report/mssm-tech-report/R/03-sampling-changes.R
In pbs-assess/gfsynopsis: Data Synopsis Reports for PBS Groundfish
if (!('mssm_loaded' %in% ls())) {
source(here::here('report', 'mssm-tech-report', 'R', '00-load.R'))
}
# --- Gear change ----
net_comp_df <- mssm_dat |>
filter(fishing_event_id %in% comp_trawls$fishing_event_id) |>
left_join(comp_trawls) |>
group_by(net, species_common_name) |>
summarise(mean_catch = mean(catch)) |>
ungroup() |>
mutate(net = factor(net, levels = c('NMFS', 'American'))) |>
group_by(species_common_name) |>
filter(sum(mean_catch) > 0) |>
ungroup()
net_comp_df |> filter(net == 'NMFS', mean_catch == 0)
net_comp_df |> filter(net == 'American', mean_catch == 0)
tow_plot <-
ggplot(data = net_comp_df, aes(x = net, y = mean_catch, colour = species_common_name, group = species_common_name)) +
geom_point() +
geom_line() +
scale_y_continuous(trans = 'log10', breaks = c(0.01, 0.1, 1, 10, 100), labels = scales::label_number(accuracy = 0.01), limits = c(0.001, NA)) +
scale_x_discrete() +
guides(colour = 'none') +
coord_cartesian(clip = "off", xlim = c(1, 1.7)) +
ggrepel::geom_text_repel(
data = net_comp_df %>% filter(net == 'NMFS'),
aes(label = species_common_name, x = net, y = mean_catch, colour = species_common_name),
size = 3.5, hjust = 0, segment.color = 'grey85',
nudge_x = -0.55, box.padding = 0.1, point.padding = 0.8,
direction = "y"
) +
labs(y = 'Catch (kg)', x = 'Net')
tow_plot
ggsave(filename = file.path(mssm_figs, 'net-comp.png'), width = 6.5, height = 6)
# ------------------------------------------------------------------------------
# --- Look at effect of 2003 sampling protocol on mean annual catch ------------
yearbin_catch <- mssm_dat |>
filter(year < 2003) |>
group_by(species_common_name) |>
summarise(mean_catch = mean(catch, na.rm = TRUE)) |>
mutate(yearbin_catch = ifelse(mean_catch == 0, 0, 1)) |>
distinct(species_common_name, yearbin_catch)
post_2003_spp <- filter(yearbin_catch, yearbin_catch == 0)$species_common_name
saveRDS(post_2003_spp, file.path(mssm_dir, 'data-outputs', 'post-2003-spp.rds'))
sampling_2003 <-
mssm_dat |>
group_by(species_common_name, species_science_name, species_code, year) |>
summarise(mean_catch = mean(catch, na.rm = TRUE), .groups = 'drop') |>
left_join(yearbin_catch) |>
mutate(mean_catch = ifelse((species_common_name %in% post_2003_spp & year < 2003), NA, mean_catch))
over3_spp <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
mutate(presence = ifelse(mean_catch == 0, 0, 1)) |>
group_by(species_common_name) |>
filter(sum(presence) > 3) |>
ungroup() |>
distinct(species_common_name) |> pluck('species_common_name')
plot_samp_spp <- function(dat) {
dat |>
mutate(species_common_name = stringr::str_to_title(species_common_name)) |>
mutate(species_common_name = forcats::fct_reorder(species_common_name, species_code)) |>
ggplot(data = _, aes(x = year, y = mean_catch)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray85", alpha = 0.2) +
geom_point() +
geom_line(alpha = 0.5) +
geom_vline(xintercept = 2001, colour = 'grey50') +
theme(axis.text = element_blank()) +
facet_wrap(~ species_common_name, scales = 'free_y', ncol = 3) +
labs(x = "Year", y = "Mean annual catch (kg)")
}
pre_2003_spp_plot_less451 <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
filter(!(as.numeric(species_code) >= 451)) |>
filter(species_common_name %in% over3_spp) |>
plot_samp_spp()
pre_2003_spp_plot_over451 <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
filter((as.numeric(species_code) >= 451)) |>
filter(species_common_name %in% over3_spp) |>
plot_samp_spp()
pre_2003_spp_plot <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
plot_samp_spp()
ggsave(file.path(mssm_figs, 'sampling-2003.png'), plot = pre_2003_spp_plot,
width = 9, height = 12)
# Comparison of pcod, pollock, tomcod and possible misidentification
cod_comparison <-
mssm_dat |>
filter(species_common_name %in% c('pacific cod', 'walleye pollock', 'pacific tomcod')) |>
group_by(species_common_name, species_code, year) |>
summarise(mean_catch = mean(catch, na.rm = TRUE), .groups = 'drop') |>
left_join(yearbin_catch) |>
mutate(species_common_name = stringr::str_to_title(species_common_name)) |>
mutate(species_common_name = forcats::fct_reorder(species_common_name, species_code)) |>
ggplot(data = _, aes(x = year, y = mean_catch)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf), alpha = 0.1, fill = "grey90", colour = NA) +
geom_point() +
geom_line() +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
guides(colour = "none") +
#facet_wrap(~ species_common_name, scales = 'free_y', nrow = 3) +
facet_wrap(~ species_common_name, nrow = 3) +
labs(x = "Year", y = "Mean annual catch (kg)")
cod_comparison
ggsave(file.path(mssm_figs, 'sampling-cod.png'), plot = cod_comparison,
width = 5.5, height = 5.5)
# ---- Fish aggregates ---------------------
# How I got to more-mssm-spp.rds:
#fish <- gfdata::run_sql('GFBioSQL', query = "SELECT * FROM SPECIES")
# saveRDS(fish, file.path(mssm_appendix, 'gfbio_fish_list.rds'))
# fish <- readRDS(file.path(mssm_appendix, 'gfbio_fish_list.rds') |> as_tibble()
# # Look at higher order species classifications and choose species aggregations
# # to look for in the MSSM data
# distinct(fish, SPECIES_DESC, .keep_all = TRUE) |>
# filter(TAXONOMIC_RANK != 'Species') |>
# arrange(SPECIES_DESC) |>
# select(SPECIES_CODE, SPECIES_DESC, SPECIES_COMMON_NAME, SPECIES_SCIENCE_NAME)
# spp_string <- textConnection("01P\tSANDDABS
# 042\tDOGFISH SHARKS
# 051\tSKATES
# 060\tCARTILAGINOUS FISH (SHARKS, SKATES, RAYS, RATFISH)
# 065\tRATFISHES
# 221\tCODFISHES
# 227\tCODS/HAKES/GRENADIERS
# 231\tEELPOUTS
# 234\tEELPOUT
# 388\tSCORPIONFISHES
# 389\tROCKFISHES
# 402\tROCKFISHES
# 465\tLINGCOD
# 472\tSCULPINS
# 477\tSCULPINS/POACHERS/SCORPIONFISH
# 595\tLEFTEYE FLOUNDERS
# 597\tFLATFISHES
# 599\tRIGHTEYE FLOUNDERS
# 618\tPLEURONECTES"
# )
# more_spp_codes <- as_tibble(read.table(spp_string, col.names = c("species_code", "species_common_name"), header = FALSE, sep = "\t"))
# GFBIO query to get additional species
# more_spp <- get_survey_sets2(species = more_spp_codes$species_code, ssid = 7)
# saveRDS(more_spp, file.path(mssm_data, 'higher-taxonomic-mssm-spp.rds')
more_spp <- readRDS(file.path(mssm_data, 'higher-taxonomic-mssm-spp.rds')) |>
filter(grouping_desc %in% c('WCVI Shrimp Survey Area 124', 'WCVI Shrimp Survey Area 125'))
# Check non-zero catches
more_spp %>% filter(catch_weight > 0 | catch_count > 0) |>
#filter(species_common_name == 'cartilaginous fish (sharks, skates, rays, ratfish)') |>
count(species_common_name, year) |>
arrange(species_common_name, year) |>
distinct(species_common_name)
more_spp %>% filter(catch_weight == 0 & catch_count == 0) |>
#filter(species_common_name == 'cartilaginous fish (sharks, skates, rays, ratfish)') |>
count(species_common_name, year) |>
arrange(species_common_name, year) |>
distinct(species_common_name)
more_spp_summ <- more_spp |>
group_by(species_common_name, species_science_name, year) |>
summarise(mean_catch = mean(catch_weight, na.rm = TRUE), .groups = 'drop') |>
mutate(parent_taxonomic_unit = species_science_name, parent_taxonomic_unit)
spp_group_df <- mssm_dat |>
group_by(species_common_name, species_science_name, year) |>
summarise(mean_catch = mean(catch_weight, na.rm = TRUE), .groups = 'drop') |>
left_join(gfsynopsis::get_spp_names()) |>
mutate(parent_taxonomic_unit = ifelse(species_common_name == "pacific halibut", "pleuronectidae(righteye flounders)", parent_taxonomic_unit)) |>
mutate(parent_taxonomic_unit = ifelse(species_common_name == "pacific hake", "merlucciidae", parent_taxonomic_unit)) |>
mutate(parent_taxonomic_unit = ifelse(is.na(parent_taxonomic_unit), species_science_name, parent_taxonomic_unit))
# Baseplot
agg_plot <- function(df, ncol = 1, scales = 'free_y') {
ggplot(data = df) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray85", alpha = 0.2) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point(aes(x = year, y = mean_catch)) +
labs(x = "Year", y = "Mean annual catch (kg)") +
facet_wrap(~ species_common_name, scales = scales, ncol = ncol)
}
# All higher order species aggregations f families caught in MSSM
spp_group_plot <-
more_spp_summ |>
filter(species_common_name %in% c('eelpouts', 'flatfishes', 'rockfishes', 'sculpins', 'skates')) |>
mutate(species_common_name = str_to_title(species_common_name)) |>
agg_plot(ncol = 3)
spp_group_plot
ggsave(spp_group_plot, filename = file.path(mssm_figs, 'aggregated-spp-plot.png'), width = 8, height = 4)
#
plot_combined_spp <- function(family, common_name) {
df1 <- spp_group_df |>
filter(str_detect(parent_taxonomic_unit, family)) |>
group_by(year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'species combined')
df2 <- more_spp_summ |>
filter(str_detect(parent_taxonomic_unit, family)) |>
group_by(year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = common_name)
dat <- bind_rows(df1, df2)
ggplot(data = dat) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray90", alpha = 0.1) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point(aes(x = year, y = mean_catch, colour = species_common_name)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Mean annual catch (kg)")
}
add_group_common_name <- function(df) {
df |>
mutate(group_common_name = case_when(
str_detect(parent_taxonomic_unit, "cottidae") ~ 'sculpins',
str_detect(parent_taxonomic_unit, "pleuronect|paralich") ~ 'flatfishes',
str_detect(parent_taxonomic_unit, "rajidae") ~ 'skates',
str_detect(parent_taxonomic_unit, "sebastes") ~ 'rockfishes',
str_detect(parent_taxonomic_unit, "zoarcidae") ~ 'eelpouts'))
}
plot_combined_spp('cottidae', "'sculpins'")
plot_combined_spp('pleuronect|paralich', "'flatfishes'")
plot_combined_spp('rajidae', "'skates'")
plot_combined_spp('sebastes', "'rockfishes'")
plot_combined_spp('zoarcidae', "'eelpouts'")
df1 <- spp_group_df |>
filter(str_detect(parent_taxonomic_unit, c("cottidae|pleuronect|paralich|rajidae|sebastes|zoarcidae"))) |>
add_group_common_name() |>
group_by(group_common_name, year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'Species')
df2 <- more_spp_summ |>
filter(str_detect(parent_taxonomic_unit, c("cottidae|pleuronect|paralich|rajidae|sebastes|zoarcidae"))) |>
add_group_common_name() |>
group_by(group_common_name, year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'Order/Family')
dat <- bind_rows(df1, df2) |>
mutate(group_common_name = str_to_title(group_common_name))
spp_group_plot2 <-
ggplot(data = dat, aes(x = year, y = mean_catch, colour = species_common_name, shape = species_common_name)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray90", colour = NA, alpha = 0.1) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point() +
geom_line(linewidth = 0.3) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Mean annual catch (kg)", shape = "Identification level", colour = 'Identification level') +
facet_wrap(~ group_common_name, scales = "free_y", ncol = 2) +
theme(legend.position = c(0.7, 0.15))
spp_group_plot2
ggsave(filename = file.path(mssm_figs, 'aggregated-id-level-plot.png'), width = 7.7, height = 5.2)
# --- Mystery of the eelpout spike in 2002 ---
select_simple <- function(df) {
select(df, species_code, fishing_event_id, year, month, day, time_deployed, latitude, longitude,
depth_m, catch_weight, catch_count, species_common_name)
}
# No answer to why
eelpouts_group <- more_spp |> filter(species_common_name == 'eelpouts') |>
select_simple() |>
filter(year < 2023) |>
mutate(id_level = 'Family')
eelpouts_spp <- mssm_dat |>
left_join(gfsynopsis::get_spp_names()) |>
filter(str_detect(parent_taxonomic_unit, 'zoarcidae')) |>
group_by(year, month, day, time_deployed, latitude, longitude, depth_m) |>
summarise(catch_weight = sum(catch_weight)) |>
mutate(id_level = 'Species')
extreme_catches <- eelpouts_group |>
arrange(-catch_weight) |>
slice(1:5)
#View(extreme_catches)
bind_rows(eelpouts_group, eelpouts_spp) |>
ggplot(aes(x = year, y = catch_weight)) +
geom_point(aes(colour = id_level)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Catch (kg)", colour = 'Identification level') +
ggrepel::geom_text_repel(
data = extreme_catches,
aes(x = year, y = catch_weight, label = fishing_event_id),
size = 3.5, segment.color = 'grey85',
nudge_x = -1.5, box.padding = 0.2,
direction = "y", hjust = 1
) +
theme(legend.position = c(0.15, 0.83))
# --------
# Skates 2002
# - Does not look like there are any duplicate fishing events
skates_group <- more_spp |> filter(species_common_name == 'skates') |>
select_simple() |>
filter(year < 2023) |>
mutate(id_level = 'Family')
skates_spp <- mssm_dat |>
left_join(gfsynopsis::get_spp_names()) |>
filter(str_detect(parent_taxonomic_unit, 'rajidae')) |>
group_by(year, month, day, time_deployed, latitude, longitude, depth_m) |>
summarise(catch_weight = sum(catch_weight)) |>
mutate(id_level = 'Species')
extreme_catches <- skates_group |>
arrange(-catch_weight) |>
slice(1:5)
#View(extreme_catches)
bind_rows(skates_group, skates_spp) |>
ggplot(aes(x = year, y = catch_weight)) +
geom_point(aes(colour = id_level)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Catch (kg)", colour = 'Identification level') +
ggrepel::geom_text_repel(
data = extreme_catches,
aes(x = year, y = catch_weight, label = fishing_event_id),
size = 3.5, segment.color = 'grey85',
nudge_x = -1.5, box.padding = 0.2,
direction = "y", hjust = 1
) +
theme(legend.position = c(0.15, 0.83))
ggsave(filename = file.path(mssm_figs, 'aggregated-skates-plot.png'),
width = 7.3, height = 3.8)
# # Sculpins ---
# sculpins <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'cottidae'))
# sculpin_levels <- c('sculpins', unique(sculpins$species_common_name)[unique(sculpins$species_common_name) != 'sculpins'], 'scuplins combined')
# sculpins_all <- sculpins |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('scuplins combined', levels = sculpin_levels))
# #sculpins <- bind_rows(sculpins, sculpins_all)
# p_sculpin <- sculpins |>
# mutate(species_common_name = factor(species_common_name, levels = sculpin_levels)) |>
# agg_plot(ncol = 1, scales = 'fixed')
# p_sculpin
# # Flatfish ---
# flatfish <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'pleuronect|paralich'))
# flatfish_levels <- c('flatfishes', unique(flatfish$species_common_name)[unique(flatfish$species_common_name) != 'flatfishes'], 'flatfishes combined')
# flatfish_all <- flatfish |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('flatfishes combined', levels = flatfish_levels))
# #flatfish <- bind_rows(flatfish, flatfish_all)
# p_flatfish <- flatfish |>
# mutate(species_common_name = factor(species_common_name, levels = flatfish_levels)) |>
# agg_plot(ncol = 2)
# p_flatfish
# # Skates ---
# skates <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'rajidae'))
# skates_levels <- c('skates', unique(skates$species_common_name)[unique(skates$species_common_name) != 'skates'], 'skates combined')
# skates_all <- skates |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('skates combined', levels = skates_levels))
# #skates <- bind_rows(skates, skates_all)
# p_skate <- skates |>
# mutate(species_common_name = factor(species_common_name, levels = skates_levels)) |>
# agg_plot(ncol = 1, scales = 'fixed') +
# theme(axis.title.y = element_blank())
# # Rockfish ---
# rockfish <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'sebastes'))
# rockfish_levels <- c('rockfishes', unique(rockfish$species_common_name)[unique(rockfish$species_common_name) != 'rockfishes'], 'rockfishes combined')
# rockfish_all <- rockfish |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('rockfishes combined', levels = rockfish_levels))
# #rockfish <- bind_rows(rockfish, rockfish_all)
# p_rockfish <- rockfish |>
# mutate(species_common_name = factor(species_common_name, levels = rockfish_levels)) |>
# agg_plot(ncol = 2)
# # Eelpouts ---
# eelpouts <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'zoarcidae'))
# eelpouts_levels <- c('all eelpouts', 'eelpouts', unique(eelpouts$species_common_name)[unique(eelpouts$species_common_name) != 'eelpouts'])
# eelpouts_all <- eelpouts |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('all eelpouts', levels = eelpouts_levels))
# eelpouts <- bind_rows(eelpouts, eelpouts_all)
# p_eelpout <- eelpouts |>
# mutate(species_common_name = factor(species_common_name, levels = eelpouts_levels)) |>
# agg_plot(ncol = 1)
# design <- "
# 12
# 12
# 12
# 1#
# 1#
# "
# p_sculpin + p_skate + plot_layout(design = design)
# ggsave(filename = file.path(mssm_figs, 'agg-sculpin-skate.png'), width = 7, height = 7)
pbs-assess/gfsynopsis documentation built on March 26, 2024, 7:30 p.m.
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if (!('mssm_loaded' %in% ls())) {
source(here::here('report', 'mssm-tech-report', 'R', '00-load.R'))
}
# --- Gear change ----
net_comp_df <- mssm_dat |>
filter(fishing_event_id %in% comp_trawls$fishing_event_id) |>
left_join(comp_trawls) |>
group_by(net, species_common_name) |>
summarise(mean_catch = mean(catch)) |>
ungroup() |>
mutate(net = factor(net, levels = c('NMFS', 'American'))) |>
group_by(species_common_name) |>
filter(sum(mean_catch) > 0) |>
ungroup()
net_comp_df |> filter(net == 'NMFS', mean_catch == 0)
net_comp_df |> filter(net == 'American', mean_catch == 0)
tow_plot <-
ggplot(data = net_comp_df, aes(x = net, y = mean_catch, colour = species_common_name, group = species_common_name)) +
geom_point() +
geom_line() +
scale_y_continuous(trans = 'log10', breaks = c(0.01, 0.1, 1, 10, 100), labels = scales::label_number(accuracy = 0.01), limits = c(0.001, NA)) +
scale_x_discrete() +
guides(colour = 'none') +
coord_cartesian(clip = "off", xlim = c(1, 1.7)) +
ggrepel::geom_text_repel(
data = net_comp_df %>% filter(net == 'NMFS'),
aes(label = species_common_name, x = net, y = mean_catch, colour = species_common_name),
size = 3.5, hjust = 0, segment.color = 'grey85',
nudge_x = -0.55, box.padding = 0.1, point.padding = 0.8,
direction = "y"
) +
labs(y = 'Catch (kg)', x = 'Net')
tow_plot
ggsave(filename = file.path(mssm_figs, 'net-comp.png'), width = 6.5, height = 6)
# ------------------------------------------------------------------------------
# --- Look at effect of 2003 sampling protocol on mean annual catch ------------
yearbin_catch <- mssm_dat |>
filter(year < 2003) |>
group_by(species_common_name) |>
summarise(mean_catch = mean(catch, na.rm = TRUE)) |>
mutate(yearbin_catch = ifelse(mean_catch == 0, 0, 1)) |>
distinct(species_common_name, yearbin_catch)
post_2003_spp <- filter(yearbin_catch, yearbin_catch == 0)$species_common_name
saveRDS(post_2003_spp, file.path(mssm_dir, 'data-outputs', 'post-2003-spp.rds'))
sampling_2003 <-
mssm_dat |>
group_by(species_common_name, species_science_name, species_code, year) |>
summarise(mean_catch = mean(catch, na.rm = TRUE), .groups = 'drop') |>
left_join(yearbin_catch) |>
mutate(mean_catch = ifelse((species_common_name %in% post_2003_spp & year < 2003), NA, mean_catch))
over3_spp <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
mutate(presence = ifelse(mean_catch == 0, 0, 1)) |>
group_by(species_common_name) |>
filter(sum(presence) > 3) |>
ungroup() |>
distinct(species_common_name) |> pluck('species_common_name')
plot_samp_spp <- function(dat) {
dat |>
mutate(species_common_name = stringr::str_to_title(species_common_name)) |>
mutate(species_common_name = forcats::fct_reorder(species_common_name, species_code)) |>
ggplot(data = _, aes(x = year, y = mean_catch)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray85", alpha = 0.2) +
geom_point() +
geom_line(alpha = 0.5) +
geom_vline(xintercept = 2001, colour = 'grey50') +
theme(axis.text = element_blank()) +
facet_wrap(~ species_common_name, scales = 'free_y', ncol = 3) +
labs(x = "Year", y = "Mean annual catch (kg)")
}
pre_2003_spp_plot_less451 <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
filter(!(as.numeric(species_code) >= 451)) |>
filter(species_common_name %in% over3_spp) |>
plot_samp_spp()
pre_2003_spp_plot_over451 <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
filter((as.numeric(species_code) >= 451)) |>
filter(species_common_name %in% over3_spp) |>
plot_samp_spp()
pre_2003_spp_plot <- sampling_2003 |>
filter(!(species_common_name %in% post_2003_spp)) |>
plot_samp_spp()
ggsave(file.path(mssm_figs, 'sampling-2003.png'), plot = pre_2003_spp_plot,
width = 9, height = 12)
# Comparison of pcod, pollock, tomcod and possible misidentification
cod_comparison <-
mssm_dat |>
filter(species_common_name %in% c('pacific cod', 'walleye pollock', 'pacific tomcod')) |>
group_by(species_common_name, species_code, year) |>
summarise(mean_catch = mean(catch, na.rm = TRUE), .groups = 'drop') |>
left_join(yearbin_catch) |>
mutate(species_common_name = stringr::str_to_title(species_common_name)) |>
mutate(species_common_name = forcats::fct_reorder(species_common_name, species_code)) |>
ggplot(data = _, aes(x = year, y = mean_catch)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf), alpha = 0.1, fill = "grey90", colour = NA) +
geom_point() +
geom_line() +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
guides(colour = "none") +
#facet_wrap(~ species_common_name, scales = 'free_y', nrow = 3) +
facet_wrap(~ species_common_name, nrow = 3) +
labs(x = "Year", y = "Mean annual catch (kg)")
cod_comparison
ggsave(file.path(mssm_figs, 'sampling-cod.png'), plot = cod_comparison,
width = 5.5, height = 5.5)
# ---- Fish aggregates ---------------------
# How I got to more-mssm-spp.rds:
#fish <- gfdata::run_sql('GFBioSQL', query = "SELECT * FROM SPECIES")
# saveRDS(fish, file.path(mssm_appendix, 'gfbio_fish_list.rds'))
# fish <- readRDS(file.path(mssm_appendix, 'gfbio_fish_list.rds') |> as_tibble()
# # Look at higher order species classifications and choose species aggregations
# # to look for in the MSSM data
# distinct(fish, SPECIES_DESC, .keep_all = TRUE) |>
# filter(TAXONOMIC_RANK != 'Species') |>
# arrange(SPECIES_DESC) |>
# select(SPECIES_CODE, SPECIES_DESC, SPECIES_COMMON_NAME, SPECIES_SCIENCE_NAME)
# spp_string <- textConnection("01P\tSANDDABS
# 042\tDOGFISH SHARKS
# 051\tSKATES
# 060\tCARTILAGINOUS FISH (SHARKS, SKATES, RAYS, RATFISH)
# 065\tRATFISHES
# 221\tCODFISHES
# 227\tCODS/HAKES/GRENADIERS
# 231\tEELPOUTS
# 234\tEELPOUT
# 388\tSCORPIONFISHES
# 389\tROCKFISHES
# 402\tROCKFISHES
# 465\tLINGCOD
# 472\tSCULPINS
# 477\tSCULPINS/POACHERS/SCORPIONFISH
# 595\tLEFTEYE FLOUNDERS
# 597\tFLATFISHES
# 599\tRIGHTEYE FLOUNDERS
# 618\tPLEURONECTES"
# )
# more_spp_codes <- as_tibble(read.table(spp_string, col.names = c("species_code", "species_common_name"), header = FALSE, sep = "\t"))
# GFBIO query to get additional species
# more_spp <- get_survey_sets2(species = more_spp_codes$species_code, ssid = 7)
# saveRDS(more_spp, file.path(mssm_data, 'higher-taxonomic-mssm-spp.rds')
more_spp <- readRDS(file.path(mssm_data, 'higher-taxonomic-mssm-spp.rds')) |>
filter(grouping_desc %in% c('WCVI Shrimp Survey Area 124', 'WCVI Shrimp Survey Area 125'))
# Check non-zero catches
more_spp %>% filter(catch_weight > 0 | catch_count > 0) |>
#filter(species_common_name == 'cartilaginous fish (sharks, skates, rays, ratfish)') |>
count(species_common_name, year) |>
arrange(species_common_name, year) |>
distinct(species_common_name)
more_spp %>% filter(catch_weight == 0 & catch_count == 0) |>
#filter(species_common_name == 'cartilaginous fish (sharks, skates, rays, ratfish)') |>
count(species_common_name, year) |>
arrange(species_common_name, year) |>
distinct(species_common_name)
more_spp_summ <- more_spp |>
group_by(species_common_name, species_science_name, year) |>
summarise(mean_catch = mean(catch_weight, na.rm = TRUE), .groups = 'drop') |>
mutate(parent_taxonomic_unit = species_science_name, parent_taxonomic_unit)
spp_group_df <- mssm_dat |>
group_by(species_common_name, species_science_name, year) |>
summarise(mean_catch = mean(catch_weight, na.rm = TRUE), .groups = 'drop') |>
left_join(gfsynopsis::get_spp_names()) |>
mutate(parent_taxonomic_unit = ifelse(species_common_name == "pacific halibut", "pleuronectidae(righteye flounders)", parent_taxonomic_unit)) |>
mutate(parent_taxonomic_unit = ifelse(species_common_name == "pacific hake", "merlucciidae", parent_taxonomic_unit)) |>
mutate(parent_taxonomic_unit = ifelse(is.na(parent_taxonomic_unit), species_science_name, parent_taxonomic_unit))
# Baseplot
agg_plot <- function(df, ncol = 1, scales = 'free_y') {
ggplot(data = df) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray85", alpha = 0.2) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point(aes(x = year, y = mean_catch)) +
labs(x = "Year", y = "Mean annual catch (kg)") +
facet_wrap(~ species_common_name, scales = scales, ncol = ncol)
}
# All higher order species aggregations f families caught in MSSM
spp_group_plot <-
more_spp_summ |>
filter(species_common_name %in% c('eelpouts', 'flatfishes', 'rockfishes', 'sculpins', 'skates')) |>
mutate(species_common_name = str_to_title(species_common_name)) |>
agg_plot(ncol = 3)
spp_group_plot
ggsave(spp_group_plot, filename = file.path(mssm_figs, 'aggregated-spp-plot.png'), width = 8, height = 4)
#
plot_combined_spp <- function(family, common_name) {
df1 <- spp_group_df |>
filter(str_detect(parent_taxonomic_unit, family)) |>
group_by(year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'species combined')
df2 <- more_spp_summ |>
filter(str_detect(parent_taxonomic_unit, family)) |>
group_by(year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = common_name)
dat <- bind_rows(df1, df2)
ggplot(data = dat) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray90", alpha = 0.1) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point(aes(x = year, y = mean_catch, colour = species_common_name)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Mean annual catch (kg)")
}
add_group_common_name <- function(df) {
df |>
mutate(group_common_name = case_when(
str_detect(parent_taxonomic_unit, "cottidae") ~ 'sculpins',
str_detect(parent_taxonomic_unit, "pleuronect|paralich") ~ 'flatfishes',
str_detect(parent_taxonomic_unit, "rajidae") ~ 'skates',
str_detect(parent_taxonomic_unit, "sebastes") ~ 'rockfishes',
str_detect(parent_taxonomic_unit, "zoarcidae") ~ 'eelpouts'))
}
plot_combined_spp('cottidae', "'sculpins'")
plot_combined_spp('pleuronect|paralich', "'flatfishes'")
plot_combined_spp('rajidae', "'skates'")
plot_combined_spp('sebastes', "'rockfishes'")
plot_combined_spp('zoarcidae', "'eelpouts'")
df1 <- spp_group_df |>
filter(str_detect(parent_taxonomic_unit, c("cottidae|pleuronect|paralich|rajidae|sebastes|zoarcidae"))) |>
add_group_common_name() |>
group_by(group_common_name, year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'Species')
df2 <- more_spp_summ |>
filter(str_detect(parent_taxonomic_unit, c("cottidae|pleuronect|paralich|rajidae|sebastes|zoarcidae"))) |>
add_group_common_name() |>
group_by(group_common_name, year) |>
summarise(mean_catch = mean(mean_catch), .groups = 'drop') |>
mutate(species_common_name = 'Order/Family')
dat <- bind_rows(df1, df2) |>
mutate(group_common_name = str_to_title(group_common_name))
spp_group_plot2 <-
ggplot(data = dat, aes(x = year, y = mean_catch, colour = species_common_name, shape = species_common_name)) +
geom_rect(aes(xmin = -Inf, xmax = 2003, ymin = -Inf, ymax = Inf),
fill = "gray90", colour = NA, alpha = 0.1) +
geom_vline(xintercept = 2001, colour = 'grey50') +
geom_point() +
geom_line(linewidth = 0.3) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Mean annual catch (kg)", shape = "Identification level", colour = 'Identification level') +
facet_wrap(~ group_common_name, scales = "free_y", ncol = 2) +
theme(legend.position = c(0.7, 0.15))
spp_group_plot2
ggsave(filename = file.path(mssm_figs, 'aggregated-id-level-plot.png'), width = 7.7, height = 5.2)
# --- Mystery of the eelpout spike in 2002 ---
select_simple <- function(df) {
select(df, species_code, fishing_event_id, year, month, day, time_deployed, latitude, longitude,
depth_m, catch_weight, catch_count, species_common_name)
}
# No answer to why
eelpouts_group <- more_spp |> filter(species_common_name == 'eelpouts') |>
select_simple() |>
filter(year < 2023) |>
mutate(id_level = 'Family')
eelpouts_spp <- mssm_dat |>
left_join(gfsynopsis::get_spp_names()) |>
filter(str_detect(parent_taxonomic_unit, 'zoarcidae')) |>
group_by(year, month, day, time_deployed, latitude, longitude, depth_m) |>
summarise(catch_weight = sum(catch_weight)) |>
mutate(id_level = 'Species')
extreme_catches <- eelpouts_group |>
arrange(-catch_weight) |>
slice(1:5)
#View(extreme_catches)
bind_rows(eelpouts_group, eelpouts_spp) |>
ggplot(aes(x = year, y = catch_weight)) +
geom_point(aes(colour = id_level)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Catch (kg)", colour = 'Identification level') +
ggrepel::geom_text_repel(
data = extreme_catches,
aes(x = year, y = catch_weight, label = fishing_event_id),
size = 3.5, segment.color = 'grey85',
nudge_x = -1.5, box.padding = 0.2,
direction = "y", hjust = 1
) +
theme(legend.position = c(0.15, 0.83))
# --------
# Skates 2002
# - Does not look like there are any duplicate fishing events
skates_group <- more_spp |> filter(species_common_name == 'skates') |>
select_simple() |>
filter(year < 2023) |>
mutate(id_level = 'Family')
skates_spp <- mssm_dat |>
left_join(gfsynopsis::get_spp_names()) |>
filter(str_detect(parent_taxonomic_unit, 'rajidae')) |>
group_by(year, month, day, time_deployed, latitude, longitude, depth_m) |>
summarise(catch_weight = sum(catch_weight)) |>
mutate(id_level = 'Species')
extreme_catches <- skates_group |>
arrange(-catch_weight) |>
slice(1:5)
#View(extreme_catches)
bind_rows(skates_group, skates_spp) |>
ggplot(aes(x = year, y = catch_weight)) +
geom_point(aes(colour = id_level)) +
scale_colour_brewer(palette = "Dark2", type = 'qual') +
labs(x = "Year", y = "Catch (kg)", colour = 'Identification level') +
ggrepel::geom_text_repel(
data = extreme_catches,
aes(x = year, y = catch_weight, label = fishing_event_id),
size = 3.5, segment.color = 'grey85',
nudge_x = -1.5, box.padding = 0.2,
direction = "y", hjust = 1
) +
theme(legend.position = c(0.15, 0.83))
ggsave(filename = file.path(mssm_figs, 'aggregated-skates-plot.png'),
width = 7.3, height = 3.8)
# # Sculpins ---
# sculpins <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'cottidae'))
# sculpin_levels <- c('sculpins', unique(sculpins$species_common_name)[unique(sculpins$species_common_name) != 'sculpins'], 'scuplins combined')
# sculpins_all <- sculpins |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('scuplins combined', levels = sculpin_levels))
# #sculpins <- bind_rows(sculpins, sculpins_all)
# p_sculpin <- sculpins |>
# mutate(species_common_name = factor(species_common_name, levels = sculpin_levels)) |>
# agg_plot(ncol = 1, scales = 'fixed')
# p_sculpin
# # Flatfish ---
# flatfish <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'pleuronect|paralich'))
# flatfish_levels <- c('flatfishes', unique(flatfish$species_common_name)[unique(flatfish$species_common_name) != 'flatfishes'], 'flatfishes combined')
# flatfish_all <- flatfish |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('flatfishes combined', levels = flatfish_levels))
# #flatfish <- bind_rows(flatfish, flatfish_all)
# p_flatfish <- flatfish |>
# mutate(species_common_name = factor(species_common_name, levels = flatfish_levels)) |>
# agg_plot(ncol = 2)
# p_flatfish
# # Skates ---
# skates <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'rajidae'))
# skates_levels <- c('skates', unique(skates$species_common_name)[unique(skates$species_common_name) != 'skates'], 'skates combined')
# skates_all <- skates |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('skates combined', levels = skates_levels))
# #skates <- bind_rows(skates, skates_all)
# p_skate <- skates |>
# mutate(species_common_name = factor(species_common_name, levels = skates_levels)) |>
# agg_plot(ncol = 1, scales = 'fixed') +
# theme(axis.title.y = element_blank())
# # Rockfish ---
# rockfish <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'sebastes'))
# rockfish_levels <- c('rockfishes', unique(rockfish$species_common_name)[unique(rockfish$species_common_name) != 'rockfishes'], 'rockfishes combined')
# rockfish_all <- rockfish |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('rockfishes combined', levels = rockfish_levels))
# #rockfish <- bind_rows(rockfish, rockfish_all)
# p_rockfish <- rockfish |>
# mutate(species_common_name = factor(species_common_name, levels = rockfish_levels)) |>
# agg_plot(ncol = 2)
# # Eelpouts ---
# eelpouts <- filter(spp_group_df, str_detect(parent_taxonomic_unit, 'zoarcidae'))
# eelpouts_levels <- c('all eelpouts', 'eelpouts', unique(eelpouts$species_common_name)[unique(eelpouts$species_common_name) != 'eelpouts'])
# eelpouts_all <- eelpouts |>
# group_by(year) |>
# summarise(mean_catch = sum(mean_catch)) |>
# mutate(species_common_name = factor('all eelpouts', levels = eelpouts_levels))
# eelpouts <- bind_rows(eelpouts, eelpouts_all)
# p_eelpout <- eelpouts |>
# mutate(species_common_name = factor(species_common_name, levels = eelpouts_levels)) |>
# agg_plot(ncol = 1)
# design <- "
# 12
# 12
# 12
# 1#
# 1#
# "
# p_sculpin + p_skate + plot_layout(design = design)
# ggsave(filename = file.path(mssm_figs, 'agg-sculpin-skate.png'), width = 7, height = 7)
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