$ACF[2]),data=uds_data_LBa10_60d)
Anova(model10_60d)
#Post-hoc test
data10d<-subset(uds_data_LBa10_60d, day==10)
pairwise.t.test(data10d$adult, data10d$chamber,p.adjust.method="holm")
pairwise.t.test(data10d$adult[-c(1:16)], data10d$treatment[-c(1:16)],p.adjust.method="holm")

#Summary statistics for graph
sum10_60d = groupwiseMean(adult ~ treatment + day, data = uds_data_LBa10_60d, conf = 0.95, digits = 3,traditional = FALSE, percentile  = TRUE)

#graphics
pd = position_dodge(3)
g_line<-ggplot(sum10_60d, aes(y=Mean, x=day)) + xlim(5,65) + ylim(0,4) + coord_fixed(ratio = 60/4)
g_line<-g_line+geom_line(aes(colour=treatment),size=1, position=pd) 
g_line<- g_line+geom_errorbar(aes(ymin=Percentile.lower, ymax = Percentile.upper, colour=treatment),alpha=0.9,size=1, width=0, position=pd)
g_line<-g_line+geom_point(aes(colour=treatment, shape=treatment),size=3,alpha=1.0, position=pd) 
print(g_line)
```

#### Summary of monthly scale results (Fig.1c)
- There was no statistically significant difference bet. U vs (S + D), i.e., chamber effect.
- By the add-hoc test,
  - there was no difference bet. U vs (S + D) at 10d
  - but a difference bet. S vs D at 10d. 

## Ordinations for community composition
### Preparing the function for Fig.2 & Fig.S1
```{r}
disper_uds_centroid_graph <- function(data_name, c_LB, meth, adj=NULL, type="violin", lim_min=-0.6, lim_max=0.4) {
  if(c_LB == "wLB") {
    uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    uds_each<-uds_data1[,c(-1,-2,-3, -77,-78, -79, -80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
    uds_each<-uds_data1[,c(-1,-2,-3,-4,-77,-78,-79, -80)]
  }  #When leaf beele is NOT included
  if(c_LB == "LBonly") {
    uds_data1 <- subset(data_name, abundance2 > 0)
    uds_each <- subset(uds_data1, day==date)[, c(4)]  #picking up LB only
  }
  
  sample_each <- uds_data1$sample
  #meth="chao"
  uds_bray_each0.d<-vegdist(uds_each, method=meth)
  if(c_LB == "LBonly") tp <- "centroid"
  else tp <- "median"
  uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
  uds_median <- data.frame(uds_dist_each0$centroids)  #calculate the centroid (spatial median) for repeated measures
  
  sample <- row.names(uds_median)
  #sample <- sample_each
  #Replace the sample name into the treatment
  sample[grep("1n", sample)]<- "U10"
  sample[grep("2n", sample)]<- "U32"
  sample[grep("3n", sample)]<- "U60"
  sample[grep("1t", sample)]<- "S10"
  sample[grep("2t", sample)]<- "S32"
  sample[grep("3t", sample)]<- "S60"
  sample[grep("1w", sample)]<- "D10"
  sample[grep("2w", sample)]<- "D32"
  sample[grep("3w", sample)]<- "D60"
  
  treatment_each <- as.factor(sample) #just conversion

  uds_each.d<-vegdist(uds_median, method="euclidean")

  uds_dist_each <-betadisper(uds_each.d, treatment_each, type="centroid", bias.adjust = TRUE)  #Choose centroid because of euclidean distance
  #Plot
  if(c_LB == "LBonly") boxplot(uds_dist_each)
  else {
    #boxplot(uds_dist_each)
    #ordiplot(uds_dist_each$centroids, type="text", choices=c(1,2), cex=1, xlim=c(-0.6,0.4), ylim=c(-0.6, 0.5))
    #par(new=T)

    if(type == "violin") {
      #boxplot(uds_dist_each)
      ##from ggplot2
      #convert to data.frame (which is necessary for ggplot2)
      
      uds_dist_each2 <- cbind.data.frame(uds_dist_each$distances, as.character(uds_dist_each$group))
      
      treatment<-as.character(uds_dist_each$group)
      #Replace the sample name into the treatment
      treatment[grep("U", treatment)]<- "U"
      treatment[grep("D", treatment)]<- "D"
      treatment[grep("S", treatment)]<- "S"
      
      uds_dist_each2<-cbind.data.frame(uds_dist_each2, treatment)  
      colnames(uds_dist_each2) <- c("distances", "group", "treatment")
      uds_dist_each2$distances <- as.numeric(as.character(uds_dist_each2$distances))
      
      g_box<-ggplot(uds_dist_each2, aes(y=distances, x=group,colour=treatment))
      g_box<-g_box + geom_violin()
      g_box<-g_box + geom_boxplot(fill="black", width=0.1) 
      g_box<-g_box + coord_fixed(ratio = 15/1)
      print(g_box)
    }      
      else if(type == "PCoA") {
        #PCoA 
        test0<-capscale(uds_each.d~1)  
        test<-cmdscale(uds_each.d, eig=FALSE, k = 2)
        #prepare dataframe for each point
        test2<-summary(test)
        #uds_dist_each_test<-data.frame(PCoA1=-test2$sites[,1], PCoA2=-test2$sites[,2])
        uds_dist_each_test<-as.data.frame(test)
        uds_dist_each_test<-cbind.data.frame(sample, uds_dist_each_test)
        treatment<-as.character(sample)
        day<-as.character(sample)
        #Replace the sample name into the treatment & day
        treatment[grep("U", treatment)]<- "U"
        treatment[grep("D", treatment)]<- "D"
        treatment[grep("S", treatment)]<- "S"
        day[grep("10", day)]<-"Day10"
        day[grep("32", day)]<-"Day32"
        day[grep("60", day)]<-"Day60"
        
        uds_dist_each_test<-cbind.data.frame(cbind.data.frame(treatment, day), uds_dist_each_test)
        colnames(uds_dist_each_test)<-c("treatment", "day", "group", "PCoA1", "PCoA2")
        class(uds_dist_each_test$day)
        #prepare dataframe for centroid
        uds_dist_each_centr<-as.data.frame(uds_dist_each$centroids)
        treatment<-as.character(rownames(uds_dist_each_centr))
        day<-as.character(rownames(uds_dist_each_centr))
        #Replace the sample name into the treatment & day
        treatment[grep("U", treatment)]<- "U"
        treatment[grep("D", treatment)]<- "D"
        treatment[grep("S", treatment)]<- "S"
        day[grep("10", day)]<-"Day10"
        day[grep("32", day)]<-"Day32"
        day[grep("60", day)]<-"Day60"
        
        uds_dist_each_centr<-cbind.data.frame(uds_dist_each_centr, data.frame(treatment=treatment, day=day, group=rownames(uds_dist_each_centr)))
        
        g_PCoA <- ggplot(uds_dist_each_test, aes(x=PCoA1,y=PCoA2))
        g_PCoA <- g_PCoA + geom_point(aes(colour=treatment, shape=day), size=3, alpha=0.5)
        g_PCoA <- g_PCoA + xlim(lim_min, lim_max) + ylim(lim_min,lim_max) + coord_fixed(ratio = 1/1)
        #Add the centroid information
        g_PCoA <- g_PCoA + layer(
          data=uds_dist_each_centr, 
          mapping=aes(x=PCoA1, y=PCoA2, colour=treatment, shape=day), 
          params=list(size=5),
          geom="point", 
          stat="identity", 
          position="identity"
        )
        print(g_PCoA)
       
        #Performance of PCoA  http://d.hatena.ne.jp/hoxo_m/20120313/p1
        summary(test0)
    }
  }
  
  #Performance of PCoA  http://d.hatena.ne.jp/hoxo_m/20120313/p1
  #summary(test0)
  
  if(type == "NMDS") {
    uds_each.mds<-metaMDS(uds_median, distance="euclidean")
    rownames(uds_each.mds$points)<-treatment_each
    ordiplot(uds_each.mds$points, type='points', xlim=c(-1.0, 1.0))
    text(uds_each.mds$points,labels=treatment_each, col=unclass(treatment_each),cex=0.8)
  }

}
```
### Figure 2a & 2b
- With excluding leaf beetle and using chao distance
```{r}
#Figure 2a
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="chao", type="violin")
#Figure 2b
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="chao", type="PCoA")
```
### Figure S1
- (a) Using bray-curtis dissimilarity w/o LB
- (b) Using Chao dissimilarity w/ LB
```{r}
####For Figure S1a
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="woLB", meth="bray", type="PCoA", lim_min=-0.75, lim_max=0.5) 
####For Figure S1b
disper_uds_centroid_graph(data_name=uds_data_all_day2, c_LB="wLB", meth="chao", type="PCoA", lim_min=-0.75, lim_max=0.5)   
```

### Statistical tests corresponding to the results shown in Fig.2
#### Definitions of functions fot statistical test
```{r}
#///////////////////////Pre-Evaluation of effectiveness of PERMANOVA (or can be used for assessing beta diversity) through PERMDISP for each of 10d, 32d, 60d/////////////////////
disper_uds_centroid <- function(data_name, date, c_LB, meth, adj=NULL, plot="box", dist_out=FALSE) {
    
    if(c_LB == "wLB") {
      uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
      uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3, -77,-78, -79, -80)]
    }  #When leaf beetle is included
    if(c_LB == "woLB") {
      uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
      uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-4,-77,-78,-79, -80)]
    }  #When leaf beetle is NOT included 
  
    if(c_LB == "LBonly") {  #For leaf beetle only but consider the case with other insects only
      uds_data1 <- subset(data_name, abundance2 > 0)
      #uds_data1 <-data_name
      uds_each <- subset(uds_data1, day==date)[, c(4)]  #picking up LB only
    }
    
    sample_each <- subset(uds_data1, day==date)$sample
    #length(uds_each)
    #length(sample_each)
    
    ##In case of leaf beetle only, different calculation
    if(c_LB == "LBonly") {
      sample_ID<-as.factor(sample_each)
      LB_average<-tapply(uds_each, sample_ID, mean)  #calculate the average of short-term repeated measurement
      LB_average2<-LB_average #just copy
      sample <- row.names(LB_average2)
      sample2 <- row.names(LB_average2)
      #Replace the sample name into the treatment
      sample[grep("n", sample)]<- "U" #undamaged
      sample[grep("t", sample)]<- "S" #Signaled
      sample[grep("w", sample)]<- "D" #Damaged
      treatment_each <- as.factor(sample) #just conversion
      #print(treatment_each)
      
      #Replace the sample name into the chamber
      sample2[grep("n", sample2)]<- "C1" #undamaged
      sample2[grep("t", sample2)]<- "C2" #Signaled
      sample2[grep("w", sample2)]<- "C2" #Damaged
      chamber_each <- as.factor(sample2) #just conversion
      
      average_treatment<-tapply(LB_average, treatment_each, mean)   #calculate the average of U, D, S, respectively
      average_chamber<-tapply(LB_average2, chamber_each, mean)   #calculate the average of C1 amd C2
      #print(average_treatment)
      for(i in 1: length(LB_average)) {  #normalizing the original data to exclude the effect of average on the distance to centroid
        if(treatment_each[i] == "D" && average_treatment[1] > 0.0) LB_average[i] = LB_average[i]/average_treatment[1] 
        if(treatment_each[i] == "S" && average_treatment[2] > 0.0) LB_average[i] = LB_average[i]/average_treatment[2]
        if(treatment_each[i] == "U" && average_treatment[3] > 0.0) LB_average[i] = LB_average[i]/average_treatment[3]
      }
      
      for(i in 1: length(LB_average)) {  #normalizing the original data to exclude the effect of average on the distance to centroid
        if(chamber_each[i] == "C1" && average_chamber[1] > 0.0) LB_average2[i] = LB_average2[i]/average_chamber[1] 
        if(chamber_each[i] == "C2" && average_chamber[2] > 0.0) LB_average2[i] = LB_average2[i]/average_chamber[2]
      }
      
    uds_median<-LB_average  #send the data for further analysis, distinguishing D,S,and U
    uds_median2<-LB_average2  #send the data for further analysis, distinguishing C1 and C2
    #print(uds_median)
    if(dist_out == "C1") uds_median <- subset(uds_median, chamber_each=="C1")
    else if(dist_out == "C2") uds_median <- subset(uds_median, chamber_each=="C2")
    #print(uds_median)
    }
    
    else {
      uds_bray_each0.d<-vegdist(uds_each, method=meth)
      
      tp <- "median"
      uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
      uds_median <- data.frame(uds_dist_each0$centroids)  #calculate the centroid (spatial median) for repeated measures
      
      sample <- row.names(uds_median)
      sample2 <- sample #just copy
      #Replace the sample name into the treatment
      sample[grep("n", sample)]<- "U" #undamaged
      sample[grep("t", sample)]<- "S" #Signaled
      sample[grep("w", sample)]<- "D" #Damaged
      treatment_each <- as.factor(sample) #just conversion
      #Replace the sample name into the chamber
      sample2[grep("n", sample2)]<- "C1" #undamaged
      sample2[grep("t", sample2)]<- "C2" #Signaled
      sample2[grep("w", sample2)]<- "C2" #Damaged
      chamber_each <- as.factor(sample2) #just conversion
      
      if(dist_out == "C1") uds_median <- uds_median[chamber_each=="C1",]
      else if(dist_out == "C2") uds_median <- uds_median[chamber_each=="C2",]
      #print(uds_median)
    }
    
    
    uds_each.d<-vegdist(uds_median, method="euclidean")
    if(dist_out!= FALSE) return(uds_each.d)
    
    #treatment_each
    uds_dist_each <-betadisper(uds_each.d, treatment_each, type="centroid", bias.adjust = TRUE)  #Choose centroid because of euclidean distance
    #chamber_each
    uds_dist_each_C <-betadisper(uds_each.d, chamber_each, type="centroid", bias.adjust = TRUE)  
    #Plot
    if(c_LB == "LBonly") boxplot(uds_dist_each)
    else {
      if(plot=="box") {
        boxplot(uds_dist_each_C)
        boxplot(uds_dist_each)
      }
      else{
        plot(uds_dist_each)
        ordilabel(scores(uds_dist_each, "centroids"))
        plot(uds_dist_each_C)
        ordilabel(scores(uds_dist_each_C, "centroids"))
        
      }
    }
    
    #PERDISP & permutation 
    perm_result_C <- permutest(uds_dist_each_C, pairwise=FALSE, permutations=9999)
    perm_result <- permutest(uds_dist_each, pairwise=TRUE, permutations=9999)
    
    cat("Sampling day was\n")
    print(subset(uds_data1, day==date)$day[1])
    cat("Comparison between chambers\n")
    print(perm_result_C)
    cat("Comparison between treatments\n")
    print(perm_result)
    
    #In fact, the adjustment is not necessary because permutest.betadisper does not repeat permutation for each pair
    if(!is.null(adj)) {
      pairwise_adj<-p.adjust(perm_result$pairwise$permuted, method = adj , n = length(perm_result$pairwise$permuted))
      cat("Pairwise comparision by permutation, adjusted by")
      print(as.character(adj))
      print(pairwise_adj[1])
      print(pairwise_adj[2])
      print(pairwise_adj[3])
    }

    
}

#///PERMANOVA for each initial conditions, based on the spatial median for repeated measures
com_div_uds_centroid <- function(data_name, date, c_LB, meth, adj)
{
  if(c_LB == "wLB") {
    uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-77,-78, -79, -80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
    uds_each<-subset(uds_data1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79, -80)]
    uds_each_LB<-subset(uds_data1, day==date)[,c(4)]  #extract leaf beetle abundance
  }  #When leaf beele is NOTincluded
  
  sample_each <- subset(uds_data1, day==date)$sample

  uds_each.d<-vegdist(uds_each, method=meth)
  uds_dist_each <-betadisper(uds_each.d, sample_each, type="median", bias.adjust = TRUE)
  uds_median <- data.frame(uds_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  

  sample <- row.names(uds_median)
  sample2 <- row.names(uds_median)
  #Replace the sample name into the treatment
  sample[grep("n", sample)]<- "U"
  sample[grep("t", sample)]<- "S"
  sample[grep("w", sample)]<- "D"
  treatment_each <- as.factor(sample) #just conversion
  
  #Replace the sample name into the chamber
  sample2[grep("n", sample2)]<- "C1" #undamaged
  sample2[grep("t", sample2)]<- "C2" #Signaled
  sample2[grep("w", sample2)]<- "C2" #Damaged
  chamber_each <- as.factor(sample2) #just conversion
      
  
  adonis_result_C<-adonis(uds_median ~ chamber_each, method="euclidean", permutations=9999)
  adonis_result_T<-adonis(uds_median ~ treatment_each, method="euclidean", permutations=9999)

  print(adonis_result_C) #Chamber effect
  print(adonis_result_T) #Treatment effect
  
  #For pairwise comparison (only between D and S)
  cat("The pairwise PERMANOVA (adonis) analysis:\n")
  uds_dataDS0<-subset(data_name,  treatment != "N") #We still need the symbols N, W, and T because it is used in the raw data files
  uds_dataSU0<-subset(data_name,  treatment != "W")
  uds_dataUD0<-subset(data_name,  treatment != "T")
  if(c_LB == "wLB") {
    uds_dataDS1 <-subset(uds_dataDS0, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    DS_each<-subset(uds_dataDS1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
    uds_dataSU1 <-subset(uds_dataSU0, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    SU_each<-subset(uds_dataSU1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
    uds_dataUD1 <-subset(uds_dataUD0, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    UD_each<-subset(uds_dataUD1, day==date)[,c(-1,-2,-3,-77,-78,-79,-80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_dataDS1 <-subset(uds_dataDS0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    DS_each<-subset(uds_dataDS1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79, -80)]
    uds_dataSU1 <-subset(uds_dataSU0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    SU_each<-subset(uds_dataSU1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79,-80)]
    uds_dataUD1 <-subset(uds_dataUD0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    UD_each<-subset(uds_dataUD1, day==date)[,c(-1,-2,-3,-4,-77,-78, -79,-80)]
  }  #When leaf beetle is NO Tincluded
  
  sample_DS <- subset(uds_dataDS1, day==date)$sample
  sample_SU <- subset(uds_dataSU1, day==date)$sample
  sample_UD <- subset(uds_dataUD1, day==date)$sample
  
  DS_bray_each.d<-vegdist(DS_each, method=meth)
  SU_bray_each.d<-vegdist(SU_each, method=meth)
  UD_bray_each.d<-vegdist(UD_each, method=meth)
  
  DS_dist_each <-betadisper(DS_bray_each.d, sample_DS, type="median", bias.adjust = TRUE)
  SU_dist_each <-betadisper(SU_bray_each.d, sample_SU, type="median", bias.adjust = TRUE)
  UD_dist_each <-betadisper(UD_bray_each.d, sample_UD, type="median", bias.adjust = TRUE)
  
  DS_median <- data.frame(DS_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  SU_median <- data.frame(SU_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  UD_median <- data.frame(UD_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  
  DS_sample <- row.names(DS_median)
  SU_sample <- row.names(SU_median)
  UD_sample <- row.names(UD_median)
  
  #Replace the sample name into the treatment
  DS_sample[grep("n", DS_sample)]<- "U"
  DS_sample[grep("t", DS_sample)]<- "S"
  DS_sample[grep("w", DS_sample)]<- "D"
  treatment_DS <- as.factor(DS_sample) #just conversion
  SU_sample[grep("n", SU_sample)]<- "U"
  SU_sample[grep("t", SU_sample)]<- "S"
  SU_sample[grep("w", SU_sample)]<- "D"
  treatment_SU <- as.factor(SU_sample) #just conversion
  UD_sample[grep("n", UD_sample)]<- "U"
  UD_sample[grep("t", UD_sample)]<- "S"
  UD_sample[grep("w", UD_sample)]<- "D"
  treatment_UD <- as.factor(UD_sample) #just conversion

  adonis_resultDS<-adonis(DS_median ~ treatment_DS, method="euclidean", permutations=9999)
  adonis_resultSU<-adonis(SU_median ~ treatment_SU, method="euclidean", permutations=9999)
  adonis_resultUD<-adonis(UD_median ~ treatment_UD, method="euclidean", permutations=9999)
  
  p_list <- c(adonis_resultDS$aov$Pr[1], adonis_resultSU$aov$Pr[1], adonis_resultUD$aov$Pr[1])
  
  cat("The comparision between D and S is:\n")
  print(adonis_resultDS)
  
  pairwise_adj<-p.adjust(p_list, method = adj , n = length(p_list))
  
  #cat("Pairwise comparision by permutation, adjusted by")
  #print(adj)
  #cat("The adjusted  P-value for S vs D was\n")
  #print(pairwise_adj[1])
  #cat("The adjusted  P-value for U vs S was\n")
  #print(pairwise_adj[2])
  #cat("The adjusted  P-value for D vs U was\n")
  #print(pairwise_adj[3])
  
}

#///PERMANOVA for each date, based on the spatial median for repeated measures
com_div_DAY_centroid <- function(data_name, treat, c_LB, meth, adj=NULL) {
  
  #data_name <-uds_data_all_day2
  #treat <- "T"
  #meth="chao"
  #date <- "10d","32d", or "60d", or any
  if(c_LB == "wLB") {
    uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3, -77,-78, -79,-80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
    uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
  }  #When leaf beele is NOTincluded
  if(c_LB == "LBonly") {
    uds_data1 <- subset(data_name, abundance2 > 0)
    uds_each <- subset(uds_data1, treatment==treat)[, c(4)]  #picking up LB only
  }
  
  sample_each <- subset(uds_data1, treatment==treat)$sample
  day_each <- subset(uds_data1, treatment==treat)$day

  uds_bray_each0.d<-vegdist(uds_each, method=meth)
  if(c_LB == "LBonly") tp <- "centroid"
  else tp <- "median"
  uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
  uds_median <- data.frame(uds_dist_each0$centroids)  #calculate the centroid (spatial median) for repeasted measures

  sample <- row.names(uds_median)
  
  #Replace the sample name into the date
  sample[grep("^1", sample)]<- "day10"
  sample[grep("^2", sample)]<- "day32"
  sample[grep("^3", sample)]<- "day60"
  
  day_each <- as.factor(sample) #just conversion
  
  adonis_result<-adonis(uds_median ~ day_each, method="euclidean", permutations=9999)
  
  print(adonis_result)
  
  #For pairwise comarison (with Bonferroni adjustment)
  cat("The pairwise PERMANOVA (adonis) analysis:\n")
  uds_dataDay1032_0<-subset(data_name,  day != "60d")
  uds_dataDay1060_0<-subset(data_name,  day != "32d")
  uds_dataDay3260_0<-subset(data_name,  day != "10d")
  
  if(c_LB == "wLB") {
    uds_dataDay1032_1 <-subset(uds_dataDay1032_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day1032_each<-subset(uds_dataDay1032_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
    uds_dataDay1060_1 <-subset(uds_dataDay1060_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day1060_each<-subset(uds_dataDay1060_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
    uds_dataDay3260_1 <-subset(uds_dataDay3260_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day3260_each<-subset(uds_dataDay3260_1, treatment==treat)[,c(-1,-2,-3,-77,-78,-79,-80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_dataDay1032_1 <-subset(uds_dataDay1032_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day1032_each<-subset(uds_dataDay1032_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
    uds_dataDay1060_1 <-subset(uds_dataDay1060_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day1060_each<-subset(uds_dataDay1060_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
    uds_dataDay3260_1 <-subset(uds_dataDay3260_0, abundance2 > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    Day3260_each<-subset(uds_dataDay3260_1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
  }  #When leaf beele is NOTincluded
  
  sample_1032 <- subset(uds_dataDay1032_1, treatment==treat)$sample
  sample_1060 <- subset(uds_dataDay1060_1, treatment==treat)$sample
  sample_3260 <- subset(uds_dataDay3260_1, treatment==treat)$sample

  Day1032_each.d<-vegdist(Day1032_each, method=meth)
  Day1060_each.d<-vegdist(Day1060_each, method=meth)
  Day3260_each.d<-vegdist(Day3260_each, method=meth)
  
  Day1032_dist_each <-betadisper(Day1032_each.d, sample_1032, type="median", bias.adjust = TRUE)
  Day1060_dist_each <-betadisper(Day1060_each.d, sample_1060, type="median", bias.adjust = TRUE)
  Day3260_dist_each <-betadisper(Day3260_each.d, sample_3260, type="median", bias.adjust = TRUE)

  Day1032_median <- data.frame(Day1032_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  Day1060_median <- data.frame(Day1060_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  Day3260_median <- data.frame(Day3260_dist_each$centroids)  #calculate the centroid (spatial median) for repeasted measures
  
  Day1032_sample <- row.names(Day1032_median)
  Day1060_sample <- row.names(Day1060_median)
  Day3260_sample <- row.names(Day3260_median)
  
  #Replace the sample name into the day
  Day1032_sample[grep("^1", Day1032_sample)]<- "day10"
  Day1032_sample[grep("^2", Day1032_sample)]<- "day32"
  day_Day1032 <- as.factor(Day1032_sample) #just conversion
  Day1060_sample[grep("^1", Day1060_sample)]<- "day10"
  Day1060_sample[grep("^3", Day1060_sample)]<- "day60"
  day_Day1060 <- as.factor(Day1060_sample) #just conversion
  Day3260_sample[grep("^2", Day3260_sample)]<- "day32"
  Day3260_sample[grep("^3", Day3260_sample)]<- "day60"
  day_Day3260 <- as.factor(Day3260_sample) #just conversion

  adonis_resultDay1032<-adonis(Day1032_median ~ day_Day1032, method="euclidian", permutations=9999)
  adonis_resultDay1060<-adonis(Day1060_median ~ day_Day1060, method="euclidian", permutations=9999)
  adonis_resultDay3260<-adonis(Day3260_median ~ day_Day3260, method="euclidian", permutations=9999)
  
  p_list <- c(adonis_resultDay1032$aov$Pr[1], adonis_resultDay1060$aov$Pr[1], adonis_resultDay3260$aov$Pr[1])
  
  
  pairwise_adj<-p.adjust(p_list, method = adj , n = length(p_list))
  
  cat("Pairwise comparision by permutation, adjusted by")
  print(adj)
  cat("The adjusted  P-value for day10 vs day32 was\n")
  print(pairwise_adj[1])
  cat("The adjusted  P-value for day10 vs day60 was\n")
  print(pairwise_adj[2])
  cat("The adjusted  P-value for day32 vs day60 was\n")
  print(pairwise_adj[3])
}


#///PERMDISP or each date, based on the spatial median for repeated measures
disper_DAY_centroid <- function(data_name, treat, c_LB, meth, adj=NULL) {
  
  #resolving inconsistency of the symbols to represent the treatments (and chamber)
  if(treat == "U") treat <- "N"
  else if(treat == "E") treat <- "T"
  else if(treat == "D") treat <- "W"
  else if(treat == "S") treat <- "T"
  
  if(c_LB == "wLB") {
    uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3, -77,-78, -79,-80)]
  }  #When leaf beele is included
  if(c_LB == "woLB") {
    uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
    uds_each<-subset(uds_data1, treatment==treat)[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
  }  #When leaf beele is NOTincluded
  if(c_LB == "LBonly") {
    uds_data1 <- subset(data_name, abundance2 > 0)
    uds_each <- subset(uds_data1, treatment==treat)[, c(4)]  #picking up LB only
  }
  
  sample_each <- subset(uds_data1, treatment==treat)$sample
  day_each <- subset(uds_data1, treatment==treat)$day
  
  
  uds_bray_each0.d<-vegdist(uds_each, method=meth)
  if(c_LB == "LBonly") tp <- "centroid"
  else tp <- "median"
  uds_dist_each0 <-betadisper(uds_bray_each0.d, sample_each, type=tp, bias.adjust = TRUE)
  uds_median <- data.frame(uds_dist_each0$centroids)  #calculate the centroid (spatial median) for repeasted measures
  
  sample <- row.names(uds_median)
  
  #Replace the sample name into the date
  sample[grep("^1", sample)]<- "day10"
  sample[grep("^2", sample)]<- "day32"
  sample[grep("^3", sample)]<- "day60"
  
  day_each <- as.factor(sample) #just conversion
  
  #treatment_each
  uds_each.d<-vegdist(uds_median, method="euclidean")
  uds_dist_each <-betadisper(uds_each.d, day_each, type="centroid", bias.adjust = TRUE)  #Choose centroid becasue of euclidean distance
  #Plot
  if(c_LB == "LBonly") boxplot(uds_dist_each)
  else {
    boxplot(uds_dist_each)
    ordilabel(scores(uds_dist_each, "centroids"))
  }
  #PERDISP & ANOVA & MULTIPLE COMPARISON
  #anova_dist <- anova(uds_dist_each)
  #TukeyHSD(uds_dist_each, which = "group", ordered = FALSE, conf.levels = 0.95)
  
  #PERDIPST & permutation 
  perm_result <- permutest(uds_dist_each, pairwise=TRUE, permutations=9999)
  #perm_result
  
  #print (anova_dist$Pr[1]) 
  cat("Target treatment was\n")
  print(treat)
 
  print(uds_dist_each)
  print(perm_result)
  #In fact, the adjustment is not necessary because permutest.betadisper does not repeat permutation for each pair
  if(!is.null(adj)) {
    pairwise_adj<-p.adjust(perm_result$pairwise$permuted, method = adj, n = length(perm_result$pairwise$permuted))
    cat("Pairwise comparision by permutation, adjusted by")
    print(as.character(adj))
    print(pairwise_adj[1])
    print(pairwise_adj[2])
    print(pairwise_adj[3])
  }
  
  #TukeyHSD(uds_dist_each)
  
}


```

#### Statistical tests for Fig.2
##### For PERMDISP: comparison between dates (Fig.2A)\
```{r}
#Temporal variability in beta diversity for manuscript
disper_DAY_centroid(data_name=uds_data_all_day2, treat="U", c_LB="woLB", meth="chao")
disper_DAY_centroid(data_name=uds_data_all_day2, treat="S", c_LB="woLB", meth="chao")
disper_DAY_centroid(data_name=uds_data_all_day2, treat="D", c_LB="woLB", meth="chao")

```
#####For PERMDISP: comparison between chambers and treatments (Fig.2a)\
```{r}
#Results from 2-3 times measurement in 10d, 32d, and 60d without leaf beetle through spatial median
#With the recommended measure (Chao index) (for Fig.2b)
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao")
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao")
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao")
```

##### For PERMANOVA (Fig.2b)\
```{r}
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao", adj="holm")
```

##### PERMANOVA (for Supplementary Material)
```{r}
com_div_DAY_centroid(data_name=uds_data_all_day2, treat="N", c_LB="woLB", meth="chao", adj="holm")
com_div_DAY_centroid(data_name=uds_data_all_day2, treat="T", c_LB="woLB", meth="chao", adj="holm")
com_div_DAY_centroid(data_name=uds_data_all_day2, treat="W", c_LB="woLB", meth="chao", adj="holm")
```


#### Summary for the statistical tests on Figure 2
- Dispersion with time
  - Dispersion of U (Chamber 1) decreased with time (day10=day32, but day10 > day60)
  - No differences in dispersion bet. dates in S(E) and D treatments
- Dispersion between chambers and treatment\
  - Dispersion of U (Chamber 1) was smaller than chamber 2 (D+S) at 60d (P=0.0075).
  - No differences in dispersion bet. S(E) and D treatments at 60d
- PERMANOVA (with PERMDISP) results tell that:
  - the centroid community composition was not different between U vs (D + S) (no chamber effects).
  - the difference between D and E at 32d (F = 2.4132, df=2, P = 0.0036)
- Temporal dynamics of centroid within each treatment
  - For all U, D, and E, all pairwise PERMANOVA resulted in P < 0.001
  - But due to heterogeneous dispersion day10 day32 > day60 for U, we cannot conclude the centroid of U at day 60 is difference from day 10 not day 32.
  
#### Statistical tests for Figure S1
##### For PERMDISP: comparision between chambers and treatments with bray-curtis measure
```{r}
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="bray")
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="bray")
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="bray")
```
##### For PERMANOVA: comparision between chambers and treatments with bray-curtis measure
```{r}
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="bray", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="bray", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="bray", adj="holm")
```

##### For PERMDISP: comparision between chambers and treatments with Chao measure but with leaf beetle
```{r}
disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="wLB", meth="chao")
disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="wLB", meth="chao")
disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="wLB", meth="chao")
```
##### For PERMANOVA: comparision between chambers and treatments with bray-curtis measure
```{r}
com_div_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="wLB", meth="chao", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="wLB", meth="chao", adj="holm")
com_div_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="wLB", meth="chao", adj="holm")
```

### Temporal divergence on each willow tree (Decided not to present)
#### Preparation of functions to calculate/visualize temporal divergence
```{r}
turnover_comparison_short <- function(data_name, date, c_LB, meth, output = NULL)
{
 
 #data_name <- uds_data_all_day2
 #date <- "10d"
 #meth<-"chao"
 #date <- "10d","32d", or "60d", or any
 if(c_LB == "wLB") {
   uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
   uds_data2 <-subset(uds_data1, day==date)
   uds_each<-uds_data2[,c(-1,-2,-3, -77,-78, -79,-80)]
 }  #When leaf beetle is included
 if(c_LB == "woLB") {
   uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
   uds_data2 <-subset(uds_data1, day==date)
   uds_each<-uds_data2[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
 }  #When leaf beetle is NOT included
 
 sample_each <- subset(uds_data1, day==date)$sample
 treatment_each <- subset(uds_data1, day==date)$treatment
 chamber_each <- subset(uds_data1, day==date)$chamber
 #View(chamber_each)
 time_each <- subset(uds_data1, day==date)$time
 
 #prepare dummy vectors
 sample_n <- NULL
 distance_n <-NULL
 treatment_n <-NULL
 chamber_n <-NULL
 timei_n <-NULL
 timej_n <-NULL
 
 #calculate distance between 1P, 2P, and 3P
 for(i in 1:(length(sample_each) - 1)) {
   for(j in (i + 1): length(sample_each)) {
     if(sample_each[i] == sample_each[j]) {
       sample_n <- append(sample_n, as.character(sample_each[i]))
       distance_n <- append(distance_n, as.numeric(vegdist(rbind(uds_each[i,], uds_each[j,])), method=meth))
       treatment_n <-append(treatment_n, as.character(treatment_each[i]))
       chamber_n <- append(chamber_n, as.character(chamber_each[i]))
       timei_n <-append(timei_n, as.character(time_each[i]))
       timej_n <-append(timej_n, as.character(time_each[j]))
     }
    }
 }
 turnover<- data.frame(sample=sample_n, distance=distance_n, treatment=treatment_n, chamber=chamber_n, timei=timei_n, timej=timej_n)

 turnover2 <-subset(turnover, !(timei == "1P" & timej=="3P"))  #exclude the combination of 1P & 3P (redundant)
 
 #turnoverN <- subset(turnover2, treatment=="N")  #separate data by treatment
 turnoverT <- subset(turnover2, treatment=="T")  #separate data by treatment
 turnoverW <- subset(turnover2, treatment=="W")  #separate data by treatment
 turnoverC1 <- subset(turnover2, chamber=="C1") #separate data by chamber
 turnoverC2 <- subset(turnover2, chamber=="C2") #separate data by chamber
 
 #calculate average of 1P vs 2P & 2P vs 3P if any
 #average_turnoverN<-by(as.numeric(turnoverN$distance), turnoverN$sample, mean)
 average_turnoverT<-by(as.numeric(turnoverT$distance), turnoverT$sample, mean)
 average_turnoverW<-by(as.numeric(turnoverW$distance), turnoverW$sample, mean)
 average_turnoverC1<-by(as.numeric(turnoverC1$distance), turnoverC1$sample, mean)
 average_turnoverC2<-by(as.numeric(turnoverC2$distance), turnoverC2$sample, mean)

 #average_N<-subset(average_turnoverN, average_turnoverN>=0)
 average_T<-subset(average_turnoverT, average_turnoverT>=0)
 average_W<-subset(average_turnoverW, average_turnoverW>=0)
 average_C1<-subset(average_turnoverC1, average_turnoverC1>=0)
 average_C2<-subset(average_turnoverC2, average_turnoverC2>=0)
 
 length(average_T)
 #trN<-NULL
 trT<-NULL
 trW<-NULL
 trC1<-NULL
 trC2<-NULL
 #for(i in 1:length(average_N)) trN <-append(trN, "N")
 for(i in 1:length(average_T)) trT <-append(trT, "E")
 for(i in 1:length(average_W)) trW <-append(trW, "D")
 for(i in 1:length(average_C1)) trC1 <-append(trC1, "C1")
 for(i in 1:length(average_C2)) trC2 <-append(trC2, "C2")
 
 #av_N<-data.frame(treatment=trN, distance=average_N)
 av_T<-data.frame(treatment=trT, distance=average_T)
 av_W<-data.frame(treatment=trW, distance=average_W)
 av_C1<-data.frame(chamber=trC1, distance=average_C1)
 av_C2<-data.frame(chamber=trC2, distance=average_C2)
 
 #summary dataframe
 turnover_short <- rbind.data.frame(av_T, av_W)
 turnover_shortC <- rbind.data.frame(av_C1, av_C2)
 
 #print(turnover_shortC$distance)
 #View(turnover_shortC)
 #View(turnover_short)
 #print(distance_n)
 
 g_box<-ggplot(turnover_shortC, aes(y=distance, x=chamber))
 g_box<-g_box + geom_violin()
 g_box<-g_box + geom_boxplot(width=0.1) 
 g_box<-g_box + coord_fixed(ratio = 5/1) + coord_cartesian(ylim = c(0, 1))
 print(g_box)
 g_box2<-ggplot(turnover_short, aes(y=distance, x=treatment))
 g_box2<-g_box2 + geom_violin()
 g_box2<-g_box2 + geom_boxplot(width=0.1) 
 g_box2<-g_box2 + coord_fixed(ratio = 5/1) + coord_cartesian(ylim = c(0, 1))
 print(g_box2)
 
 #plotmeans(turnover_shortC$distance~turnover_shortC$chamber)
 #plotmeans(turnover_short$distance~turnover_short$treatment)

 print(bartlett.test(turnover_shortC$distance~turnover_shortC$chamber))
 #One-way ANOVA with unequal-variance
 print(oneway.test(turnover_shortC$distance~turnover_shortC$chamber,var.equal=FALSE)) 
 
 print(bartlett.test(turnover_short$distance~turnover_short$treatment))
 #One-way ANOVA with unequal-variance
 print(oneway.test(turnover_short$distance~turnover_short$treatment,var.equal=FALSE))
 #Pairwise comparison with Welch's t test
 #print(pairwise.t.test(turnover_short$distance, turnover_short$treatment, p.adj="holm"))
 
 #if(output==TRUE) return(turnover_short)
 
}  

turnover_comparison_long <- function(data_name, from_date, c_LB, meth="chao", output = NULL)
{
  
  if(c_LB == "wLB") {
    uds_data1 <-subset(data_name, abundance > 0)  #delete the sampling data with 0 total abundance when leaf beetle
    uds_each<-uds_data1[,c(-1,-2,-3, -77,-78,-79,-80)]
  }  #When leaf beetle is included
  if(c_LB == "woLB") {
    uds_data1 <-subset(data_name, abundance2 > 0) #OR, delete the sampling when species other than leaf beetle does not exist
    uds_each<-uds_data1[,c(-1,-2,-3,-4,-77,-78,-79,-80)]
  }  #When leaf beetle is NOT included
  
  sample_each <- uds_data1$sample
  chamber_each <- uds_data1$chamber
  treatment_each <- uds_data1$treatment
  time_each <- uds_data1$time
  
  uds_time.d<-vegdist(uds_each, method=meth)
  uds_dist_time <-betadisper(uds_time.d, sample_each, type="median", bias.adjust = TRUE)
  
  uds_day.median <- data.frame(uds_dist_time$centroids)  #calculate the centroid (spatial median) for repeated measures
 
  #Replace the sample name into the treatment & day 
  sample0 <- row.names(uds_day.median)
  sample1 <- sample0
  sample2 <- sample0
  sample3 <- sample0
 
  sample0<-gsub("1n", "n", sample0)
  sample0<-gsub("2n", "n", sample0)
  sample0<-gsub("3n", "n", sample0)
  sample0<-gsub("1t", "t", sample0)
  sample0<-gsub("2t", "t", sample0)
  sample0<-gsub("3t", "t", sample0)
  sample0<-gsub("1w", "w", sample0)
  sample0<-gsub("2w", "w", sample0)
  sample0<-gsub("3w", "w", sample0)
  
  sample1[grep("n", sample1)]<- "C1"
  sample1[grep("t", sample1)]<- "C2"
  sample1[grep("w", sample1)]<- "C2"
  
  sample2[grep("n", sample2)]<- "U"
  sample2[grep("t", sample2)]<- "E"
  sample2[grep("w", sample2)]<- "D"
  
  sample3[grep("1n", sample3)] <-"10d"
  sample3[grep("2n", sample3)] <-"32d"
  sample3[grep("3n", sample3)] <-"60d"
  sample3[grep("1t", sample3)] <-"10d"
  sample3[grep("2t", sample3)] <-"32d"
  sample3[grep("3t", sample3)] <-"60d"
  sample3[grep("1w", sample3)] <-"10d"
  sample3[grep("2w", sample3)] <-"32d"
  sample3[grep("3w", sample3)] <-"60d"
  
  sample_each <- as.factor(sample0)
  chamber_each <- as.factor(sample1)
  treatment_each <- as.factor(sample2) #just conversion
  day_each <- as.factor(sample3) #just conversion
  
  uds_day.median2<-cbind(chamber=chamber_each, uds_day.median)
  uds_day.median2<-cbind(treatment=treatment_each, uds_day.median2)
  uds_day.median2<-cbind(day=day_each, uds_day.median2)
  uds_day.median2<-cbind(sample_each, uds_day.median2)
  
  
  uds_day.median2[1,c(-1,-2,-3)] 
  uds_day.median2$sample_each[2]
  #View(uds_day.median2)
  
  #prepare dummy vectors
  sample_n <- NULL
  distance_n <-NULL
  chamber_n <-NULL
  treatment_n <-NULL
  dayi_n <-NULL
  dayj_n <-NULL
  
  #calculate distance between 10d, 32d, and 60d
  for(i in 1:(length(sample_each) - 1)) {
    for(j in (i + 1): length(sample_each)) {
      if(sample_each[i] == sample_each[j]) {
        sample_n <- append(sample_n, as.character(sample_each[i]))
        distance_n <- append(distance_n, as.numeric(vegdist(rbind.data.frame(uds_day.median2[i,c(-1,-2,-3,-4)], uds_day.median2[j,c(-1,-2,-3,-4)]), method="euclidean")))
        chamber_n <-append(chamber_n, as.character(chamber_each[i]))
        treatment_n <-append(treatment_n, as.character(treatment_each[i]))
        dayi_n <-append(dayi_n, as.character(day_each[i]))
        dayj_n <-append(dayj_n, as.character(day_each[j]))
      }
    }
  }
  turnover<- data.frame(sample=sample_n, distance=distance_n, chamber=chamber_n, treatment=treatment_n, dayi=dayi_n, dayj=dayj_n)
  
  if(from_date == "10d") turnover_long <-subset(turnover, (dayi == "10d" & dayj=="32d"))  
  #turnover_10d60d <-subset(turnover, (dayi == "10d" & dayj=="60d"))  #not used excluding the combination of 10d & 60d (redundanct)
  if(from_date == "32d") turnover_long <-subset(turnover, (dayi == "32d" & dayj=="60d"))  
 
  turnover_long_tr <- subset(turnover_long, chamber == "C2")
  
  View(turnover_long)
  View(turnover_long_tr)
  
  g_box<-ggplot(turnover_long, aes(y=distance, x=chamber))
  g_box<-g_box + geom_violin()
  g_box<-g_box + geom_boxplot(width=0.1) 
  g_box<-g_box + coord_fixed(ratio = 5/1)
  print(g_box)
  g_box2<-ggplot(turnover_long_tr, aes(y=distance, x=treatment))
  g_box2<-g_box2 + geom_violin()
  g_box2<-g_box2 + geom_boxplot(width=0.1) 
  g_box2<-g_box2 + coord_fixed(ratio = 5/1)
  print(g_box2)
  #plotmeans(turnover_long$distance~turnover_long$chamber)
  #plotmeans(turnover_long_tr$distance~turnover_long_tr$treatment)
  
  print(bartlett.test(turnover_long$distance~turnover_long$chamber))
  print(bartlett.test(turnover_long_tr$distance~turnover_long_tr$treatment))
  #One-way ANOVA with unequal-variance
  print(oneway.test(turnover_long$distance~turnover_long$chamber,var.equal=FALSE))
  print(oneway.test(turnover_long_tr$distance~turnover_long_tr$treatment,var.equal=FALSE))
  
}
```
####Calculate/Visualize within day turnover (which is not used in the manuscript)
```{r}
#(No difference at all @ 10 day between time)  ####For Fig. S1
#turnover_comparison_short(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao")  #error due to lack of replication within day
turnover_comparison_short(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao")
turnover_comparison_short(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao")
```

####Calculate/Visualize between dates turnover (not used)
```{r}

#(No difference at all @ 10 day between time) ####For Fig. S1
turnover_comparison_long(data_name=uds_data_all_day2, from_date="10d", c_LB="woLB", meth="chao")
turnover_comparison_long(data_name=uds_data_all_day2, from_date="32d", c_LB="woLB", meth="chao")
```
#### Summary of the species turnover analysis (Decided not to use)
- No differences between Chambers
- No differences between E and D

### Relationship between leaf beetle population size and community composition
#### Preparation for Mantel test
```{r}
LB_diff_C1_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="LBonly", meth="euclidean",dist_out="C12")
LB_diff_C1_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="LBonly", meth="euclidean",dist_out="C12")
LB_diff_C1_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C1")
LB_diff_C2_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C2")
LB_diff_C12_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="LBonly", meth="euclidean",dist_out="C12")

comm_diff_C1_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C1")
comm_diff_C2_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C2")
comm_diff_C12_d10.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="10d", c_LB="woLB", meth="chao",dist_out="C12")
comm_diff_C1_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C1")
comm_diff_C2_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C2")
comm_diff_C12_d32.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="32d", c_LB="woLB", meth="chao",dist_out="C12")
comm_diff_C1_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C1")
comm_diff_C2_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C2")
comm_diff_C12_d60.d<-disper_uds_centroid(data_name=uds_data_all_day2, date="60d", c_LB="woLB", meth="chao",dist_out="C12")

```
#### Mantel test and simple plots
```{r}
plot(LB_diff_C1_d10.d, comm_diff_C1_d10.d)
mantel(LB_diff_C1_d10.d, comm_diff_C1_d10.d, method="pearson", permutations=9999)
plot(LB_diff_C2_d10.d, comm_diff_C2_d10.d)
mantel(LB_diff_C2_d10.d, comm_diff_C2_d10.d, method="pearson", permutations=9999)
plot(LB_diff_C12_d10.d, comm_diff_C12_d10.d)
mantel(LB_diff_C12_d10.d, comm_diff_C12_d10.d, method="pearson", permutations=9999)

plot(LB_diff_C1_d32.d, comm_diff_C1_d32.d)
mantel(LB_diff_C1_d32.d, comm_diff_C1_d32.d, method="pearson", permutations=9999)
plot(LB_diff_C2_d32.d, comm_diff_C2_d32.d)
mantel(LB_diff_C2_d32.d, comm_diff_C2_d32.d, method="pearson", permutations=9999)
plot(LB_diff_C12_d32.d, comm_diff_C12_d32.d)
mantel(LB_diff_C12_d32.d, comm_diff_C12_d32.d, method="pearson", permutations=9999)

plot(LB_diff_C1_d60.d, comm_diff_C1_d60.d)
#mantel(LB_diff_C1_d60.d, comm_diff_C1_d60.d, method="pearson", permutations=9999)
plot(LB_diff_C2_d60.d, comm_diff_C2_d60.d)
mantel(LB_diff_C2_d60.d, comm_diff_C2_d60.d, method="pearson", permutations=9999)
plot(LB_diff_C12_d60.d, comm_diff_C12_d60.d)
mantel(LB_diff_C12_d60.d, comm_diff_C12_d60.d, method="pearson", permutations=9999)

```
#### ggplot for mantel test
Only for day 32
```{r}
library(reshape)
#C1 only
m_LB <- as.matrix(LB_diff_C1_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,]
names(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C1_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,]
names(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)

g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C1 only")
g_points<-g_points+geom_point(size=2,alpha=0.3) 
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
summary(result)
result$coefficients[1]
g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=2,colour="blue") # slope
print(g_points)

#C2 only
m_LB <- as.matrix(LB_diff_C2_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,]
names(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C2_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,]
names(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)

g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C2 only")
g_points<-g_points+geom_point(size=2,alpha=0.3) 
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
summary(result)
result$coefficients[1]
g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=1,colour="blue") # slope
print(g_points)

#C1 + C2 
m_LB <- as.matrix(LB_diff_C12_d32.d)
m2_LB <- melt(m_LB)[melt(upper.tri(m_LB))$value,]
names(m2_LB) <- c("t1", "t2", "distance_LB")
#View(m2_LB)
m_com <- as.matrix(comm_diff_C12_d32.d)
m2_com <- melt(m_com)[melt(upper.tri(m_com))$value,]
names(m2_com) <- c("tt1", "tt2", "distance_com")
m2_LB_com <- cbind.data.frame(m2_LB, m2_com)
#View(m2_LB_com)

g_points<-ggplot(m2_LB_com, aes(y=distance_com, x=distance_LB)) + xlim(0,8) + ylim(0,1.5) + coord_fixed(8/1.5) + ggtitle("C1 + C2")
g_points<-g_points+geom_point(size=2,alpha=0.3) 
result<-lm(m2_LB_com$distance_com~m2_LB_com$distance_LB)
result$coefficients[1]
g_points<-g_points+geom_abline(slope = result$coefficients[2],intercept = result$coefficients[1],size=1,linetype=1,colour="blue") # slope
print(g_points)

```


#### Summary of mantel test
- Positive relationship between LB population size and community composition at day 32 for the whole data
- Positive relationship between LB population size and community composition at day 32 for C2 chamber
- No relationship between LB population size and community composition at day 32 for C1 chamber


## Species richness analysis (Fig.3 & Fig.S3)
### Preparation of functions with iNEXT package
```{r}

alpha_hill_number <- function(data_name, date, q)
{
  #data_name <-uds_data_all_day2
  #date <- "32d"
  #Including leaf_beetle
  
  uds_data1 <-subset(data_name, abundance2 >= 0) #OR, delete the sampling when species other than leaf beetle does not exist
  uds_data2 <-subset(uds_data1, day==date)
  
  #if excluding leaf beetle
  #uds_each<-uds_data2[,c(-1,-2,-3,-4,-77,-78,-79)]
  #OR inclcding leaf beetle
  uds_each<-uds_data2[,c(-1,-2,-3,-77,-78,-79,-80)]
  
  
  #Hill number q = 0, 1, 2
  uds_hill<-renyi(uds_each, scales=c(0,1,2), hill=TRUE)
  uds_hill2<-cbind(uds_hill, uds_data2[,c(77,78,79,80)])
  colnames(uds_hill2) <-c("H0", "H1", "H2", "day", "treatment", "time", "chamber")
  
  #convert NA to 0
  uds_hill2$H0[is.na(uds_hill2$H0)] <- 0 
  
  
  uds_hill3 <- subset(uds_hill2,chamber=="C2")
  uds_hill3$treatment[uds_hill3$treatment=="T"] <- "E"
  uds_hill3$treatment[uds_hill3$treatment=="W"] <- "D"
  #View(uds_hill2)
  if(q == 0) {
     g_box<-ggplot(uds_hill2, aes(y=H0, x=chamber))
     g_box<-g_box + geom_violin()
     g_box<-g_box + geom_boxplot(width=0.1) 
     g_box<-g_box + coord_fixed(ratio = 1/2)
     print(g_box)
     g_box2<-ggplot(uds_hill3, aes(y=H0, x=treatment))
     g_box2<-g_box2 + geom_violin()
     g_box2<-g_box2 + geom_boxplot(width=0.1) 
     g_box2<-g_box2 + coord_fixed(ratio = 1/2)
     print(g_box2)
     
     plotmeans(uds_hill2$H0~uds_hill2$chamber) 
     
     View(uds_hill2)
    
    #homogeneity of variance
    print(bartlett.test(uds_hill2$H0~uds_hill2$chamber))
    print(bartlett.test(uds_hill3$H0~uds_hill3$treatment))
    #One-way ANOVA with unequal-variance between chambers
    print(oneway.test(uds_hill2$H0~uds_hill2$chamber,var.equal=FALSE))
    print(oneway.test(uds_hill3$H0~uds_hill3$treatment,var.equal=FALSE))
    #Pairwise comparison with Welch's t test
    #print(pairwise.t.test(uds_hill2$H0, uds_hill2$treatment, p.adj="holm"))
  }
  if(q == 1) {
    g_box<-ggplot(uds_hill2, aes(y=H1, x=chamber))
     g_box<-g_box + geom_violin()
     g_box<-g_box + geom_boxplot(width=0.1) 
     g_box<-g_box + coord_fixed(ratio = 1/2)
     print(g_box)
     g_box2<-ggplot(uds_hill3, aes(y=H1, x=treatment))
     g_box2<-g_box2 + geom_violin()
     g_box2<-g_box2 + geom_boxplot(width=0.1) 
     g_box2<-g_box2 + coord_fixed(ratio = 1/2)
     print(g_box2)
     
     plotmeans(uds_hill2$H1~uds_hill2$chamber) 
     
     View(uds_hill2)
    
    #homogeneity of variance
    print(bartlett.test(uds_hill2$H1~uds_hill2$chamber))
    print(bartlett.test(uds_hill3$H1~uds_hill3$treatment))
    #One-way ANOVA with unequal-variance between chambers
    print(oneway.test(uds_hill2$H1~uds_hill2$chamber,var.equal=FALSE))
    print(oneway.test(uds_hill3$H1~uds_hill3$treatment,var.equal=FALSE))
    
  }
  if(q == 2) {
    g_box<-ggplot(uds_hill2, aes(y=H2, x=chamber))
     g_box<-g_box + geom_violin()
     g_box<-g_box + geom_boxplot(width=0.1) 
     g_box<-g_box + coord_fixed(ratio = 1/2)
     print(g_box)
     g_box2<-ggplot(uds_hill3, aes(y=H2, x=treatment))
     g_box2<-g_box2 + geom_violin()
     g_box2<-g_box2 + geom_boxplot(width=0.1) 
     g_box2<-g_box2 + coord_fixed(ratio = 1/2)
     print(g_box2)
     
     plotmeans(uds_hill2$H1~uds_hill2$chamber) 
     
     View(uds_hill2)
    
    #homogeneity of variance
    print(bartlett.test(uds_hill2$H2~uds_hill2$chamber))
    print(bartlett.test(uds_hill3$H2~uds_hill3$treatment))
    #One-way ANOVA with unequal-variance between chambers
    print(oneway.test(uds_hill2$H2~uds_hill2$chamber,var.equal=FALSE))
    print(oneway.test(uds_hill3$H2~uds_hill3$treatment,var.equal=FALSE))
    
  }
  
    
}

```
### Temporal dynamics of species richness
#### Observed species richness per plant individuals (Fig.S2)
```{r}
#all samplings are treated as independent, used in the manuscript 
####For Figure S2
alpha_hill_number(uds_data_all_day2, date="10d", q=0)
alpha_hill_number(uds_data_all_day2, date="32d", q=0)
alpha_hill_number(uds_data_all_day2, date="60d", q=0)

```
#### Summary of observerd richness
- There were significant differences bet.chambers (C1 < C2) at day 32 and day 60 but the difference is minor 
- There were no differences bet. D and E at any days

### Species richness estimates analyzed by iNEXT (Fig.3)
#### Loading data for iNEXT analyses
```{r}
incidence_whole <- read.csv("monthly-for-R_iNEXT.csv", header=T)  #for incidence data calculated by Excel
```
#### Estimating the coverage-based standardized richness (Fig.3a) & asymptotic richness (Fig.3b)
For standardized richness (Results for Fig.3a)\
```{r}
####This is code for graphics in the manuscript (Fig.3)
stat_test<-DataInfo(incidence_whole, datatype = "incidence")  ##This is necessary to find the minimum value of the coverage
estimateD(incidence_whole, datatype="incidence_freq", base="coverage", level=min(stat_test$SC))
```
For asymptotic richness (Results for Fig.3b)\
```{r}
#When Short-term repeated measurement is assumed to be independent
ChaoRichness(incidence_whole, datatype="incidence")  #It is OK to use dataframe, this is from iNEXT
```

#### Loading data for graphics
The above iNEXT results were manually summarized in excel and csv.\
```{r}
diversity_summary <- read.csv("iNEXT_summary.csv",header=T)
class(diversity_summary)
View(diversity_summary)
diversity_summary2<-diversity_summary[1:20,]
class(diversity_summary2$day)
```

#### Figure 3a
```{r}
#colours <- c("#F8766D", "#EE55B7", "#00BA38", "#619CFF", "#E76BF3")
g_div_a <- ggplot(data=diversity_summary2, aes(x=ID,y=SR))
g_div_a <- g_div_a + geom_errorbar(aes(ymax = Hconf, ymin = Lconf, colour=treatment), width = 0.0,size=5, alpha=0.5) #+ scale_colour_manual("Group", values = colours)
g_div_a <- g_div_a + geom_point(aes(colour=treatment), size=3) #+ scale_colour_manual("Group", values = colours)
g_div_a <- g_div_a +  geom_point(aes(y=Observed), size=3) + coord_fixed(ratio = 1/5) #+ scale_colour_manual("Group", values = colours) 
print(g_div_a)
```

#### Figure 3b
```{r}
#Plot Asymptotic richness (Chao index)(Fig3b)
diversity_summary3<-diversity_summary[16:20,]
diversity_summary3$ID <- c("1U", "2ED", "3E", "4D", "5UED")
#colours <- c("#F8766D", "#00BA38", "#619CFF", "#E76BF3", "#00B6EB", "#A58AFF", "#FB61D7")
g_div_b <- ggplot(data=diversity_summary3, aes(x=ID,y=AsyEST))
g_div_b <- g_div_b + geom_errorbar(aes(ymax = Hconf_asy, ymin = Lconf_asy, colour=treatment), width = 0.0,size=5, alpha=1.0) #+ scale_colour_manual("Group", values = colours)
g_div_b <- g_div_b + geom_point(size=5) 
print(g_div_b)
```

#### Summary for richness analysis
- Observed richness (Fig.3a)
  - the whole richness at each day was greater than any of single treatment or single chamber
  - the richness in the whole period was greater than any of single date for every treatment and chamber
- Standardized richness (Fig.3a)

- Asymptotic richness (Fig.3b)
  - Chamber 2 (E&D) had a greater richness than Chamber 1 (U)
  - Chamber 2 had a greater richness than D
  - The whole richness (UED) was greater than Chamber 1 and D
  
#### Species richness partitioning (Table S2, Fig. S3, and Fig.3C)
```{r}
partitioning_summary <- read.csv("richness_partitioning.csv",header=T)

g <- ggplot(partitioning_summary, aes(x = partition, y = richness, fill = fraction))
g <- g + geom_bar(stat = "identity")
g <- g + geom_text(aes(label = richness), position = position_stack(vjust = 0.5))
plot(g)

```


