inst/Updates/LFA41/2023/PrimaryIndicators_2023.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
require(bio.lobster)
require(bio.utilities)
require(dplyr)
require(devtools)
la()
p=list()
p$lfas = c("41") # specify lfa
p$current.assessment.year =2023
p$yrs = 1947:p$current.assessment.year
assessment.year = p$current.assessment.year ########### check the year ############### !!!!!!!!!!!
fp = file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','indicators')
#jpeg("BM41_2023TEST.jpeg", width = 9, height = 7, units = "in",res=600, bg = "white")
par(mfrow = c(2,2),las=1,mar = c(2,2,2,2),omi=c(0.5,0.5,0.25,0.25))
###DFO RV survey
all.out=read.csv(file.path(fp,'DFO.restratified.All.csv'))
out=read.csv(file.path(fp,'DFO.restratified.commercial.csv'))
a = merge(all.out,out,by='yr')
#median(a$w.Yst.y/a$w.Yst.x) #0.876 proportion of total weight that comprises commercial animals....assuming constant over
ao = all.out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst * 0.876
ao$w.Yst[ao$yr %in% out$yr[which(!is.na(out$yr))]] <- out$w.Yst[which(!is.na(out$yr))]
#ao$w.Yst[ao$yr %in% out$yr] <- out$w.Yst[which(!is.na(out$yr))]
ao$w.Yst = ao$w.Yst/1000
#ao is full time series of biomasses
ao<-ao[-c(52),]
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2000:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Georges###
rm(out)
aout=read.csv(file.path(fp,'DFO.Georges.All.csv'))
aout = aout[,c('yr','w.Yst')]
out=read.csv(file.path(fp,'DFO.Georges.commercial.csv'))
out = subset(out,yr>=2007)
out = out[,c('yr','w.Yst')]
aa=merge(out,aout,all.x=T,by='yr')
#sum(aa[,2])/sum(aa[,3]) #0.872 commercial
aout$w.Yst = aout$w.Yst * 0.872
aout$w.Yst[aout$yr %in% out$yr] <- out$w.Yst
ao = aout
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
#based on bcp
ub = median(subset(ao,yr %in% 2000:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Spring ###
out=read.csv(file.path(fp,'NEFSC.spring.restratified.commercial.csv'))
ao = out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2001:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Fall###
out=read.csv(file.path(fp,'NEFSC.fall.restratified.commercial.csv'))
ao = out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2001:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
mtext(" ",1,1,outer=T, cex= 2)
mtext("Stratified Total Weight (kt)",2,1,outer=T,las=0, cex = 1.5)
################### REPRODUCTIVE POTENTIAL ##########################
require(bio.lobster)
la()
ff = c(
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41polygonSummerRV.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41NEFSCspringrestratified.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41NEFSCfallrestratified.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41dfogeorges.rdata'))
#jpeg("RP41_2022_FR.jpeg", width = 11, height = 8, units = "in",res=600, bg = "white")
par(mfrow = c(2,2),las=1,mar = c(2,2,2,2),omi=c(0.5,0.5,0.25,0.25))
#DFO summer
i=1
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2000:2015,select=Fecm)[,1]) * 0.4
abline(h=ub,col='lightcyan4',lwd=2,lty = 4)
# DFO Georges
i=4
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
nub = median(subset(out,yr %in% 1999:2015,select=Fecm)[,1])
#NEFSC Spring survey
i=2
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Mat = ifelse(g$FLEN<120, g$Mat/2,g$Mat*(2/3))
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
if(sum(g$n.Yst,na.rm=T)>0){
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2001:2015,select=Fecm)[,1]) * 0.4
llb = out$Fecm[which(out$Fecm>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty = 4 )
#NEFSC Autumn survey
i=3
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
if(sum(g$n.Yst,na.rm=T)>0){
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2000:2015,select=Fecm)[,1]) * 0.4
llb = out$Fecm[which(out$Fecm>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2,lty = 4)
mtext(" ",1,1,outer=T, cex= 2)
mtext("Reproductive Potential ",2,1,outer=T,las=0, cex = 1.5)
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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require(bio.lobster)
require(bio.utilities)
require(dplyr)
require(devtools)
la()
p=list()
p$lfas = c("41") # specify lfa
p$current.assessment.year =2023
p$yrs = 1947:p$current.assessment.year
assessment.year = p$current.assessment.year ########### check the year ############### !!!!!!!!!!!
fp = file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','indicators')
#jpeg("BM41_2023TEST.jpeg", width = 9, height = 7, units = "in",res=600, bg = "white")
par(mfrow = c(2,2),las=1,mar = c(2,2,2,2),omi=c(0.5,0.5,0.25,0.25))
###DFO RV survey
all.out=read.csv(file.path(fp,'DFO.restratified.All.csv'))
out=read.csv(file.path(fp,'DFO.restratified.commercial.csv'))
a = merge(all.out,out,by='yr')
#median(a$w.Yst.y/a$w.Yst.x) #0.876 proportion of total weight that comprises commercial animals....assuming constant over
ao = all.out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst * 0.876
ao$w.Yst[ao$yr %in% out$yr[which(!is.na(out$yr))]] <- out$w.Yst[which(!is.na(out$yr))]
#ao$w.Yst[ao$yr %in% out$yr] <- out$w.Yst[which(!is.na(out$yr))]
ao$w.Yst = ao$w.Yst/1000
#ao is full time series of biomasses
ao<-ao[-c(52),]
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2000:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Georges###
rm(out)
aout=read.csv(file.path(fp,'DFO.Georges.All.csv'))
aout = aout[,c('yr','w.Yst')]
out=read.csv(file.path(fp,'DFO.Georges.commercial.csv'))
out = subset(out,yr>=2007)
out = out[,c('yr','w.Yst')]
aa=merge(out,aout,all.x=T,by='yr')
#sum(aa[,2])/sum(aa[,3]) #0.872 commercial
aout$w.Yst = aout$w.Yst * 0.872
aout$w.Yst[aout$yr %in% out$yr] <- out$w.Yst
ao = aout
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
#based on bcp
ub = median(subset(ao,yr %in% 2000:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Spring ###
out=read.csv(file.path(fp,'NEFSC.spring.restratified.commercial.csv'))
ao = out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2001:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
###Fall###
out=read.csv(file.path(fp,'NEFSC.fall.restratified.commercial.csv'))
ao = out[,c('yr','w.Yst')]
ao$w.Yst = ao$w.Yst/1000
plot(ao$yr,ao$w.Yst,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(ao$yr,ao$w.Yst,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(ao$yr,ao$w.Yst)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(ao,yr %in% 2001:2015,select=w.Yst)[,1]) * 0.4
llb = ao$w.Yst[which(ao$w.Yst>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty=4)
mtext(" ",1,1,outer=T, cex= 2)
mtext("Stratified Total Weight (kt)",2,1,outer=T,las=0, cex = 1.5)
################### REPRODUCTIVE POTENTIAL ##########################
require(bio.lobster)
la()
ff = c(
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41polygonSummerRV.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41NEFSCspringrestratified.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41NEFSCfallrestratified.rdata'),
file.path(project.datadirectory('bio.lobster'),'assessments','LFA41','2023','maturefemaleLengthFrequenciesLFA41dfogeorges.rdata'))
#jpeg("RP41_2022_FR.jpeg", width = 11, height = 8, units = "in",res=600, bg = "white")
par(mfrow = c(2,2),las=1,mar = c(2,2,2,2),omi=c(0.5,0.5,0.25,0.25))
#DFO summer
i=1
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2000:2015,select=Fecm)[,1]) * 0.4
abline(h=ub,col='lightcyan4',lwd=2,lty = 4)
# DFO Georges
i=4
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
nub = median(subset(out,yr %in% 1999:2015,select=Fecm)[,1])
#NEFSC Spring survey
i=2
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
#g$Mat = exp(-17.8583 +(0.1894 *g$FLEN)) / (1+exp(-17.8583 +(0.1894 *g$FLEN))) #pezzack and duggan 1989
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Mat = ifelse(g$FLEN<120, g$Mat/2,g$Mat*(2/3))
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
if(sum(g$n.Yst,na.rm=T)>0){
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2001:2015,select=Fecm)[,1]) * 0.4
llb = out$Fecm[which(out$Fecm>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2, lty = 4 )
#NEFSC Autumn survey
i=3
load(ff[i])
yll = max(aa$n.yst)
h = split(aa,f=aa$yr)
out= c()
for(j in 1:length(h)) {
g = h[[j]]
y = unique(g$yr)
g$Mat = 1 / (1+exp(-(-22.5522 +0.2455 *g$FLEN))) #gaudette 2016 from offshore lobsters collected in 2016
g$Fec = (g$Mat * 0.0031829 * g$FLEN ^ 3.353501) # campbell and Robinson 1983
g$Fecm = g$Fec * g$n.Yst / 1000000
if(sum(g$n.Yst,na.rm=T)>0){
n = aggregate(cbind(n.Yst,Fecm)~yr,data = g, FUN=sum, na.rm=T)
out = rbind(out,n)
}}
plot(out$yr,out$Fecm,type='n',xlab=' ',ylab = paste(" ",sep=" "))
points(out$yr,out$Fecm,type='p',lty=1,pch=16,lwd=2)
rmm = rmed(out$yr,out$Fecm)
lines(rmm[[1]],rmm[[2]],lty=1,lwd=3,col='red')
ub = median(subset(out,yr %in% 2000:2015,select=Fecm)[,1]) * 0.4
llb = out$Fecm[which(out$Fecm>0)]
llb = median(sort(llb)[1:5])
abline(h=llb,col='dodgerblue4',lwd=2)
abline(h=ub,col='lightcyan4',lwd=2,lty = 4)
mtext(" ",1,1,outer=T, cex= 2)
mtext("Reproductive Potential ",2,1,outer=T,las=0, cex = 1.5)
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