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# Eudriano Costa
# R code used in the article " Costa et al. (2022). Effects of biological and environmental factors on the variability
# of Paralonchurus brasiliensis (Sciaenidae) density: An GAMLSS application"
# This code was used to apply Generalized Additive Models for Location, Scale, and Shape (GAMLSS) in order to analyze
# the relationship between the response variable fish density (Dens) and several explanatory variables including biomass,
# RI, HSI, K, Sex ratio (Sexrat), temperature (Temp), salinity (Sal), organic matter (OM), mean grain size of the sediment
# (φ), and depth. The analysis also takes into account region and month as factors.
# Costa, E.F.S., Teixeira, G.M., Freire, F.A.M., Dias, J.F., Fransozo, A. (2022). Effects of biological and environmental
# factors on the variability of Paralonchurus brasiliensis (Sciaenidae) density: An GAMLSS application. Journal of Sea
# Research 183, 102203. doi: https://doi.org/10.1016/j.seares.2022.102203
###################################################################################################################
######################### FULL(0) pabr dataset= Paralonchurus brasiliensis ####################################
###################################################################################################################
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
datapabrM<-read.csv("Data_Pabr_Dens.csv",dec=".",sep=";",header=T)
attach(datapabrM)
library("gamlss")
library("car")
str(datapabrM)
#edit(datapabrM)
##############################Binary GAMLSS for negative and positive stations (0- values) -- Book- GAMLSS pag 211
tiff("Probability with negative stations.tiff", width = 6, height = 5.5,units = 'in', res=300)
datapabrM = na.omit(datapabrM)
pabrm1<- gamlss(Bin~pb(Depth), data=datapabrM, family=BI)
plot(Bin~Depth, data=datapabrM, type="n",xlab="Depth (m)", ylab="Estimated probability",lwd=2,cex.lab=1.3,font.lab=1)
points(datapabrM$Bin~jitter(datapabrM$Depth), pch="|", col="blue")
with (datapabrM, lines(fitted(pabrm1)[order(Depth)]~Depth[order(Depth)],col="red"))
summary(pabrm1)
dev.off()
############################## creating another dataframe with no negative stations, i.e. zero values ##################
pabr1<-datapabrM[apply(datapabrM!=0, 1, all),] # zero values have been removed
pabr1= na.omit(pabr1)
attach(pabr1)
pabr1<-transform(pabr1, Transec = as.factor(Transec))
pabr1<-transform(pabr1, Month_ = as.factor(Month_))
str(pabr1)
############################################ Matrix Corr
library("gamlss.dist")
data("pabr1")
with(pabr1, plot(data.frame(DensT,K,HSI,RI,Sexrat,BT,BS,Phi,OM,Depth)))
################################# histogramas de freuencia que da variavel dependente que estima a densidade de probabilidade de forma nao parametrica e sajusta a distribuição que suspeitamos ser a mais adequada
tiff("Plots_Dist.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(2,4))
par(mar = c(3.5,3.5,6,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(2, 0.5, 0))#axis title, axis labels and axis line default c(3,1,0)
layout.show(8)
histDist(DensT,family = LOGNO, main="Log normal", ylim=c(0,0.07), xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = GA,main="Gamma", ylim=c(0,0.07),xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = WEI, main="Weibull",ylim=c(0,0.07),xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = WEI3, main="Weibull (mean)",ylim=c(0,0.08), xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = BCT, main="Box-cox t",ylim=c(0,0.07), xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = NO, main="Normal",ylim=c(0,0.07), xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
histDist(DensT,family = NOF,main="Normal family",ylim=c(0,0.07), xlab = "Fish density", ylab = NULL,line.wd = 2,line.ty = 1,
col.hist="white",border.hist="black", line.col = "black", col.main = "black",col.lab="black", col.axis="black",fg.hist= "black",data=pabr1)
dev.off()
######################################################## GAMLSS ###################################################
###################################################################################################################
######################################## VIF- Multicollinearity ###################################################
###################################################################################################################
# VIF todas as variaveis
modVIF1<-(lm(DensT~Sexrat+RI+K+HSI+BT+BS+OM+Phi,data=pabr1)) #acima de 3 = excluir
vround<-vif(modVIF1)
round(vround, digits = 2)
#Ver https://rstudio-pubs-static.s3.amazonaws.com/118082_4777e197a79c4135857444bbcca879b7.html
library(usdm)
vifcor(pabr1[,c(15:17,19,30,32,34,35:36)], th=0.4)# Testar Modelos assim
tiff("PairPlots.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(1,1))
par(mar = c(1.5,3,8.5,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(1.8,0.5, 0))#axis title, axis labels and axis line default c(3,1,0)
layout.show(1)
pabr1Mat<-pabr1[,c(15:17,32,34,35:36)] # dens= 10, biomass 19 RI =
pabr1Mat
library("PerformanceAnalytics")
chart.Correlation(pabr1Mat, method="spearman",histogram=TRUE, pch=19)
dev.off()
r <- cor(pabr1Mat, use="complete.obs")
round(r,2)
library(ggplot2)
library(ggcorrplot)
ggcorrplot(r)
tiff("Coplots2.tiff", width = 6, height = 5.5,units = 'in', res=600)
library(corrplot)
library(RColorBrewer)
corrplot(r, type="upper", order="hclust",addCoef.col = 'black',
col=brewer.pal(n=8, name="RdYlBu"))
dev.off()
library(GGally)
ggpairs(pabr1Mat,
upper = list(continuous = "points"))
###################################################################################################################
######################################## Testando a distribuicao da variavel resposta #############################
###################################################################################################################
modGamma<-gamlss(DensT ~ 1, family=GA, trace=F)
modNormal<-gamlss(DensT ~ 1, family=NO, trace=F)
modWeibull1<-gamlss(DensT ~ 1, family=WEI,trace=F)
modWeibull3<-gamlss(DensT ~ 1, family=WEI3,trace=F)
modLogNorm <-gamlss(DensT ~ 1, family=LOGNO,trace=F)
modFamNorm <-gamlss(DensT ~ 1, family=NOF, trace=F)
modBCT <-gamlss(DensT ~ 1, family=BCT, trace=F)
# GAIC Test to select the best distribution
modGaic<-GAIC(modGamma, modNormal, modLogNorm, modFamNorm, modWeibull1, modWeibull3,modBCT) # AIC test
modGaic
###################################################################################################################
#################### Achando grau de liberdade para cada umas das variveis explicativas ###########################
###################################################################################################################
modx<-quote(gamlss(DensT~cs(K, df=p[1])+ cs(RI, df=p[2])+ cs(HSI, df=p[3])+cs(BT,df=p[4]) + cs(BS, df=p[5])+ cs(OM, df=p[6])+ cs(Phi, df=p[7])+ cs(Sexrat, df=p[8])+ cs(Juv, df=p[9]),family=LOGNO))
op<-find.hyper(model=modx, parameters=c(3,3,3,3,3,3,3,3,3), lower=c(1,1,1,1,1,1,1,1,1), upper = c(30, 30, 30,30, 30,30, 30, 30,30),trace=FALSE)
op
##################################################################################################################
##################################### GAMLSS MOEDELS #############################################################
##################################################################################################################
# Global model
mod0<-(gamlss(DensT~cs(BiomassT,df=2)+cs(HSI,df=3)+cs(K,df=3)+cs(Sexrat,df=1)+cs(BT,df=1)+cs(BS,df=2)+cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=2.5)+Region+Month,family=LOGNO))
predict(mod0,what="sigma", type="response")
predict(mod0,what="mu", type="response")
# Abiotic Models
mod1<-(gamlss(DensT~cs(BT,df=1)+cs(BS,df=2)+cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=3)+Region+Month,family=LOGNO))
mod2<-(gamlss(DensT~cs(BT,df=1)+cs(BS,df=2)+cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=3)+Region,family=LOGNO))
mod3<-(gamlss(DensT~cs(BT,df=1)+cs(BS,df=2)+cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=3),family=LOGNO))
mod4<-(gamlss(DensT~cs(BT,df=1)+cs(BS,df=2)+cs(Depth,df=3)+Region,family=LOGNO))
mod5<-(gamlss(DensT~cs(BT,df=1)+cs(BS,df=2)+cs(Depth,df=3),family=LOGNO))
mod6<-(gamlss(DensT~cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=3)+Region,family=LOGNO))
mod7<-(gamlss(DensT~cs(OM,df=3)+cs(Phi,df=4)+cs(Depth,df=3),family=LOGNO))
GAIC(mod0,mod1, mod2, mod3, mod4, mod5, mod6 , mod7)
# Biotic Models
mod8<-(gamlss(DensT~cs(K,df=3)+cs(HSI,df=3)+cs(Sexrat,df=1)+cs(BiomassT,df=6)+Region+Month,family=LOGNO))
mod9<-(gamlss(DensT~cs(K,df=3)+cs(HSI,df=3)+cs(Sexrat,df=1)+cs(BiomassT,df=6)+Region,family=LOGNO))
mod10<-(gamlss(DensT~cs(K,df=3)+cs(HSI,df=3)+cs(Sexrat,df=1)+cs(BiomassT,df=6),family=LOGNO))
mod11<-(gamlss(DensT~cs(K,df=3)+cs(HSI,df=3),family=LOGNO))
mod12<-(gamlss(DensT~cs(Sexrat,df=1)+cs(BiomassT,df=6),family=LOGNO))
rGAIC<-GAIC(mod0,mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8, mod9, mod10, mod11,mod12)
round(rGAIC, digits = 1)
###################################################################################################################
#################### Extracting fitted values from mod0 MU & SIGMA ################################################
###################################################################################################################
tiff("Mu parameter.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(3,3))
par(mar = c(3.4,3,1.0,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(2,0.8, 0))#axis title, axis labels and axis line default c(3,1,0)
layout.show(9)
plot(BiomassT, fitted(mod0,"mu")) #will plot the fitted values of µ distribution parameter against x
plot(HSI, fitted(mod0,"mu"))
plot(K, fitted(mod0,"mu"))
plot(Sexrat, fitted(mod0,"mu"))
plot(BT, fitted(mod0,"mu"))
plot(BS, fitted(mod0,"mu"))
plot(OM, fitted(mod0,"mu"))
plot(Phi, fitted(mod0,"mu"))
plot(Depth, fitted(mod0,"mu"))
#The constant estimated scale parameter (the standard deviation of the normal distribution in this case) can be obtained:
fitted(mod0,"sigma")[1] #where [1] indicates the first element of the vector
fitted(mod0,"mu")[1]
predict(mod0,what='sigma',type='response')
dev.off()
###################################################################################################################
#################### Modelling both µ and ?? ####################################################################
###################################################################################################################
edfAll(mod0)#is used to obtain the effective degrees of freedom for all parameters
###################################################################################################################
#################### Diagnostic plots ####################################################################
###################################################################################################################
#Once a GAMLSS model is fitted, it is important to assess the adequacy of the fitted model by examining the model residuals
# Plots of Residuals
tiff("ResidualPlots.tiff", width = 6, height = 5.5,units = 'in', res=300)
plot(mod0)#shows plots of the residuals: (top left) against the fitted values of µ; (top right) against
#an index (i.e. case number); (bottom left) a nonparametric kernel density estimate; (bottom
#right) a normal Q-Q plot.
dev.off()
# Worm Plots
wp(mod0) #The worm plot (see Section ??) is a de-trended normal Q-Q plot of the residuals. Model
#inadequacy is indicated when many points plotted lie outside the (dotted) point-wise 95%
#confidence bands
wp(mod0, ylim.all=3)
title("Ximboca")
#Plotting mu & sigma
tiff("Mu e sigma.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(3,3))
par(mar = c(3.4,3,1.0,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(2,0.8, 0))#axis title, axis labels and axis line default c(3,1,0)
layout.show(9)
fittedPlot(mod0, x=pabr1$BiomassT, line.type = TRUE)#plot of the smooth fitted values for all the parameters
fittedPlot(mod0, x=pabr1$HSI, line.type = TRUE)
fittedPlot(mod0, x=pabr1$K, line.type = TRUE)
fittedPlot(mod0, x=pabr1$Sexrat, line.type = TRUE)
fittedPlot(mod0, x=pabr1$BT, line.type = TRUE)
fittedPlot(mod0, x=pabr1$BS, line.type = TRUE)
fittedPlot(mod0, x=pabr1$OM, line.type = TRUE)
fittedPlot(mod0, x=pabr1$Phi, line.type = TRUE)
fittedPlot(mod0, x=pabr1$Depth, line.type = TRUE)
dev.off()
#Centile fan plot
tiff("Centile fan plot.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(3,3))
par(mar = c(3.4,3,1.0,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(2,0.8, 0))#axis title, axis labels and axis line default c(3,1,0)
layout.show(9)
# Legend
centiles.fan(mod0, xvar=pabr1$BiomassT, cent=c(3,10,25,50,75,90,97), #Centile fan plot for the m6 model showing the 3%, 10%, 25%, 50%, 75%, 90% and
colors="terrain",ylab="Density", xlab="Biomass (g)",main="") #97% centiles for the fitted LOGNO distribution
centiles.fan(mod0, xvar=pabr1$HSI, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Hepatosomatic index",main="")
centiles.fan(mod0, xvar=pabr1$K, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Condition factor",main="")
centiles.fan(mod0, xvar=pabr1$Sexrat, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Sex ratio",main="")
centiles.fan(mod0, xvar=pabr1$BT, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Temperature",main="")
centiles.fan(mod0, xvar=pabr1$BS, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Salinity",main="")
centiles.fan(mod0, xvar=pabr1$OM, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Organic matter",main="")
centiles.fan(mod0, xvar=pabr1$Phi, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Phi",main="")
centiles.fan(mod0, xvar=pabr1$Depth, cent=c(3,10,25,50,75,90,97),
colors="terrain",ylab="Density", xlab="Depth (m)",main="")
dev.off()
# Centile fan plot for the mod0 model showing the 3%, 10%, 25%, 50%, 75%, 90% and 97% centiles for the fitted LOGNO distribution
library(gamlss.util) # NÂO FOCIU LEGAM COM ESSA DISTRIBUIÇÂO
library(colorspace)
plotSimpleGamlss(DensT,BiomassT, model=mod0, data=pabr1, val=10, N=1000, ylim=c(0,70), family= LOGNO,
cols=heat_hcl(100))
plotSimpleGamlss(DensT,HSI, model=mod0, data=pabr1, val=10, N=1000, ylim=c(0,70),
cols=heat_hcl(100))
# ......
###################################################################################################################
#################### OUT PUT ####################################################################
###################################################################################################################
#Significance of smoothing terms
drop1(mod0) #melhor
summary(mod0)
#provides (standard error based) confidence intervals for the the fitted coefficients
confint(mod0)
# ????
acfResid(mod0)
histDist(mod0)
###################################################################################################################
#################### TERM PLOT- FINAL ####################################################################
###################################################################################################################
tiff("TermPlotsLOGNO.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(3,4))
par(mar = c(3.4,3,1.0,0.5))#outer margin: bottom, left, top and right side
par(mgp = c(2,0.8, 0))#axis title, axis labels and axis line default c(3,1,0)
#par(oma = c(1.5,2.8,1.5,0))
layout.show(12)
term.plot(mod0, se = T, scheme = c("lines"),col.res = rgb(0, 1, 0,max = 255, alpha = 100, names = "gray"),col.rug = "black", col.term = "black",col.se = "red",
col.shaded = "gray",partial = T,lwd.se = 1.5,lwd.term=1.8, rug=TRUE,ylim = "common") #pages=1 #rgb(0, 0, 255, max = 255, alpha = 0.4, names = "blue50") transparente
wp(mod0)
dev.off()
###################################################################################################################
#################### mu and sigma - LOGNO ################################################################
###################################################################################################################
# tentativa de analisar os dois paramatros da distribuição LOGNO-local and scale
Res_mod<-residuals (mod0)
plot(Res_mod)
fitted(mod0)
Res_DensT<-DensT-fitted(mod0)#How come this aren't the residuals?
plot(Res_DensT)
tiff("Local and scale param.tiff", width = 6, height = 5.5,units = 'in', res=600)
par(no.readonly = TRUE)
par(mfrow = c(1,2))
par(mar = c(3.4,3,1.0,0.5))
par(mgp = c(2,0.8, 0))
layout.show(2)
fit_mu<-fitted(mod0, "mu")
fit_sig<-fitted(mod0, "sigma")
plot(fit_mu,ylab="Fitted values from mod0 (mu parameter)")
plot(fit_sig,ylab="Fitted values from mod0 (sigma parameter)")
formula(mod0,"mu")
dev.off()
tiff("mu_plot.tiff", width = 6, height = 5.5,units = 'in', res=300)
pdf.plot(mod0,min=0, max=15, step=.1) # mu and sigma
dev.off()
###################################################################################################################
#################### generalised R-squared of Nagelkerke (1991) #########################################
###################################################################################################################
Rsq(mod0)
Rsq(mod1)
Rsq(mod2)
Rsq(mod3)
Rsq(mod4)
Rsq(mod5)
Rsq(mod6)
Rsq(mod7)
Rsq(mod8)
Rsq(mod9)
Rsq(mod10)
Rsq(mod11)
Rsq(mod12)
term.plot(mod0, pages=1, what="sigma", ask=FALSE)
drop1(mod0, what="sigma")
################################################# END for the manuscript ####################################
###################################################################################################################
#################### Supporting Materials ################################################################
###################################################################################################################
############################### BoxPlots#########################################################
library(ggplot2)
library(tidyr)
library(dplyr)
library (lemon)
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
Biomass_n<-read.csv("Biomass_n.csv",dec=".",sep=";",header=T)
attach(Biomass_n)
###############################################################################
############ N_total/Density/Biomass ~ Month + Depth ########################
SubUba<-Biomass_n[1:59,c("Regions","Month","BiomassT","N_total","Subs","Transec","DensT")]
SubCar<-Biomass_n[60:118,c("Regions","Month","BiomassT","N_total","Subs","Transec","DensT")]
#·--------------------- New figures BIOTICS
Biomass_n1<-Biomass_n[,-7]# delete sub
DataBioLong1<-pivot_longer(Biomass_n1, -c(Regions,Month,Month_,Season,Transec),
values_to = "BioValues", names_to = "BioVar")
DataBioLong1
DataBioLong<-transform(DataBioLong1, Regions = as.factor(Regions),
Month_ = as.factor(Month_),
Transec = as.factor(Transec),
BioVar = as.factor(BioVar),
BioValues = as.numeric(BioValues))
DataBioLong$Month_ <- factor(DataBioLong$Month_ , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
Names_var <- c(
'N_total'="Total number of individuals",
'BiomassT'="Total biomass (g)",
'DensT'="Density (fish/m2)*1000" )
#reorder plots
DataBioLong$facetOerder = factor(DataBioLong$BioVar, levels = c("N_total",
"DensT",
"BiomassT"))
str(DataBioLong)
#----------------------------- ~ Month ----------------------------------------#
load("SuppData.RData")
BioVar_Month<-ggplot(DataBioLong, aes(x=Month_, y=BioValues, fill=Regions)) +
geom_boxplot() +#
scale_fill_manual(values = c("white", "gray55"),
labels = c("Ubatuba (n= 12643)", "Caraguatatuba (n= 17166)"))+
ylab("")+
xlab("")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetOerder ~ ., scale="free",labeller = as_labeller(Names_var)) +
#facet_wrap(~facetOerder, scale="free", ncol = 1,labeller = as_labeller(Names_var))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top",#c(0.79, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
BioVar_Month
ggsave(filename = "./Figures/BioVar_Month.png",
plot = BioVar_Month,
units = "cm", width = 15, height = 15, dpi = 600)
#----------------------------- ~ Depth ----------------------------------------#
load("SuppData.RData")
BioVar_Depth<-ggplot(DataBioLong, aes(x=Transec, y=BioValues, fill=Regions)) +
geom_boxplot() + #
scale_fill_manual(values = c("white", "gray55"),
labels = c("Ubatuba (n= 12643)", "Caraguatatuba (n= 17166)"))+
ylab("")+
xlab("")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetOerder ~ ., scale="free",labeller = as_labeller(Names_var)) +
#facet_wrap(~facetOerder, scale="free", ncol = 1,labeller = as_labeller(Names_var))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top",#c(0.79, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
BioVar_Depth
ggsave(filename = "./Figures/BioVar_Depth.png",
plot = BioVar_Depth,
units = "cm", width = 15, height = 15, dpi = 600)
#--------------------------- / / ---------------------------------------#
################################################################################
############### Condition indices ~ Month + Depth ##########################
library(ggplot2)
library(tidyr)
library(dplyr)
library (lemon)
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
CondInd<-read.csv("Size_index2.csv",dec=".",sep=";",header=T)
attach(CondInd)
CondInd2<-CondInd %>% filter(str_detect(Sex, "Adults|JUveniles"))
summary(CondInd2)
str(CondInd2)
CondInd1<-CondInd2[,c("Region","Month_","Season","Trans",
"SL","TW","HSI","K","RI")]# Filtering
DataCondLong1<-pivot_longer(CondInd1, -c(Region,Month_,Trans,Season),
values_to = "CondValues", names_to = "CondVar")
DataCondLong1
str(DataCondLong1)
DataCondLong<-transform(DataCondLong1, Region = as.factor(Region),
Month_ = as.factor(Month_),
Season = as.factor(Season),
Trans = as.factor(Trans),
CondVar = as.factor(CondVar),
CondValues = as.numeric(CondValues))
DataCondLong$Month_ <- factor(DataCondLong$Month_ , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
Names_var <- c(
'SL'="SL (mm)",
'TW'="TW (g)",
'HSI'="HSI",
'K'="K",
'RI'="RI")
#reorder plots
DataCondLong$facetOrder = factor(DataCondLong$CondVar, levels = c("SL",
"TW",
"HSI",
"K",
"RI"))
#----------------------------- Adults~ Month ----------------------------------------#
#load("SuppData.RData")
#ADULTS
CondVAr_Month<-ggplot(DataCondLong, aes(x=Month_, y=CondValues, fill=Region)) +
geom_boxplot() +
#stat_summary(fun=mean, geom="point", shape=20, size=4.5, color="red", fill="red")+
scale_fill_manual(values = c("white", "gray55"),
labels = c("Ubatuba (n= 2860)", "Caraguatatuba (n= 3133)"))+
ylab("")+
xlab("")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetOrder ~ ., scale="free",labeller = as_labeller(Names_var)) +
#facet_wrap(~facetOrder, scale="free", ncol = 2,labeller = as_labeller(Names_var))+
theme(legend.position = "top",
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"),
legend.direction = "horizontal")+
guides(fill=guide_legend(""))+ # legend title
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top", #c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
CondVAr_Month
ggsave(filename = "./Figures/CondVAr_Month_All.png",
plot = CondVAr_Month,
units = "cm", width = 15, height = 15, dpi = 600)
#----------------------------- Juveniles~ Month ----------------------------------------#
CondIndJuv<-CondInd %>% filter(str_detect(Sex, "Juveniles"))
summary(CondIndJuv)
str(CondIndJuv)
CondIndJuv<-CondIndJuv[,c("Region","Month_","Season","Trans",
"SL","TW","HSI","K","RI")]# Filtering
DataCondLongJuv<-pivot_longer(CondIndJuv, -c(Region,Month_,Trans,Season),
values_to = "CondValues", names_to = "CondVar")
DataCondLongJuv
str(DataCondLongJuv)
DataCondLongJuv<-transform(DataCondLongJuv, Region = as.factor(Region),
Month_ = as.factor(Month_),
Season = as.factor(Season),
Trans = as.factor(Trans),
CondVar = as.factor(CondVar),
CondValues = as.numeric(CondValues))
DataCondLongJuv$Month_ <- factor(DataCondLongJuv$Month_ , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
Names_var <- c(
'SL'="SL (mm)",
'TW'="TW (g)",
'HSI'="HSI",
'K'="K",
'RI'="RI")
#reorder plots
DataCondLongJuv$facetOrder = factor(DataCondLongJuv$CondVar, levels = c("SL",
"TW",
"HSI",
"K",
"RI"))
CondVAr_Month_Juv<-ggplot(DataCondLongJuv, aes(x=Month_, y=CondValues, fill=Region)) +
geom_boxplot() +
#stat_summary(fun=mean, geom="point", shape=20, size=4.5, color="red", fill="red")+
scale_fill_manual(values = c("white", "gray55"),
labels = c("Ubatuba (n= 436)", "Caraguatatuba (n= 370)"))+
ylab("")+
xlab("")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetOrder ~ ., scale="free",labeller = as_labeller(Names_var)) +
#facet_wrap(~facetOrder, scale="free", ncol = 2,labeller = as_labeller(Names_var))+
theme(legend.position = "top",
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"),
legend.direction = "horizontal")+
guides(fill=guide_legend(""))+ # legend title
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top", #c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
CondVAr_Month_Juv
ggsave(filename = "./Figures/CondVAr_Month_Juv.png",
plot = CondVAr_Month,
units = "cm", width = 15, height = 15, dpi = 600)
#----------------------------- ~ Depth ----------------------------------------#
#load("SuppData.RData")
CondVAr_Depth<-ggplot(DataCondLong, aes(x=Trans, y=CondValues, fill=Region)) +
geom_boxplot() +
scale_fill_manual(values = c("white", "gray55"),
labels = c("Ubatuba (n= 2860)", "Caraguatatuba (n= 3133)"))+
ylab("Value")+
xlab("Month")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetOrder ~ ., scale="free",labeller = as_labeller(Names_var)) +
theme(legend.position = "top",
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"),
legend.direction = "horizontal")+
guides(fill=guide_legend(""))+ # legend title
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top",#c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
CondVAr_Depth
ggsave(filename = "./Figures/CondVAr_Depth.png",
plot = CondVAr_Depth,
units = "cm", width = 15, height = 15, dpi = 600)
#--------------------------- / / ---------------------------------------#
################################################################################
###################### Juveniles ~ Month + Depth ##########################
library(ggplot2)
library(tidyr)
library(dplyr)
library (lemon)
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
JuvDat<-read.csv("Size_index.csv",dec=".",sep=";",header=T)
attach(JuvDat)
str(JuvDat)
JuvDat1<-JuvDat[,c("Region","Month_","Trans","Sex",
"SL", "N_total")]# Filtering
JuvDat1<-transform(JuvDat1, Region = as.factor(Region),
Month_ = as.factor(Month_),
Trans = as.factor(Trans),
Sex = as.factor(Sex),
SL = as.numeric(SL),
N_total = as.numeric(N_total))
str(JuvDat1)
summary(JuvDat1)
JuvDat2<-JuvDat1 %>%
group_by (Region, Month_, Sex) %>%
summarise(n= n())
Juv_wideFrq<-JuvDat2 %>%
pivot_wider(values_from = "n", names_from = "Sex") %>%
replace_na(list(Juveniles = 0)) %>%
mutate(FrqAdults=(Adults*100)/(Adults+Juveniles)) %>%
mutate(FrqJuveniles=(Juveniles*100)/(Adults+Juveniles)) %>%
mutate(Teste=(FrqAdults+FrqJuveniles))
Juv_wideFrq
SexLong<-pivot_longer(Juv_wideFrq[,c(-3,-4,-7)], -c(Region,Month_),
values_to = "SexValues", names_to = "SexVar")
SexLong
SexLong$Month_ <- factor(SexLong$Month_ , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
Names_var <- c(
'UBA'="Ubatuba",
'CAR'="Caraguatatuba")
#reorder plots
SexLong$facetJuv = factor(SexLong$Region, levels = c("UBA",
"CAR"))
#----------------------------- FR ~ Month -------------------------------------#
load("SuppData.RData")
FR_Juv<-ggplot(SexLong, aes(x = Month_, y = SexValues, fill = SexVar)) +
geom_bar(stat="identity") +
scale_fill_manual(values = c("gray75", "black"),name = "",
labels = c("Adults (n= 5187)", "Juveniles (n= 806)"))+
ylab("Frequency (%)")+
xlab("Month")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(facetJuv ~ ., scale="free",labeller = as_labeller(Names_var)) +
coord_capped_cart(bottom='both', left='both')+
theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top",#c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
FR_Juv
ggsave(filename = "./Figures/FR_Juv.png",
plot = FR_Juv,
units = "cm", width = 15, height = 15, dpi = 600)
#----------------------------- SL ~ Month -------------------------------------#
load("SuppData.RData")
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
JuvDat<-read.csv("Size_index.csv",dec=".",sep=";",header=T)
attach(JuvDat)
str(JuvDat)
JuvDat1<-JuvDat[,c("Region","Month_","Trans","Sex",
"SL", "N_total")]# Filtering
JuvDat1<-transform(JuvDat1, Region = as.factor(Region),
Month_ = as.factor(Month_),
Trans = as.factor(Trans),
Sex = as.factor(Sex),
SL = as.numeric(SL),
N_total = as.numeric(N_total))
str(JuvDat1)
summary(JuvDat1)
JuvDat1$Month_ <- factor(JuvDat1$Month_ , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
SL_Month<-ggplot(JuvDat1, aes(x=SL, fill=Sex)) +
geom_histogram(alpha = 0.5, position = "identity",bins = 30) +
scale_fill_manual(values = c("gray55", "black"),
labels = c("Adults (n= 5187)", "Juveniles (n= 806)"))+
ylab("Absolute frequency")+
xlab("Standard length (mm)")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 8, face = "plain",
angle = 90, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 8),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 10)) +
facet_grid(Region ~ Month_, scale="free") +
theme(legend.position = "top",
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"),
legend.direction = "horizontal")+
guides(fill=guide_legend(""))+ # legend title
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
#theme_bw() +
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top", #c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
SL_Month
ggsave(filename = "./Figures/SL_Month.png",
plot = SL_Month,
units = "cm", width = 18, height = 10, dpi = 600)
#--------------------- Boxplot SL ~ Month -------------------------------------#
load("SuppData.RData")
BoxplotSL_Month<-ggplot(JuvDat1, aes(x=Month_, y=SL, fill=Sex)) +
geom_boxplot() +
scale_fill_manual(values = c("white", "gray55"),name="",
labels = c("Adults (n= 5187)", "Juveniles (n= 806)"))+
ylab("Standard length (mm)")+
xlab("Month")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="plain", colour ="black"),
legend.title = element_text(size = 10, face = "plain"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 0.5),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12)) +
facet_grid(Region ~ ., scale="free",labeller = as_labeller(Names_var))+
theme(legend.position = "top",
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"),
legend.direction = "horizontal")+
guides(fill=guide_legend(""))+ # legend title
guides(fill=guide_legend(""))+# legend title
coord_capped_cart(bottom='both', left='both')+
theme(panel.border=element_blank(), axis.line=element_line(),
legend.position = "top", #c(0.89, 0.96),
legend.background = element_rect(fill = NA, color = NA),
legend.key = element_rect(fill = NA, color = NA),
legend.key.size = unit(0.7, "cm"))
BoxplotSL_Month
ggsave(filename = "./Figures/BoxplotSL_Month.png",
plot = BoxplotSL_Month,
units = "cm", width = 15, height = 15, dpi = 600)
#--------------------------- / / ---------------------------------------#
save(JuvDat1,DataBioLong, DataCondLong,SexLong, file="SuppData.RData")
load("SuppData.RData")
################################################################################
###################### Juveniles ~ Month + Depth ##########################
library(tidyr)
library(dplyr)
setwd("~/efc/2021_SUBMITTED_Biological factors as proxies of/2021_SUBMITTED_J Sea Research/Script_Figuras")
AbioF<-read.csv("Abio_updated.csv",dec=".",sep=";",header=T)
attach(AbioF)
str(AbioF)
EnvVar<-AbioF[,c("Region","Month","Depth",
"BT", "BS")]# Filtering
str(EnvVar)
EnvVar<-transform(EnvVar, Month = as.factor(Month))
EnvVar<-transform(EnvVar, Region = as.factor(Region))
str(EnvVar)
EnvVar$Month <- factor(EnvVar$Month , levels = c("Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec"))
TempAbio<-ggplot(EnvVar, aes(x=Month, y=BT, fill=Region)) +
geom_boxplot() +
#stat_summary(fun="mean", color="white", shape=15)+
scale_fill_manual(values = c("white", "gray75"))+
ylab("Temperature (ºC)")+
xlab("Month")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="bold", colour ="black"),
legend.title = element_text(size = 11, face = "bold"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12))
TempAbio
ggsave(filename = "./Figures/TempAbio.png",
plot = TempAbio,
units = "cm", width = 15, height = 21, dpi = 600)
SalAbio<-ggplot(EnvVar , aes(x=Month, y=BS, fill=Region)) +
geom_boxplot() +
#stat_summary(fun="mean", color="white", shape=15)+
scale_fill_manual(values = c("white", "gray75"))+
ylab("Salinity")+
xlab("Month")+
theme(axis.text=element_text(size=12),
legend.key=element_blank(),
axis.text.x = element_text(colour = "black", size = 12, face = "plain", angle = 360, vjust = 1, hjust = 0.5),
axis.text.y = element_text(colour = "black", face = "plain", size = 10),
legend.text = element_text(size = 10, face ="bold", colour ="black"),
legend.title = element_text(size = 11, face = "bold"), panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.position = "top", panel.grid.major.y = element_line(colour = "grey93"),
panel.grid.minor.y = element_line(colour = "grey93"),
strip.text = element_text(size = 12))
SalAbio
ggsave(filename = "./Figures/SalAbio.png",
plot = SalAbio,
units = "cm", width = 15, height = 21, dpi = 600)
#--------------------------------------------------------