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statistics_and_graphs.r
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statistics_and_graphs.r
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#restructuring and aggregating data formats
install.packages("reshape2")
#fits parametric distributions of data
install.packages("fitdistrplus")
#fitting GAM models
install.packages("gam")
#GAM model extensions
install.packages("mgcv")
#methods for auto-determiniation of axis breaks and scales
install.packages("scales")
#creates plots
install.packages("ggplot2")
#accessory themes for ggplot2
install.packages("ggthemes")
#customise ggplot2 outputs
install.packages("ggpubr")
#creating high quality figure outputs from ggplot2
install.packages("cowplot")
#grid graphic formats for plots
install.packages("gridExtra")
#models to determin egrowth rates from experimental data
install.packages("growthrates")
#fit simple non-linear models
install.packages("easynls")
#fit and compare linear and nl mixed effect models
install.packages("nlme")
#modification of nls algorithm
install.packages("minpack.lm")
#analysis of deose-response curves
install.packages("drc")
#tests and confidence intervals for general linear hypotheses
install.packages("multcomp")
#pairwise multiple comparisons of mean ranked sums
install.packages("PMCMRplus")
#time series analysis
install.packagaboutes("xts")
#fitting models to perform sensitivity analysis
install.packages("FME")
#breaks down data/models into smaller pieces
install.packages("plyr")
#visualises fits of regression models
install.packages("visreg")
# Download libraries
library(mgcv)
library(gam)
library(cowplot)
library(fitdistrplus)
library(scales)
library(multcomp)
library(ggplot2)
library(ggthemes)
library(reshape2)
library(gridExtra)
library(PMCMR)
library(drc)
library(FSA)
library(minpack.lm)
library(easynls)
library(FME)
library(growthrates)
library(nlme)
library(plyr)
library(visreg)
library(ggpubr)
# Graphs for experimental data
# read in data
data = read.table("Calcium_juvenile_calc_rates.txt", header = TRUE)
data
data2 = read.table("Bicarbonate_juvenile_calc_rates.txt", header = TRUE)
data2
# format data strings as factors
data2 = read.table("Bicarbonate_juvenile_calc_rates.txt", header = TRUE)
data2$salinity = as.factor(data2$salinity)
data2$salinity = as.character(data2$salinity)
data2$salinity = factor(data2$salinity, levels = c("16", "11", "6"))
# Van Bertalanffy growth curve
VBGM = nlsLM(SM~Linf*(1-exp(-K*(bicarbonate_cont-t0))), data=data2,
start=list(Linf=20.33,K=1.27,t0=354))
### nls logistic curve
F3 = formula(SM~a*(1+380*(exp(-c*bicarbonate_cont)))^-1|salinity)
LC = nlsList(F3, data=data2,
start=list(a=30,c=0.01))
summary(LC)
### Gompertz model
data2m6 = data2m[c(1:5), c(1:5)]
data2m6
data2m11 = data2m[c(6:10), c(1:5)]
data2m11
data2m16 = data2m[c(11:15), c(1:5)]
data2m16
GP6 = nls(SM~a*exp(-380*exp(-c*bc)),
start=list(a=5, c=0.01),
data=data26)
GP11 = nls(SM~a*exp(-380*exp(-c*bicarbonate_cont)),
start=list(a=40, c=0.01), data=data211)
GP16 = nls(SM~a*exp(-380*exp(-c*bicarbonate_cont)),
start=list(a=35, c=0.01), data=data216)
summary(GP6)
summary(GP11)
summary(GP16)
#data26$VBGM = predict(fitTypical) # create new vector for mode predictions
plot(SM ~ bicarbonate_cont, data=data2, col = data2$salinity,
xlim=c(0, 2500),
ylim=c(0, 60))
lines(data26$bicarbonate_cont, predict(fitTypical6), col = "green")
lines(data26$bicarbonate_cont, predict(fitTypical11), col = "red")
lines(data26$bicarbonate_cont, predict(fitTypical16), col = "black")
#### michaelis menten model
mm6 = nls(SM ~ Vm * bicarbonate_cont / (K + bicarbonate_cont),
start=list(Vm=4, K=500), data=data26)
mm11 = nls(SM ~ Vm * bicarbonate_cont / (K + bicarbonate_cont),
start=list(Vm=30, K=500), data=data211)
mm16 = nls(SM ~ Vm * bicarbonate_cont / (K + bicarbonate_cont),
start=list(Vm=30, K=500), data=data216)
summary(mm6)
summary(mm11)
summary(mm16)
######Yield-loss density
yld = nls(SM ~ (Vm/K) * bicarbonate_cont / (1+ ((Vm/K)*bicarbonate_cont)/Vm),
start=list(Vm=6, K=400), data=data26)
summary(yld)
#exponential decay model
F2 = formula(log(SM+3) ~ Vm * (1-exp(-bicarbonate_cont/K))|salinity)
edm = nlsList(F2, data=data2,
start=list(Vm=10.33,K=300))
summary(edm)
AIC(VBGM, GP, LC, yld)
# format data as factors
data$salinity = as.factor(data$salinity)
data$salinity = as.character(data$salinity)
data$salinity = factor(data$salinity, levels = c("16", "11", "6"))
# plot data
SM_CA =
ggplot(data, aes(x = Ca, y = SM, colour = salinity)) +
geom_point(size = 2.5, aes(x = Ca, y = SM, shape = salinity)) +
stat_smooth(method = "lm", formula = y ~ x, size = 0.5, se = F, linetype = "dashed") +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab(expression(Calcium ~ conc. ~ (mmol ~ kg^{-1} ))) +
ylab(expression(Calcification ~ rate ~ (?g ~ day^{-1} ))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5,
linetype="dotted"),
legend.justification=c(1,0),
legend.position=c("none"),
legend.title=element_blank(),
legend.text = element_text(size = 7),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(-5, 60), expand = c(0, 0)) +
scale_x_continuous(limits=c(0, 4), expand = c(0, 0))
SM_BI =
ggplot(data2, aes(x = bicarbonate_cont, y = SM, colour = salinity)) +
geom_point(size=2.5, aes(x = bicarbonate_cont, y = SM, shape = salinity)) +
geom_line(data = data2, aes(x=bicarbonate_cont, y=VBGM), linetype = "dashed", size = 0.5) +
#geom_smooth(method="nls",
# formula=y~Vmax*(1-exp(-x/K)),
# method.args = list(start=c(K=1000,Vmax=2)),
# se=FALSE, colour="gray") +
#stat_smooth(method = "lm", formula = y ~ log(x), size = 1.2, se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab(expression(Bicarbonate ~ conc. ~ (?mol ~ kg^{-1} ))) +
ylab(expression(Calcification ~ rate ~ (?g ~ day^{-1} ))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5,
linetype="dotted"),
legend.justification=c(1,0),
legend.position=c(0.13, 0.45),
legend.title=element_blank(),
legend.text = element_text(size = 7),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(-5, 60), expand = c(0, 0)) +
scale_x_continuous(limits=c(0, 2500), expand = c(0, 0))
Linf_BI =
ggplot(data2_2, aes(x = salinity, y = Linf, colour = salinity)) +
geom_point(size=4) +
geom_errorbar(aes(ymin=Linf-X95_CI, ymax=Linf+X95_CI), width=.1) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Salinity") +
ylab("Linf") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5), legend.position="none",
panel.grid.major.x=element_blank(), panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0, 40), expand = c(0, 0)) +
scale_x_discrete()
#plot all graphs onto one output file
ion_plots_SM = ggdraw(xlim = c(0, 1.15), ylim = c(0, 1.15)) +
draw_plot(SM_BI, x = 0, y = 0.55, width = 1.1, height = 0.55) +
draw_plot(SM_CA, x = 0, y = 0, width = 0.55, height = 0.55) +
draw_plot(Linf_BI, x = 0.55, y = 0.001, width = 0.55, height = 0.55)
pdf("ion_growth_plots_SM.pdf", width = 6, height = 5)
ion_plots_SM
dev.off()
######################################## Field SL growth rates
###############SL-SM relationships
dataSLSM = read.table("SL_SM_relationship.txt", header=T)
dataSLSM
dataSLSM$Population = as.factor(dataSLSM$Population)
shapiro.test(dataSLSM$CaCO3_ug)
leveneTest(CaCO3_ug ~ Population, data=dataSLSM)
lmLM1 = lm(CaCO3_ug ~ (SL_mm)^2 * as.factor(Population), data=dataSLSM)
summary(lmLM1)
anova(lmLM1)
###################### subsett SL-SM data by population
dataSLkie = dataSLSM[c(1:124), c(2:3)]
dataSLkie
dataSLahp = dataSLSM[c(125:216), c(2:3)]
dataSLahp
dataSLuse = dataSLSM[c(217:296), c(2:3)]
dataSLuse
power_kie = nlsLM(CaCO3_ug~a*SL_mm^b, data=dataSLkie,
start=list(a=1, b=1))
power_ahp = nlsLM(CaCO3_ug~a*SL_mm^b, data=dataSLahp,
start=list(a=1, b=1))
power_use = nlsLM(CaCO3_ug~a*SL_mm^b, data=dataSLuse,
start=list(a=1, b=1))
summary(power_use)
anova(power_kie, power_ahp, power_use)
plot(CaCO3_ug ~ (SL_mm)^2, data=dataSLSM)
dataSLSM$Population = as.factor(dataSLSM$Population)
dataSLSM$Population = factor(dataSLSM$Population, levels = c("Kiel", "Ahp", "Use"))
pdf("SL_SM_relationship.pdf", width = 5, height = 5)
ggplot(dataSLSM, aes(x = SL_mm, y = CaCO3_ug, colour = Population)) +
geom_point(size = 1, position = position_dodge(width = .2)) +
stat_smooth(method = 'nls', formula = 'y~a*x^b', start = list(a = 1,b=1),se=F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Shell length (mm)") +
ylab("Shell mass (?g)") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position=c(0.3,0.77),
legend.title=element_blank(),
legend.text = element_text(size = 10),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_continuous(limits=c(0, 20), expand = c(0, 0)) +
scale_y_continuous(limits=c(0, 250))
dev.off()
dataSL = read.table("field_shell_growth_2016-2017.txt", header = T)
dataSL
dataSL6 = subset(dataSL, population=="Usedom", drop=T)
dataSL11 = subset(dataSL, population=="Ahrenshoop", drop=T)
dataSL16 = subset(dataSL, population=="Kiel", drop=T)
dataSL$month = as.numeric(dataSL$month)
dataSLm$month = as.numeric(dataSLm$month)
dataSL$population = as.factor(dataSL$population)
cdata = ddply(dataSL, c("month", "population"), summarise,
N = length(SM),
mean = mean(SM),
sd = sd(SM),
se = sd/sqrt(N))
cdata
dataSMm = data.frame(cdata)
dataSMm
write.table(dataSMm,"SM_growth_means.txt",sep="\t",row.names=T)
dataSMm = read.table("SM_growth_means.txt", header = T)
dataSMm
dataSMm17$SM_ug = (dataSMm17$mean*1000)
dataSMm17$day = (dataSMm17$month*30)
dataSMm17 = dataSMm[-c(10, 11, 12, 13, 14, 15, 16, 17, 18), ]
dataSMm17
lm17 = lm(log(SM_ug) ~ day * population, data=dataSMm17)
summary(lm17)
shapiro.test(log(dataSL$SM_pow))
plot1 = plot(log(SM_ug) ~ day, colour = population, data = dataSMm17)
lm1 = aov(mean ~ I(month^2) * as.factor(population), data=dataSMm)
anova(lm1)
summary(lm1)
coef(lm1)
lm2 = aov(mean ~ I(month^2) * as.factor(population), data=dataSMm)
anova(lm2)
TukeyHSD(lm1, "as.factor(population)", conf.level = 0.95)
z = dataSL$population = as.factor(dataSL$population)
plotdist(dataSLm$mean, histo = TRUE, demp = TRUE)
descdist(dataSL$SM, discrete = F, boot=1000)
qqnorm(dataSL$SM);qqline(log(dataSL$SM), col = 2)
hist(log(dataSL$SM))
TukeyHSD(aov1, "as.factor(population)", conf.level = 0.95)
dataSMm
plot(mean ~ month, colour = population, data = dataSMm19)
#plot mean environmental conditions with calcification rates and compare with experiments
dataSMm$population = as.factor(dataSMm$population)
dataSMm$population = factor(dataSMm$population, levels = c("kiel", "ahp", "use"))
pdf("SM_field.pdf", width = 5, height = 5)
ggplot(dataSMm, aes(x = month, y = mean, colour = population)) +
geom_point(size = 3.5, position = position_dodge(width = .2)) +
#geom_smooth(method = "nls", formula=y~(a*x)^b, start=c(a=1, b=1), se=F) +
stat_smooth(method='lm', formula = y~I(x^2), se = F,
# method.args = list(family = "exp"),
linetype = "dashed", size = 0.55) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.2,
position=position_dodge(.2)) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Month") +
ylab("Shell mass (mg)") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position=c(0.3,0.77),
legend.title=element_blank(),
legend.text = element_text(size = 10),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_continuous(limits=c(0, 16), expand = c(0, 0)) +
scale_y_continuous(limits=c(0, 2500))
dev.off()
# Field Temperature
install.packages("dunn.test")
descdist(datac$pH, discrete = F)
fit.norm = fitdist(log(datac$HCO3), "norm")
plot(fit.norm)
leveneTest(datac$pH ~ as.factor(site), data=datac)
shapiro.test(datac$HCO3)
lmph = aov(pH ~ as.factor(site), data = datac)
anova(lmph)
TukeyHSD(lmph, "as.factor(site)", conf.level = 0.95)
# plot field pH
datac = read.table("Carb_chem_all.txt", header = TRUE)
datac
datac$date = as.Date(datac$date, "%d/%m/%Y")
#datac$date = as.Date(datac$date, "%Y-%m-%d")
datac$site = as.character(datac$site)
datac$site = factor(datac$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
pH_all_sp =
ggplot(datac, aes(x = date, y = pH, colour = site)) +
geom_point(size = 2) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Date") +
#ylab(expression(italic(C)[T] ~ (?mol ~ kg^{-1}))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2014-12-01"), to=as.Date("2017-12-01"), by="6 month"),
labels=date_format("%b. '%y"), limits=as.Date(c("2014-12-01", "2017-12-01"), expand = c(0, 0))) +
scale_y_continuous(limits=c(7.4, 8.4), expand = c(0, 0))
pH_all_bp =
ggplot(datac, aes(x = site, y = pH, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("deepskyblue1", "seagreen4", "coral1")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(7.4, 8.4), expand = c(0, 0))
field_pH_plots = ggdraw(xlim = c(0, 3), ylim = c(0, 1)) +
draw_plot(pH_all_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(pH_all_bp, x = 2, y = 0, width = 1, height = 1)
pdf("field_pH_plots.pdf", width = 9, height = 3)
field_pH_plots
dev.off()
# Field Salinity plots
descdist(log(data_S$value[1:5000]), discrete = F)
fit.norm = fitdist(log(data_S$value[1:5000]), "norm")
plot(fit.norm)
lm2 = aov(log(value+3) ~ site, data = data_S)
anova(lm2)
TukeyHSD(lm2, "site", conf.level = 0.95)
kruskal.test(value ~ site, data = data_S)
dunnTest(data_S$value, g = data_S$site, kw=TRUE, label=TRUE, wrap=FALSE, alpha=0.05)
data_S = read.table("Salinity_all_2015_2018.txt", sep = "\t", header = TRUE)
data_S
data_S$date = as.Date(data_S$date, "%d.%m.%Y")
data_S$date = as.Date(data_S$date, "%Y-%m-%d")
data_S$site = as.character(data_S$site)
data_S$site = factor(data_S$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
sal_all_sp = ggplot(data_S, aes(x = date, y = value, colour = site)) +
geom_point(size = .1) +
#stat_smooth(method = "gam", formula = y ~ s(x, bs = "ps", k = 12), size = 1, se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Date") +
ylab("Salinity (psu)") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5,
linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2015-8-01"), to=as.Date("2017-12-01"), by="4 month"),
labels=date_format("%b. '%y"), limits=c(), expand = c(0, 0)) +
scale_y_continuous(limits=c(0, 25), expand = c(0, 0))
sal_all_bp = ggplot(data_S, aes(x = site, y = value, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
stat_smooth(method = "gam", formula = y ~ s(x, bs = "ps", k = 12), size = 1.5, se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5,
linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0, 25), expand = c(0, 0))
S_plots = ggdraw(xlim = c(0, 3), ylim = c(0, 1)) +
draw_plot(sal_all_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(sal_all_bp, x = 2, y = 0, width = 1, height = 1)
pdf("S_plots.pdf", width = 9, height = 3)
S_plots
dev.off()
#C hl_a monitoring data
descdist(log(data_chla$chla), discrete = F)
fit.norm = fitdist(log(data_chla$chla), "norm")
plot(fit.norm)
lm3 = aov(log(chla) ~ Site, data = data_chla)
anova(lm3)
TukeyHSD(lm3, "Site", conf.level = 0.95)
kruskal.test(value ~ site, data = data_S)
dunnTest(data_S$value, g = data_S$site, kw=TRUE, label=TRUE, wrap=FALSE, alpha=0.05)
data_chla = read.table("chla_monitored_2015-2017.txt", sep="\t", header = T)
data_chla
mean(data_chla$chla[1:25])
data_chla$date = as.Date(data_chla$date, "%d/%m/%Y")
data_chla$Site = as.character(data_chla$Site)
data_chla$Site = factor(data_chla$Site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
data_chla$chla = as.numeric(data_chla$chla)
chl_monitor_sp =
ggplot(data_chla, aes(x = date, y = chla, colour = Site, shape = Site)) +
geom_point(size = 4) +
#stat_smooth(method = "loess", formula = y ~ x, size = .6, se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("") +
ylab(expression(Chl-italic(a) ~ (?g ~ L^{-1} ))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2014-12-01"), to=as.Date("2017-12-01"), by="6 month"),
labels=date_format("%b. '%y"), limits=as.Date(c("2014-12-01", "2017-12-01"), expand = c(0, 0))) +
scale_y_continuous(limits=c(0, 20), expand = c(0, 0))
chl_monitor_bp =
ggplot(data_chla, aes(x = Site, y = chla, colour = Site)) +
geom_boxplot(size = .5, outlier.size = .05) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_text(size=8),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0, 20), expand = c(0, 0))
chla_plots = ggdraw(xlim = c(0, 3.03), ylim = c(0, 1)) +
draw_plot(chl_monitor_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(chl_monitor_bp, x = 2.03, y = 0, width = 1, height = 1)
pdf("chl_monitor.pdf", width = 9, height = 3)
chla_plots
dev.off()
# modelled chla data
descdist(log(data_chla$chla), discrete = F)
fit.norm = fitdist(log(data_chla$chla), "norm")
plot(fit.norm)
lm3 = aov(log(chla) ~ Site, data = data_chla)
anova(lm3)
TukeyHSD(lm3, "Site", conf.level = 0.95)
kruskal.test(value ~ site, data = data_S)
dunnTest(data_S$value, g = data_S$site, kw=TRUE, label=TRUE, wrap=FALSE, alpha=0.05)
data_chla2 = read.table("chla_modelled_2015-2017.txt", sep="\t", header = T)
data_chla2
data_chla3 = read.table("mean_chla_modelled_2015-2017.txt", sep="\t", header = T)
data_chla3
data_chla3$date = as.Date(data_chla3$date, "%d/%m/%Y")
data_chla3$site = as.character(data_chla3$site)
data_chla3$site = factor(data_chla3$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
data_chla3$chla = as.numeric(data_chla3$chl)
chl2_model_sp =
ggplot(data_chla3, aes(x = date, y = chl, colour = site)) +
geom_line(size = 1) +
#stat_smooth(method = "gam", formula = y ~ s(x), size = .6, se = F) +
scale_color_manual(values=c("deepskyblue1", "seagreen4", "coral1")) +
xlab("Date") +
ylab(expression(Chl-italic(a) ~ (?g ~ L^{-1} ))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(), panel.grid.minor.x=element_blank(), panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2016-07-01"),
to=as.Date("2017-12-01"),
by="3 month"),
labels=date_format("%b. %y")) +
scale_y_continuous(limits=c(0, 25), expand = c(0, 0))
chl2_model_bp =
ggplot(data_chla3, aes(x = site, y = chl, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
scale_color_manual(values=c("deepskyblue1", "seagreen4", "coral1")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_text(size=8),
legend.text = element_text(size = 8),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0, 25), expand = c(0, 0))
chla2_plots = ggdraw(xlim = c(0, 3.03), ylim = c(0, 1)) +
draw_plot(chl2_model_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(chl2_model_bp, x = 2.03, y = 0, width = 1, height = 1)
pdf("chl_modelled.pdf", width = 9, height = 3)
chla2_plots
dev.off()
# Field HCO3
descdist(log(datac$HCO3), discrete = F)
fit.norm = fitdist(log(datac$HCO3), "norm")
plot(fit.norm)
leveneTest(datac$HCO3 ~ as.factor(site), data=datac)
shapiro.test(datac$HCO3)
lm5 = aov(HCO3 ~ as.factor(site), data = datac)
anova(lm5)
TukeyHSD(lm5, "as.factor(site)", conf.level = 0.95)
datac = read.table("Carb_chem_all.txt", header = TRUE)
datac
datac$date = as.Date(datac$date, "%d/%m/%Y")
#datac$date = as.Date(datac$date, "%Y-%m-%d")
datac$site = as.character(datac$site)
datac$site = factor(datac$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
cc_all_sp =
ggplot(datac, aes(x = date, y = HCO3, colour = site, shape = site)) +
geom_point(size = 4) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Date") +
#ylab(expression(italic(C)[T] ~ (?mol ~ kg^{-1}))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2014-12-01"), to=as.Date("2017-12-01"), by="6 month"),
labels=date_format("%b. '%y"), limits=as.Date(c("2014-12-01", "2017-12-01"), expand = c(0, 0))) +
scale_y_continuous(limits=c(1500, 2100), expand = c(0, 0))
cc_all_bp =
ggplot(datac, aes(x = site, y = HCO3, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(1500, 2100), expand = c(0, 0))
field_hco3_plots = ggdraw(xlim = c(0, 3), ylim = c(0, 1)) +
draw_plot(cc_all_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(cc_all_bp, x = 2, y = 0, width = 1, height = 1)
pdf("field_hco3_plots.pdf", width = 9, height = 3)
field_hco3_plots
dev.off()
# Field omega values
descdist(log(datac$omega_ara), discrete = F)
fit.norm = fitdist(log(datac$omega_ara), "norm")
plot(fit.norm)
leveneTest(datac$omega_ara ~ as.factor(site), data=datac)
shapiro.test(log(datac$omega_ara))
lm6 = aov(log(omega_ara) ~ as.factor(site), data = datac)
anova(lm6)
TukeyHSD(lm6, "as.factor(site)", conf.level = 0.95)
datac = read.table("Carb_chem_all.txt", header = TRUE)
datac
datac$date = as.Date(datac$date, "%d/%m/%Y")
#datac$date = as.Date(datac$date, "%Y-%m-%d")
datac$site = as.character(datac$site)
datac$site = factor(datac$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
cc_omega_sp =
ggplot(datac, aes(x = date, y = omega_ara, colour = site, shape = site)) +
geom_point(size = 4) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Date") +
#ylab(expression(italic(C)[T] ~ (?mol ~ kg^{-1}))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2014-12-01"), to=as.Date("2017-12-01"), by="6 month"),
labels=date_format("%b. '%y"), limits=as.Date(c("2014-12-01", "2017-12-01"), expand = c(0, 0))) +
scale_y_continuous(limits=c(0,2), expand = c(0, 0))
cc_omega_bp =
ggplot(datac, aes(x = site, y = omega_ara, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0,2), expand = c(0, 0))
field_omega_plots = ggdraw(xlim = c(0, 3), ylim = c(0, 1)) +
draw_plot(cc_omega_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(cc_omega_bp, x = 2, y = 0, width = 1, height = 1)
pdf("field_omega_plots.pdf", width = 9, height = 3)
field_omega_plots
dev.off()
# Field ESIR values
descdist(log(datac$ex_SIR), discrete = F)
fit.norm = fitdist(log(datac$ex_SIR), "norm")
plot(fit.norm)
leveneTest(datac$ex_SIR ~ as.factor(site), data=datac)
shapiro.test(log(datac$ex_SIR))
lm7 = aov(log(ex_SIR) ~ as.factor(site), data = datac)
anova(lm7)
TukeyHSD(lm7, "as.factor(site)", conf.level = 0.95)
datac = read.table("Carb_chem_all.txt", header = TRUE)
datac
datac$date = as.Date(datac$date, "%d/%m/%Y")
#datac$date = as.Date(datac$date, "%Y-%m-%d")
datac$site = as.character(datac$site)
datac$site = factor(datac$site, levels = c("Kiel", "Ahrenshoop", "Usedom"))
cc_ESIR_sp =
ggplot(datac, aes(x = date, y = ex_SIR, colour = site, shape = site)) +
geom_point(size = 4) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Date") +
#ylab(expression(italic(C)[T] ~ (?mol ~ kg^{-1}))) +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_x_date(breaks=seq(from=as.Date("2014-12-01"), to=as.Date("2017-12-01"), by="6 month"),
labels=date_format("%b. '%y"), limits=as.Date(c("2014-12-01", "2017-12-01"), expand = c(0, 0))) +
scale_y_continuous(limits=c(0,1.6), breaks=c(0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2), expand = c(0, 0))
cc_ESIR_bp =
ggplot(datac, aes(x = site, y = ex_SIR, colour = site)) +
geom_boxplot(size = .5, outlier.size = .05) +
#geom_smooth(method = "lm", se = F) +
scale_color_manual(values=c("yellow3", "aquamarine3", "steelblue4")) +
xlab("Site") +
ylab("") +
theme_bw() +
theme(legend.background = element_rect(fill="white", size=.5, linetype="dotted"),
legend.justification=c(1,0),
legend.position="none",
legend.title=element_blank(),
legend.text = element_text(size = 12),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank(),
axis.text=element_text(size=10, colour = "black")) +
scale_y_continuous(limits=c(0,1.6), breaks=c(0, 0.4, 0.8, 1.2, 1.6),
expand = c(0, 0))
field_ESIR_plots = ggdraw(xlim = c(0, 3), ylim = c(0, 1)) +
draw_plot(cc_ESIR_sp, x = 0, y = 0, width = 2, height = 1) +
draw_plot(cc_ESIR_bp, x = 2, y = 0, width = 1, height = 1)
pdf("field_ESIR_plots.pdf", width = 9, height = 3)
field_ESIR_plots
dev.off()
# SIR Omega
descdist(log(data_S_O$ESIR), discrete = F)
descdist(data_S_O$shell_growth, discrete = F)
fit.norm = fitdist(log(data_S_O$ESIR), "norm")
plot(fit.norm)
leveneTest(log(data_S_O$ESIR) ~ as.factor(salinity), data=data_S_O)
shapiro.test(log(data_S_O$ESIR))
lm9 = lm(log(shell_growth+3) ~ ESIR, data = data_S_O)
anova(lm9)
coef(lm8)
TukeyHSD(lm7, "as.factor(site)", conf.level = 0.95)
data_S_O = read.table("SIR_omega_calcification.txt", sep = "\t", header = T)
data_S_O
data_S_O$salinity = as.character(data_S_O$salinity)
data_S_O$salinity = factor(data_S_O$salinity, levels = c("16", "11", "6"))
# saturation curve / MM constant
MM_SIR = nlsLM(shell_growth ~ Vm * SIR / (K + SIR),
start=list(Vm=0.7, K=35), data=data_S_O)
EM_SIR = nlsLM(shell_growth ~ 1 + Vm * (1-exp(-SIR/K)),
start=list(Vm=0.7, K=32), data=data_S_O)
MM_ESIR = nlsLM(shell_growth ~ Vm * ESIR / (K + ESIR),
start=list(Vm=0.7, K=35), data=data_S_O)
EM_ESIR = nlsLM(shell_growth ~ 1 + Vm * (1-exp(-ESIR/K)),
start=list(Vm=0.7, K=32), data=data_S_O)
MM_omega = nlsLM(shell_growth ~ Vm * omega_ara / (K + omega_ara),