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analysis_hr.R
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analysis_hr.R
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### ------------------------------------------------------------------------ ###
### analysis of constant harvest rate rule ####
### ------------------------------------------------------------------------ ###
library(mse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(doParallel)
library(scales)
library(cowplot)
library(patchwork)
library(RColorBrewer)
library(akima)
library(GA)
source("funs.R")
source("funs_GA.R")
# cl <- makeCluster(3)
# registerDoParallel(cl)
# clusterEvalQ(cl = cl, expr = {library(mse)})
### stock list
stocks <- read.csv("input/stocks.csv", stringsAsFactors = FALSE)
stocks_subset <- stocks$stock[21:29]
names(stocks_subset) <- stocks_subset
### brps
brps <- readRDS("input/brps.rds")
### ------------------------------------------------------------------------ ###
### collate data ####
### ------------------------------------------------------------------------ ###
### ------------------------------------------------------------------------ ###
### collate data: pollack HR components ####
pol_GA <- foreach(stock = "pol", .combine = bind_rows) %:%
#foreach(optimised = c(TRUE, FALSE), .combine = rbind) %:%
foreach(objective = c("MSY", "MSY-PA"), .combine = bind_rows) %:%
foreach(fhist = c("one-way", "random"), .combine = bind_rows) %:%
foreach(parameters = c("none",
"multiplier",
"idx_lag", "idx_range", "b_multiplier",
"interval",
"upper_cap", "lower_cap", "caps",
"all_no_caps", "all",
"mult_cond_cap", "all_cond_cap"),
.combine = bind_rows) %:%
foreach(scenario = c("GA"),
.combine = bind_rows) %:%
foreach(catch_rule = "hr", .combine = bind_rows) %:%
foreach(stat_yrs = "all", .combine = bind_rows) %do% {#browser()
### find files
file_prefix <- switch(parameters,
"all" = paste0("idxB_lag-idxB_range_3-comp_b_multiplier-interval-",
"multiplier-upper_constraint-lower_constraint"),
"all_no_caps" = paste0("idxB_lag-idxB_range_3-comp_b_multiplier-interval",
"-multiplier"),
"caps" = "upper_constraint-lower_constraint",
"upper_cap" = "upper_constraint",
"lower_cap" = "lower_constraint",
"multiplier" = "multiplier",
"interval" = "interval",
"idx_range" = "idxB_range_3",
"idx_lag" = "idxB_lag",
"b_multiplier" = "comp_b_multiplier",
"none" = "multiplier",
"mult_cond_cap" = "multiplier-upper_constraint1.2-lower_constraint0.7",
"all_cond_cap" = paste0("idxB_lag-idxB_range_3-comp_b_multiplier-",
"interval-multiplier-upper_constraint1.2-",
"lower_constraint0.7")
)
file_suffix <- switch(objective,
"MSY" = paste0("obj_SSB_C_risk_ICV"),
"MSY-PA" = "obj_ICES_MSYPA")
file_res <- paste0(file_prefix, "--", file_suffix, "_res.rds")
file_runs <- paste0(file_prefix, "--", file_suffix, "_runs.rds")
scenario <- ifelse(parameters %in% c("mult_cond_cap", "all_cond_cap"),
"GA_cond_cap", scenario)
if (!file.exists(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_res))) {
return(NULL)
}
df_res <- data.frame(parameters = parameters, objective = objective,
scenario = scenario, catch_rule = catch_rule,
stat_yrs = stat_yrs, fhist = fhist)
### load GA results
res <- readRDS(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_res))
runs <- readRDS(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_runs))
solution <- res@solution
### if several equal best solutions, pick first
if (isTRUE(nrow(solution) > 1)) {
solution <- solution[1,, drop = FALSE]
}
solution[c(1, 2, 5)] <- round(solution[c(1, 2, 5)])
solution[c(3, 4)] <- round(solution[c(3, 4)], 1)
solution[c(6, 7, 8)] <- round(solution[c(6, 7, 8)], 2)
### default (non-optimised?)
if (identical(parameters, "none")) {
solution[] <- c(1, 1, 1, 1.4, 1, 1, Inf, 0)
}
df_res <- cbind(df_res, solution)
solution_c <- paste0(solution, collapse = "_")
### replace NaN for upper_constraint with Inf
solution_c <- gsub(x = solution_c, pattern = "NaN", replacement = "Inf")
stats <- runs[[solution_c]]$stats
stats <- as.data.frame(t(data.frame(unlist(stats),
row.names = rownames(stats))))
stats <- stats %>% select(risk_Blim:ICV)
row.names(stats) <- NULL
df_res <- cbind(df_res, stats)
### prepare risk penalty
penalty_tmp <- penalty(x = stats$risk_Blim,
negative = FALSE, max = 5,
inflection = 0.05 + 0.01,
steepness = 0.5e+3)
if (!identical(parameters, "none")) {
df_res$fitness <- res@fitnessValue
df_res$iter <- res@iter
} else {
if (isTRUE(objective == "MSY")) {
df_res$fitness <- -sum(abs(stats$SSB_rel - 1), abs(stats$Catch_rel - 1),
stats$risk_Blim, stats$ICV)
} else if (isTRUE(objective == "MSY-PA")) {
df_res$fitness <- -sum(abs(stats$SSB_rel - 1),
abs(stats$Catch_rel - 1),
stats$ICV,
penalty_tmp)
}
df_res$iter <- NA
}
df_res$risk_penalty <- ifelse(isTRUE(objective == "MSY-PA"),
penalty_tmp, NA)
return(df_res)
}
pol_GA <- pol_GA %>%
mutate(parameters = factor(as.character(parameters),
levels = c("none",
"multiplier",
"idx_lag", "idx_range", "b_multiplier",
"interval",
"upper_cap", "lower_cap", "caps",
"all_no_caps", "all", "mult_cond_cap", "all_cond_cap"),
labels = c("not optimised",
"multiplier",
"time lag", "index range", "index trigger\nbuffer",
"interval",
"upper cap", "lower cap", "both caps",
"all without caps", "all",
"multiplier (cond. cap)",
"all (cond. cap)")),
) %>%
mutate(fhist = factor(as.character(fhist),
levels = c("one-way", "random")))
saveRDS(pol_GA, file = "output/hr_pol_comps_stats.rds")
write.csv(pol_GA, file = "output/hr_pol_comps_stats.csv",
row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate data: all stocks ####
all_GA <- foreach(stock = stocks$stock, .combine = bind_rows) %:%
foreach(parameters = c("none", "mult", "all"), .combine = bind_rows) %:%
foreach(objective = c("MSY", "MSY-PA"), .combine = bind_rows) %:%
foreach(scenario = c("GA", "GA_cond_cap"), .combine = bind_rows) %:%
foreach(catch_rule = "hr", .combine = bind_rows) %:%
foreach(stat_yrs = "all", .combine = bind_rows) %:%
foreach(fhist = c("one-way", "random"), .combine = bind_rows) %do% {#browser()
### find files
file_prefix <- switch(parameters,
"none" = "multiplier",
"mult" = "multiplier",
"all" = paste0("idxB_lag-idxB_range_3-comp_b_multiplier-interval-",
"multiplier-upper_constraint-lower_constraint")
)
if (identical(scenario, "GA_cond_cap")) {
if (identical(parameters, "all")) {
file_prefix <- gsub(x = file_prefix,
pattern = "upper_constraint-lower_constraint",
replacement = "upper_constraint1.2-lower_constraint0.7")
} else if (isTRUE(parameters %in% c("none", "mult"))) {
file_prefix <- paste0(file_prefix, "-",
"upper_constraint1.2-lower_constraint0.7")
}
}
file_suffix <- switch(objective,
"MSY" = paste0("obj_SSB_C_risk_ICV"),
"MSY-PA" = "obj_ICES_MSYPA")
file_res <- paste0(file_prefix, "--", file_suffix, "_res.rds")
file_runs <- paste0(file_prefix, "--", file_suffix, "_runs.rds")
if (!file.exists(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_res))) {
return(NULL)
}
df_res <- data.frame(parameters = parameters, objective = objective,
scenario = scenario, catch_rule = catch_rule,
stat_yrs = stat_yrs, fhist = fhist, stock = stock)
### load GA results
res <- readRDS(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_res))
runs <- readRDS(paste0("output/", catch_rule, "/500_50/",
scenario, "/", fhist, "/", stock, "/", file_runs))
solution <- res@solution
### if several equal best solutions, pick first
if (isTRUE(nrow(solution) > 1)) {
solution <- solution[1,, drop = FALSE]
}
solution[c(1, 2, 5)] <- round(solution[c(1, 2, 5)])
solution[c(3, 4)] <- round(solution[c(3, 4)], 1)
solution[c(6, 7, 8)] <- round(solution[c(6, 7, 8)], 2)
### default (non-optimised?)
if (identical(parameters, "none")) {
solution[6] <- 1 ### multiplier
}
df_res <- cbind(df_res, solution)
solution_c <- paste0(solution, collapse = "_")
### replace NaN for upper_constraint with Inf
solution_c <- gsub(x = solution_c, pattern = "NaN", replacement = "Inf")
stats <- runs[[solution_c]]$stats
stats <- as.data.frame(t(data.frame(unlist(stats),
row.names = rownames(stats))))
stats <- stats %>% select(risk_Blim:ICV)
row.names(stats) <- NULL
df_res <- cbind(df_res, stats)
### prepare risk penalty
penalty_tmp <- penalty(x = stats$risk_Blim,
negative = FALSE, max = 5,
inflection = 0.05 + 0.01,
steepness = 0.5e+3)
if (!identical(parameters, "none")) {
df_res$fitness <- res@fitnessValue
df_res$iter <- res@iter
} else {
if (isTRUE(objective == "MSY")) {
df_res$fitness <- -sum(abs(stats$SSB_rel - 1), abs(stats$Catch_rel - 1),
stats$risk_Blim, stats$ICV)
} else if (isTRUE(objective == "MSY-PA")) {
df_res$fitness <- -sum(abs(stats$SSB_rel - 1),
abs(stats$Catch_rel - 1),
stats$ICV,
penalty_tmp)
}
df_res$iter <- NA
}
df_res$risk_penalty <- ifelse(isTRUE(objective == "MSY-PA"),
penalty_tmp, NA)
return(df_res)
}
saveRDS(all_GA, file = "output/hr_all_GA_stats.rds")
write.csv(all_GA, file = "output/hr_all_GA_stats.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate data: all stocks - multiplier with cond. cap - all runs ####
all_mult <- foreach(stock = stocks$stock, .combine = bind_rows) %:%
foreach(parameters = c("mult"), .combine = bind_rows) %:%
foreach(objective = c("MSY-PA"), .combine = bind_rows) %:%
foreach(scenario = c("GA_cond_cap", "GA_cond_cap_mult"), .combine = bind_rows) %:%
foreach(catch_rule = "hr", .combine = bind_rows) %:%
foreach(stat_yrs = "all", .combine = bind_rows) %:%
foreach(fhist = c("one-way", "random"), .combine = bind_rows) %:%
foreach(years = c(50, 100), .combine = bind_rows) %do% {#browser()
### find files
file_prefix <- "multiplier-upper_constraint1.2-lower_constraint0.7"
file_suffix <- "obj_ICES_MSYPA"
file_res <- paste0(file_prefix, "--", file_suffix, "_res.rds")
file_runs <- paste0(file_prefix, "--", file_suffix, "_runs.rds")
if (!file.exists(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_res))) {
return(NULL)
}
df_res <- data.frame(parameters = parameters, objective = objective,
scenario = scenario, catch_rule = catch_rule,
stat_yrs = stat_yrs, fhist = fhist, stock = stock,
years = years)
### load GA results
res <- readRDS(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_res))
runs <- readRDS(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_runs))
solution <- c(res@solution[, "multiplier"])
runs_df <- lapply(runs, function(x) {
tmp <- cbind(as.data.frame(t(x$pars)),
as.data.frame(t(unlist(x$stats[1:11, ]))))
penalty_tmp <- penalty(x = tmp$risk_Blim,
negative = FALSE, max = 5,
inflection = 0.05 + 0.01,
steepness = 0.5e+3)
if (isTRUE(objective == "MSY")) {
tmp$fitness <- -sum(abs(tmp$SSB_rel - 1), abs(tmp$Catch_rel - 1),
tmp$risk_Blim, tmp$ICV)
} else if (isTRUE(objective == "MSY-PA")) {
tmp$fitness <- -sum(abs(tmp$SSB_rel - 1),
abs(tmp$Catch_rel - 1),
tmp$ICV,
penalty_tmp)
}
return(tmp)
})
runs_df <- do.call(rbind, runs_df)
row.names(runs_df) <- NULL
runs_df$optimum <- ifelse(round(runs_df$multiplier, 2) == round(solution, 2),
TRUE, FALSE)
df_res <- cbind(df_res, runs_df)
return(df_res)
}
saveRDS(all_mult, file = "output/hr_all_mult_runs_stats.rds")
write.csv(all_mult, file = "output/hr_all_mult_runs_stats.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate data: all stocks - multiplier without cap - all runs ####
all_mult <- foreach(stock = stocks$stock, .combine = bind_rows) %:%
foreach(parameters = c("mult"), .combine = bind_rows) %:%
foreach(objective = c("MSY-PA"), .combine = bind_rows) %:%
foreach(scenario = c("GA"), .combine = bind_rows) %:%
foreach(catch_rule = "hr", .combine = bind_rows) %:%
foreach(stat_yrs = "all", .combine = bind_rows) %:%
foreach(fhist = c("one-way", "random", "roller-coaster"),
.combine = bind_rows) %:%
foreach(years = c(50), .combine = bind_rows) %do% {#browser()
### find files
file_prefix <- "multiplier"
file_suffix <- "obj_ICES_MSYPA"
file_res <- paste0(file_prefix, "--", file_suffix, "_res.rds")
file_runs <- paste0(file_prefix, "--", file_suffix, "_runs.rds")
if (!file.exists(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_res))) {
return(NULL)
}
df_res <- data.frame(parameters = parameters, objective = objective,
scenario = scenario, catch_rule = catch_rule,
stat_yrs = stat_yrs, fhist = fhist, stock = stock,
years = years)
### load GA results
res <- readRDS(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_res))
runs <- readRDS(paste0("output/", catch_rule, "/500_", years, "/",
scenario, "/", fhist, "/", stock, "/", file_runs))
solution <- c(res@solution[, "multiplier"])
runs_df <- lapply(runs, function(x) {
tmp <- cbind(as.data.frame(t(x$pars)),
as.data.frame(t(unlist(x$stats[1:11, ]))))
penalty_tmp <- penalty(x = tmp$risk_Blim,
negative = FALSE, max = 5,
inflection = 0.05 + 0.01,
steepness = 0.5e+3)
if (isTRUE(objective == "MSY")) {
tmp$fitness <- -sum(abs(tmp$SSB_rel - 1), abs(tmp$Catch_rel - 1),
tmp$risk_Blim, tmp$ICV)
} else if (isTRUE(objective == "MSY-PA")) {
tmp$fitness <- -sum(abs(tmp$SSB_rel - 1),
abs(tmp$Catch_rel - 1),
tmp$ICV,
penalty_tmp)
}
return(tmp)
})
runs_df <- do.call(rbind, runs_df)
row.names(runs_df) <- NULL
runs_df$optimum <- ifelse(round(runs_df$multiplier, 2) == round(solution, 2),
TRUE, FALSE)
df_res <- cbind(df_res, runs_df)
return(df_res)
}
saveRDS(all_mult, file = "output/hr_all_mult_runs_stats_nocaps.rds")
write.csv(all_mult, file = "output/hr_all_mult_runs_stats_nocaps.csv",
row.names = FALSE)
all_mult %>% filter(stock == "pol") %>%
ggplot(aes(x = multiplier, y = risk_Blim, colour = as.factor(fhist))) +
geom_line()
all_mult %>% filter(stock == "pol") %>%
ggplot(aes(x = multiplier, y = Catch_rel, colour = as.factor(fhist))) +
geom_line()
### ------------------------------------------------------------------------ ###
### figures for paper ####
### ------------------------------------------------------------------------ ###
### ------------------------------------------------------------------------ ###
### visualise calculation of target HR - pollack ####
### ------------------------------------------------------------------------ ###
stock <- "pol"
input <- readRDS(paste0("input/hr/500_50/OM_2_mp_input/random/", stock, ".rds"))
lhist <- stocks[stocks$stock == stock, ]
brp <- brps$pol
refpts(brp)
### find iteration with increasing F
pos <- which(fbar(input$om@stock)[, ac(50)] < 0.05 * c(refpts(brp)["crash","harvest"]) &
fbar(input$om@stock)[, ac(100)] > 0.95 * c(refpts(brp)["crash","harvest"]))
### 478
plot(input$om@stock, iter = 478)
### recreate mean catch length without noise
Lc <- calc_lc(stk = input$om@stock[, ac(50:100)],
a = lhist$a, b = lhist$b)
pars_l <- FLPar(a = lhist$a, b = lhist$b, Lc = Lc)
idxL = lmean(stk = input$om@stock[, ac(50:100)], params = pars_l)
LFeM <- (lhist$linf + 2*1.5*c(Lc)) / (1 + 2*1.5)
plot(idxL, iter = 478) + geom_hline(yintercept = LFeM)
df_idxL <- as.data.frame(idxL) %>%
filter(iter == pos) %>%
mutate(year = year - 100)
df_catch <- as.data.frame(catch(input$om@stock[, ac(50:100)])) %>%
filter(iter == pos) %>%
mutate(year = year - 100)
df_idxB <- as.data.frame(input$oem@observations$idx$idxB[, ac(50:100)]) %>%
filter(iter == pos) %>%
mutate(year = year - 100)
df_cr <- as.data.frame(catch(input$om@stock[, ac(50:100)]) /
input$oem@observations$idx$idxB[, ac(50:100)]) %>%
filter(iter == pos) %>%
mutate(year = year - 100)
### combine dfs and save
df_list <- list(idxL = df_idxL, catch = df_catch, idxB = df_idxB, cr = df_cr)
saveRDS(df_list, file = "output/hr_data_pol_visualisation.rds")
df_list <- readRDS("output/hr_data_pol_visualisation.rds")
df_idxL <- df_list$idxL
df_catch <- df_list$catch
df_idxB <- df_list$idxB
df_cr <- df_list$cr
### find years where L > LFeM
df_years <- df_idxL %>% filter(data >= LFeM) %>% select(year) %>% unlist()
### mean length
p_idxL1 <- df_idxL %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
facet_wrap(~ "step 1", strip.position = "top") +
labs(x = "year", y = "mean catch length [cm]") +
theme_bw(base_size = 8) +
theme(strip.text = element_text(size = 8))
p_idxL1
### add reference length
p_idxL2 <- df_idxL %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
geom_hline(yintercept = LFeM, linetype = "dashed") +
facet_wrap(~ "step 2", strip.position = "top") +
labs(x = "year", y = "mean catch length [cm]") +
theme_bw(base_size = 8) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text = element_text(size = 8))
p_idxL2
### add points
p_idxL3 <- df_idxL %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
geom_hline(yintercept = LFeM, linetype = "dashed") +
geom_point(data = df_idxL %>% filter(year %in% df_years),
colour = "red", size = 0.4) +
facet_wrap(~ "step 3", strip.position = "top") +
labs(x = "year", y = "mean catch length [cm]") +
theme_bw(base_size = 8) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text = element_text(size = 8))
p_idxL3
### catch
p_catch <- df_catch %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
labs(x = "year", y = "catch") +
theme_bw(base_size = 8) +
theme(axis.title.x = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
panel.border = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "transparent", colour = NA))
p_catch
### biomass index
p_idxB <- df_idxB %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
labs(x = "year", y = "index") +
theme_bw(base_size = 8) +
theme(axis.title.x = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
panel.border = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "transparent", colour = NA))
p_idxB
### catch rate
p_cr1 <- df_cr %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
facet_wrap(~ "step 4", strip.position = "top") +
labs(x = "year", y = "catch/index") +
ylim(c(0, 0.4)) +
theme_bw(base_size = 8) +
theme(strip.text = element_text(size = 8)) +
annotation_custom(grob = ggplotGrob(p_catch),
xmin = -50, xmax = -35,
ymin = 0.3, ymax = 0.4) +
annotation_custom(grob = ggplotGrob(p_idxB),
xmin = -30, xmax = -15,
ymin = 0.3, ymax = 0.4) +
annotate("segment", x = -34, xend = -31, y = 0.3, yend = 0.4,
colour = "black", size = 1)
p_cr1
### catch rate & points
p_cr2 <- df_cr %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
geom_point(data = df_cr %>% filter(year %in% df_years),
colour = "red", size = 0.4) +
facet_wrap(~ "step 5", strip.position = "top") +
labs(x = "year", y = "catch/index") +
ylim(c(0, 0.4)) +
theme_bw(base_size = 8) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text = element_text(size = 8))
p_cr2
### catch rate & points & reference
p_cr3 <- df_cr %>%
ggplot(aes(x = year, y = data)) +
geom_line() +
geom_point(data = df_cr %>% filter(year %in% df_years),
colour = "red", size = 0.4) +
geom_hline(yintercept = df_cr %>%
filter(year %in% df_years) %>%
summarise(mean(data)) %>% unlist(),
colour = "red") +
facet_wrap(~ "step 6", strip.position = "top") +
labs(x = "year", y = "catch/index") +
ylim(c(0, 0.4)) +
theme_bw(base_size = 8) +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.text = element_text(size = 8))
p_cr3
### combine all plots
plot_grid(p_idxL1, p_idxL2, p_idxL3,
ncol = 3, rel_widths = c(1.12, 1, 1))
ggsave(filename = "output/plots/length_procedure_combined_1.png",
width = 17, height = 6, units = "cm", dpi = 600,
type = "cairo")
ggsave(filename = "output/plots/length_procedure_combined_1.pdf",
width = 17, height = 6, units = "cm")
ggsave(filename = "output/plots/length_procedure_combined_1_.pdf",
width = 17, height = 6, units = "cm")
plot_grid(p_cr1, p_cr2, p_cr3,
ncol = 3, rel_widths = c(1.12, 1, 1))
ggsave(filename = "output/plots/length_procedure_combined_2.png",
width = 17, height = 6, units = "cm", dpi = 600,
type = "cairo")
ggsave(filename = "output/plots/length_procedure_combined_2.pdf",
width = 17, height = 6, units = "cm")
### ------------------------------------------------------------------------ ###
### HR principle visualisation ####
### ------------------------------------------------------------------------ ###
### no colours
data.frame(x = c(0, 1, 2),
y = c(0, 1, 1)) %>%
ggplot(aes(x = x, y = y)) +
geom_line() +
scale_x_continuous("Index I", expand = c(0, 0),
breaks = c(0, 1), labels = c(0, expression(I[trigger]))) +
scale_y_continuous("Harvest rate", limits = c(0, 1.2), expand = c(0, 0),
breaks = c(0, 1),
labels = c(0, expression(H[target]))) +
annotate(geom = "segment", x = 1, xend = 1, y = 0, yend = 1,
linetype = "dotted") +
annotate(geom = "segment", x = 0, xend = 1, y = 1, yend = 1,
linetype = "dotted") +
theme_classic()
ggsave(filename = "output/plots/paper/HR_principle_bw.png",
width = 8.5, height = 5, units = "cm", dpi = 600,
type = "cairo")
ggsave(filename = "output/plots/paper/HR_principle_bw.pdf",
width = 8.5, height = 5, units = "cm")
### ------------------------------------------------------------------------ ###
### HR principle: plot catch and index selectivity ####
### ------------------------------------------------------------------------ ###
### extract fishery selectivity and maturity for all stocks
df_sel <- foreach(stock = stocks$stock, .combine = bind_rows) %do% {
#browser()
brp_i <- brps[[stock]]
df_i <- as.data.frame(FLQuants(maturity = brp_i@mat,
selectivity = [email protected]/
max([email protected]))) %>%
select(age, data, qname) %>%
mutate(stock = stock)
return(df_i)
}
df_sel <- df_sel %>%
full_join(stocks %>%
select(stock, k) %>%
mutate(ID = 1:29)) %>%
mutate(stock_id = paste0(stock, "~(italic(k)==", sprintf(k, fmt = "%.2f"),
")"))
df_sel <- df_sel %>%
mutate(stock_id = factor(stock_id, levels = unique(df_sel$stock_id))) %>%
mutate(qname = factor(qname, levels = c("maturity", "selectivity"))) %>%
rename(value = data)
df_sel %>%
ggplot(aes(x = age, y = value, colour = qname)) +
geom_line(size = 0.3) +
facet_wrap(~ stock_id, scales = "free_x", labeller = "label_parsed",
ncol = 5) +
labs(x = "Age [years]", y = "Maturity | Selectivity") +
scale_x_continuous(breaks = pretty_breaks(n = 3), limits = c(0, NA)) +
scale_colour_discrete("") +
theme_bw(base_size = 8) +
theme(legend.position = c(0.9, 0.075),
legend.key.height = unit(0.5, "lines"),
legend.key.width = unit(0.5, "lines"))
ggsave(filename = "output/plots/paper/HR_OMs_sel_mat.png",
width = 16, height = 16, units = "cm", dpi = 600,
type = "cairo")
ggsave(filename = "output/plots/paper/HR_OMs_sel_mat.pdf",
width = 16, height = 16, units = "cm")
### ------------------------------------------------------------------------ ###
### sensitivity to simulation assumptions ####
### ------------------------------------------------------------------------ ###
### use pollack as example
### default: 50 yrs, 500 iterations
### some generic parameters
brp <- readRDS("input/brps.rds")[["pol"]]
Blim <- brp@Blim
Bmsy <- c(refpts(brp)["msy", "ssb"])
MSY <- c(refpts(brp)["msy", "yield"])
### stats over time
stats_sens_time <- foreach(fhist = c("random", "one-way", "roller-coaster"),
.combine = rbind) %do% {
#browser()
file <- ifelse(fhist %in% c("random", "one-way"),
"mp_length_1_TRUE_1_1_1_Inf_0",
"mp_length_1_TRUE_1_1_1_Inf_0_0.2_0.2_0_0_0.6_0_0.75")
res <- readRDS(paste0("output/hr/500_100/sensitivity/", fhist,
"/pol/", file, ".rds"))
### collapse correction
res_corrected <- collapse_correction(stk = res@stock, yrs = 101:200)
### template
tmp <- data.frame(year = 1:100)
### Blim risk
tmp$risk_average <- sapply(1:100, function(x) {
mean(c(res_corrected$ssb[, ac(seq(from = 101, length.out = x))] < Blim),
na.rm = TRUE)
})
tmp$risk_annual <- c(apply(res_corrected$ssb < Blim, 2, mean, na.rm = TRUE))
### SSB
tmp$SSB_annual <- sapply(1:100, function(x) {
median(c(res_corrected$ssb[, x]/Bmsy), na.rm = TRUE)
})
tmp$SSB_average <- sapply(1:100, function(x) {
median(c(res_corrected$ssb[, ac(seq(from = 101, length.out = x))]/Bmsy),
na.rm = TRUE)
})
### Catch
tmp$Catch_annual <- sapply(1:100, function(x) {
median(c(res_corrected$catch[, x]/MSY), na.rm = TRUE)
})
tmp$Catch_average <- sapply(1:100, function(x) {
median(c(res_corrected$catch[, ac(seq(from = 101, length.out = x))]/MSY),
na.rm = TRUE)
})
tmp <- tmp %>%
pivot_longer(2:7, names_to = c(".value", "period"), names_sep = "_")
### full data.frame
df_i <- data.frame(
stock = "pol", multiplier = 1, comp_b = TRUE, idxB_lag = 1,
idxB_range_3 = 1, interval = 1, upper_constraint = Inf,
lower_constraint = 0, sigmaL = 0.2, sigmaB = 0.2, sigmaR = 0.6,
sigmaR_rho = 0,
risk_Blim = tmp$risk,
SSB_rel = tmp$SSB,
Catch_rel = tmp$Catch,
stat_metric = tmp$period,
fhist = fhist,
n_yrs = tmp$year,
n_iter = 10000,
steepness = 0.75,
sensitivity = "period") %>%
arrange(stat_metric, n_yrs)
return(df_i)
}
### stock status
stats_sens_status <- foreach(fhist = c("random", "one-way"),
.combine = rbind) %do% {
#browser()
res <- readRDS(paste0("output/hr/10000_50/sensitivity/", fhist,
"/pol/mp_length_1_TRUE_1_1_1_Inf_0.rds"))
### collapse correction
res_corrected <- collapse_correction(stk = res@stock, yrs = 101:150)
### starting condition
SSBs0 <- ssb(res@stock)[, ac(100)]
SSBs0 <- SSBs0/Bmsy
SSBs0 <- c(SSBs0)
SSB_breaks <- seq(from = 0, to = max(SSBs0), by = 0.1)
SSB_groups <- cut(SSBs0, breaks = SSB_breaks)
SSB_levels <- unique(as.character(SSB_groups))
### number of replicates per group
group_n <- sapply(SSB_levels, function(x) {
length(which(SSB_groups %in% x))
})
# group_n[sort(names(group_n))]
### Blim risk per group
### SSB is on absolute scale
risk_group <- sapply(SSB_levels, function(x) {
tmp <- res_corrected$ssb[,,,,, which(SSB_groups %in% x)]
mean(tmp < (Blim))
})
### SSB (long-term median) per group
SSB_group <- sapply(SSB_levels, function(x) {
tmp <- res_corrected$ssb[,,,,, which(SSB_groups %in% x)]/Bmsy
median(tmp)
})
### Catch (long-term median) per group
Catch_group <- sapply(SSB_levels, function(x) {
tmp <- res_corrected$catch[,,,,, which(SSB_groups %in% x)]/MSY
median(tmp)
})
### get starting conditions
SSB_levels <- sapply(SSB_levels, function(x) {
x <- gsub(x = x, pattern = "\\(|\\]", replacement = "")
x <- unlist(strsplit(x, split = ","))
mean(as.numeric(x))
})
pos_remove <- which(is.na(SSB_levels))
df_i <- data.frame(
stock = "pol", multiplier = 1, comp_b = TRUE, idxB_lag = 1,
idxB_range_3 = 1, interval = 1, upper_constraint = Inf,
lower_constraint = 0, sigmaL = 0.2, sigmaB = 0.2, sigmaR = 0.6,
sigmaR_rho = 0,
risk_Blim = unlist(risk_group)[-pos_remove],
SSB_rel = unlist(SSB_group)[-pos_remove],
Catch_rel = unlist(Catch_group)[-pos_remove],
SSB0_rel = unlist(SSB_levels)[-pos_remove],
n_iter_part = unlist(group_n)[-pos_remove],
fhist = fhist,
n_yrs = 50,
n_iter = 10000,
steepness = 0.75,
sensitivity = "stock_status")
row.names(df_i) <- NULL
df_i <- df_i[order(df_i$SSB0_rel), ]
return(df_i)
}
### recruitment & observations
stats_sens_more <- foreach(sensitivity = c("rec_var", "rec_rho",
"rec_steepness",
#"obs_idx_sd", "obs_lngth_sd",
"obs_idx_lngth_sd",
"obs_idx_lngth_rho"),
.combine = bind_rows) %:%
foreach(fhist = c("one-way", "roller-coaster", "random"),
.combine = bind_rows) %do% {
#browser()
if (fhist %in% c("one-way", "random")) {
file_name <- switch(sensitivity,
"rec_var" = "1_1_1_1_1_Inf_0_0.2_0.2_0-1_0.rds",
"rec_rho" = "1_1_1_1_1_Inf_0_0.2_0.2_0.6_0-1_0.75.rds",
"rec_steepness" = "1_1_1_1_1_Inf_0_0.2_0.2_0.6_0_0-1.rds",
"obs_idx_sd" = "1_1_1_1_1_Inf_0_0.2_0-1_0.6_0.rds",
"obs_lngth_sd" = "1_1_1_1_1_Inf_0_0-1_0.2_0.6_0.rds",
"obs_idx_lngth_sd" = "1_1_1_1_1_Inf_0_0-1_0-1_0.6_0.rds",
"obs_idx_lngth_rho" = "1_1_1_1_1_Inf_0_0.2_0.2_0-1_0-1_0.6_0_0.75.rds"
)
} else {
file_name <- switch(sensitivity,
"rec_var" = "1_1_1_1_1_Inf_0_0.2_0.2_0_0_0-1_0_0.75.rds",
"rec_rho" = "1_1_1_1_1_Inf_0_0.2_0.2_0_0_0.6_0-1_0.75.rds",
"rec_steepness" = "1_1_1_1_1_Inf_0_0.2_0.2_0_0_0.6_0_0-1.rds",
"obs_idx_sd" = "1_1_1_1_1_Inf_0_0.2_0-1_0_0_0.6_0_0.75.rds",
"obs_lngth_sd" = "1_1_1_1_1_Inf_0_0-1_0.2_0_0_0.6_0_0.75.rds",
"obs_idx_lngth_sd" = "1_1_1_1_1_Inf_0_0-1_0-1_0_0_0.6_0_0.75.rds",
"obs_idx_lngth_rho" = "1_1_1_1_1_Inf_0_0.2_0.2_0-1_0-1_0.6_0_0.75.rds"
)
}
file_name <- paste0("collated_stats_length_", file_name)
tmp <- readRDS(paste0("output/hr/500_50/sensitivity/", fhist, "/pol/",
file_name))
tmp <- as.data.frame(lapply(tmp, unlist))
tmp$sensitivity <- sensitivity
tmp$fhist <- fhist
tmp$n_yrs <- 50
tmp$n_iter <- 500
return(tmp)
}
### combine all sensitivity runs
stats_sens <- bind_rows(stats_sens_more, stats_sens_status, stats_sens_time)
saveRDS(stats_sens, "output/hr_pol_sensitivity.rds")
write.csv(stats_sens, "output/hr_pol_sensitivity.csv", row.names = FALSE)
stats_sens <- readRDS("output/hr_pol_sensitivity.rds")
### plot sensitivity analysis
stats_sens_plot <- stats_sens %>%
pivot_longer(c(risk_Blim, SSB_rel, Catch_rel)) %>%
mutate(name = factor(name, levels = c("SSB_rel", "Catch_rel", "risk_Blim"),
labels = c("SSB/B[MSY]", "Catch/MSY", "B[lim]~risk")),
fhist = factor(fhist, levels = c("one-way", "roller-coaster",
"random")))
df_blank <- data.frame(name = rep(c("SSB/B[MSY]", "Catch/MSY", "B[lim]~risk"),
each = 2),
x = c(0, 1, 0, 1, 0, 1),
value = c(0, 1.8, 0, 1.4, 0, 1),
fhist = NA)
res_def_colours <- c("one-way" = brewer.pal(n = 4, name = "Set1")[1],
"roller-coaster" = brewer.pal(n = 4, name = "Set1")[4],
"random" = brewer.pal(n = 4, name = "Set1")[2])
res_def_linetype <- c("one-way" = "solid",
"roller-coaster" = "1212",
"random" = "3232")
p_sens_rec <- stats_sens_plot %>%
filter(sensitivity == "rec_var") %>%
ggplot(aes(x = sigmaR, y = value, fill = fhist, colour = fhist,
linetype = fhist)) +
geom_vline(xintercept = 0.6, size = 0.4, colour = "grey") +
stat_smooth(n = 50, span = 0.2, se = FALSE, geom = "line", size = 0.4,
show.legend = FALSE) +
geom_point(size = 0.15, stroke = 0, shape = 21, show.legend = FALSE) +
geom_blank(data = df_blank, aes(x = x, y = value)) +
facet_grid(name ~ "'Recruitment\n\ \ variability'", scales = "free",
labeller = "label_parsed",
switch = "y") +
scale_linetype_manual("fishing history", values = res_def_linetype) +
scale_colour_manual("fishing history", values = res_def_colours) +
scale_fill_manual("fishing history", values = res_def_colours) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(x = expression(sigma[R])) +
theme_bw(base_size = 8) +
theme(strip.placement = "outside",
strip.text.y = element_text(size = 8),
strip.text.x = element_text(margin = margin(8, 0, 0, 0)),
strip.background.y = element_blank(),
axis.title.y = element_blank(),
strip.switch.pad.grid = unit(0, "pt"),
plot.margin = unit(c(2, 2, 4, 4), "pt"))
p_sens_rec_rho <- stats_sens_plot %>%
filter(sensitivity == "rec_rho" &
sigmaR_rho < 0.99) %>%
ggplot(aes(x = sigmaR_rho, y = value, fill = fhist, colour = fhist,
linetype = fhist)) +
geom_vline(xintercept = 0.0, size = 0.4, colour = "grey") +
stat_smooth(n = 50, span = 0.2, se = FALSE, geom = "line", size = 0.4,
show.legend = FALSE) +
geom_point(size = 0.15, stroke = 0, shape = 21, show.legend = FALSE) +
geom_blank(data = df_blank, aes(x = x, y = value)) +
facet_grid(name ~ "'\ \ \ Recruitment\nauto-correlation'", scales = "free",
labeller = "label_parsed",
switch = "y") +
scale_linetype_manual("fishing history", values = res_def_linetype) +
scale_colour_manual("fishing history", values = res_def_colours) +
scale_fill_manual("fishing history", values = res_def_colours) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(x = expression(italic(rho)[R])) +
theme_bw(base_size = 8) +
theme(strip.placement = "outside",
strip.text.y = element_blank(),
strip.text.x = element_text(margin = margin(8, 0, 1.2, 0)),
strip.background.y = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.switch.pad.grid = unit(0, "pt"),
plot.margin = unit(c(2, 2, 4, 0), "pt"))
p_sens_rec_steepness <- stats_sens_plot %>%
filter(sensitivity == "rec_steepness" &
steepness > 0.2) %>%
ggplot(aes(x = steepness, y = value, fill = fhist, colour = fhist,
linetype = fhist)) +
geom_vline(xintercept = 0.75, size = 0.4, colour = "grey") +
stat_smooth(n = 50, span = 0.2, se = FALSE, geom = "line", size = 0.4,
show.legend = FALSE) +
geom_point(size = 0.15, stroke = 0, shape = 21, show.legend = FALSE) +
geom_blank(data = df_blank, aes(x = x, y = value)) +
facet_grid(name ~ "'Recruitment\n\ steepness'", scales = "free",
labeller = "label_parsed",
switch = "y") +
scale_linetype_manual("fishing history", values = res_def_linetype) +
scale_colour_manual("fishing history", values = res_def_colours) +
scale_fill_manual("fishing history", values = res_def_colours) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(x = expression(italic(h))) +
theme_bw(base_size = 8) +
theme(strip.placement = "outside",
strip.text.y = element_blank(),
strip.text.x = element_text(margin = margin(8, 0, 0, 0)),
strip.background.y = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.switch.pad.grid = unit(0, "pt"),
plot.margin = unit(c(2, 2, 4, 0), "pt"))
p_sens_obs <- stats_sens_plot %>%
filter(sensitivity == "obs_idx_lngth_sd") %>%
ggplot(aes(x = sigmaB, y = value, fill = fhist, colour = fhist,
linetype = fhist)) +
geom_vline(xintercept = 0.2, size = 0.4, colour = "grey") +
stat_smooth(n = 50, span = 0.2, se = FALSE, geom = "line", size = 0.4,
show.legend = FALSE) +
geom_point(size = 0.15, stroke = 0, shape = 21, show.legend = FALSE) +
geom_blank(data = df_blank, aes(x = x, y = value)) +
facet_grid(name ~ "'Observation\n\ uncertainty'", scales = "free",
labeller = "label_parsed",
switch = "y") +
scale_linetype_manual("fishing history", values = res_def_linetype) +
scale_colour_manual("fishing history", values = res_def_colours) +
scale_fill_manual("fishing history", values = res_def_colours) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(x = expression(italic(sigma)[obs])) +
theme_bw(base_size = 8) +
theme(strip.placement = "outside",
strip.text.y = element_blank(),
strip.text.x = element_text(margin = margin(8, 0, 0, 0)),
strip.background.y = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.switch.pad.grid = unit(0, "pt"),
plot.margin = unit(c(2, 2, 4, 0), "pt"))
p_sens_obs_rho <- stats_sens_plot %>%
filter(sensitivity == "obs_idx_lngth_rho" & sigmaB_rho < 1) %>%
ggplot(aes(x = sigmaB_rho, y = value, fill = fhist, colour = fhist,
linetype = fhist)) +
geom_vline(xintercept = 0.0, size = 0.4, colour = "grey") +
stat_smooth(n = 50, span = 0.2, se = FALSE, geom = "line", size = 0.4,
show.legend = FALSE) +
geom_point(size = 0.15, stroke = 0, shape = 21, show.legend = FALSE) +
geom_blank(data = df_blank, aes(x = x, y = value)) +
facet_grid(name ~ "'\ \ \ Observation\nauto-correlation'", scales = "free",
labeller = "label_parsed",
switch = "y") +
scale_linetype_manual("fishing history", values = res_def_linetype) +
scale_colour_manual("fishing history", values = res_def_colours) +
scale_fill_manual("fishing history", values = res_def_colours) +
scale_x_continuous(breaks = c(0, 0.5, 1)) +
labs(x = expression(italic(rho)[obs])) +
theme_bw(base_size = 8) +
theme(strip.placement = "outside",
strip.text.y = element_blank(),
strip.text.x = element_text(margin = margin(8, 0, 1, 0)),
strip.background.y = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
strip.switch.pad.grid = unit(0, "pt"),
plot.margin = unit(c(2, 2, 4, 0), "pt"))
p_sens_status <- stats_sens_plot %>%
filter(sensitivity == "stock_status" &
SSB0_rel <= 2) %>%
mutate(value = ifelse(fhist == "one-way", NA, value)) %>%
ggplot(aes(x = SSB0_rel, y = value, fill = fhist, colour = fhist,