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SOC_Decay.R
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################################################################################
# Article: Soil organic carbon preservation and decay trends in tidal marsh, mangrove and seagrass blue carbon ecosystems
# 1/1
# Author: Nerea Piñeiro-Juncal (https://github.com/NPJuncal)
# V 4.3
# Year: 2025
################################################################################
library(ggplot2)
library(maps)
library(tidyr)
library(magrittr)
library(mosaic)
library(gridExtra)
library(nlstools)
library(dplyr)
library(MASS)
library(reshape2)
library(reshape)
library(psych)
# Loading the dataset ------------------------------------------------------
File <- "Data/Cores.csv"
Cores <- read.csv(File,
header = T,
sep = ";",
dec = ".")
Cores <- as.data.frame(Cores)
# check numeric variables in dataset
is.numeric(Cores$DBD)
is.numeric(Cores$Corg)
is.numeric(Cores$Corg_.gcm3)
is.numeric(Cores$d13C)
is.numeric(Cores$Mud)
Cores$Max.Depth<-as.numeric(Cores$Max.Depth)
#clean the rows without DBD and OC data
Cores$DBD <- as.numeric(Cores$DBD)
Cores <- Cores[!is.na(Cores$DBD),]
Cores$Corg <- as.numeric(Cores$Corg)
Cores <- Cores[!is.na(Cores$Corg),]
# estimation of carbon density for those samples where there are bulk density and oc percentage available
Cores$Corg_.gcm3 <- as.numeric(Cores$Corg_.gcm3)
for (i in 1:nrow(Cores)) {
if (is.na(Cores[i, "Corg_.gcm3"]) & !is.na(Cores[i, "DBD"]) & !is.na(Cores[i, "Corg"])) {
Cores[i, "Corg_.gcm3"] <- Cores[i, "DBD"] * (Cores[i, "Corg"]/100)}}
### we create a folder to save the results
Folder = "Decay2023"
dir.create(Folder)
### We select only vegetated or un-vegetated (Bare) cores
unique(Cores[, 5])
B <- subset(Cores, Cores$V.vs.B == "Vegetated" | Cores$V.vs.B == "vegetated")
unique(B[, 5])
B$Corg <- as.numeric(B$Corg)
B$Mud <- as.numeric(B$Mud)
B$DBD <- as.numeric(B$DBD)
# we only keep cores with more than 4 samples
temp <- B[0,]
X<-split(B, B$Core)
for (i in 1:length(X)) {
data<-X[[i]]
if(nrow(data)>4) {
temp<-rbind(temp, data)}}
length(unique(temp$Core))
B<-temp
#
length(unique(B$Core))
SingleCore<-B[!duplicated(B$Core),]
table(SingleCore$Ecosystem)
write.csv(SingleCore[,c(1,3,9,10)],
file.path(Folder, "singlecore.csv"),
sep = ";",
dec = ".")
# Sampling sites Map (Figure 4) -------------------------------------------
#load a world map
WM <- map_data("world")
global<- B %>%
ggplot() + ggtitle("Sampling sites") + xlab("Longitude") + ylab("Latitude") +
geom_polygon(data = WM, aes(x = long, y = lat, group = group)) +
#geom_point(aes(x = long, y = lat))+
geom_point(aes(x = Long, y = Lat, fill = Ecosystem), pch = 21, size = 1.8) +
coord_sf(xlim = c(-140, 150), ylim = c(-40, 75)) +
scale_fill_manual(values = c("blue", "green","orange")) +
theme(plot.title = element_text(hjust = 0.5))
nam<-B %>%
ggplot() + xlab("Longitude") + ylab("Latitude") +
geom_polygon(data = WM, aes(x = long, y = lat, group = group)) +
#geom_point(aes(x = long, y = lat))+
geom_point(aes(x = Long, y = Lat, fill = Ecosystem), pch = 21, size = 1.8) +
coord_sf(xlim = c(-150, -50), ylim = c(-20, 80)) +
scale_fill_manual(values = c("blue", "green","orange")) +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none")
eu<-B %>%
ggplot() + xlab("Longitude") + ylab("Latitude") +
geom_polygon(data = WM, aes(x = long, y = lat, group = group)) +
#geom_point(aes(x = long, y = lat))+
geom_point(aes(x = Long, y = Lat, fill = Ecosystem), pch = 21, size = 1.8) +
coord_sf(xlim = c(-10, 50), ylim = c(20, 60)) +
scale_fill_manual(values = c("blue", "green","orange")) +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none")
aus<-B %>%
ggplot() + xlab("Longitude") + ylab("Latitude") +
geom_polygon(data = WM, aes(x = long, y = lat, group = group)) +
#geom_point(aes(x = long, y = lat))+
geom_point(aes(x = Long, y = Lat, fill = Ecosystem), pch = 21, size = 1.8) +
coord_sf(xlim = c(110, 155), ylim = c(-40, -5)) +
scale_fill_manual(values = c("blue", "green","orange")) +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none")
ssp<-grid.arrange(global, nam, eu, aus,
layout_matrix = rbind(c(1, 1, 1),
c(2, 3, 4)))
ggsave(
plot = ssp,
path = Folder,
filename = "Sampling sites.jpg",
units = "cm",
width = 20,
height = 15
)
# Average and median OC content per core (full length and top 25 c --------
ADT <- B[, c("Core", "Ecosystem", "Max.Depth", "Corg", "Mud", "d13C")]
X <- split(ADT, ADT$Core)
CM <- data.frame(
ID = character(),
Ecosystem = character(),
Av_C = numeric(),
SE_C = numeric(),
M_C = numeric(),
Av_Mud = numeric(),
SE_Mud = numeric(),
M_Mud = numeric(),
Av_13C = numeric(),
SE_13C = numeric(),
M_13C = numeric(),
Av_C_25 = numeric(),
SE_C_25 = numeric(),
M_C_25 = numeric(),
Av_Mud_25 = numeric(),
SE_Mud_25 = numeric(),
M_Mud_25 = numeric(),
Av_13C_25 = numeric(),
SE_13C_25 = numeric(),
M_13C_25 = numeric()
)
std <- function(x) sd(x, na.rm=TRUE)/sqrt(length(x))
for (i in 1:length(X)) {
CM[i, 1] <- names(X[i])
Data <- as.data.frame(X[i])
colnames(Data) <- colnames(ADT)
CM[i, 2] <- Data[1, which(colnames(Data) == "Ecosystem")]
#Data<-na.omit(Data)
CM[i, 3] <- mean(Data[, which(colnames(Data) == "Corg")], na.rm=TRUE)
CM[i, 4] <- std(Data[, which(colnames(Data) == "Corg")])
CM[i, 5] <- median(Data[, which(colnames(Data) == "Corg")], na.rm=TRUE)
CM[i, 6] <- mean(Data[, which(colnames(Data) == "Mud")], na.rm=TRUE)
CM[i, 7] <- std(Data[, which(colnames(Data) == "Mud")])
CM[i, 8] <- median(Data[, which(colnames(Data) == "Mud")], na.rm=TRUE)
CM[i, 9] <- mean(Data[, which(colnames(Data) == "d13C")], na.rm=TRUE)
CM[i, 10] <- std(Data[, which(colnames(Data) == "d13C")])
CM[i, 11] <- median(Data[, which(colnames(Data) == "d13C")], na.rm=TRUE)
Data25 <-
Data %>% filter(Data[, which(colnames(Data) == "Max.Depth")] <= 25)
CM[i, 12] <- mean(Data25[, which(colnames(Data25) == "Corg")], na.rm=TRUE)
CM[i, 13] <- std(Data25[, which(colnames(Data25) == "Corg")])
CM[i, 14] <- median(Data25[, which(colnames(Data) == "Corg")], na.rm=TRUE)
CM[i, 15] <- mean(Data25[, which(colnames(Data25) == "Mud")], na.rm=TRUE)
CM[i, 16] <- std(Data25[, which(colnames(Data25) == "Mud")])
CM[i, 17] <- median(Data[, which(colnames(Data) == "Mud")], na.rm=TRUE)
CM[i, 18] <- mean(Data25[, which(colnames(Data25) == "d13C")], na.rm=TRUE)
CM[i, 19] <- std(Data25[, which(colnames(Data25) == "d13C")])
CM[i, 20] <- median(Data[, which(colnames(Data) == "d13C")], na.rm=TRUE)
}
write.csv(CM,
file.path(Folder, "AvMdC.csv"),
sep = ";",
dec = ".")
tapply(CM$Av_C_25, CM$Ecosystem, summary)
tapply(CM$Av_C_25, CM$Ecosystem, sd, na.rm=T)
# Sediment accretion rate -------------------------------------------------
File <- "Data/Acc_Mass-Age_F.csv"
Dates <- read.csv(File,
header = T,
sep = ";",
dec = ".")
Dates <- as.data.frame(Dates)
# estimates SAR last 150 years
SAR <- data.frame(
ID = character(),
Ecosystem = character(),
SAR_Age = numeric(),
SAR_Pb = numeric(),
SAR_C = numeric(),
SAR_25 = numeric())
X<-split(Dates, Dates$Core)
for (i in 1:length(X)) {
SAR[i, 1] <- names(X[i])
Data <- as.data.frame(X[i])
colnames(Data) <- colnames(Dates)
Eco <- substr(Data[1,"Core"], 1, 2)
if (Eco == "Mg") { SAR[i, 2] <- "Mangrove"}
if (Eco == "Sg") { SAR[i, 2] <- "Seagrass"}
if (Eco == "Sm") { SAR[i, 2] <- "Tidal Marsh"}
#correlation depth-age to predict depth at 150 yr old
DataAge <- Data[!is.na(Data$Age),]
DataAge<- DataAge[c(1:(length(which(DataAge$Age <=150)))),]
if (nrow(DataAge)>2) {SAR[i, 3] <- max(DataAge$Depth)/max(DataAge$Age)}
DataPb <- Data[!is.na(Data$Age.Pb),]
DataPb<- DataPb[c(1:(length(which(DataPb$Age.Pb <=150)))),]
if (nrow(DataPb)>2) {SAR[i, 4] <- max(DataPb$Depth)/max(DataPb$Age.Pb)}
DataC <- Data[!is.na(Data$Age.C),]
DataC<- DataC[c(1:(length(which(DataC$Age.C <=150)))),]
if (nrow(DataC)>2) {SAR[i, 5] <- max(DataC$Depth)/max(DataC$Age.C)}
if (nrow(DataAge)>2) {
Data25 <- Data[!is.na(Data$Age),]
Data25<- Data25[c(1:(length(which(Data25$Depth <=25)))),]
if (nrow(Data25)>2) {SAR[i, 6] <- max(Data25$Depth)/max(Data25$Age)}
} else {
Data25 <- Data[!is.na(Data$Age.Pb),]
Data25<- Data25[c(1:(length(which(Data25$Depth <=25)))),]
if (nrow(Data25)>2) {SAR[i, 6] <- max(Data25$Depth)/max(Data25$Age.Pb)}}
}
SAR<-SAR[-1,]
SAR$SAR<-"NA"
SAR$SAR<-as.numeric(SAR$SAR)
for (i in 1:nrow(SAR)) {
if (is.na(SAR[i,"SAR_Age"]) == FALSE) {SAR[i,"SAR"]<-SAR[i,"SAR_Age"]}
else {
if (is.na(SAR[i,"SAR_Pb"]) == FALSE) {SAR[i,"SAR"]<-SAR[i,"SAR_Pb"]}
else {if (is.na(SAR[i,"SAR_C"]) == FALSE) {SAR[i,"SAR"]<-SAR[i,"SAR_C"]}}}}
ggplot(SAR, aes(Ecosystem, SAR)) +
geom_boxplot() +
geom_jitter()
pairwise.wilcox.test(SAR$SAR, SAR$Ecosystem,
p.adjust.method = "BH") # are significantly different (p < 0.05)
# Corg trends with depth --------------------------------------------------
#### spearman correlation between depth and Corg content, as data no normal nor expected to follow lineal correlation
#### return DT data.frame (Core ID, rho, p value, trend)
ADT <- B[, c("Core", "Ecosystem", "Max.Depth", "Corg")]
X <- split(ADT, ADT$Core)
DT <- data.frame(
ID = character(),
Ecosystem = character(),
C_rho = numeric(),
C_p = numeric(),
C_Gr = character()
)
for (i in 1:length(X)) {
DT[i, 1] <- names(X[i])
Data <- as.data.frame(X[i])
colnames(Data) <- colnames(ADT)
DT[i, 2] <- Data[1, which(colnames(Data) == "Ecosystem")]
#Data<-na.omit(Data)
cor <-
cor.test(x = Data[, which(colnames(Data) == "Max.Depth")], y = Data[, which(colnames(Data) ==
"Corg")], method = "spearman")
DT[i, 3] <- cor$estimate
DT[i, 4] <- cor$p.value
}
##### from spearman correlations we discriminate 3 groups: NT, no trend with depth; DEC, decrease with depth; INC, increase with depth
#lower keys: IDs, capital keys: data frame with core data
# return data frames per group (NT,DEC and INC) and add trend tipe to DT
Nt <- DT$ID[DT$C_p > 0.5]
Trend <- DT[!(DT$C_p > 0.5), ]
Dec <- Trend$ID[Trend$C_rho < 0]
Inc <- Trend$ID[Trend$C_rho > 0]
NT <- B[is.element(B$Core, Nt), ]
DEC <- B[is.element(B$Core, Dec), ]
INC <- B[is.element(B$Core, Inc), ]
for (i in 1:nrow(DT)) {
if (is.element(DT[i, 1], Nt)) {
DT[i, 5] <- "NT"
}
else if (is.element(DT[i, 1], Dec)) {
DT[i, 5] <- "DEC"
}
else {
DT[i, 5] <- "INC"
}
}
write.csv(DT,
file.path(Folder, "DepthTrends.csv"),
sep = ";",
dec = ".")
#### Count cores per group and get the percentages
NGr <- DT %>% group_by(C_Gr) %>% count()
NGr %>% mutate(proc = ((n * 100) / sum(NGr[, 2])))
DT %>% group_by(Ecosystem, C_Gr) %>% count()
NGrSg <- subset(DT, Ecosystem == "Seagrass") %>% group_by(C_Gr) %>% count()
NGrSg %>% mutate(proc = ((n * 100) / sum(NGrSg[, 2])))
NGrSm <-
subset(DT, Ecosystem == "Tidal Marsh") %>% group_by(C_Gr) %>% count()
NGrSm %>% mutate(proc = ((n * 100) / sum(NGrSm[, 2])))
NGrMg <- subset(DT, Ecosystem == "Mangrove") %>% group_by(C_Gr) %>% count()
NGrMg %>% mutate(proc = ((n * 100) / sum(NGrMg[, 2])))
# Figures and tables tendency with depth ----------------------------------
### Figure 1 ###
DT2<-DT
DT2$C_Gr <- recode(DT2$C_Gr, DEC = 'Decreasing',
INC = 'No decreasing',
NT = 'No decreasing')
DT2<-cbind(CM, DT2)
DT2<-DT2[,c(1:20, 25)]
DT2 <-merge(DT2, SAR[,c(1,3:7)], by = "ID", all = TRUE)
cor.test(DT2$Av_Mud_25, DT2$Av_13C_25)
plot(DT2$Av_Mud_25, DT2$Av_13C_25)
DT2[, c(2,12,21)] %>%
group_by(Ecosystem, C_Gr) %>%
summarise_all("mean", na.rm=TRUE)
DT2[, c(2,12,21)] %>%
group_by(Ecosystem, C_Gr) %>%
summarise_all("median", na.rm=TRUE)
DT2 [, c(2,12,21)]%>%
group_by(Ecosystem, C_Gr) %>%
summarise_all("sd", na.rm=TRUE)
temp<-subset(DT2, !is.na(DT2 [, c(2,12,21)]$Av_C_25))
temp [, c(2,12,21)]%>%
group_by(Ecosystem, C_Gr) %>%
summarise_all("std")
### data from BIO-Oracle ##
File <- "Data/bio_oracle.csv"
oracle <- read.csv(File,
header = T,
sep = ";",
dec = ".")
oracle <- as.data.frame(oracle)
DT3 <-merge(DT2, oracle[c(1,7:12)], by = "ID", all = TRUE)
DT3<-subset(DT3, !is.na(DT3$C_Gr))
# seagrass specie and trend -----------------------------------------------
Sg_DT<-subset(DT, Ecosystem=="Seagrass")
Sg_DT$Life.form<-"NA"
for (i in 1:nrow(Sg_DT)) {
life_form<- unique(SingleCore[c(which(SingleCore$Core==Sg_DT[i,which(colnames(Sg_DT)=="ID")])),which(colnames(SingleCore)=="Life.form")])
Sg_DT[i,which(colnames(Sg_DT)=="Life.form")]<- life_form
}
# percentage of dec in persistent and another life forms
#persistent
temp <- subset(Sg_DT, Life.form=="Persistent") %>% group_by(C_Gr) %>% count()
temp %>% mutate(proc = ((n * 100) / sum(temp[, 2])))
#no persistent
temp <- subset(Sg_DT, !Life.form=="Persistent") %>% group_by(C_Gr) %>% count()
temp %>% mutate(proc = ((n * 100) / sum(temp[, 2])))
# percentage of life.forms in dec trends
temp <- subset(Sg_DT, C_Gr=="DEC") %>% group_by(Life.form) %>% count()
temp %>% mutate(proc = ((n * 100) / sum(temp[, 2])))
# percentage of life.forms in dec trends
temp <- subset(Sg_DT, !C_Gr=="DEC") %>% group_by(Life.form) %>% count()
temp %>% mutate(proc = ((n * 100) / sum(temp[, 2])))
##
#Seagrass meadows
DT2Sg<-subset(DT2, Ecosystem=="Seagrass")
pairwise.wilcox.test(DT2Sg$Av_13C, DT2Sg$Life.form,
p.adjust.method = "BH") # are significantly different (p < 0.05)
# Figure 1 ----------------------------------------------------------------
#Seagrass meadows
DT2Sg<-subset(DT2, Ecosystem=="Seagrass")
cor.test(DT2Sg$Av_C_25, DT2Sg$Av_13C_25, method="spearman")
plot(DT2Sg$Av_C_25, DT2Sg$Av_13C_25)
#organic carbon
SC<-ggplot(DT2Sg, aes(C_Gr, Av_C_25)) + ggtitle("Seagrass")+
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.1)+
scale_y_log10()+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = 0.003,
label = table(subset(DT2Sg, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_blank())
#mud
SM<-ggplot(DT2Sg, aes(C_Gr, Av_Mud_25)) +
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.5)+
ylim(-10,110)+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = -7,
label = table(subset(DT2Sg, !is.na(Av_Mud_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
#SAR
SS<-ggplot(DT2Sg, aes(C_Gr, SAR_25)) +
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.5)+
ylim(-0.15,1.1)+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = -0.1,
label = table(subset(DT2Sg, !is.na(SAR_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
#d13C
d13S<-ggplot(DT2Sg, aes(C_Gr, Av_13C_25)) + ylab(expression(delta~"13C (‰)")) + ggtitle("Seagrass")+
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.3)+
ylim(-30,-5)+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = -30,
label = table(subset(DT2Sg, !is.na(Av_13C_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5),)
#temperature
ST<-ggplot(subset(DT3, Ecosystem=="Seagrass"), aes(C_Gr, temperature)) +
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.1)+
ylim(2,35)+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = 2.5,
label = table(subset(DT2Sg, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_blank())
#current velocity
SCV<-ggplot(subset(DT3, Ecosystem=="Seagrass"), aes(C_Gr, water.velocity)) +
ylab(expression(paste("Current v. (m ",s^-1,")"))) + ggtitle("Seagrass")+
geom_boxplot()+
geom_jitter(color="green4", alpha = 0.3)+
annotate("text",
x = 1:length(table(DT2Sg$C_Gr)),
y = -0.05,
label = table(subset(DT2Sg, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank())
#Tidal marshes
DT2Sm<-subset(DT2, Ecosystem=="Tidal Marsh")
#organic carbon
TMC<-ggplot(DT2Sm, aes(C_Gr, Av_C_25)) + ylab("log(OC%)") + ggtitle("Tidal Marsh")+
geom_boxplot()+
geom_jitter(color="orange", alpha = 0.1)+
scale_y_log10()+
annotate("text",
x = 1:length(table(DT2Sm$C_Gr)),
y = 0.003,
label = table(subset(DT2Sm, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank())
#mud
TMM<-ggplot(DT2Sm, aes(C_Gr, Av_Mud_25)) + ylab("Mud%") +
geom_boxplot()+
geom_jitter(color="orange", alpha = 0.5)+
ylim(-10,110)+
annotate("text",
x = 1:length(table(DT2Sm$C_Gr)),
y = -7,
label = table(subset(DT2Sm, !is.na(Av_Mud_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank())
#SAR
TMS<-ggplot(DT2Sm, aes(C_Gr, SAR_25)) + ylab(expression(paste("Sed. acc. (cm ",yr^-1,")"))) +
geom_boxplot()+
geom_jitter(color="orange", alpha = 0.5)+
ylim(-0.15,1.1)+
annotate("text",
x = 1:length(table(DT2Sm$C_Gr)),
y = -0.1,
label = table(subset(DT2Sm, !is.na(SAR_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank())
#d13C
d13T<-ggplot(DT2Sm, aes(C_Gr, Av_13C_25)) + ylab(expression(delta~"13C (‰)")) +
geom_boxplot()+
geom_jitter(color="orange", alpha = 0.5)+
annotate("text",
x = 1:length(table(DT2Sm$C_Gr)),
y = -30,
label = table(subset(DT2Sm, !is.na(Av_13C_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank())
#temperature
TT<-ggplot(subset(DT3, Ecosystem=="Tidal Marsh"), aes(C_Gr, temperature)) + ylab("Sea Water Tª (\u00B0C)") +
geom_boxplot()+
geom_jitter(color="orange", alpha = 0.1)+
ylim(2,35)+
annotate("text",
x = 1:length(table(DT2Sm$C_Gr)),
y = 2.5,
label = table(subset(DT2Sm, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank())
#Mangroves
DT2Mg<-subset(DT2, Ecosystem=="Mangrove")
#organic carbon
MGC<-ggplot(DT2Mg, aes(C_Gr, Av_C_25)) + ggtitle("Mangrove")+
geom_boxplot()+
geom_jitter(color="blue", alpha = 0.1)+
annotate("text",
x = 1:length(table(DT2Mg$C_Gr)),
y = 0.003,
label = table(subset(DT2Mg, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
scale_y_log10()+
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_blank())
#mud
TGM<-ggplot(DT2Mg, aes(C_Gr, Av_Mud_25)) +
geom_boxplot()+
geom_jitter(color="blue", alpha = 0.5)+
ylim(-10,110)+
annotate("text",
x = 1:length(table(DT2Mg$C_Gr)),
y = -7,
label = table(subset(DT2Mg, !is.na(Av_Mud_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
#SAR
TGS<-ggplot(DT2Mg, aes(C_Gr, SAR_25)) +
geom_boxplot()+
geom_jitter(color="blue", alpha = 0.5)+
ylim(-0.15,1.1)+
annotate("text",
x = 1:length(table(DT2Mg$C_Gr)),
y = -0.1,
label = table(subset(DT2Mg, !is.na(SAR_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
#d13C
d13M<-ggplot(DT2Mg, aes(C_Gr, Av_13C_25)) +
geom_boxplot()+
geom_jitter(color="blue", alpha = 0.5)+
ylim(-30,-5)+
annotate("text",
x = 1:length(table(DT2Mg$C_Gr)),
y = -30,
label = table(subset(DT2Mg, !is.na(Av_13C_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
#temperature
MT<-ggplot(subset(DT3, Ecosystem=="Mangrove"), aes(C_Gr, temperature)) +
geom_boxplot()+
geom_jitter(color="blue", alpha = 0.1)+
ylim(2,35)+
annotate("text",
x = 1:length(table(DT2Mg$C_Gr)),
y = 2.5,
label = table(subset(DT2Mg, !is.na(Av_C_25))[,"C_Gr"]),
col = "black")+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
todos_CM<-ggpubr::ggarrange( TMC, MGC, SC, TMM, TGM, SM, TMS, TGS, SS, TT, MT, ST, d13S, SCV,
labels = c("A","B", "C","D" , "E","F", "G","H", "I", "J", "K", "L", "M", "N"),
ncol=3, nrow= 5)
ggsave( plot = todos_CM,
path = Folder,
filename = "C_Mud_Gr.jpg",
units = "cm",
width = 20,
height = 25)
## diff sig
#Seagrass
pairwise.wilcox.test(DT2Sg$Av_C_25, DT2Sg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Sg$Av_Mud_25, DT2Sg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Sg$SAR_25, DT2Sg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(subset(DT3, Ecosystem=="Seagrass")$temperature, subset(DT3, Ecosystem=="Seagrass")$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Sg$Av_13C_25, DT2Sg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(subset(DT3, Ecosystem=="Seagrass")$water.velocity, subset(DT3, Ecosystem=="Seagrass")$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
#Tidal marshes
pairwise.wilcox.test(DT2Sm$Av_C_25, DT2Sm$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Sm$Av_Mud_25, DT2Sm$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Sm$SAR_25, DT2Sm$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(subset(DT3, Ecosystem=="Tidal Marsh")$temperature, subset(DT3, Ecosystem=="Tidal Marsh")$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
#Mangroves
pairwise.wilcox.test(DT2Mg$Av_C_25, DT2Mg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Mg$Av_Mud_25, DT2Mg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(DT2Mg$SAR_25, DT2Mg$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
pairwise.wilcox.test(subset(DT3, Ecosystem=="Mangrove")$temperature, subset(DT3, Ecosystem=="Mangrove")$C_Gr,
p.adjust.method = "BH") # are significantly different (p < 0.05)
# average per ecosystem
std <- function(x) sd(x, na.rm=TRUE)/sqrt(length(x))
aggregate(DT2[,c(12, 15, 18, 25)], list(DT2$Ecosystem), FUN=mean, na.rm=T)
aggregate(DT2[,c(12, 15, 18, 25)], list(DT2$Ecosystem), FUN=std)
aggregate(DT3[,c(27:32)], list(DT3$Ecosystem), FUN=mean, na.rm=T)
aggregate(DT3[,c(6:11)], list(DT3$Ecosystem), FUN=std)
# correlation with time ---------------------------------------------------
### chronological models estimation
File <- "Data/Acc_Mass-Age_F.csv"
Dates <- read.csv(File,
header = T,
sep = ";",
dec = ".")
Dates <- as.data.frame(Dates)
# Create a dataframe with those core with age-acc mass models
X <- split(B, B$Core)
C <- data.frame(matrix(ncol = 26, nrow = 0))
colnames(C) <- c(colnames(B), colnames(Dates[, 2:6]))
for (i in 1:length(X)) {
Data <- as.data.frame(X[i])
colnames(Data) <- colnames(B)
if (Data[1, 1] %in% Dates$Core == TRUE) {
temp <- subset(Dates, Dates$Core == Data[1, 1])
temp <- temp[c(1:nrow(Data)), ]
Data <- cbind(Data, temp[, c(3:6)])
#check if there is at least 5 samples with dates
if (count(!is.na(Data$Age))>5 | count(!is.na(Data$Age.Pb))>5 | count(!is.na(Data$Age.C))>5) {
C <- rbind(C, Data)}
} else
next
}
length(unique(C$Core))
single_core_dates<-C[!duplicated(C$Core),]
table(single_core_dates$Ecosystem)
table(subset(single_core_dates,Ecosystem=="Seagrass")[,"Specie"])
TAll = filter(C, !is.na(Age) | !is.na(Age.Pb) | !is.na(Age.C))# df with core with any model
length(unique(TAll$Core))
# number of cores with age-depth models per ecosystem
table((TAll[!duplicated(TAll$Core),])[,"Ecosystem"])
TPb = subset(C, !is.na(Age) | !is.na(Age.Pb) )# df with core with Pb model
length(unique(TPb$Core))
TC = filter(C, !is.na(Age.C))# df with core with C model
length(unique(TC$Core))
TPbandC = filter(C, !is.na(Age) | !is.na(Age.Pb))# df with core with C and Pb model
TPbandC = filter(TPbandC, !is.na(Age.C))
length(unique(TPbandC$Core))
#### Homogenize Age
# Decay models ---------------------------------------------------
# function to generate different df depending on their tendency with time (DEC, INC and NT), generate figures per group
# and return a dfs: TDT, TDEC, TINC and TNT. Needs a df with columns: c("Core", "Ecosystem","Min.Depth","Max.Depth", "FAge", "Corg"
# a max age (MA) and a name for the plots
tendency<- function (df, pnames) {
ADT <- df[, c("Core", "Ecosystem","Min.Depth","Max.Depth", "FAge", "Corg")]
X <- split(ADT, ADT$Core)
TDT <- data.frame(
ID = character(),
Ecosystem = character(),
C_rho = numeric(),
C_p = numeric(),
C_Gr = character()
)
for (i in 1:length(X)) {
TDT[i, 1] <- names(X[i])
Data <- as.data.frame(X[i])
colnames(Data) <- colnames(ADT)
TDT[i, 2] <- Data[1, which(colnames(Data) == "Ecosystem")]
#Data<-na.omit(Data)
if (nrow(Data)<3) next
cor <- cor.test(x = Data$FAge, y = Data$Corg, method = "spearman")
TDT[i, 3] <- cor$estimate
TDT[i, 4] <- cor$p.value
}
##### from spearman correlations we discriminate 3 groups: NT, no trend with depth; DEC, decrease with depth; INC, increase with depth
#lower keys: IDs, capital keys: data frame with core data
# return data frames per group (NT,DEC and INC) and add trend tipe to DT
Nt <- TDT$ID[TDT$C_p > 0.5]
Trend <- TDT[!(TDT$C_p > 0.5), ]
Dec <- Trend$ID[Trend$C_rho < 0]
Inc <- Trend$ID[Trend$C_rho > 0]
TNT <- df[is.element(df$Core, Nt), ]
TDEC <- df[is.element(df$Core, Dec), ]
TINC <- df[is.element(df$Core, Inc), ]
for (i in 1:nrow(TDT)) {
if (is.element(TDT[i, 1], Nt)) {
TDT[i, 5] <- "NT"
}
else if (is.element(TDT[i, 1], Dec)) {
TDT[i, 5] <- "DEC"
}
else {
TDT[i, 5] <- "INC"
}
}
#### Count cores per group and get the percentages
library(dplyr)
NGr <- TDT %>% group_by(C_Gr) %>% count()