Commit 265babba authored by matbuoro's avatar matbuoro
Browse files

Tables CIEM

parent 322ad298
rm(list=ls()) # Clear memory
# TO DO
# - Scorff: revoir les données de l année N-1 pour prendre en compte les bécards
# - Oir: vérifier les données adultes de l'année 1986' (différence capturés vs marqués NEGATIVE!!!)
# - Oir : demander les fecondités des 1SW et MSW à Fred
# - OIR: chercher les données de surface de production accessible aux spawners dans les données de juvéniles
# ' Bresle: vérifier tx fecondité et sex-ratio
year <- "2016"
year <- 2016
###################################################################################################################
######## Table 7 - Exploitation rate in the rivers Scorff ######
###################################################################################################################
## SCORFF
site <- "Scorff"
......@@ -12,11 +24,17 @@ stade <- "adult"
# load dataset
load(paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/data/data_',stade,"_",year,'.Rdata',sep="")) # chargement des données
## Cm_F[t,a]: Annual number of marked fish caught by fishing per sea age category and showed at Moulin des Princes. 1:1SW, 2:MSW
## Cum_F[t,a]: Annual number of unmarked fish caught by fishing per sea age category and showed at Moulin des Princes. 1:1SW, 2:MSW
C_F_1SW <- data$Cm_F[,1] + data$Cum_F[,1] # marked + unmarked 1SW fish caugth by fishing
C_F_MSW <- data$Cm_F[,2] + data$Cum_F[,2] # marked + unmarked MSW fish caugth by fishing
# /!\ Annual number of fish caught by fishing per sea age category from 1994 to 2002 / Not all reported then
#data$C_F # 94 -> 2002
C_F_1SW[1:length(data$C_F[,1])] <- data$C_F[,1]
C_F_MSW[1:length(data$C_F[,2])] <- data$C_F[,2]
# load estimations of size popualtions
load("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/results/Results_adult_2016.RData")
n_1SW <- fit$median$n_1SW # medians
......@@ -29,22 +47,27 @@ Expl_rate <- cbind(
Expl_rate <- rbind(Expl_rate,colMeans(Expl_rate))
rownames(Expl_rate) <- c(seq(1994,2016,1), "Average")
rownames(Expl_rate) <- c(seq(1994,year,1), "Average")
colnames(Expl_rate) <- c("1SW (%)", "MSW (%)")
write.csv(round(Expl_rate,1), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table7_Scorff_',year,'.csv',sep=""))
######## Table 8 - Index rivers :spawning stock, egg deposition and attainment of CLs ######
## NIVELLE
###################################################################################################################
######## Table 8 - Index rivers :spawning stock, egg deposition and attainment of CLs ######
###################################################################################################################
##________________________NIVELLE (starting in 1984)
site <- "Nivelle"
stade <- "adult"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/adult/results/Results_adult_",year,".RData",sep=""))
years <- seq(1987, 2016, 1)
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c("1SW", "MSW", "eggs (million)", "eggs/CL")
rownames(table) <- years
......@@ -52,9 +75,298 @@ rownames(table) <- years
#Conservation Limit:
CL = 1.44
# Spawners:
table[,"1SW"] <- fit$median$e_1SW # spawners 1SW
table[,"MSW"] <- fit$median$e_MSW # spawners MSW
# Eggs
table[,"eggs (million)"] <- fit$median$eggs_tot / 1e6 # depose eggs
table[,"eggs/CL"] <- table[,"eggs (million)"] / CL
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table8_',site,"_",year,'.csv',sep=""))
##________________________ SCORFF (starting in 1994)
site <- "Scorff"
stade <- "adult"
nyear <- length(seq(1994,year,1))
load("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/tacon/data/data_tacon_2016.Rdata") # DATA
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c("1SW", "MSW", "eggs (million)", "eggs/CL")
rownames(table) <- years
# Spawners:
table[,"1SW"] <- c(rep(NA,10), fit$median$e_1SW) # spawners 1SW
table[,"MSW"] <- c(rep(NA,10),fit$median$e_MSW) # spawners MSW
# Eggs:
mcmc <- fit$sims.matrix
e_1SW.mcmc <- mcmc[,paste("e_1SW[",1:nyear,"]",sep="")]
e_MSW.mcmc <- mcmc[,paste("e_MSW[",1:nyear,"]",sep="")]
#Conservation Limit de taux de depose oeufs
CL = 300 # eggs/100m²
fec_1SW = 0.45 * 3485 # fecondité 1SW Female
fec_MSW = 0.80 * 5569 # fecondite MSW Female
eggs_tot.mcmc = e_1SW.mcmc * fec_1SW + e_MSW.mcmc * fec_MSW
eggs_tot <- apply(eggs_tot.mcmc,2,quantile, probs=0.5) #median
S_prod <- colSums(data$S_Sc) + 31 # surface de production juveniles accessible aux spawners / ajout de 31 pour tenir compte des affluents non proscptés
CL_eggs <- S_prod[2:(nyear+1)] * CL # /!\ data$S_Sc starts in 1993 instead of 1994
ratio_CL <- eggs_tot / CL_eggs
table[,"eggs (million)"] <- c(rep(NA,10),eggs_tot / 1e6) # depose eggs
table[,"eggs/CL"] <- c(rep(NA,10),ratio_CL)
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table8_',site,"_",year,'.csv',sep=""))
##_______________________________ OIR (starting in 1984)
site <- "Oir"
stade <- "adult"
nyear <- length(seq(1984,year,1))
load("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Oir/adult/data/data_adult_2016.Rdata") # DATA
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
# /!\ regarder difference capturés - marqués (data$C_MC - data$Cm_MC)-> nombre negatif en 1986!!!!
# retirer les individus non marqués des effectifs estimés n (n - (data$C_MC - data$Cm_MC))
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c("1SW", "MSW", "eggs (million)", "eggs/CL")
rownames(table) <- years
# Spawners:
table[,"1SW"] <- fit$median$Nesc_1SW # spawners 1SW
table[,"MSW"] <- fit$median$Nesc_MSW # spawners MSW
# Eggs:
mcmc <- fit$sims.matrix
e_1SW.mcmc <- mcmc[,paste("Nesc_1SW[",1:nyear,",2]",sep="")] # female only
e_MSW.mcmc <- mcmc[,paste("Nesc_MSW[",1:nyear,",4]",sep="")] # female only
#S_prod <- colSums(data$S_Sc) # surface de production juveniles accessible aux spawners
#CL_eggs <- S_prod[2:(nyear+1)] * CL
fec_1SW = 3485 # fecondité 1SW # A REVOIR avec Fred
fec_MSW = 5569 # fecondite MSW # A REVOIR avec Fred
eggs_tot.mcmc = e_1SW.mcmc * fec_1SW + e_MSW.mcmc * fec_MSW
eggs_tot <- apply(eggs_tot.mcmc,2,quantile, probs=0.5) #median
# calculs basés sur la conservation limit des tableaux CIEM!! A REFAIRE à partir des données MAIS
#/!\ chercher les données de surface d eproduction dans les données de juvéniles
#Conservation Limit de taux de depose oeufs
CL_eggs = 0.12 # issue des tableaux CIEM, A REVOIR!!!
ratio_CL <- (eggs_tot/1e6) / CL_eggs
table[,"eggs (million)"] <- eggs_tot / 1e6 # depose eggs
table[,"eggs/CL"] <- ratio_CL
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table8_',site,"_",year,'.csv',sep=""))
##_______________________________ BRESLE (starting in 1984)
site <- "Bresle"
stade <- "adult"
nyear <- length(seq(1984,year,1))
load("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/data/data_adult_2016.Rdata") # DATA
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
# /!\ regarder difference capturés - marqués (data$C_MC - data$Cm_MC)-> nombre negatif en 1986!!!!
# retirer les individus non marqués des effectifs estimés n (n - (data$C_MC - data$Cm_MC))
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c("1SW", "MSW", "eggs (million)", "eggs/CL")
rownames(table) <- years
# Spawners:
table[,"1SW"] <- fit$median$n_1SW # spawners 1SW
table[,"MSW"] <- fit$median$n_MSW # spawners MSW
# Eggs:
mcmc <- fit$sims.matrix
e_1SW.mcmc <- mcmc[,paste("n_1SW[",1:nyear,"]",sep="")] # female only
e_MSW.mcmc <- mcmc[,paste("n_MSW[",1:nyear,"]",sep="")] # female only
#S_prod <- colSums(data$S_Sc) # surface de production juveniles accessible aux spawners
#CL_eggs <- S_prod[2:(nyear+1)] * CL
fec_1SW = 0.45 * 3485 # fecondité 1SW Female # A REVOIR
fec_MSW = 0.80 * 5569 # fecondite MSW Female # A REVOIR
eggs_tot.mcmc = e_1SW.mcmc * fec_1SW + e_MSW.mcmc * fec_MSW
eggs_tot <- apply(eggs_tot.mcmc,2,quantile, probs=0.5) #median
# calculs basés sur la conservation limit des tableaux CIEM!! A REFAIRE à partir des données MAIS
#/!\ chercher les données de surface d eproduction dans les données de juvéniles
#Conservation Limit de taux de depose oeufs
CL_eggs = 0.36 # issue des tableaux CIEM, A REVOIR!!!
ratio_CL <- (eggs_tot/1e6) / CL_eggs
table[,"eggs (million)"] <- eggs_tot / 1e6 # depose eggs
table[,"eggs/CL"] <- ratio_CL
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table8_',site,"_",year,'.csv',sep=""))
###################################################################################################################
######## Table 9 - juvenile and adult salmon numbers (estim.), in-river return rate in the monitored rivers ######
###################################################################################################################
# Nota : juvenile fish are smolts, except in r. Nivelle (parrs O+). Adult numbers refer to the smolt year N: runs of N+1 and N+2
##________________________NIVELLE (starting in 1984)
site <- "Nivelle"
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c( "smolt year","0+ parr", "adults", "survival rate (%)")
rownames(table) <- years
smolt.years <- years+1
table[,1] <- smolt.years
## JUVENILES
stade <- "tacon"
#load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
YOY_tot_q <- read.table(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/",stade,"/results/YOY_tot.txt",sep=""), header = TRUE)
table[,2] <- YOY_tot_q[,"q0.5"]
## ADULTS
stade <- "adult"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
n_1SW <- fit$median$n_1SW # spawners 1SW
n_MSW <- fit$median$n_MSW # spawners MSW
for (y in 1:(nrow(table))){
table[y,3] <- n_1SW[y+2] + n_MSW[y+3] # Parr 0+ become 1SW 2 years later / MSW 3 years later; /!\ NA reported if one of the two is missing!!!
}
## SURVIVAL
table[,4] <- (table[,3] / table[,2])*100
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table9_',site,"_",year,'.csv',sep=""), row.names = FALSE)
##________________________OIR (starting in 1984)
site <- "Oir"
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c( "smolt year","smolt", "adults", "survival rate (%)")
rownames(table) <- years
smolt.years <- years
table[,1] <- smolt.years
## JUVENILES
stade <- "smolt"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
table[,2] <- c(NA,NA,fit$median$Nesc) # capture of smolts started in 1986
## ADULTS
stade <- "adult"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
n_1SW <- fit$median$Nesc_1SW # spawners 1SW
n_MSW <- fit$median$Nesc_MSW # spawners MSW
for (y in 1:(nrow(table))){
table[y,3] <- n_1SW[y+1] + n_MSW[y+2] # Smolt 1+ become 1SW 1 years later / MSW 2 years later; /!\ NA reported if one of the two is missing!!!
}
## SURVIVAL
table[,4] <- (table[,3] / table[,2])*100
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table9_',site,"_",year,'.csv',sep=""), row.names = FALSE)
##________________________BRESLE (starting in 1984)
site <- "Bresle"
years <- seq(1982, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c( "smolt year","smolt", "adults", "survival rate (%)")
rownames(table) <- years
smolt.years <- years
table[,1] <- smolt.years
## JUVENILES (1982 to now ; # /!\ NO CAPTURE IN 1988 to 1991 & 2001)
stade <- "smolt"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
Nesc <- fit$median$Nesc # escapement from river
table[1:6,2] <- Nesc[1:6] # 1982 to 1987
table[11:19,2] <- Nesc[7:15] # 1992 to 2000
table[21:nrow(table),2] <- Nesc[16:length(Nesc)] # 2002 to now
## ADULTS (1984 to now)
stade <- "adult"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
n_1SW <- c(NA,NA,fit$median$n_1SW) # spawners 1SW
n_MSW <- c(NA,NA,fit$median$n_MSW) # spawners MSW
for (y in 1:(nrow(table))){
table[y,3] <- n_1SW[y+1] + n_MSW[y+2] # Smolt 1+ become 1SW 1 years later / MSW 2 years later; /!\ NA reported if one of the two is missing!!!
}
## SURVIVAL
table[,4] <- (table[,3] / table[,2])*100
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table9_',site,"_",year,'.csv',sep=""), row.names = FALSE)
##________________________SCORFF (starting in 1994)
site <- "Scorff"
years <- seq(1984, year, 1)
table <- array(, dim=c(length(years), 4))
colnames(table) <- c( "smolt year","smolt", "adults", "survival rate (%)")
rownames(table) <- years
smolt.years <- years
table[,1] <- smolt.years
## JUVENILES from 1995 to now on
stade <- "smolt"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
Nesc <- fit$median$Nesc # escapement from river
table[12:nrow(table),2] <- Nesc # 1995 to now
## ADULTS (1984 to now)
stade <- "adult"
load(paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance/",site,"/",stade,"/results/Results_",stade,"_",year,".RData",sep=""))
n_1SW <- c(rep(NA,10), fit$median$e_1SW) # spawners 1SW
n_MSW <- c(rep(NA,10),fit$median$e_MSW) # spawners MSW
for (y in 1:(nrow(table))){
table[y,3] <- n_1SW[y+1] + n_MSW[y+2] # Smolt 1+ become 1SW 1 years later / MSW 2 years later; /!\ NA reported if one of the two is missing!!!
}
## SURVIVAL
table[,4] <- (table[,3] / table[,2])*100
write.csv(round(table,2), file=paste('~/Documents/RESEARCH/PROJECTS/ORE/Abundance/CIEM/Table9_',site,"_",year,'.csv',sep=""), row.names = FALSE)
"","1SW (%)","MSW (%)"
"1994",3.7,21.3
"1995",6.4,5.7
"1996",4.9,6.9
"1997",5.3,8.2
"1998",9.8,18.5
"1999",7.8,3.3
"2000",12.7,11.9
"2001",5.8,10
"2002",2.1,4.3
"1994",9,21.3
"1995",9.9,12.5
"1996",13.5,12.6
"1997",7.7,11
"1998",12.5,18.5
"1999",9.7,7.6
"2000",14,23.8
"2001",9.9,15
"2002",4.1,4.3
"2003",0,11.9
"2004",7.7,15.4
"2005",6,20.2
......@@ -22,4 +22,4 @@
"2014",8.2,9
"2015",8.7,17.1
"2016",5.4,12.2
"Average",6.4,12.2
"Average",7.8,13.8
"","1SW","MSW","eggs (million)","eggs/CL"
"1984",61,47,0.31,0.86
"1985",101,40,0.34,0.95
"1986",172.5,41,0.46,1.28
"1987",129,45,0.41,1.14
"1988",97,37,0.33,0.91
"1989",183,50,0.53,1.48
"1990",67,58,0.37,1.02
"1991",158,32,0.4,1.1
"1992",134,47,0.42,1.18
"1993",45,28,0.2,0.56
"1994",43,19,0.16,0.44
"1995",110,8,0.21,0.59
"1996",34,27,0.18,0.49
"1997",32,15,0.12,0.33
"1998",221,15,0.41,1.15
"1999",27,52,0.28,0.78
"2000",57,14,0.17,0.46
"2001",89,20,0.25,0.69
"2002",107,36,0.34,0.93
"2003",27,25,0.16,0.44
"2004",81,16,0.2,0.56
"2005",257,52,0.64,1.77
"2006",106,122,0.72,1.99
"2007",71,23,0.22,0.61
"2008",128,12,0.26,0.72
"2009",85,59,0.4,1.11
"2010",138,49,0.44,1.21
"2011",136,39,0.39,1.09
"2012",132,41,0.4,1.1
"2013",138,81,0.59,1.64
"2014",125,66,0.5,1.38
"2015",122,68,0.5,1.38
"2016",177,35,0.44,1.22
"","1SW","MSW","eggs (million)","eggs/CL"
"1984",67,16,0.26,0.18
"1985",39,28,0.24,0.17
"1986",186,30,0.58,0.4
"1987",74,23,0.31,0.22
"1988",75,13,0.23,0.16
"1989",136,52,0.61,0.42
"1990",225,38,0.81,0.56
"1991",153,54,0.62,0.43
"1992",179,54,0.77,0.53
"1993",415,42,1.35,0.94
"1994",279,42,0.99,0.69
"1995",155,54,0.7,0.49
"1996",141,40,0.64,0.44
"1997",91,12,0.34,0.23
"1998",141,12,0.36,0.25
"1999",131,31,0.45,0.31
"2000",139,29,0.44,0.3
"2001",176,28,0.53,0.37
"2002",380,37,1.07,0.74
"2003",20,59,0.4,0.28
"2004",77,16,0.27,0.19
"2005",75,14,0.29,0.2
"2006",33,30,0.25,0.18
"2007",52,15,0.19,0.13
"2008",55,28,0.32,0.22
"2009",46,19,0.21,0.15
"2010",137,20,0.37,0.26
"2011",38,43,0.32,0.22
"2012",61,16,0.2,0.14
"2013",81,42,0.41,0.29
"2014",62,46,0.35,0.24
"2015",44,38,0.31,0.21
"2016",53,22,0.21,0.15
"","1SW","MSW","eggs (million)","eggs/CL"
"1984",229,32,0.94,7.83
"1985",228,70,1.05,8.74
"1986",84,84,0.78,6.52
"1987",167,13,0.65,5.42
"1988",156,84,0.85,7.12
"1989",198,41,0.77,6.38
"1990",80,22,0.37,3.12
"1991",46,5,0.19,1.61
"1992",43,12,0.18,1.53
"1993",119,13,0.43,3.54
"1994",52,15,0.23,1.89
"1995",141,4,0.49,4.1
"1996",246,7,0.91,7.54
"1997",62,6,0.18,1.46
"1998",159,16,0.64,5.31
"1999",202,33,0.88,7.31
"2000",231,33,1,8.33
"2001",176,15,0.7,5.84
"2002",281,37,1.19,9.91
"2003",183,32,0.81,6.75
"2004",325,73,1.55,12.88
"2005",139,24,0.63,5.22
"2006",176,37,0.82,6.87
"2007",182,28,0.8,6.64
"2008",95,20,0.45,3.76
"2009",139,25,0.63,5.29
"2010",495,23,1.84,15.36
"2011",202,70,1.1,9.15
"2012",206,53,1.04,8.66
"2013",256,64,1.26,10.47
"2014",228.5,54,1.12,9.34
"2015",123,44,0.69,5.72
"2016",270,30,1.12,9.31
"","1SW","MSW","eggs (million)","eggs/CL"
"1984",NA,NA,NA,NA
"1985",NA,NA,NA,NA
"1986",NA,NA,NA,NA
"1987",NA,NA,NA,NA
"1988",NA,NA,NA,NA
"1989",NA,NA,NA,NA
"1990",NA,NA,NA,NA
"1991",NA,NA,NA,NA
"1992",NA,NA,NA,NA
"1993",NA,NA,NA,NA
"1994",430,60,0.96,1.59
"1995",657,36,1.2,2
"1996",608,62,1.24,2.05
"1997",411,33,0.8,1.33
"1998",499,21,0.88,1.46
"1999",228,60,0.63,1.04
"2000",250,4,0.42,0.69
"2001",286,32,0.59,0.99
"2002",549,20,0.96,1.59
"2003",233,40,0.55,0.87
"2004",1027,53,1.85,2.93
"2005",414,98,1.09,1.72
"2006",816,67,1.58,2.5
"2007",435,81,1.05,1.66
"2008",274,71,0.75,1.19
"2009",257,101,0.86,1.36
"2010",692,61,1.36,2.16
"2011",354,195,1.43,2.27
"2012",338,109,1.02,1.43
"2013",561,95,1.31,1.9
"2014",628,120,1.53,2.22
"2015",525,138,1.44,2.1
"2016",483,62,1.04,1.52
"smolt year","smolt","adults","survival rate (%)"
1982,1373,NA,NA
1983,2030,103,5.07
1984,2101,139,6.62
1985,3646.5,216,5.92
1986,1889,162,8.58
1987,593.5,140,23.59
1988,NA,235,NA
1989,NA,100,NA
1990,NA,210,NA
1991,NA,158,NA
1992,898,70,7.8
1993,1653,53,3.21
1994,2403,154,6.41
1995,628.5,51,8.11
1996,1398,43,3.08
1997,6281.5,280,4.46
1998,1578,54,3.42
1999,545.5,108,19.8
2000,1594,167,10.48
2001,NA,140,NA
2002,1771.5,50,2.82
2003,2863.5,135,4.71
2004,7413.5,374,5.04
2005,5348,128,2.39
2006,2703,92,3.4
2007,5202,199,3.83
2008,6541,131,2
2009,1191,169,14.19
2010,2666,171,6.41
2011,6269.5,207,3.3
2012,6397.5,208,3.25
2013,2005.5,193,9.62
2014,2287.5,158,6.91
2015,7631,NA,NA
2016,7587,NA,NA
"smolt year","0+ parr","adults","survival rate (%)"
1985,NA,375,NA
1986,1301,226,17.37
1987,7835.5,188,2.4
1988,11664,286,2.45
1989,10952,342,3.12
1990,10377,220,2.12
1991,5625.5,253,4.5
1992,6902.5,521,7.55
1993,4668.5,393,8.42
1994,4613,232,5.03
1995,13567,184,1.36
1996,5668.5,126,2.22
1997,6925,196,2.83
1998,7481,179,2.39
1999,6615.5,173,2.62
2000,9499,225,2.37
2001,10662.5,455,4.27
2002,12190.5,50,0.41
2003,14865.5,103,0.69
2004,6947,119,1.71
2005,4378,64,1.46
2006,9264,93,1
2007,2390,83,3.47
2008,3092,76,2.46
2009,5112,192,3.76
2010,2730.5,60,2.2
2011,6885,104,1.51
2012,4669,129,2.76
2013,15019.5,100,0.67
2014,4501,66,1.47
2015,6749,NA,NA
2016,7744,NA,NA
2017,9459,NA,NA