SPRFMO Jack Mackerel — Catch/Fishery Data Quality Checks

This report runs quality-control checks on the commercial fishery (catch) data in the per-member files (Chile.csv, China.csv, … Russia.csv). # 1. Load and prepare data

Member–fleet allowlist

Each member fishes a specific fleet (or two for Chile). The standard ALK template includes empty sheets for all fleets, so we filter to keep only the (member, fleet) combinations that actually correspond to each member’s fishery:

member_fleet <- tribble(
  ~member,    ~fleet_canon,
  "Chile",    "F1",     # Fleet 1: Northern Chile (inside EEZ)
  "Chile",    "F2",     # Fleet 2: South-Central Chile (EEZ/HS)
  "Peru",     "F3",     # Fleet 3: Peru & Ecuador (inside EEZ)
  "Ecuador",  "F3",     # Fleet 3 (Ecuador, same fleet as Peru)
  "EU",       "F4",     # Fleet 4: Offshore (outside EEZ)
  "Korea",    "F4",     # Fleet 4
  "China",    "F4",     # Fleet 4
  "Russia",   "F4"      # Fleet 4
)

member_fleet %>% kable(caption = "Allowed (member, fleet) combinations")

Allowed (member, fleet) combinations

member	fleet_canon
Chile	F1
Chile	F2
Peru	F3
Ecuador	F3
EU	F4
Korea	F4
China	F4
Russia	F4

Load CSVs

data_dir <- params$data_dir

read_member <- function(m) {
  fp <- file.path(data_dir, paste0(m, ".csv"))
  if (!file.exists(fp)) {
    warning("Missing: ", fp); return(NULL)
  }
  read_csv(fp, show_col_types = FALSE,
           col_types = cols(
             submission_year = col_integer(),
             data_year = col_integer(),
             member = col_character(),
             fleet = col_character(),
             variable = col_character(),
             quarter = col_character(),
             length_cm = col_double(),
             age = col_integer(),
             metric_detail = col_character(),
             value = col_double(),
             unit = col_character(),
             version = col_character(),
             data_source = col_character(),
             source_file = col_character()
           ))
}

raw <- bind_rows(lapply(params$members, read_member))
cat("Total rows loaded:", format(nrow(raw), big.mark=","), "\n")

## Total rows loaded: 136,751

Normalize fleet labels

Some submissions use full descriptive names (“Fleet 1: Northern Chile (inside EEZ)”), others just “Fleet 1” or “Fleet1”. Collapse to a canonical short label F1/F2/F3/F4:

canon_fleet <- function(x) {
  x[is.na(x)] <- ""
  x_low <- tolower(x)
  x_clean <- str_replace_all(x_low, "[^a-z0-9 ]", " ")
  case_when(
    str_detect(x_clean, "fleet ?1|northern chile") ~ "F1",
    str_detect(x_clean, "fleet ?2|south.?central|eez/hs") ~ "F2",
    str_detect(x_clean, "fleet ?3|peru") ~ "F3",
    str_detect(x_clean, "fleet ?4|offshore") ~ "F4",
    x == "" ~ NA_character_,
    TRUE ~ x
  )
}

raw <- raw %>% mutate(fleet_canon = canon_fleet(fleet))

For the legacy data sources couldn’t infer a fleet, fall back to the member’s expected fleet (only valid because each member except Chile has exactly one fleet — Chile’s NA-fleet rows would be ambiguous and are dropped):

raw <- raw %>%
  left_join(
    member_fleet %>% group_by(member) %>%
      summarise(default_fleet = ifelse(n() == 1, fleet_canon, NA_character_),
                .groups = "drop"),
    by = "member"
  ) %>%
  mutate(fleet_canon = coalesce(fleet_canon, default_fleet)) %>%
  select(-default_fleet)

Apply filters: version, member-fleet allowlist, zeros, latest submission

n_raw <- nrow(raw)

# 1) version filter
df <- raw %>% filter(!str_detect(version, params$exclude_versions))
cat(sprintf("After version filter: %s rows (%.1f%% of raw)\n",
            format(nrow(df), big.mark=","), 100 * nrow(df) / n_raw))

## After version filter: 128,188 rows (93.7% of raw)

# 2) member-fleet allowlist - drop rows from fleets that the member doesn't fish
n_before_allowlist <- nrow(df)
df <- df %>% inner_join(member_fleet, by = c("member","fleet_canon"))
cat(sprintf("After member-fleet allowlist: %s rows (dropped %s)\n",
            format(nrow(df), big.mark=","),
            format(n_before_allowlist - nrow(df), big.mark=",")))

## After member-fleet allowlist: 107,875 rows (dropped 20,313)

# 3) drop zero-valued rows (zero placeholders are noise, not data)
n_before_zero <- nrow(df)
df <- df %>% filter(!is.na(value), value != 0)
cat(sprintf("After dropping zero/NA values: %s rows (dropped %s)\n",
            format(nrow(df), big.mark=","),
            format(n_before_zero - nrow(df), big.mark=",")))

## After dropping zero/NA values: 77,935 rows (dropped 29,940)

# 4) dedupe by latest submission per identical
#    (member, fleet, year, quarter, length, age, variable, metric_detail, data_source)
if (isTRUE(params$pick_latest_submission)) {
  n_before_dedup <- nrow(df)
  df <- df %>%
    group_by(data_year, member, fleet_canon, variable, quarter,
             length_cm, age, metric_detail, data_source) %>%
    slice_max(submission_year, n = 1, with_ties = TRUE) %>%
    ungroup()
  cat(sprintf("After picking latest submission: %s rows (dropped %s)\n",
              format(nrow(df), big.mark=","),
              format(n_before_dedup - nrow(df), big.mark=",")))
}

## After picking latest submission: 69,557 rows (dropped 8,378)

# Member × fleet labels for plotting
df <- df %>%
  mutate(mf  = paste(member, fleet_canon, sep = " "),
         qtr = factor(quarter, levels = c("Q1","Q2","Q3","Q4","all")))

# Stable factor ordering for mf so plots come out in the same member order
mf_levels <- df %>% distinct(member, fleet_canon, mf) %>%
  arrange(member, fleet_canon) %>% pull(mf)
df <- df %>% mutate(mf = factor(mf, levels = mf_levels))

Coverage table

df %>%
  count(member, fleet_canon, variable) %>%
  pivot_wider(names_from = variable, values_from = n, values_fill = 0) %>%
  arrange(member, fleet_canon) %>%
  kable(caption = "Row count per (member × fleet) × variable (after all filters)") %>%
  kable_styling(bootstrap_options = c("striped","hover"), font_size = 11)

Row count per (member × fleet) × variable (after all filters)

member	fleet_canon	age_length_key	catch	catch_at_age	catch_at_age_proportion	length_freq	mean_length_at_age	mean_weight_at_age	numbers_at_age	numbers_at_age_proportion	sampling	weight_at_length	age_freq_at_length	age_proportion_at_length
Chile	F1	2373	64	1096	1096	1453	1559	1559	1559	1096	189	3500	0	0
Chile	F2	4921	67	1955	1955	2089	2576	2576	2576	1955	224	3800	0	0
China	F4	54	55	0	0	787	18	18	18	0	54	77	0	0
EU	F4	1722	49	0	0	1752	231	230	221	0	112	377	5884	5883
Ecuador	F3	0	13	0	0	14	0	0	0	0	0	0	0	0
Korea	F4	0	33	0	0	617	0	0	0	0	52	237	0	0
Peru	F3	4560	59	0	0	1688	324	324	325	0	129	238	0	0
Russia	F4	1421	36	0	0	691	253	255	255	0	104	149	0	0

2. Catch

Catch is reported by quarter (Q1–Q4). The “all”-quarter aggregate row is excluded to avoid double-counting.

catch <- df %>%
  filter(variable == "catch",
         metric_detail == "official_landings",
         quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr) %>%
  summarise(catch_t = sum(value, na.rm = TRUE), .groups = "drop")

catch_annual <- catch %>%
  group_by(member, fleet_canon, mf, data_year) %>%
  summarise(catch_t = sum(catch_t, na.rm = TRUE), .groups = "drop")

Annual totals

ggplot(catch_annual, aes(data_year, catch_t / 1000, fill = mf)) +
  geom_col() +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_x_continuous(breaks = pretty_breaks(6)) +
  scale_y_continuous(labels = comma) +
  guides(fill = "none") +
  labs(title = "Annual catch (official landings)",
       x = NULL, y = "Landings (thousand tonnes)")

Year × quarter heatmap

ggplot(catch, aes(data_year, qtr, fill = catch_t / 1000)) +
  geom_tile() +
  facet_wrap(~ mf, ncol = 2) +
  scale_fill_viridis_c(option = "C", labels = comma, name = "kt") +
  scale_x_continuous(breaks = pretty_breaks(6)) +
  labs(title = "Catch (thousand tonnes) by year × quarter",
       subtitle = "Empty cells = no catch reported that quarter",
       x = NULL, y = "Quarter")

Quarter share within year

share <- catch %>%
  group_by(member, fleet_canon, mf, data_year) %>%
  mutate(share = catch_t / sum(catch_t, na.rm = TRUE)) %>%
  ungroup() %>%
  filter(is.finite(share))

ggplot(share, aes(data_year, share, fill = quarter)) +
  geom_area(position = "stack") +
  facet_wrap(~ mf, ncol = 2) +
  scale_x_continuous(breaks = pretty_breaks(6)) +
  scale_y_continuous(labels = percent) +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Within-year catch share by quarter",
       x = NULL, y = "Share")

Catch table

catch %>%
  pivot_wider(names_from = quarter, values_from = catch_t, values_fill = 0) %>%
  mutate(annual = Q1 + Q2 + Q3 + Q4) %>%
  arrange(member, fleet_canon, data_year) %>%
  mutate(across(c(Q1,Q2,Q3,Q4,annual), ~ round(.x))) %>%
  kable(caption = "Catch (tonnes) by quarter, with annual sum") %>%
  kable_styling(bootstrap_options = c("striped","hover"), font_size = 11) %>%
  scroll_box(height = "400px")

Catch (tonnes) by quarter, with annual sum

member	fleet_canon	mf	data_year	qtr	Q1	Q2	Q3	Q4	annual
Chile	F1	Chile F1	2015	Q1	29539	0	0	0	29539
Chile	F1	Chile F1	2015	Q2	0	5311	0	0	5311
Chile	F1	Chile F1	2015	Q3	0	0	7	0	7
Chile	F1	Chile F1	2015	Q4	0	0	0	29	29
Chile	F1	Chile F1	2016	Q1	6	0	0	0	6
Chile	F1	Chile F1	2016	Q2	0	7102	0	0	7102
Chile	F1	Chile F1	2016	Q3	0	0	12361	0	12361
Chile	F1	Chile F1	2017	Q1	17992	0	0	0	17992
Chile	F1	Chile F1	2017	Q2	0	10153	0	0	10153
Chile	F1	Chile F1	2017	Q3	0	0	18	0	18
Chile	F1	Chile F1	2017	Q4	0	0	0	175	175
Chile	F1	Chile F1	2019	Q1	134	0	0	0	134
Chile	F1	Chile F1	2019	Q2	0	1537	0	0	1537
Chile	F1	Chile F1	2019	Q3	0	0	169	0	169
Chile	F1	Chile F1	2019	Q4	0	0	0	12719	12719
Chile	F1	Chile F1	2020	Q1	7649	0	0	0	7649
Chile	F1	Chile F1	2020	Q2	0	27784	0	0	27784
Chile	F1	Chile F1	2020	Q4	0	0	0	2567	2567
Chile	F1	Chile F1	2021	Q1	35458	0	0	0	35458
Chile	F1	Chile F1	2021	Q2	0	19334	0	0	19334
Chile	F1	Chile F1	2021	Q3	0	0	212	0	212
Chile	F1	Chile F1	2021	Q4	0	0	0	7140	7140
Chile	F1	Chile F1	2022	Q1	10925	0	0	0	10925
Chile	F1	Chile F1	2022	Q2	0	18102	0	0	18102
Chile	F1	Chile F1	2022	Q3	0	0	12252	0	12252
Chile	F1	Chile F1	2022	Q4	0	0	0	15202	15202
Chile	F1	Chile F1	2023	Q1	60778	0	0	0	60778
Chile	F1	Chile F1	2023	Q2	0	57325	0	0	57325
Chile	F1	Chile F1	2023	Q3	0	0	3927	0	3927
Chile	F1	Chile F1	2023	Q4	0	0	0	14074	14074
Chile	F1	Chile F1	2024	Q1	86264	0	0	0	86264
Chile	F1	Chile F1	2024	Q2	0	71058	0	0	71058
Chile	F1	Chile F1	2024	Q3	0	0	14566	0	14566
Chile	F1	Chile F1	2024	Q4	0	0	0	15722	15722
Chile	F1	Chile F1	2025	Q1	32802	0	0	0	32802
Chile	F2	Chile F2	2015	Q1	38872	0	0	0	38872
Chile	F2	Chile F2	2015	Q2	0	145825	0	0	145825
Chile	F2	Chile F2	2015	Q3	0	0	55453	0	55453
Chile	F2	Chile F2	2015	Q4	0	0	0	10177	10177
Chile	F2	Chile F2	2016	Q1	108489	0	0	0	108489
Chile	F2	Chile F2	2016	Q2	0	133419	0	0	133419
Chile	F2	Chile F2	2016	Q3	0	0	14182	0	14182
Chile	F2	Chile F2	2017	Q1	57198	0	0	0	57198
Chile	F2	Chile F2	2017	Q2	0	158755	0	0	158755
Chile	F2	Chile F2	2017	Q3	0	0	66026	0	66026
Chile	F2	Chile F2	2017	Q4	0	0	0	12084	12084
Chile	F2	Chile F2	2018	Q1	146376	0	0	0	146376
Chile	F2	Chile F2	2019	Q1	205560	0	0	0	205560
Chile	F2	Chile F2	2019	Q2	0	165061	0	0	165061
Chile	F2	Chile F2	2019	Q3	0	0	37077	0	37077
Chile	F2	Chile F2	2019	Q4	0	0	0	20891	20891
Chile	F2	Chile F2	2020	Q1	228190	0	0	0	228190
Chile	F2	Chile F2	2020	Q2	0	128557	0	0	128557
Chile	F2	Chile F2	2020	Q3	0	0	17550	0	17550
Chile	F2	Chile F2	2020	Q4	0	0	0	11016	11016
Chile	F2	Chile F2	2021	Q1	276076	0	0	0	276076
Chile	F2	Chile F2	2021	Q2	0	217959	0	0	217959
Chile	F2	Chile F2	2021	Q3	0	0	48888	0	48888
Chile	F2	Chile F2	2021	Q4	0	0	0	53659	53659
Chile	F2	Chile F2	2022	Q1	214628	0	0	0	214628
Chile	F2	Chile F2	2022	Q2	0	221402	0	0	221402
Chile	F2	Chile F2	2022	Q3	0	0	74873	0	74873
Chile	F2	Chile F2	2022	Q4	0	0	0	67950	67950
Chile	F2	Chile F2	2023	Q1	657516	0	0	0	657516
Chile	F2	Chile F2	2023	Q2	0	545881	0	0	545881
Chile	F2	Chile F2	2023	Q3	0	0	48614	0	48614
Chile	F2	Chile F2	2023	Q4	0	0	0	228396	228396
Chile	F2	Chile F2	2024	Q1	718491	0	0	0	718491
Chile	F2	Chile F2	2024	Q2	0	491306	0	0	491306
Chile	F2	Chile F2	2024	Q3	0	0	210989	0	210989
Chile	F2	Chile F2	2024	Q4	0	0	0	319834	319834
Chile	F2	Chile F2	2025	Q1	371493	0	0	0	371493
China	F4	China F4	2016	Q1	4302	0	0	0	4302
China	F4	China F4	2016	Q2	0	24402	0	0	24402
China	F4	China F4	2016	Q3	0	0	9147	0	9147
China	F4	China F4	2016	Q4	0	0	0	2564	2564
China	F4	China F4	2017	Q2	0	9984	0	0	9984
China	F4	China F4	2017	Q3	0	0	6601	0	6601
China	F4	China F4	2018	Q1	1726	0	0	0	1726
China	F4	China F4	2018	Q2	0	15571	0	0	15571
China	F4	China F4	2018	Q3	0	0	7091	0	7091
China	F4	China F4	2019	Q1	3017	0	0	0	3017
China	F4	China F4	2019	Q2	0	9935	0	0	9935
China	F4	China F4	2019	Q3	0	0	9762	0	9762
EU	F4	EU F4	2016	Q1	6484	0	0	0	6484
EU	F4	EU F4	2022	Q2	0	21341	0	0	21341
EU	F4	EU F4	2022	Q3	0	0	18862	0	18862
EU	F4	EU F4	2022	Q4	0	0	0	4061	4061
EU	F4	EU F4	2023	Q2	0	570	0	0	570
EU	F4	EU F4	2023	Q3	0	0	10500	0	10500
EU	F4	EU F4	2025	Q2	0	205	0	0	205
EU	F4	EU F4	2025	Q3	0	0	6450	0	6450
Ecuador	F3	Ecuador F3	2016	Q4	0	0	0	289	289
Ecuador	F3	Ecuador F3	2021	Q1	3	0	0	0	3
Ecuador	F3	Ecuador F3	2021	Q2	0	1	0	0	1
Ecuador	F3	Ecuador F3	2021	Q3	0	0	0	0	0
Ecuador	F3	Ecuador F3	2021	Q4	0	0	0	4	4
Korea	F4	Korea F4	2015	Q2	0	758	0	0	758
Korea	F4	Korea F4	2015	Q3	0	0	4990	0	4990
Korea	F4	Korea F4	2016	Q2	0	662	0	0	662
Korea	F4	Korea F4	2016	Q3	0	0	3176	0	3176
Korea	F4	Korea F4	2016	Q4	0	0	0	2593	2593
Korea	F4	Korea F4	2017	Q2	0	91	0	0	91
Korea	F4	Korea F4	2017	Q3	0	0	1144	0	1144
Korea	F4	Korea F4	2024	Q2	0	0	0	0	0
Korea	F4	Korea F4	2024	Q3	0	0	1557	0	1557
Korea	F4	Korea F4	2024	Q4	0	0	0	240	240
Peru	F3	Peru F3	2015	Q1	6602	0	0	0	6602
Peru	F3	Peru F3	2015	Q2	0	6189	0	0	6189
Peru	F3	Peru F3	2015	Q3	0	0	6256	0	6256
Peru	F3	Peru F3	2015	Q4	0	0	0	3111	3111
Peru	F3	Peru F3	2016	Q1	5094	0	0	0	5094
Peru	F3	Peru F3	2016	Q2	0	4116	0	0	4116
Peru	F3	Peru F3	2016	Q3	0	0	3751	0	3751
Peru	F3	Peru F3	2016	Q4	0	0	0	2127	2127
Peru	F3	Peru F3	2017	Q1	1480	0	0	0	1480
Peru	F3	Peru F3	2017	Q2	0	1739	0	0	1739
Peru	F3	Peru F3	2017	Q3	0	0	2832	0	2832
Peru	F3	Peru F3	2017	Q4	0	0	0	2763	2763
Peru	F3	Peru F3	2018	Q1	2824	0	0	0	2824
Peru	F3	Peru F3	2018	Q2	0	2993	0	0	2993
Peru	F3	Peru F3	2018	Q3	0	0	22074	0	22074
Peru	F3	Peru F3	2018	Q4	0	0	0	26957	26957
Peru	F3	Peru F3	2019	Q1	84679	0	0	0	84679
Peru	F3	Peru F3	2019	Q2	0	6865	0	0	6865
Peru	F3	Peru F3	2019	Q3	0	0	24101	0	24101
Peru	F3	Peru F3	2019	Q4	0	0	0	20139	20139
Peru	F3	Peru F3	2020	Q1	89237	0	0	0	89237
Peru	F3	Peru F3	2020	Q2	0	4266	0	0	4266
Peru	F3	Peru F3	2020	Q3	0	0	28039	0	28039
Peru	F3	Peru F3	2020	Q4	0	0	0	18458	18458
Peru	F3	Peru F3	2021	Q1	77972	0	0	0	77972
Peru	F3	Peru F3	2021	Q2	0	13886	0	0	13886
Peru	F3	Peru F3	2021	Q3	0	0	14533	0	14533
Peru	F3	Peru F3	2021	Q4	0	0	0	17237	17237
Peru	F3	Peru F3	2022	Q1	101707	0	0	0	101707
Peru	F3	Peru F3	2022	Q2	0	23975	0	0	23975
Peru	F3	Peru F3	2022	Q3	0	0	6	0	6
Peru	F3	Peru F3	2022	Q4	0	0	0	33915	33915
Peru	F3	Peru F3	2023	Q1	120792	0	0	0	120792
Peru	F3	Peru F3	2023	Q2	0	9997	0	0	9997
Peru	F3	Peru F3	2024	Q1	7047	0	0	0	7047
Peru	F3	Peru F3	2024	Q2	0	56934	0	0	56934
Peru	F3	Peru F3	2024	Q3	0	0	149603	0	149603
Peru	F3	Peru F3	2024	Q4	0	0	0	3628	3628
Peru	F3	Peru F3	2025	Q1	99725	0	0	0	99725
Peru	F3	Peru F3	2025	Q2	0	7061	0	0	7061
Russia	F4	Russia F4	2019	Q1	432	0	0	0	432
Russia	F4	Russia F4	2019	Q2	0	2745	0	0	2745
Russia	F4	Russia F4	2019	Q3	0	0	6246	0	6246
Russia	F4	Russia F4	2020	Q3	0	0	3378	0	3378
Russia	F4	Russia F4	2020	Q4	0	0	0	1867	1867
Russia	F4	Russia F4	2021	Q1	156	0	0	0	156
Russia	F4	Russia F4	2021	Q2	0	4523	0	0	4523
Russia	F4	Russia F4	2021	Q3	0	0	7472	0	7472
Russia	F4	Russia F4	2022	Q2	0	9136	0	0	9136
Russia	F4	Russia F4	2022	Q3	0	0	9111	0	9111
Russia	F4	Russia F4	2022	Q4	0	0	0	11660	11660
Russia	F4	Russia F4	2023	Q1	2774	0	0	0	2774
Russia	F4	Russia F4	2023	Q2	0	10099	0	0	10099
Russia	F4	Russia F4	2023	Q3	0	0	15131	0	15131
Russia	F4	Russia F4	2023	Q4	0	0	0	13137	13137
Russia	F4	Russia F4	2024	Q2	0	261	0	0	261
Russia	F4	Russia F4	2024	Q3	0	0	7968	0	7968
Russia	F4	Russia F4	2024	Q4	0	0	0	3881	3881

3. Sampling effort

sampling <- df %>%
  filter(variable == "sampling",
         metric_detail %in% c("samples","samples_1","samples_2","measured","aged",
                              "total_of_sample")) %>%
  mutate(stream = case_when(
    metric_detail == "aged" ~ "aged",
    metric_detail == "total_of_sample" ~ "sampled_weight_t",
    metric_detail %in% c("measured","samples","samples_1","samples_2") ~ "measured"
  )) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, stream) %>%
  summarise(value = sum(value, na.rm = TRUE), .groups = "drop")

Annual measured / aged

sampling %>%
  filter(quarter == "all", stream %in% c("measured","aged")) %>%
  mutate(year_f = factor(data_year)) %>%
  ggplot(aes(year_f, value, fill = stream)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.75) +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_y_continuous(labels = comma) +
  scale_fill_brewer(palette = "Set1") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(title = "Annual sampling effort: number of fish measured / aged",
       x = NULL, y = "Fish (n)", fill = NULL)

Annual sampled weight

sw <- sampling %>%
  filter(quarter == "all", stream == "sampled_weight_t")

if (nrow(sw)) {
  sw %>%
    mutate(year_f = factor(data_year)) %>%
    ggplot(aes(year_f, value, fill = mf)) +
    geom_col(width = 0.75) +
    facet_wrap(~ mf, scales = "free_y", ncol = 2) +
    scale_y_continuous(labels = comma) +
    guides(fill = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    labs(title = "Annual sampled weight (tonnes)",
         x = NULL, y = "Sampled weight (tonnes)")
} else {
  cat("No annual sampled weight data after filtering.\n")
}

Year × quarter (measured)

sampling %>%
  filter(stream == "measured", quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  ggplot(aes(data_year, qtr, fill = value)) +
  geom_tile() +
  facet_wrap(~ mf, ncol = 2) +
  scale_fill_viridis_c(option = "C", labels = comma, name = "fish") +
  labs(title = "Number of fish measured by year × quarter",
       x = NULL, y = "Quarter")

Year × quarter (aged)

sampling %>%
  filter(stream == "aged", quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  ggplot(aes(data_year, qtr, fill = value)) +
  geom_tile() +
  facet_wrap(~ mf, ncol = 2) +
  scale_fill_viridis_c(option = "C", labels = comma, name = "fish") +
  labs(title = "Number of fish aged by year × quarter",
       x = NULL, y = "Quarter")

Year × quarter (sampled weight)

sw_q <- sampling %>%
  filter(stream == "sampled_weight_t", quarter %in% c("Q1","Q2","Q3","Q4"))

if (nrow(sw_q)) {
  ggplot(sw_q, aes(data_year, qtr, fill = value)) +
    geom_tile() +
    facet_wrap(~ mf, ncol = 2) +
    scale_fill_viridis_c(option = "C", labels = comma, name = "tonnes") +
    labs(title = "Sampled weight (tonnes) by year × quarter",
         x = NULL, y = "Quarter")
} else {
  cat("No quarterly sampled-weight data after filtering.\n")
}

Sampling table

sampling %>%
  filter(quarter == "all") %>%
  pivot_wider(names_from = stream, values_from = value) %>%
  arrange(member, fleet_canon, data_year) %>%
  mutate(across(where(is.numeric) & !data_year, ~ round(.x, 1))) %>%
  kable(caption = "Annual sampling: fish measured / aged and sampled weight (tonnes)") %>%
  kable_styling(bootstrap_options = c("striped","hover"), font_size = 11) %>%
  scroll_box(height = "400px")

Annual sampling: fish measured / aged and sampled weight (tonnes)

member	fleet_canon	mf	data_year	quarter	qtr	aged	measured	sampled_weight_t
Chile	F1	Chile F1	2015	all	all	54	118	25982.3
Chile	F1	Chile F1	2016	all	all	NA	38	NA
Chile	F1	Chile F1	2017	all	all	230	7882	NA
Chile	F1	Chile F1	2019	all	all	123	409	13258.8
Chile	F1	Chile F1	2020	all	all	175	646	38338.2
Chile	F1	Chile F1	2021	all	all	342	1175	62144.7
Chile	F1	Chile F1	2022	all	all	551	2375	56481.0
Chile	F1	Chile F1	2023	all	all	1376	6944	136104.6
Chile	F1	Chile F1	2024	all	all	1954	11664	187609.5
Chile	F1	Chile F1	2025	all	all	207	1289	32802.4
Chile	F2	Chile F2	2015	all	all	1995	7849	132943.5
Chile	F2	Chile F2	2016	all	all	414	4231	NA
Chile	F2	Chile F2	2017	all	all	1872	50776	NA
Chile	F2	Chile F2	2018	all	all	616	17136	NA
Chile	F2	Chile F2	2019	all	all	1926	19836	438567.6
Chile	F2	Chile F2	2020	all	all	1578	25215	388214.8
Chile	F2	Chile F2	2021	all	all	1833	28997	596290.8
Chile	F2	Chile F2	2022	all	all	1725	21638	578853.0
Chile	F2	Chile F2	2023	all	all	4064	71164	1480406.9
Chile	F2	Chile F2	2024	all	all	3910	65336	1740620.0
Chile	F2	Chile F2	2025	all	all	427	10604	371493.1
China	F4	China F4	2016	all	all	600	2660	1330.0
China	F4	China F4	2018	all	all	NA	24378	6.1
China	F4	China F4	2019	all	all	NA	23802	12.6
EU	F4	EU F4	2016	all	all	NA	7699	3819.0
EU	F4	EU F4	2017	all	all	1625	13943	4817.0
EU	F4	EU F4	2018	all	all	1000	7542	4120.0
EU	F4	EU F4	2019	all	all	251	5204	5185.0
EU	F4	EU F4	2021	all	all	212	11929	5808.8
EU	F4	EU F4	2022	all	all	535	21416	19619.8
EU	F4	EU F4	2023	all	all	794	28110	7.7
EU	F4	EU F4	2024	all	all	67	8844	17772.9
EU	F4	EU F4	2025	all	all	NA	11220	NA
Korea	F4	Korea F4	2014	all	all	NA	NA	0.1
Korea	F4	Korea F4	2015	all	all	NA	4482	1.0
Korea	F4	Korea F4	2016	all	all	NA	16340	2.2
Korea	F4	Korea F4	2017	all	all	NA	3134	0.5
Korea	F4	Korea F4	2019	all	all	NA	14314	1.1
Korea	F4	Korea F4	2024	all	all	NA	13198	NA
Peru	F3	Peru F3	2015	all	all	NA	3169	110.0
Peru	F3	Peru F3	2017	all	all	NA	19064	233.1
Peru	F3	Peru F3	2018	all	all	NA	43581	20428.6
Peru	F3	Peru F3	2019	all	all	NA	60454	8408.8
Peru	F3	Peru F3	2020	all	all	NA	67113	55984.3
Peru	F3	Peru F3	2021	all	all	NA	48704	13229.7
Peru	F3	Peru F3	2022	all	all	NA	54409	24270.6
Peru	F3	Peru F3	2023	all	all	NA	40134	34655.7
Peru	F3	Peru F3	2024	all	all	NA	46198	23282.9
Peru	F3	Peru F3	2025	all	all	NA	23010	11750.9
Russia	F4	Russia F4	2019	all	all	1548	24833	4114.0
Russia	F4	Russia F4	2020	all	all	529	18373	3935.0
Russia	F4	Russia F4	2021	all	all	778	22614	7.9
Russia	F4	Russia F4	2022	all	all	2226	36627	12466.3
Russia	F4	Russia F4	2023	all	all	2023	61922	15040.0
Russia	F4	Russia F4	2024	all	all	698	13184	1959.0

4. Length composition

We use only the standard template’s length-frequency rows (data_source == "STANDARD_TEMPLATE").

lfd_q <- df %>%
  filter(variable == "length_freq",
         data_source == "STANDARD_TEMPLATE",
         quarter %in% c("Q1","Q2","Q3","Q4"),
         !is.na(length_cm)) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, length_cm) %>%
  summarise(n_thousands = sum(value, na.rm = TRUE), .groups = "drop")

lfd_yr <- df %>%
  filter(variable == "length_freq",
         data_source == "STANDARD_TEMPLATE",
         quarter == "all",
         !is.na(length_cm)) %>%
  group_by(member, fleet_canon, mf, data_year, length_cm) %>%
  summarise(n_thousands = sum(value, na.rm = TRUE), .groups = "drop")

# Numbers-weighted-quantile helper
wq <- function(x, w, p) {
  ord <- order(x)
  x <- x[ord]; w <- w[ord]
  if (!length(x) || !is.finite(sum(w)) || sum(w) <= 0) return(NA_real_)
  cw <- cumsum(w) / sum(w)
  approx(cw, x, xout = p, ties = "ordered", rule = 2)$y
}

lfd_stats_q <- lfd_q %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr) %>%
  summarise(n_total_thousands = sum(n_thousands),
            mean_len = sum(length_cm * n_thousands) / sum(n_thousands),
            p25_len  = wq(length_cm, n_thousands, 0.25),
            median_len = wq(length_cm, n_thousands, 0.50),
            p75_len  = wq(length_cm, n_thousands, 0.75),
            mode_len = length_cm[which.max(n_thousands)],
            .groups = "drop")

Annual LFD curves

ggplot(lfd_yr, aes(length_cm, n_thousands, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.4) +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_color_viridis_c() +
  scale_y_continuous(labels = comma) +
  labs(title = "Annual length-frequency (one line per year)",
       x = "Fork length (cm)", y = "Numbers (thousands)", color = "Year")

Quarterly LFD curves

ggplot(lfd_q, aes(length_cm, n_thousands, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.35) +
  facet_grid(mf ~ qtr, scales = "free_y") +
  scale_color_viridis_c() +
  scale_y_continuous(labels = comma) +
  labs(title = "Quarterly length-frequency (one line per year)",
       x = "Fork length (cm)", y = "Numbers (thousands)", color = "Year")

Mean length over time, by quarter

ggplot(lfd_stats_q, aes(data_year, mean_len, color = qtr)) +
  geom_line() + geom_point(size = 1.2) +
  facet_wrap(~ mf, ncol = 2) +
  scale_color_brewer(palette = "Set1", name = "Quarter") +
  scale_x_continuous(breaks = pretty_breaks(6)) +
  labs(title = "Numbers-weighted mean length per quarter, by year",
       x = NULL, y = "Mean fork length (cm)")

Composition (proportion) by year × quarter, per fleet

For each (member, fleet) the length-frequency distribution within each (year, quarter) cell, normalized to a proportion (sums to 1 per year × quarter). Y-axis is proportion, X-axis is fork length, panels are year × quarter. Lets you see shifts in the distribution within a year and across years.

# Compute per (mf, year, quarter) proportion-at-length
lfd_prop <- lfd_q %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr) %>%
  mutate(prop = n_thousands / sum(n_thousands, na.rm = TRUE)) %>%
  ungroup() %>%
  filter(is.finite(prop), prop > 0)

plot_lfd_prop <- function(this_mf) {
  d <- lfd_prop %>% filter(mf == this_mf)
  if (!nrow(d)) {
    cat("No data for", this_mf, "\n"); return(invisible(NULL))
  }
  ggplot(d, aes(length_cm, prop)) +
    geom_col(fill = "steelblue", width = 1) +
    facet_grid(data_year ~ qtr, switch = "y") +
    scale_y_continuous(labels = percent_format(accuracy = 1),
                       breaks = pretty_breaks(2)) +
    scale_x_continuous(breaks = pretty_breaks(5)) +
    theme(strip.text.y.left = element_text(angle = 0),
          panel.spacing = unit(0.2, "lines")) +
    labs(title = paste0("Length composition (proportion) — ", this_mf),
         x = "Fork length (cm)", y = "Proportion")
}

Chile F1

plot_lfd_prop("Chile F1")

Chile F2

plot_lfd_prop("Chile F2")

China F4

plot_lfd_prop("China F4")

Ecuador F3

plot_lfd_prop("Ecuador F3")

EU F4

plot_lfd_prop("EU F4")

Korea F4

plot_lfd_prop("Korea F4")

Peru F3

plot_lfd_prop("Peru F3")

Russia F4

plot_lfd_prop("Russia F4")

Length range with non-zero counts

lfd_range <- lfd_yr %>%
  filter(n_thousands > 0) %>%
  group_by(member, fleet_canon, mf, data_year) %>%
  summarise(min_len = min(length_cm), max_len = max(length_cm),
            n_lengths = n_distinct(length_cm),
            total_thousands = sum(n_thousands),
            .groups = "drop")

ggplot(lfd_range, aes(data_year)) +
  geom_linerange(aes(ymin = min_len, ymax = max_len), color = "steelblue") +
  geom_point(aes(y = min_len), size = 1.5, color = "steelblue") +
  geom_point(aes(y = max_len), size = 1.5, color = "steelblue") +
  facet_wrap(~ mf, ncol = 2) +
  geom_hline(yintercept = c(10, 75), linetype = "dashed", color = "red") +
  labs(title = "Length range observed per year",
       subtitle = "Red dashed lines = biologically plausible range (10–75 cm)",
       x = NULL, y = "Fork length (cm)")

Length-out-of-range flags

lfd_flags <- lfd_yr %>%
  filter(n_thousands > 0, (length_cm < 10 | length_cm > 75)) %>%
  arrange(desc(n_thousands))

if (nrow(lfd_flags)) {
  lfd_flags %>%
    head(50) %>%
    kable(caption = paste0("Lengths outside 10–75 cm with non-zero counts (top 50 of ",
                          nrow(lfd_flags), ")"), digits = 1) %>%
    kable_styling(bootstrap_options = c("striped","hover"), font_size = 10) %>%
    scroll_box(height = "300px")
} else {
  cat("No length values outside 10–75 cm.\n")
}

Lengths outside 10–75 cm with non-zero counts (top 50 of 13)

member	fleet_canon	mf	data_year	length_cm	n_thousands
Chile	F1	Chile F1	2019	9	244511.1
Chile	F1	Chile F1	2019	8	181100.9
Chile	F2	Chile F2	2019	9	25645.7
Chile	F2	Chile F2	2019	8	20516.6
Chile	F1	Chile F1	2023	9	3171.2
Chile	F1	Chile F1	2023	8	1934.5
Chile	F1	Chile F1	2015	9	311.3
Chile	F1	Chile F1	2023	7	281.2
Chile	F1	Chile F1	2024	9	214.2
Peru	F3	Peru F3	2021	9	64.6
Chile	F1	Chile F1	2015	8	51.9
Chile	F1	Chile F1	2021	9	29.8
Peru	F3	Peru F3	2021	8	2.8

Year-on-year LFD-total scale shifts

Successive years’ LFD totals differing by more than 100× usually indicate a unit/scaling change between submissions.

lfd_year_totals <- lfd_yr %>%
  group_by(member, fleet_canon, mf, data_year) %>%
  summarise(total_thousands = sum(n_thousands), .groups = "drop") %>%
  arrange(member, fleet_canon, data_year) %>%
  group_by(member, fleet_canon, mf) %>%
  mutate(prev = lag(total_thousands),
         ratio = ifelse(prev > 0, total_thousands / prev, NA_real_)) %>%
  ungroup()

scale_shifts <- lfd_year_totals %>% filter(!is.na(ratio), (ratio > 100 | ratio < 1/100))

if (nrow(scale_shifts)) {
  scale_shifts %>%
    arrange(desc(abs(log10(ratio)))) %>%
    mutate(across(c(total_thousands, prev), ~ formatC(.x, format="e", digits=2)),
           ratio = round(ratio, 2)) %>%
    kable(caption = "LFD-total ratio outside [1/100, 100]") %>%
    kable_styling(bootstrap_options = c("striped","hover"), font_size = 10) %>%
    scroll_box(height = "300px")
} else {
  cat("No suspicious 100x year-on-year LFD scale changes.\n")
}

LFD-total ratio outside [1/100, 100]

member	fleet_canon	mf	data_year	total_thousands	prev	ratio
Chile	F2	Chile F2	2017	6.50e+08	2.76e+05	2350.41
Chile	F2	Chile F2	2020	6.38e+05	9.39e+08	0.00
Chile	F1	Chile F1	2017	1.91e+08	1.63e+05	1169.87
Chile	F1	Chile F1	2020	1.23e+05	3.11e+07	0.00

5. Mean weight at length

The standard template’s WLK sheet is reported by quarter only — there is no annual aggregate row. So this section shows quarterly views only.

wlk <- df %>%
  filter(variable == "weight_at_length",
         data_source == "STANDARD_TEMPLATE",
         !is.na(length_cm), value > 0) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, length_cm) %>%
  summarise(mean_w_kg = mean(value), .groups = "drop")

Curves by quarter

wlk %>%
  filter(quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  ggplot(aes(length_cm, mean_w_kg, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.35) +
  facet_grid(mf ~ qtr, scales = "free_y") +
  scale_color_viridis_c() +
  labs(title = "Mean weight-at-length by quarter",
       x = "Fork length (cm)", y = "Mean weight (kg)", color = "Year")

6. Numbers at age (CANUM)

canum_q <- df %>%
  filter(variable == "numbers_at_age",
         metric_detail == "numbers_at_age",
         quarter %in% c("Q1","Q2","Q3","Q4"),
         !is.na(age)) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, age) %>%
  summarise(n_thousands = sum(value), .groups = "drop")

canum_yr <- df %>%
  filter(variable == "numbers_at_age",
         metric_detail == "numbers_at_age",
         quarter == "all",
         !is.na(age)) %>%
  group_by(member, fleet_canon, mf, data_year, age) %>%
  summarise(n_thousands = sum(value), .groups = "drop")

Annual age distributions

ggplot(canum_yr, aes(age, n_thousands, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6) +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  scale_y_continuous(labels = comma) +
  labs(title = "Numbers at age — annual",
       x = "Age", y = "Numbers (thousands)", color = "Year")

Quarterly age distributions

ggplot(canum_q, aes(age, n_thousands, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.35) +
  facet_grid(mf ~ qtr, scales = "free_y") +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  scale_y_continuous(labels = comma) +
  labs(title = "Numbers at age — quarterly",
       x = "Age", y = "Numbers (thousands)", color = "Year")

Mean age by quarter

mean_age_q <- canum_q %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr) %>%
  summarise(mean_age = sum(age * n_thousands, na.rm=TRUE) / sum(n_thousands, na.rm=TRUE),
            .groups = "drop")

ggplot(mean_age_q, aes(data_year, mean_age, color = qtr)) +
  geom_line() + geom_point(size = 1.2) +
  facet_wrap(~ mf, ncol = 2) +
  scale_color_brewer(palette = "Set1", name = "Quarter") +
  scale_x_continuous(breaks = pretty_breaks(6)) +
  labs(title = "Mean age in catch over time, by quarter",
       x = NULL, y = "Mean age (years)")

Composition (proportion) by year × quarter, per fleet

For each (member, fleet) the age distribution within each (year, quarter) cell, normalized to a proportion (sums to 1 per year × quarter). Y-axis is proportion, X-axis is age, panels are year × quarter. Lets you visually track cohort progression.

canum_prop <- canum_q %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr) %>%
  mutate(prop = n_thousands / sum(n_thousands, na.rm = TRUE)) %>%
  ungroup() %>%
  filter(is.finite(prop), prop > 0)

plot_canum_prop <- function(this_mf) {
  d <- canum_prop %>% filter(mf == this_mf)
  if (!nrow(d)) {
    cat("No data for", this_mf, "\n"); return(invisible(NULL))
  }
  ggplot(d, aes(age, prop)) +
    geom_col(fill = "darkgreen", width = 0.85) +
    facet_grid(data_year ~ qtr, switch = "y") +
    scale_y_continuous(labels = percent_format(accuracy = 1),
                       breaks = pretty_breaks(2)) +
    scale_x_continuous(breaks = 0:12) +
    theme(strip.text.y.left = element_text(angle = 0),
          panel.spacing = unit(0.2, "lines")) +
    labs(title = paste0("Age composition (proportion) — ", this_mf),
         x = "Age", y = "Proportion")
}

Chile F1

plot_canum_prop("Chile F1")

Chile F2

plot_canum_prop("Chile F2")

China F4

plot_canum_prop("China F4")

EU F4

plot_canum_prop("EU F4")

Peru F3

plot_canum_prop("Peru F3")

Russia F4

plot_canum_prop("Russia F4")

Age range with non-zero numbers

age_range <- canum_yr %>%
  filter(n_thousands > 0) %>%
  group_by(member, fleet_canon, mf, data_year) %>%
  summarise(min_age = min(age), max_age = max(age),
            n_ages = n_distinct(age),
            total_thousands = round(sum(n_thousands)),
            .groups = "drop") %>%
  arrange(member, fleet_canon, data_year)

age_range %>%
  kable(caption = "Age range and total numbers per (mf × year)") %>%
  kable_styling(bootstrap_options = c("striped","hover"), font_size = 11) %>%
  scroll_box(height = "400px")

Age range and total numbers per (mf × year)

member	fleet_canon	mf	data_year	min_age	max_age	n_ages	total_thousands
Chile	F1	Chile F1	2015	1	8	8	163287
Chile	F1	Chile F1	2017	1	12	12	191021732
Chile	F1	Chile F1	2019	0	12	13	31113983
Chile	F1	Chile F1	2020	2	12	10	122977
Chile	F1	Chile F1	2021	0	12	13	121214
Chile	F1	Chile F1	2022	0	12	12	92941
Chile	F1	Chile F1	2023	0	12	13	214187
Chile	F1	Chile F1	2024	0	11	12	266236
Chile	F1	Chile F1	2025	1	12	12	47784
Chile	F2	Chile F2	2015	1	12	12	413967
Chile	F2	Chile F2	2016	1	12	11	276589
Chile	F2	Chile F2	2017	1	12	12	649653564
Chile	F2	Chile F2	2018	3	12	10	353682021
Chile	F2	Chile F2	2019	0	12	13	939068051
Chile	F2	Chile F2	2020	2	12	11	637768
Chile	F2	Chile F2	2021	1	12	12	1010447
Chile	F2	Chile F2	2022	0	12	13	732050
Chile	F2	Chile F2	2023	1	12	12	2072759
Chile	F2	Chile F2	2024	2	12	11	2955686
Chile	F2	Chile F2	2025	2	12	11	710427
China	F4	China F4	2016	3	9	7	47914
EU	F4	EU F4	2016	2	12	8	25235
EU	F4	EU F4	2017	1	8	8	90481
EU	F4	EU F4	2018	2	10	9	13336
EU	F4	EU F4	2019	2	10	9	11657
EU	F4	EU F4	2021	1	6	6	109769
EU	F4	EU F4	2022	2	12	11	227023
EU	F4	EU F4	2023	2	12	11	207262
EU	F4	EU F4	2024	1	9	9	9693
Peru	F3	Peru F3	2017	0	7	8	81340
Peru	F3	Peru F3	2018	1	7	7	204490
Peru	F3	Peru F3	2019	1	7	7	340229
Peru	F3	Peru F3	2020	1	7	7	284476
Peru	F3	Peru F3	2021	0	12	9	173103
Peru	F3	Peru F3	2022	1	12	8	222692
Peru	F3	Peru F3	2023	1	12	8	196917
Peru	F3	Peru F3	2024	1	12	8	227336
Peru	F3	Peru F3	2025	1	12	8	108686
Russia	F4	Russia F4	2019	1	12	12	44609
Russia	F4	Russia F4	2020	1	11	11	24947
Russia	F4	Russia F4	2021	0	12	12	49059
Russia	F4	Russia F4	2022	0	12	13	131953
Russia	F4	Russia F4	2023	1	12	12	167108
Russia	F4	Russia F4	2024	0	11	12	40347

7. Mean length-at-age and weight-at-age

mlaa <- df %>%
  filter(variable == "mean_length_at_age",
         metric_detail == "mean_length_cm",
         !is.na(age)) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, age) %>%
  summarise(mean_len = mean(value, na.rm = TRUE), .groups = "drop")

mwaa <- df %>%
  filter(variable == "mean_weight_at_age",
         metric_detail == "mean_weight",
         !is.na(age)) %>%
  group_by(member, fleet_canon, mf, data_year, quarter, qtr, age) %>%
  summarise(mean_w_kg = mean(value, na.rm = TRUE), .groups = "drop")

Mean length-at-age (annual)

mlaa %>%
  filter(quarter == "all") %>%
  ggplot(aes(age, mean_len, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6) +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  labs(title = "Mean length at age (annual)",
       x = "Age", y = "Mean length (cm)", color = "Year")

Mean length-at-age (quarterly)

mlaa %>%
  filter(quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  ggplot(aes(age, mean_len, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.35) +
  facet_grid(mf ~ qtr, scales = "free_y") +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  labs(title = "Mean length at age — by quarter",
       x = "Age", y = "Mean length (cm)", color = "Year")

Mean weight-at-age (annual)

mwaa %>%
  filter(quarter == "all") %>%
  ggplot(aes(age, mean_w_kg, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6) +
  facet_wrap(~ mf, scales = "free_y", ncol = 2) +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  labs(title = "Mean weight at age (annual)",
       x = "Age", y = "Mean weight (kg)", color = "Year")

Mean weight-at-age (quarterly)

mwaa %>%
  filter(quarter %in% c("Q1","Q2","Q3","Q4")) %>%
  ggplot(aes(age, mean_w_kg, group = data_year, color = data_year)) +
  geom_line(alpha = 0.6, linewidth = 0.35) +
  facet_grid(mf ~ qtr, scales = "free_y") +
  scale_color_viridis_c() +
  scale_x_continuous(breaks = 0:12) +
  labs(title = "Mean weight at age — by quarter",
       x = "Age", y = "Mean weight (kg)", color = "Year")