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-This is a breakpoint analysis in R for metal(loid) data obtained from individual coring sites. I used data containing more than 20 metals, and their concentrations at each depth of the impacted core.
-My data files can be found in the Github repo, called FileS7_Metalloids_CSM-IMP.csv and FileS8_Metalloids_CSM-REF.csv. Access the files here.
Begin by installing all the required packages using install/packages() if you haven’t already.
Once installed, load the packages in R
- -Load your data for impact and reference sites into R
-metals_IMP <- read.csv("Data/FileS6_Metalloids_CSM-IMP.csv")
-metals_REF <- read.csv("Data/FileS7_Metalloids_CSM-REF.csv")Transform the data into long format
-metals_long_IMP <- metals_IMP %>%
- pivot_longer(!depth, names_to = "metals", values_to = "concentration")Make sure depth column is numeric
- -![[Bosch et al. 2024]: Dendrogram (LEFT) showing the distinct breakpoints in the metal(loid)s data across the core depth (cm) of CSM-IMP-2, and a line plot (RIGHT) showing the broken stick dispersion plot for the breakpoint analysis, where value is a measure of the magnitude of the variable being analyzed. Created using the tidyverse and tidypaleo packages available in R (v.4.2.1) software.](images/FigureS3.png)
--Figure 2.1: [Bosch et al. 2024]: Dendrogram (LEFT) showing the distinct breakpoints in the metal(loid)s data across the core depth (cm) of CSM-IMP-2, and a line plot (RIGHT) showing the broken stick dispersion plot for the breakpoint analysis, where value is a measure of the magnitude of the variable being analyzed. Created using the tidyverse and tidypaleo packages available in R (v.4.2.1) software. -
-I used data from the following files found in the Github repo FileS3_Dating_CSM-IMP.csv and FileS4_Dating_CSM-REF.csv. Access the files here.
Load your data for impact and reference sites into R
-dp_IMP2 <- read.csv("Data/FileS2_Dating_CSM-IMP.csv")
-dp_REF3 <- read.csv("Data/FileS3_Dating_CSM-REF.csv") Define columns for core depth/year, activity, sedimentation rate (with error) in both files
-#core depth
-depth_IMP2 <- dp_IMP2$midpoint
-depth_REF3 <- dp_REF3$midpoint
-
-#core year
-year_IMP <- dp_IMP2$year
-year_REF <- dp_REF3$year
-
-#210-Pb activity
-act_IMP2 <- dp_IMP2$activity
-act_REF3 <- dp_REF3$activity
-
-# sedimentation rate
-sed_IMP2 <- dp_IMP2$sedimetation_rate
-sed_REF3 <- dp_REF3$sedimetation_rate
-
-error_IMP <- dp_IMP2$sr_error
-error_REF <- dp_REF3$sr_errorPlot the activity and sedimentation rate
-activity <- ggplot() +
- geom_point(data = dp_IMP2, (aes(x= depth_IMP2, y=act_IMP2)), shape=22, fill="paleturquoise4", size=3, color = "paleturquoise4", alpha=0.5) +
- geom_line(data = dp_IMP2, (aes(x = depth_IMP2, y=act_IMP2)), color="paleturquoise4") +
- geom_point(data = dp_REF3, (aes(x = depth_REF3, y=act_REF3)), size =3, color="palegreen3") +
- geom_line(data = dp_REF3, (aes(x = depth_REF3, y=act_REF3)), color="palegreen3") +
- labs(x="Midpoint depth (cm)", y="Pb-210 Activity (Bq/kg)") +
- scale_x_continuous(breaks=seq(0,19, by= 1), limits = c(0,19)) +
- theme_classic()+
- theme(text = element_text(size = 12), panel.grid.major.x=element_line(), axis.text.y = element_text(size=12), axis.title.y = element_text(margin = margin(t=0, r= 20, b=0, l=0)))
-
-activitysed.rate <- ggplot() +
- geom_point(data = dp_IMP2, (aes(x = depth_IMP2, y=sed_IMP2)),shape=21, color = "paleturquoise4") +
- geom_line(data = dp_IMP2, (aes(x = depth_IMP2, y=sed_IMP2)), color="paleturquoise4") +
- geom_point(data = dp_REF3, (aes(x = depth_REF3, y=sed_REF3)), color="palegreen3") +
- geom_line(data = dp_REF3, (aes(x = depth_REF3, y=sed_REF3)), color="palegreen3") +
- labs(x = "Core Depth (cm/yr)", y = "Sedimentation rate (cm/yr)") +
- scale_x_continuous(breaks=seq(0,19, by= 1), limits = c(0,19)) +
- theme_classic() +
- theme(panel.grid.major.x=element_line(), text = element_text(size = 12), axis.text.y = element_text(size=12),
- axis.text.x = element_text(size=12), axis.title.y = element_text(margin = margin(t=0, r= 20, b=0, l=0)))
-
-par(mar = c(10, 10,10,10))Plot year based on core depth
-year <- ggplot() +
- geom_point(data = dp_IMP2, (aes(x = year_IMP, y= depth_IMP2)), na.rm=TRUE, shape=22, fill="paleturquoise4", size=3, color = "paleturquoise4", alpha=0.5) +
- geom_line(data = dp_IMP2, (aes(x = year_IMP, y= depth_IMP2)), na.rm=TRUE, color = "paleturquoise4") +
- stat_smooth(dp_IMP2, mapping=aes(year_IMP, depth_IMP2), method="lm") +
- geom_errorbar(aes(x=year_IMP, xmin=year_IMP-error_IMP, xmax=year_IMP+error_IMP, y=depth_IMP2, ymax=NULL, ymin=NULL), width=.2) +
- labs(x = "Year (CRS model)", y = "Midpoint depth (cm)") +
- scale_x_continuous(breaks=seq(1800,2023, by= 20)) +
- scale_y_reverse(breaks=seq(0,15, by=1), expand=c(0,0)) +
- theme_classic() +
- theme(panel.grid.major.x=element_line(), text = element_text(size = 12), axis.text.y = element_text(size=12), axis.text.x = element_text(size=12))
-
-year![[Bosch et al. 2024]: 210-Pb activity (Bq/kg) over the depth of the impact core (CSM-IMP-2, blue squares) and the reference core (CSM-REF-3, green circles). The 210-Pb dates assigned to the midpoint depth of the cores using a constant rate of supply (CRS) dating model are shown in the smaller, inset graph, where pink trend lines represent the second order polynomial regression for each core.](images/Dating-profiles.png)
--Figure 1.1: [Bosch et al. 2024]: 210-Pb activity (Bq/kg) over the depth of the impact core (CSM-IMP-2, blue squares) and the reference core (CSM-REF-3, green circles). The 210-Pb dates assigned to the midpoint depth of the cores using a constant rate of supply (CRS) dating model are shown in the smaller, inset graph, where pink trend lines represent the second order polynomial regression for each core. -
-Data used in this analysis can be found in the github repo in the following files: FileS4_Impact_ProxyData.xlsx, FileS5_Reference_ProxyData.xlsx. Access the files here. Each proxy should have a seperate file containing z_score data, which I used in the analysis.
Install the following packages
- -If you’re using ubuntu, run this first to install the dependency RCPArmadillo and mvnfast for gratia
-sudo apt-get update -y
-sudo apt-get install -y r-cran-rcpparmadillo
-sudo apt-get install -y r-cran-mvnfastLoad packages
- -Here I use the mixed model approach via restricted maximum likelihood (REML).
-Start by loading a local function for first derivatives
-signifD <- function(x, d, upper, lower, eval = 0) {
- miss <- upper > eval & lower < eval
- incr <- decr <- x
- want <- d > eval
- incr[!want | miss] <- NA
- want <- d < eval
- decr[!want | miss] <- NA
- list(incr = incr, decr = decr)
-}Read the chlorophyll a data into R:
- -Check that data looks correct
-Plot your results
- -And now add standard error
-fit_gcv<- predict(chlA_gam, data = pd_chlA, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(chlA_gam))
-chlA_new <- data.frame(Year = pd_chlA[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-chlA_new <- transform(chlA_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))You need equal lengths everywhere (i.e., 200 rows) for the following functions to work
-chlA_new_sigperiod<-data.frame(approx(chlA_new$Year, chlA_new$fit, n=200))
-
-colnames(chlA_new_sigperiod)<-c("Year", "fit")Check that it looks right using head(chlA_new_sigperiod)
chlA<-ggplot()+
- geom_ribbon(data=chlA_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= chlA_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= chlA_new_sigperiod, mapping=aes(y=BTP_chlA_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=chlA_new_sigperiod, mapping=aes(y=BTP_chlA_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_chlA, aes(y=chlA, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(-0.01, 0.16))+
- labs(x=NULL, y=NULL) +
- theme_classic() +
- theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x=element_blank(), text = element_text(size = 10, face = "bold"))
-
-chlANow do the same for every other proxy, here my dataset includes data for d15N, d13C, Phosphorus, Zinc/Al, Cadmium/Al, and relative abundance of two dominant diatom species.
-pd_d15N <-read.csv("d15N.csv")
-head(pd_d15N) #Check that data looks correct
-
-d15N_gam<-gam(d15N~s(year, k = 20), data= pd_d15N, method="REML")
-gam.check(d15N_gam)
-plot(d15N_gam)
-
-fit_gcv<- predict(d15N_gam, data = pd_d15N, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(d15N_gam))
-d15N_new <- data.frame(Year = pd_d15N[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-d15N_new <- transform(d15N_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-
-d15N_new_sigperiod<-data.frame(approx(d15N_new$Year, d15N_new$fit, n=200))
-colnames(d15N_new_sigperiod)<-c("Year", "fit")
-head(d15N_new_sigperiod) # Check that it looks right.
-
-BTP_d15N_der<-derivatives(d15N_gam, type="central", n=200)
-draw(BTP_d15N_der)
-
-BTP_d15N_sig<-signifD(d15N_new_sigperiod$fit, d= BTP_d15N_der$derivative, BTP_d15N_der$upper, BTP_d15N_der$lower)
-BTP_d15N_sig
-
-d15N<-ggplot()+
- geom_ribbon(data=d15N_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= d15N_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= d15N_new_sigperiod, mapping=aes(y=BTP_d15N_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=d15N_new_sigperiod, mapping=aes(y=BTP_d15N_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_d15N, aes(y= d15N, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(-2, 13))+
- labs(x=NULL, y=NULL) +
- theme_classic() +
- theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x=element_blank(), text = element_text(size = 10, face = "bold"))
-
-d15Npd_d13C <-read.csv("d13C.csv")
-head(pd_d13C) #Check that data looks correct
-
-d13C_gam<-gam(d13C~s(year, k = 19), data= pd_d13C, method="REML")
-gam.check(d13C_gam)
-plot(d13C_gam)
-
-fit_gcv<- predict(d13C_gam, data = pd_d13C, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(d13C_gam))
-d13C_new <- data.frame(Year = pd_d13C[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-d13C_new <- transform(d13C_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-d13C_new_sigperiod<-data.frame(approx(d13C_new$Year, d13C_new$fit, n=200))
-colnames(d13C_new_sigperiod)<-c("Year", "fit")
-head(d13C_new_sigperiod)
-
-BTP_d13C_der<-derivatives(d13C_gam, type="central", n=200)
-draw(BTP_d13C_der)
-
-BTP_d13C_sig<-signifD(d13C_new_sigperiod$fit, d= BTP_d13C_der$derivative, BTP_d13C_der$upper, BTP_d13C_der$lower)
-BTP_d13C_sig
-
-d13C<-ggplot()+
- geom_ribbon(data=d13C_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= d13C_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= d13C_new_sigperiod, mapping=aes(y=BTP_d13C_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=d13C_new_sigperiod, mapping=aes(y=BTP_d13C_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_d13C, aes(y= d13C, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- theme(text = element_text(size = 10, face = "bold")) +
- ylab("d13C")+
- xlab("year Year (cm)")+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(-28, -23))+
- labs(x=NULL, y=NULL) +
- theme_classic() +
- theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x=element_blank(), text = element_text(size = 10, face = "bold"))
-
-d13Cpd_metals <-read.csv("metals.csv")
-head(pd_metals)
-
-P_gam<-gam(P~s(year, k = 15), data= pd_metals, method="REML")
-gam.check(P_gam)
-plot(P_gam)
-
-fit_gcv<- predict(P_gam, data = pd_metals, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(P_gam))
-P_new <- data.frame(Year = pd_metals[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-P_new <- transform(P_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-P_new_sigperiod<-data.frame(approx(P_new$Year, P_new$fit, n=200))
-colnames(P_new_sigperiod)<-c("Year", "fit")
-
-BTP_P_der<-derivatives(P_gam, type="central", n=200)
-draw(BTP_P_der)
-
-BTP_P_sig<-signifD(P_new_sigperiod$fit, d= BTP_P_der$derivative, BTP_P_der$upper, BTP_P_der$lower)
-BTP_P_sig
-
-P<-ggplot()+
- geom_ribbon(data=P_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= P_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= P_new_sigperiod, mapping=aes(y=BTP_P_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=P_new_sigperiod, mapping=aes(y=BTP_P_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_metals, aes(y=P, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- theme(text = element_text(size = 10, face = "bold")) +
- ylab("")+
- xlab("year")+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(-3000, 15000))+
- labs(x=NULL, y=NULL) +
- theme_classic() +
- theme(text = element_text(size = 10, face = "bold"))
-
-Ppd_metals <-read.csv("metals.csv")
-head(pd_metals)
-
-Zn_gam<-gam(Zn~s(year, k = 15), data= pd_metals, method="REML")
-gam.check(Zn_gam)
-plot(Zn_gam)
-
-fit_gcv<- predict(Zn_gam, data = pd_metals, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(Zn_gam))
-Zn_new <- data.frame(Year = pd_metals[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-Zn_new <- transform(Zn_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-
-Zn_new_sigperiod<-data.frame(approx(Zn_new$Year, Zn_new$fit, n=200))
-colnames(Zn_new_sigperiod)<-c("Year", "fit")
-head(Zn_new_sigperiod)
-
-BTP_Zn_der<-derivatives(Zn_gam, type="central", n=200)
-draw(BTP_Zn_der)
-
-BTP_Zn_sig<-signifD(Zn_new_sigperiod$fit, d= BTP_Zn_der$derivative, BTP_Zn_der$upper, BTP_Zn_der$lower)
-BTP_Zn_sig
-
-Zn <-ggplot() +
- geom_ribbon(data=Zn_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5) +
- geom_line(data= Zn_new, mapping=aes(y= fit, x= Year), colour="lightblue4") +
- geom_line(data= Zn_new_sigperiod, mapping=aes(y=BTP_Zn_sig$decr, x=Year), colour="deepskyblue4", size=1) + ## Periods of increase
- geom_line(data=Zn_new_sigperiod, mapping=aes(y=BTP_Zn_sig$incr, x=Year), colour="deepskyblue4", size=1) + ## Periods of decrease
- geom_point(data=pd_metals, aes(y= Zn, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5) +
- theme(text = element_text(size = 10, face = "bold")) +
- ylab("")+
- xlab("")+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(0.00006, 0.015)) +
- labs(x=NULL, y=NULL) +
- theme_classic() +
- theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x=element_blank(), text = element_text(size = 10, face = "bold"))
-
-Zn
-
-pd_metals <-read.csv("metals.csv")
-head(pd_metals)
-
-Cd_gam<-gam(Cd~s(year, k = 15), data= pd_metals, method="REML")
-gam.check(Cd_gam)
-plot(Cd_gam)
-
-fit_gcv<- predict(Cd_gam, data = pd_metals, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(Cd_gam))
-Cd_new <- data.frame(Year = pd_metals[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-Cd_new <- transform(Cd_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-Cd_new_sigperiod<-data.frame(approx(Cd_new$Year, Cd_new$fit, n=200))
-colnames(Cd_new_sigperiod)<-c("Year", "fit")
-head(Cd_new_sigperiod)
-
-BTP_Cd_der<-derivatives(Cd_gam, type="central", n=200)
-draw(BTP_Cd_der)
-
-BTP_Cd_sig<-signifD(Cd_new_sigperiod$fit, d= BTP_Cd_der$derivative, BTP_Cd_der$upper, BTP_Cd_der$lower)
-BTP_Cd_sig
-
-Cd<-ggplot()+
-geom_ribbon(data=Cd_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= Cd_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= Cd_new_sigperiod, mapping=aes(y=BTP_Cd_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=Cd_new_sigperiod, mapping=aes(y=BTP_Cd_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_metals, aes(y= Cd, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- theme(text = element_text(size = 10, face = "bold")) +
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(0.05e-05, 3e-04))+
- labs(x=NULL, y=NULL) +
- theme_classic()+
- theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x=element_blank(), text = element_text(size = 10, face = "bold"))
-
-
-Cdpd_diatoms <-read.csv("diatoms.csv")
-head(pd_diatoms)
-
-S.cons_gam<-gam(S.construens~s(year, k = 17), data= pd_diatoms, method="REML")
-gam.check(S.cons_gam)
-plot(S.cons_gam)
-
-fit_gcv<- predict(S.cons_gam, data = pd_diatoms, se.fit = TRUE, length.out=200)
-crit.t <- qt(0.975, df.residual(S.cons_gam))
-S.cons_new <- data.frame(Year = pd_diatoms[["year"]],
- fit = fit_gcv$fit,
- se.fit = fit_gcv$se.fit)
-
-S.cons_new <- transform(S.cons_new,
- upper = fit + (crit.t * se.fit),
- lower = fit - (crit.t * se.fit))
-
-S.cons_new_sigperiod<-data.frame(approx(S.cons_new$Year, S.cons_new$fit, n=200))
-colnames(S.cons_new_sigperiod)<-c("Year", "fit")
-head(S.cons_new_sigperiod)
-
-BTP_S.cons_der<-derivatives(S.cons_gam, type="central", n=200)
-draw(BTP_S.cons_der)
-
-BTP_S.cons_sig<-signifD(S.cons_new_sigperiod$fit, d= BTP_S.cons_der$derivative, BTP_S.cons_der$upper, BTP_S.cons_der$lower)
-BTP_S.cons_sig
-
-S.cons<-ggplot()+
- geom_ribbon(data=S.cons_new, mapping= aes(x= Year, ymax= upper, ymin=lower), fill="#b3e0ff", inherit.aes = FALSE, alpha=0.5)+
- geom_line(data= S.cons_new, mapping=aes(y= fit, x= Year), colour="lightblue4")+
- geom_line(data= S.cons_new_sigperiod, mapping=aes(y=BTP_S.cons_sig$decr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of increase
- geom_line(data=S.cons_new_sigperiod, mapping=aes(y=BTP_S.cons_sig$incr, x=Year), colour="deepskyblue4", size=1)+ ## Periods of decrease
- geom_point(data=pd_diatoms, aes(y= S.construens, x= year), shape= 21, fill= "#0099cc", colour="lightblue4", size = 0.5)+
- scale_x_continuous(breaks=seq(1000,2022, by= 100), limits = c(1000,2022))+
- ylim(c(-15, 115))+
- labs(x=NULL, y=NULL) +
- theme_classic()+
- theme(text = element_text(size = 10, face = "bold"))
-
-S.cons![[Bosch et al. 2024]: Proxy data from the impact core fitted with a generalized additive model (GAM) and aligned with extrapolated 210Pb-dates. Bolded blue lines represent significant periods of change and shaded blue areas represent the confidence intervals.](images/FigureS4.png)
--Figure 4.1: [Bosch et al. 2024]: Proxy data from the impact core fitted with a generalized additive model (GAM) and aligned with extrapolated 210Pb-dates. Bolded blue lines represent significant periods of change and shaded blue areas represent the confidence intervals. -
-2024-03-05
-This book can be used to learn how to conduct a paleoecological analysis in R. The project methods described here are formally reviewed and cited in the following paper:
-Bosch J, Álvarez-Manzaneda I, Smol J, Michelutti N, Robertson GJ, Wilhelm SI, Montevecchi WA. 2024 Blending census and paleolimnological data allows for tracking the establishment and growth of a major gannet colony over several centuries. Proceedings of the Royal Society B [in review].
-For this study, we gathered sediment records from a pond situated 240m upwind of the northern gannet (Morus bassanus) colony in Cape St Mary’s Ecological Reserve (Newfoundland, CA) that receives seabird nutrients. We reconstructed the population trends of the gannets using multiple proxies (δ15N, chlorophyll a, phosphorus, cadmium, zinc, ornithogenic diatoms) related to seabird nutrient inputs into freshwater catchments. By aligning shifts in seabird-related proxies to historical census data, we reveal how paleo-reconstructions can accurately follow known population counts.
-Samples were taken from Cape St. Mary’s Ecological Reserve, in Newfoundland. The main nesting site within the Reserve is named Bird Rock. A review of the paleolimnological process at play are shown in the graphical abstract below. Two sediment cores were obtained for the study, one from the impacted pond (ID: CSM-IMP-2) ~240 m away from the colony, and another from a reference pond (study control, ID: CSM-REF-3) more than 1 km away from the colony.
![[Bosch et al. 2024]: Seabird nutrient transfer from Bird Rock, in Cape St. Mary’s Ecological Reserve (Newfoundland) to a pond nearby the seabird colony. Seen in the illustration are northern gannets (Morus bassanus) and black-legged kittiwakes (Rissa tridactyla), which both nest within the Reserve on an annual basis.](images/Cover-photo.png)
--Figure 0.1: [Bosch et al. 2024]: Seabird nutrient transfer from Bird Rock, in Cape St. Mary’s Ecological Reserve (Newfoundland) to a pond nearby the seabird colony. Seen in the illustration are northern gannets (Morus bassanus) and black-legged kittiwakes (Rissa tridactyla), which both nest within the Reserve on an annual basis. -
-![[Bosch et al. 2024]: Map of sampling locations relative to Bird Rock](images/FigureS1.png)
--Figure 0.2: [Bosch et al. 2024]: Map of sampling locations relative to Bird Rock -
-Download some of the example data from my github repo - Paleo_Analysis_Using_R
-Install all of the packages used in this analysis:
-REQUIRED:
-Ggplot2and Ggpubr- Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4,
Note that installation may differ depending on the type of operating system you’re using.
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