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MP_analysis.R
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MP_analysis.R
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### ------------------------------------------------------------------------ ###
### ple.27.7e - analyse MSE results ####
### ------------------------------------------------------------------------ ###
library(mse)
library(GA)
library(tidyr)
library(dplyr)
library(cowplot)
library(patchwork)
library(ggplot2)
library(foreach)
source("funs.R")
source("funs_GA.R")
source("funs_analysis.R")
### ------------------------------------------------------------------------ ###
### collate results - baseline MPs ####
### ------------------------------------------------------------------------ ###
### baseline OM runs for all MPs (including optimisations)
res <- foreach(MP = c("rfb", "2over3", "2over3_XSA", "hr", "ICES_SAM"),
.combine = bind_rows) %:%
foreach(stock = c("ple.27.7e", "cod.27.47d20", "her.27.3a47d"),
.combine = bind_rows) %:%
foreach(OM = c("baseline"), .combine = bind_rows) %:%
foreach(optimised = c("default", "multiplier", "all"),
.combine = bind_rows) %:%
foreach(period = c("1-20", "11-20"), .combine = bind_rows) %do% {
#browser()
res_i <- data.frame(stock = stock, OM = OM, MP = MP, optimised = optimised,
period = period)
if (MP %in% c("rfb", "hr")) {
ga_path <- paste0("output/", stock, "/", OM, "/1000_20/multiplier/", MP,
"/")
if (isTRUE(MP %in% c("rfb", "hr")) &
optimised %in% c("default", "multiplier")) {
ga_prefix <- "multiplier-upper_constraint1.2-lower_constraint0.7"
} else if (identical(MP, "rfb") & identical(optimised, "all")) {
ga_prefix <- paste0("lag_idx-range_idx_1-range_idx_2-exp_r-exp_f-exp_b-",
"interval-multiplier-upper_constraint1.2-lower_",
"constraint0.7")
} else if (identical(MP, "hr") & identical(optimised, "all")) {
ga_prefix <- paste0("idxB_lag-idxB_range_3-comp_b_multiplier-interval-",
"multiplier-upper_constraint1.2-lower_constraint0.7")
}
ga_res_file <- paste0(ga_prefix, "--obj_ICES_res_", period, ".rds")
ga_runs_file <- paste0(ga_prefix, "--obj_ICES_runs.rds")
### load file
if (!file.exists(paste0(ga_path, ga_res_file))) return(NULL)
ga_res <- readRDS(paste0(ga_path, ga_res_file))
ga_solution <- as.data.frame(as.list(ga_res@solution[1, ]))
### round parameters to significance used in optimisation
if (identical(MP, "rfb")) {
ga_solution[c(1:4, 8)] <- round(ga_solution[c(1:4, 8)], 0)
ga_solution[c(5:7)] <- round(ga_solution[c(5:7)], 1)
ga_solution[c(9:11)] <- round(ga_solution[c(9:11)], 2)
} else if (identical(MP, "hr")) {
ga_solution[c(1:2, 5)] <- round(ga_solution[c(1:2, 5)], 0)
ga_solution[c(3:4)] <- round(ga_solution[c(3:4)], 1)
ga_solution[c(6:8)] <- round(ga_solution[c(6:8)], 2)
}
if (identical(optimised, "default") & identical(MP, "rfb")) {
if (stock %in% c("ple.27.7e", "cod.27.47d20")) {
ga_solution$multiplier <- 0.95
} else if (stock %in% c("her.27.3a47d")) {
ga_solution$multiplier <- 0.9
}
} else if (identical(optimised, "default") & identical(MP, "hr")) {
ga_solution$multiplier <- 1
}
### load stats
ga_runs <- readRDS(paste0(ga_path, ga_runs_file))
ga_run_i <- ga_runs[[paste0(ga_solution, collapse = "_")]]
ga_stats_i <- bind_rows(ga_run_i$stats)
res_i$generations <- ga_res@iter
res_i$fitness <- ga_res@fitnessValue
if (identical(optimised, "default")) {
res_i$generations <- NA
res_i$fitness <- NA
}
### combine definition and stats
res_i <- bind_cols(res_i, ga_solution, ga_stats_i)
### check if MP results exist
path_i <- paste0("output/", stock, "/", OM, "/1000_20/", MP, "/")
file_i <- paste0("mp_", paste0(ga_solution, collapse = "_"), ".rds")
file_i <- paste0(path_i, file_i)
res_i$file <- ifelse(file.exists(file_i), file_i, NA)
### other MPs - only default exists
} else if (identical(optimised, "default")) {
### load stats
path_i <- paste0("output/", stock, "/", OM, "/1000_20/", MP, "/")
file_i <- paste0(path_i, "stats.rds")
if (!file.exists(file_i)) return(NULL)
stats_i <- readRDS(file_i)
stats_i <- bind_rows(stats_i)
### check if MP results exist
path_mp_i <- paste0(path_i, "mp.rds")
stats_i$file <- ifelse(file.exists(path_mp_i), path_mp_i, NA)
res_i <- bind_cols(res_i, stats_i)
### catch interval
res_i$interval <- case_when(
MP %in% c("2over3") ~ 2,
MP %in% c("2over3_XSA", "ICES_SAM") ~ 1
)
} else {
return(NULL)
}
### calculate fitness
if (isTRUE(is.na(res_i$fitness)) | is.null(res_i$fitness)) {
tmp_catch <- unlist(res_i[, paste0(gsub(period, pattern = "-",
replacement = ":"),
"_Catch_rel") ])
tmp_risk <- unlist(res_i[, paste0(gsub(period, pattern = "-",
replacement = ":"),
"_risk_Blim_max") ])
res_i$fitness <- tmp_catch - penalty(x = tmp_risk,
negative = FALSE, max = 1,
inflection = 0.06,
steepness = 1000)
}
return(res_i)
}
write.csv(res, file = "output/MPs_baseline.csv", row.names = FALSE)
saveRDS(res, file = "output/MPs_baseline.rds")
res <- readRDS("output/MPs_baseline.rds")
### ------------------------------------------------------------------------ ###
### summary table for baseline OM: optimisation results ####
### ------------------------------------------------------------------------ ###
res <- readRDS("output/MPs_baseline.rds")
res %>%
filter(period == "11-20" & MP %in% c("rfb", "hr")) %>%
select(`stock`:lower_constraint, idxB_lag, idxB_range_3, comp_b_multiplier,
-OM, -period) %>%
group_by(stock, MP) %>%
### relative growth -> used because some fitness values might be negative
mutate(fitness_improvement = ((fitness - min(fitness))/abs(min(fitness)))*100,
.after = fitness) %>%
mutate(fitness_improvement = round(fitness_improvement)) %>%
write.csv("tmp.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate results - alternative OMs ####
### ------------------------------------------------------------------------ ###
### include alternative OMs
OMs_ple <- c("baseline", "M_low", "M_high", "M_Gislason",
"no_discards", "rec_no_AC", "rec_failure")
OMs_cod <- c("baseline", "rec_higher", "M_dd", "M_no_migration", "rec_failure")
OMs_her <- c("baseline", "rec_higher", "rec_failure", "M_high", "M_low")
res_alt <- res %>%
filter(period == "11-20") %>%
select(stock:period, fitness, lag_idx:lower_constraint, file) %>%
mutate(id = seq(n()))
res_alt <- foreach(OM = unique(c(OMs_ple, OMs_cod, OMs_her)),
.combine = bind_rows) %:%
foreach(i = split(res_alt, f = seq(nrow(res_alt))),
.combine = bind_rows) %do% {
#browser()
i$OM <- OM
if (identical(OM, "baseline")) return(i)
if ((identical(i$stock, "ple.27.7e") & !OM %in% OMs_ple) |
(identical(i$stock, "cod.27.47d20") & !OM %in% OMs_cod) |
(identical(i$stock, "her.27.3a47d") & !OM %in% OMs_her))
return(NULL)
file_i <- i$file
if (identical(OM, "rec_failure")) {
file_i <- gsub(x = file_i, pattern = "baseline/1000_20",
replacement = "baseline/1000_20/rec_failure")
} else {
file_i <- gsub(x = file_i, pattern = "baseline", replacement = OM)
}
i$file <- ifelse(file.exists(file_i), file_i, NA)
i$fitness <- NA
i$OM_group = case_when(
OM == "baseline" ~ "baseline",
OM %in% c("M_low", "M_high", "M_Gislason", "M_dd",
"M_no_migration") ~ "M",
OM %in% c("rec_higher", "rec_no_AC", "rec_failure") ~ "Rec",
OM %in% c("no_discards") ~ "Catch")
return(i)
}
res_alt$OM_group[res_alt$OM == "baseline"] <- "baseline"
#sum(table(res_alt$OM_group))
#nrow(res_alt)
#table(res_alt$id)
#any(is.na(res_alt$file))
#View(res_alt)
saveRDS(res_alt, file = "output/MPs_alternative_OMs.rds")
# res_alt <- readRDS("output/MPs_alternative_OMs.rds")
write.csv(res_alt, file = "output/MPs_alternative_OMs.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate results - alternative OMs - stats ####
### ------------------------------------------------------------------------ ###
res_alt <- readRDS("output/MPs_alternative_OMs.rds")
stats_alt <- foreach(i = split(res_alt, f = seq(nrow(res_alt))),
.combine = bind_rows) %do% {#browser()
### get projections and reference points
mp_i <- readRDS(i$file)
path_OM <- paste0("input/", i$stock, "/",
ifelse(identical(i$OM, "rec_failure"), "baseline", i$OM),
"/1000_100/")
refpts_i <- iterMedians(readRDS(paste0(path_OM, "refpts_mse.rds")))
### find OM stock - slot depends on version of mse package
. <- try(stk <- mp_i@om@stock, silent = TRUE)
if (is(., "try-error")) stk <- mp_i@stock
### extract metrics
stk_icv <- window(stk, start = 2030, end = 2040)
stk <- window(stk, start = 2031, end = 2040)
ssb_i <- c(ssb(stk)/refpts_i["Bmsy"])
ssb20_i <- c(ssb(stk)[, ac(2040)]/refpts_i["Bmsy"])
catch_i <- c(catch(stk)/refpts_i["Cmsy"])
fbar_i <- c(fbar(stk)/refpts_i["Fmsy"])
risk_i <- c(apply(ssb(stk) < c(refpts_i["Blim"]), 2, mean))
icv_i <- c(iav(catch(stk_icv), period = i$interval))
if (identical(i$OM, "no_discards"))
catch_i <- c(landings(stk)/refpts_i["Cmsy"])
### combine
df <- do.call(rbind, list(data.frame(val = ssb_i, metric = "SSB"),
data.frame(val = ssb20_i, metric = "SSB20"),
data.frame(val = catch_i, metric = "catch"),
data.frame(val = fbar_i, metric = "Fbar"),
data.frame(val = icv_i, metric = "ICV"),
data.frame(val = risk_i, metric = "risk")
))
return(bind_cols(i, df))
}
stats_alt <- stats_alt %>%
mutate(
OM_label = factor(OM,
levels = c("baseline", "M_low", "M_high", "M_Gislason",
"M_dd", "M_no_migration", "no_discards",
"rec_higher", "rec_no_AC", "rec_failure"),
labels = c("baseline", "low", "high", "Gislason",
"dens. dep.", "no migration", "no discards",
"higher", "no AC", "failure")), .after = "OM") %>%
mutate(OM_group = factor(OM_group, c("baseline", "M", "Catch", "Rec"))) %>%
mutate(
MP_label = factor(paste0(MP, "_", optimised),
levels = c("2over3_default", "2over3_XSA_default",
"rfb_default", "rfb_multiplier", "rfb_all",
"hr_default", "hr_multiplier", "hr_all",
"ICES_SAM_default"),
labels = c("2 over 3", "2 over 3 (XSA)",
"rfb (generic)", "rfb (multiplier)", "rfb (all)",
"hr (generic)", "hr (multiplier)", "hr (all)",
"ICES MSY")), .after = "MP") %>%
mutate(stock_label = factor(stock,
levels = c("ple.27.7e", "cod.27.47d20",
"her.27.3a47d"),
labels = c("Plaice", "Cod", "Herring")))
saveRDS(stats_alt, file = "output/MPs_alternative_OMs_stats.rds")
# stats_alt <- readRDS("output/MPs_alternative_OMs_stats.rds")
### ------------------------------------------------------------------------ ###
### wormplots for all MPs/OMs ####
### ------------------------------------------------------------------------ ###
res_alt <- readRDS("output/MPs_alternative_OMs.rds")
### go through all runs
for (i in split(res_alt, f = seq(nrow(res_alt)))) {#browser()
### find OM stock - slot depends on version of mse package
res_mp <- readRDS(i$file)
. <- try(stk_res <- res_mp@om@stock, silent = TRUE)
if (is(., "try-error")) stk_res <- res_mp@stock
path_OM <- paste0("input/", i$stock, "/",
ifelse(identical(i$OM, "rec_failure"), "baseline", i$OM),
"/1000_100/")
stk_hist <- readRDS(paste0(path_OM, "stk.rds"))
refpts <- readRDS(paste0(path_OM, "refpts_mse.rds"))
p <- plot_worm(stk = stk_res, stk_hist = stk_hist, refpts = refpts)
ggsave(filename = paste0("output/plots/wormplots/", i$stock, "_", i$OM, "_",
i$MP, "_", i$optimised, ".png"), plot = p,
width = 17, height = 8, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = paste0("output/plots/wormplots/", i$stock, "_", i$OM, "_",
i$MP, "_", i$optimised, ".pdf"), plot = p,
width = 17, height = 8, units = "cm")
}
### ------------------------------------------------------------------------ ###
### violin plots - all stocks/OMs/MPs - grouped by OM ####
### ------------------------------------------------------------------------ ###
stats_alt <- readRDS("output/MPs_alternative_OMs_stats.rds")
col_vals <- c("2 over 3" = "#A0A0A0",#"#9e9ac8",
"2 over 3 (XSA)" = "#606060",#"#6a51a3",
"rfb (generic)" = "#bdd7e7",
"rfb (multiplier)" = "#6baed6",
"rfb (all)" = "#2171b5",
"hr (generic)" = "#fcae91",
"hr (multiplier)" = "#fb6a4a",
"hr (all)" = "#cb181d",
"ICES MSY" = "#ffff00")
p_ple_catch <- stats_alt %>%
filter(stock == "ple.27.7e" &
metric == "catch") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_ple_SSB <- stats_alt %>%
filter(stock == "ple.27.7e" &
metric == "SSB") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 3.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_ple_risk <- stats_alt %>%
filter(stock == "ple.27.7e" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt %>%
filter(stock == "ple.27.7e" & metric == "risk") %>%
group_by(MP_label, OM, OM_label, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
scale_fill_manual("", values = col_vals) +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
labs(y = expression(max.~B[lim]~risk)) +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = c(0.25, 0.75),
legend.key = element_blank(),
legend.key.height = unit(0.4, "lines"),
legend.key.width = unit(0.3, "lines"),
legend.background = element_blank())
p_cod_catch <- stats_alt %>%
filter(stock == "cod.27.47d20" &
metric == "catch") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_cod_SSB <- stats_alt %>%
filter(stock == "cod.27.47d20" &
metric == "SSB") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 6)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_cod_risk <- stats_alt %>%
filter(stock == "cod.27.47d20" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt %>%
filter(stock == "cod.27.47d20" & metric == "risk") %>%
group_by(MP_label, OM, OM_label, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(max.~B[lim]~risk)) +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.key = element_blank(),
legend.key.height = unit(0.4, "lines"),
legend.key.width = unit(0.3, "lines"),
legend.background = element_blank())
p_her_catch <- stats_alt %>%
filter(stock == "her.27.3a47d" &
metric == "catch") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_her_SSB <- stats_alt %>%
filter(stock == "her.27.3a47d" &
metric == "SSB") %>%
ggplot(aes(x = OM_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 6)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_her_risk <- stats_alt %>%
filter(stock == "her.27.3a47d" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt %>%
filter(stock == "her.27.3a47d" & metric == "risk") %>%
group_by(MP_label, OM, OM_label, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ OM_group, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(max.~B[lim]~risk)) +
ylim(c(0, 1)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none")
### combine catch, SSB & risk plots for each stock
p_ple <- plot_grid(p_ple_catch, p_ple_SSB,
p_ple_risk + theme(legend.position = "none"),
ncol = 1, align = "v",
rel_heights = c(1.2, 1, 1.6))
p_cod <- plot_grid(p_cod_catch, p_cod_SSB,
p_cod_risk + theme(legend.position = "none"),
ncol = 1, align = "v",
rel_heights = c(1.2, 1, 1.6))
p_her <- plot_grid(p_her_catch, p_her_SSB,
p_her_risk + theme(legend.position = "none"),
NULL,
ncol = 1, align = "v",
rel_heights = c(1.2, 1, 1.4, 0.2))
p <- plot_grid(NULL,
plot_grid(p_ple, get_legend(p_cod_risk),
nrow = 1, rel_widths = c(1, 0.22)),
plot_grid(NULL, NULL, nrow = 1, rel_widths = c(1, 0.6),
labels = c("(b) Cod", "(c) Herring"),
label_size = 9, label_x = c(-0.025)),
plot_grid(p_cod, p_her, align = "vh", axis = "tb",
nrow = 1, rel_widths = c(1, 0.6)),
labels = c("(a) Plaice", "", "", ""),
label_size = 9, label_x = c(-0.025),
ncol = 1, rel_heights = c(0.07, 1, 0.07, 1), align = "v")
p
ggsave(filename = "output/plots/risk_MPs_all_OMs_all_violin.png", plot = p,
width = 17, height = 15, units = "cm", dpi = 600, type = "cairo",
bg = "white")
ggsave(filename = "output/plots/risk_MPs_all_OMs_all_violin.pdf", plot = p,
width = 17, height = 15, units = "cm",
bg = "white")
### ------------------------------------------------------------------------ ###
### violin plots - all stocks/OMs/MPs - grouped by MP ####
### ------------------------------------------------------------------------ ###
stats_alt <- readRDS("output/MPs_alternative_OMs_stats.rds")
### create OM labels
stats_alt_MP <- stats_alt %>%
mutate(OM_label_axis = paste0(OM_group, ": ", OM_label)) %>%
mutate(OM_label_axis = factor(OM_label_axis,
levels = c("baseline: baseline", "M: low", "M: high", "M: Gislason",
"M: dens. dep.", "M: no migration",
"Catch: no discards",
"Rec: no AC", "Rec: higher", "Rec: failure"),
labels = c("baseline", "M: low", "M:high", "M: Gislason",
"M: dens. dep.", "M: no migr.",
"Catch: no disc.",
"R: no AC", "R: higher", "R: failure")))
col_vals <- c("2 over 3" = "#A0A0A0",#"#9e9ac8",
"2 over 3 (XSA)" = "#606060",#"#6a51a3",
"rfb (generic)" = "#bdd7e7",
"rfb (multiplier)" = "#6baed6",
"rfb (all)" = "#2171b5",
"hr (generic)" = "#fcae91",
"hr (multiplier)" = "#fb6a4a",
"hr (all)" = "#cb181d",
"ICES MSY" = "#ffff00")
p_ple_risk <- stats_alt_MP %>%
filter(stock == "ple.27.7e" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt_MP %>%
filter(stock == "ple.27.7e" & metric == "risk") %>%
group_by(MP_label, OM, OM_label_axis, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label_axis, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label_axis, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
scale_fill_manual("", values = col_vals) +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
labs(y = expression(max.~B[lim]~risk), title = "(a) Plaice") +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text(face = "bold"),
legend.position = "none")
p_ple_catch <- stats_alt_MP %>%
filter(stock == "ple.27.7e" &
metric == "catch") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_ple_SSB <- stats_alt_MP %>%
filter(stock == "ple.27.7e" &
metric == "SSB") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 3.5)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",#c(0.25, 0.75),
legend.key = element_blank(),
legend.key.height = unit(0.4, "lines"),
legend.key.width = unit(0.3, "lines"),
legend.background = element_blank())
p_cod_risk <- stats_alt_MP %>%
filter(stock == "cod.27.47d20" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt_MP %>%
filter(stock == "cod.27.47d20" & metric == "risk") %>%
group_by(MP_label, OM, OM_label_axis, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label_axis, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label_axis, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
scale_fill_manual("", values = col_vals) +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
labs(y = expression(max.~B[lim]~risk), title = "(b) Cod") +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text(face = "bold"),
legend.position = "none")
p_cod_catch <- stats_alt_MP %>%
filter(stock == "cod.27.47d20" &
metric == "catch") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_cod_SSB <- stats_alt_MP %>%
filter(stock == "cod.27.47d20" &
metric == "SSB") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 6)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",#c(0.25, 0.75),
legend.key = element_blank(),
legend.key.height = unit(0.4, "lines"),
legend.key.width = unit(0.3, "lines"),
legend.background = element_blank())
p_her_risk <- stats_alt_MP %>%
filter(stock == "her.27.3a47d" & metric == "risk") %>%
ggplot() +
geom_hline(yintercept = 0.055, colour = "red") +
geom_col(data = stats_alt_MP %>%
filter(stock == "her.27.3a47d" & metric == "risk") %>%
group_by(MP_label, OM, OM_label_axis, OM_group) %>%
summarise(val = max(val)),
aes(x = OM_label_axis, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2,
position = position_dodge(width = 0.8)) +
geom_boxplot(aes(x = OM_label_axis, y = val, group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
scale_fill_manual("", values = col_vals) +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
labs(y = expression(max.~B[lim]~risk), title = "(c) Herring") +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text(face = "bold"),
legend.position = "none")
p_her_catch <- stats_alt_MP %>%
filter(stock == "her.27.3a47d" &
metric == "catch") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p_her_SSB <- stats_alt_MP %>%
filter(stock == "her.27.3a47d" &
metric == "SSB") %>%
ggplot(aes(x = OM_label_axis, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
position = position_dodge(width = 0.8), scale = "width") +
geom_boxplot(aes(group = interaction(OM, MP_label)),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ MP_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 6)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5),
panel.spacing.x = unit(0, "lines"),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",#c(0.25, 0.75),
legend.key = element_blank(),
legend.key.height = unit(0.4, "lines"),
legend.key.width = unit(0.3, "lines"),
legend.background = element_blank())
layout <- "AA\nBB\nCC\n#D\n#E\n#F\n#G\n#H\n#I\n"
p <- p_ple_risk + p_ple_catch + p_ple_SSB +
p_cod_risk + p_cod_catch + p_cod_SSB +
p_her_risk + p_her_catch + p_her_SSB +
plot_layout(design = layout, widths = c(0.195, 1))
p
ggsave(filename = "output/plots/risk_MPs_all_OMs_all_violin_MP.png", plot = p,
width = 18, height = 23, units = "cm", dpi = 600, type = "cairo",
bg = "white")
ggsave(filename = "output/plots/risk_MPs_all_OMs_all_violin_MP.pdf", plot = p,
width = 18, height = 23, units = "cm",
bg = "white")
### ------------------------------------------------------------------------ ###
### violin plots - baseline OM & all stocks & MPs ####
### ------------------------------------------------------------------------ ###
stats_alt <- readRDS("output/MPs_alternative_OMs_stats.rds")
stats_baseline <- readRDS("output/MPs_baseline.rds")
stats_baseline <- stats_baseline %>%
filter(period == "11-20")
col_vals <- c("2 over 3" = "#A0A0A0",#"#9e9ac8",
"2 over 3 (XSA)" = "#606060",#"#6a51a3",
"rfb (generic)" = "#bdd7e7",
"rfb (multiplier)" = "#6baed6",
"rfb (all)" = "#2171b5",
"hr (generic)" = "#fcae91",
"hr (multiplier)" = "#fb6a4a",
"hr (all)" = "#cb181d",
"ICES MSY" = "#ffff00")
p_catch <- stats_alt %>%
filter(stock %in% c("ple.27.7e", "cod.27.47d20", "her.27.3a47d") &
metric == "catch" & OM == "baseline") %>%
ggplot(aes(x = MP_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
scale = "width") +
geom_boxplot(fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ stock_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(Catch/MSY)) +
coord_cartesian(ylim = c(0, 2.5)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none",
strip.text.x = element_blank())
p_ssb <- stats_alt %>%
filter(stock %in% c("ple.27.7e", "cod.27.47d20", "her.27.3a47d") &
metric == "SSB" & OM == "baseline") %>%
ggplot(aes(x = MP_label, y = val)) +
geom_hline(yintercept = 1, colour = "grey") +
geom_violin(aes(fill = MP_label), size = 0.2, show.legend = FALSE,
scale = "width") +
geom_boxplot(fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
facet_grid(~ stock_label, scales = "free_x", space = "free_x") +
scale_fill_manual("", values = col_vals) +
labs(y = expression(SSB/B[MSY])) +
coord_cartesian(ylim = c(0, 7)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.text.x = element_blank(),
legend.position = "none")
p_risk <- stats_alt %>%
filter(stock %in% c("ple.27.7e", "cod.27.47d20", "her.27.3a47d") &
metric == "risk" & OM == "baseline") %>%
ggplot() +
geom_hline(yintercept = 0.0525, colour = "red", size = 0.75) +
geom_col(data = stats_alt %>%
filter(stock %in% c("ple.27.7e", "cod.27.47d20", "her.27.3a47d") &
metric == "risk" & OM == "baseline") %>%
group_by(stock_label, MP_label) %>%
summarise(val = max(val)),
aes(x = MP_label, y = val, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2) +
geom_boxplot(aes(x = MP_label, y = val),
position = position_dodge(width = 0.8),
fill = "white", width = 0.1, size = 0.2,
outlier.size = 0.35, outlier.shape = 21, outlier.stroke = 0.2,
outlier.fill = "transparent") +
scale_fill_manual("", values = col_vals) +
facet_grid(~ stock_label, scales = "free_x", space = "free_x") +
labs(y = expression(max.~B[lim]~risk)) +
ylim(c(0, NA)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
p_fitness <- stats_alt %>%
filter(stock %in% c("ple.27.7e", "cod.27.47d20", "her.27.3a47d") &
OM == "baseline") %>%
select(stock_label, MP_label, fitness) %>%
unique() %>%
ggplot() +
geom_hline(yintercept = 1, colour = "grey") +
geom_hline(yintercept = 0, colour = "grey") +
geom_col(aes(x = MP_label, y = fitness, fill = MP_label),
show.legend = TRUE, width = 0.8, colour = "black", size = 0.2) +
scale_fill_manual("", values = col_vals) +
facet_grid(~ stock_label, scales = "free_x", space = "free_x") +
labs(y = expression("fitness "*italic(phi))) +
scale_y_continuous(#limits = c(-0.27, 1.05),
breaks = c(-0.25, 0, 0.25, 0.5, 0.75, 1)) +
theme_bw(base_size = 8) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
strip.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none")
p <- p_risk + p_catch + p_ssb + p_fitness + plot_layout(ncol = 1)
p
ggsave(filename = "output/plots/risk_MPs_all_baseline_violin.png", plot = p,
width = 18, height = 16, units = "cm", dpi = 600, type = "cairo",
bg = "white")
ggsave(filename = "output/plots/risk_MPs_all_baseline_violin.pdf", plot = p,
width = 18, height = 16, units = "cm",
bg = "white")
### ------------------------------------------------------------------------ ###
### projections - baseline OM with all MPs and stocks ####
### ------------------------------------------------------------------------ ###
res_alt <- readRDS("output/MPs_alternative_OMs.rds")
res_lookup <- res_alt %>%
filter(OM == "baseline") %>%
bind_rows(data.frame(stock = c("ple.27.7e", "cod.27.47d20", "her.27.3a47d"),
MP = "history"))
refpts_cod <- readRDS("input/cod.27.47d20/baseline/1000_100/refpts_mse.rds")
refpts_cod <- iterMedians(refpts_cod)
refpts_ple <- readRDS("input/ple.27.7e/baseline/1000_100/refpts_mse.rds")
refpts_ple <- iterMedians(refpts_ple)
refpts_her <- readRDS("input/her.27.3a47d/baseline/1000_100/refpts_mse.rds")
refpts_her <- iterMedians(refpts_her)
proj <- foreach(i = split(res_lookup, f = seq(nrow(res_lookup))),
.combine = bind_rows) %do% {
# browser()
if (identical(i$MP, "history")) {
stk_i <- readRDS(paste0("input/", i$stock, "/baseline/1000_100/stk.rds"))
stk_i <- window(stk_i, end = 2020)
} else {
### find OM stock - slot depends on version of mse package
res_mp <- readRDS(i$file)
. <- try(stk_i <- res_mp@om@stock, silent = TRUE)
if (is(., "try-error")) stk_i <- res_mp@stock
}
### get metrics
qnts <- FLQuants(catch = catch(stk_i)/1000, rec = rec(stk_i)/1000,
ssb = ssb(stk_i)/1000, fbar = fbar(stk_i))
### percentiles
qnts_perc <- lapply(qnts, quantile, probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
na.rm = TRUE)
qnts_perc <- FLQuants(qnts_perc)
qnts_perc <- as.data.frame(qnts_perc)
qnts_perc <- qnts_perc %>% select(year, iter, data, qname) %>%
pivot_wider(names_from = iter, values_from = data)
return(bind_cols(i, qnts_perc))
}
proj <- proj %>%
mutate(
MP_label = factor(paste0(MP, "_", optimised),
levels = c("2over3_default", "2over3_XSA_default",
"rfb_default", "rfb_multiplier", "rfb_all",
"hr_default", "hr_multiplier", "hr_all",
"ICES_SAM_default"),
labels = c("2 over 3", "2 over 3 (XSA)",
"rfb (generic)", "rfb (multiplier)", "rfb (all)",
"hr (generic)", "hr (multiplier)", "hr (all)",
"ICES MSY")), .after = "MP") %>%
mutate(stock_label = factor(stock,
levels = c("ple.27.7e", "cod.27.47d20",
"her.27.3a47d"),
labels = c("Plaice", "Cod", "Herring")))
proj_distr <- foreach(i = split(res_lookup, f = seq(nrow(res_lookup))),
.combine = bind_rows) %do% {
# browser()
if (identical(i$MP, "history")) {
return(NULL)
} else {
### find OM stock - slot depends on version of mse package
res_mp <- readRDS(i$file)
. <- try(stk_i <- res_mp@om@stock, silent = TRUE)
if (is(., "try-error")) stk_i <- res_mp@stock
}
### get metrics & distribution in last year