diff --git a/Rscripts/logbook_analys.R b/Rscripts/logbook_analys.R
new file mode 100644
index 0000000..b394aa5
--- /dev/null
+++ b/Rscripts/logbook_analys.R
@@ -0,0 +1,87 @@
+library(dplyr)
+
+observer <- read.csv('data/confidential/wcgop_logbook/ward_oken_observer_data_2024-09-10.csv') |>
+ as_tibble() |>
+ filter(AVG_LAT > 40+1/6,
+ sector == 'Midwater Rockfish',
+ DATATYPE == 'Analysis Data')
+
+observer_yt <- observer|>
+ group_by(HAUL_ID) |>
+ summarise(has_yt = 'Yellowtail Rockfish' %in% species,
+ across(AVG_DEPTH:YEAR, first)) |>
+ filter(!has_yt) |>
+ mutate(species = 'Yellowtail Rockfish', MT = 0) |>
+ select(-has_yt) |>
+ bind_rows(
+ filter(observer, species == 'Yellowtail Rockfish')
+ ) |>
+ mutate(DRVID = as.integer(DRVID))
+
+em <- read.csv('data/confidential/wcgop_logbook/ward_oken_em_data_2024-12-03.csv') |>
+ as_tibble() |>
+ filter(AVG_LAT > 40+1/6, sector == 'Midwater Rockfish EM')
+
+em_yt <- em |>
+ group_by(HAUL_ID) |>
+ summarise(has_yt = 'Yellowtail Rockfish' %in% species,
+ across(AVG_DEPTH:YEAR, first)) |>
+ filter(!has_yt) |>
+ mutate(species = 'Yellowtail Rockfish', MT = 0) |>
+ select(-has_yt) |>
+ bind_rows(
+ filter(em, species == 'Yellowtail Rockfish')
+ )
+
+all_hauls_yt <- bind_rows(list(obs = observer_yt, em = em_yt), .id = 'program') |>
+ filter(HAUL_DURATION > 0, HAUL_DURATION < 10) |>
+ mutate(depth_std = (AVG_DEPTH - mean(AVG_DEPTH))/sd(AVG_DEPTH),
+ fyear = factor(YEAR), log_duration = log(HAUL_DURATION),
+ cpue = MT/HAUL_DURATION, month = lubridate::month(as.Date(SET_DATE)),
+ fDRVID = factor(DRVID), fport = factor(R_PORT),
+ program = factor(program)) # note D_PORT is NA in some cases, R_PORT is always defined (fish ticket)
+
+nrow(filter(all_hauls_yt, MT>0)) / nrow(all_hauls_yt)
+# good to monitor % positive hauls. This is pretty high (70%).
+
+yt_mesh <- sdmTMB::make_mesh(all_hauls_yt, xy_cols = c('AVG_LONG', 'AVG_LAT'), n_knots = 50)
+plot(yt_mesh)
+# this does not look like a st model is a good idea. very clear "hotspots" of fishing effort.
+# but we need it for sdmTMB to work (not actually used)
+
+mod_simple <- sdmTMB::sdmTMB(formula = cpue ~ fyear + s(depth_std) + s(month, bs = 'cc') + program + (1|fDRVID) + (1|fport) - 1, family = sdmTMB::delta_lognormal(),
+ data = all_hauls_yt, spatial = 'off', spatiotemporal = 'off', time = 'fyear', mesh = yt_mesh)
+
+summary(mod_simple)
+
+
+resids <- residuals(mod_simple, model = 2) # the presence/absence model looks consistently good
+qqnorm(resids)
+qqline(resids)
+
+sdmTMB::visreg_delta(mod_simple, xvar = "depth_std", model = 1, gg = TRUE)
+sdmTMB::visreg_delta(mod_simple, xvar = "depth_std", model = 2, gg = TRUE)
+sdmTMB::visreg_delta(mod_simple, xvar = "month", model = 1, gg = TRUE)
+sdmTMB::visreg_delta(mod_simple, xvar = "month", model = 2, gg = TRUE)
+
+preds <- predict(mod_simple,
+ newdata = tibble(fyear = factor(2012:2023), depth_std = 0, month = 6, program = 'obs', fDRVID = '546053', fport = 'NEWPORT'),
+ return_tmb_object = TRUE, re_form = NA, re_form_iid = NA)
+
+ind <- sdmTMB::get_index(preds, bias_correct = TRUE)
+
+ind |>
+ ggplot(aes(x = as.numeric(fyear), y = est, ymin = lwr, ymax = upr)) +
+ geom_line() +
+ geom_errorbar()
+
+
+all_hauls_yt |>
+ ggplot() +
+ geom_point(aes(x = HAUL_DURATION, y = MT))
+ geom_density(aes(x = HAUL_DURATION, col = YEAR, group = YEAR))
+
+### general observation: as I have added terms to the model, the signal in the index went from well-defined to noisy and meaningless.
+### the port (8 levels) and vessel (46 levels) effects really did it in. should think more carefully about what terms to include.
+### note 3962 hauls, 2762 positive hauls over 12 years. ~330 hauls/yr, 230 positive hauls/yr. # of hauls varies a lot by year.
+### (increases 2012-2018)
\ No newline at end of file
diff --git a/docs/data_summary_doc.qmd b/docs/data_summary_doc.qmd
index 10bea23..17e95e5 100644
--- a/docs/data_summary_doc.qmd
+++ b/docs/data_summary_doc.qmd
@@ -25,12 +25,12 @@ This documents data streams for use in the Northern Yellowtail Rockfish stock as
Some summary figures:
```{r pacfin-catch, cache=TRUE}
-load(here('data/confidential/commercial/pacfin_12-09-2024/PacFIN.YTRK.CompFT.09.Dec.2024.RData'))
+load(here('data/confidential/commercial/pacfin_12-11-2024/PacFIN.YTRK.CompFT.11.Dec.2024.RData'))
# I have checked and LANDING_YEAR = PACFIN_YEAR for all rows.
yt_n_catch_pacfin <- catch.pacfin |>
filter(
- AGENCY_CODE == 'W' | AGENCY_CODE == 'O' | COUNTY_NAME == 'DEL NORTE' | COUNTY_NAME == 'HUMBOLDT',
+ AGENCY_CODE == 'W' | AGENCY_CODE == 'O' | COUNTY_NAME == 'DEL NORTE' | COUNTY_NAME == 'HUMBOLDT', # OR, WA, N. CA
LANDING_YEAR < 2024
)
@@ -38,24 +38,29 @@ yt_n_catch_pacfin <- catch.pacfin |>
or_comm_catch <- read.csv(here("Data/Confidential/commercial/Oregon Commercial landings_433_2023.csv")) |>
tibble::as_tibble() |>
group_by(YEAR) |>
- summarise(catch_mt = sum(TOTAL))
+ summarise(catch_mt = sum(TOTAL)) |>
+ mutate(AGENCY_CODE = 'O')
-# WA URCK/UPOP reconstruction
+# WA URCK/UPOP reconstruction, use prior to 2000
wa_urck <- read.csv(here('Data/Raw_not_confidential/WA_URCK_UPOP_recon_1981-2016.csv')) |>
- filter(Species_AfterComps %in% c('YTRK', 'YTR1')) |>
+ filter(Species_AfterComps %in% c('YTRK', 'YTR1'),
+ Year < 2001) |>
group_by(Year) |>
summarise(catch_mt = sum(RoundPounds_AfterComps) * 0.453592 / 1000) |>
- mutate(AGENCY_CODE = 'W',
- model = 'URCK/UPOP')
+ mutate(AGENCY_CODE = 'W') |>
+ rename(YEAR = Year)
# Put it all together
comm_draft_landings <- yt_n_catch_pacfin |>
- filter(AGENCY_CODE != 'O') |>
+ filter(AGENCY_CODE != 'O',
+ AGENCY_CODE != 'W' | PACFIN_YEAR >= 2001
+ ) |>
group_by(PACFIN_YEAR, AGENCY_CODE)|>
summarise(catch_mt = sum(ROUND_WEIGHT_MTONS)) |>
rename(YEAR = PACFIN_YEAR) |>
bind_rows(
- mutate(or_comm_catch, AGENCY_CODE = 'O')
+ or_comm_catch,
+ wa_urck
)|>
group_by(YEAR, AGENCY_CODE) |>
summarise(catch_mt = sum(catch_mt)) |>
@@ -109,23 +114,7 @@ comm_draft_landings |>
geom_line(aes(x = Year, y = catch_mt, col = AGENCY_CODE, linetype = model), linewidth = 1) +
viridis::scale_color_viridis(discrete = TRUE) +
labs(title = 'Comparing commercial landings in 2017 model vs current', x = 'YEAR')
-
-# compare 2017 landings to current landings, WA only
-comm_draft_landings |>
- rename(Year = YEAR) |>
- mutate(model = 'PacFIN') |>
- filter(AGENCY_CODE == 'W') |>
- bind_rows(
- filter(comm2017, AGENCY_CODE == 'W')
- )|>
- bind_rows(wa_urck) |>
- ggplot() +
- geom_line(aes(x = Year, y = catch_mt, col = model), linewidth = 1) +
- viridis::scale_color_viridis(discrete = TRUE) +
- labs(title = 'Possible sources for WA landings (vert. line = 1994)', x = 'YEAR') +
- geom_vline(xintercept = 1994)
```
-URCK/UPOP = sum of RoundPounds_AfterComps for Species_AfterComps = YTR1 or YTRK, converted to mt.
Table of commercial landings by state, 1981 onward.
@@ -139,9 +128,11 @@ comm_draft_landings |>
knitr::kable(align = 'l', digits = 1)
```
-WDFW has alerted the STAT these do not include all tribal catches in recent years.
+WDFW has alerted the STAT these do not include all tribal catches in recent years. 1981-2000 are from the UPOP/URCK reconstruction, summing catches for YTRK and YTR1. 2001 onward comes from PacFIN
-Note this is for catch landed into Del Norte and Humboldt counties only.
+Oregon landings were provided directly to the STAT from ODFW and come from PacFIN and a UPOP/URCK reconstruction.
+
+California landings are for catch landed into Del Norte and Humboldt counties only.
### At-Sea landings
@@ -159,14 +150,15 @@ ashop_catch |>
```{r rec-catch-modern}
# WASHINGTON
-wa_modern <- read.csv(here('Data/Raw_not_confidential/RecFIN_WA_catch_to_2023.csv')) |>
- filter(RECFIN_WATER_AREA_NAME != 'Canada', RECFIN_YEAR <= 2023) |>
+wa_modern <- read.csv(here('Data/Confidential/Rec/CTE501-WASHINGTON-1990---2023.csv')) |>
+ filter(RECFIN_WATER_AREA_NAME != 'CANADA', RECFIN_YEAR <= 2023,
+ RECFIN_CATCH_AREA_NAME != 'SEKIU AND PILLAR POINT') |>
tibble::as_tibble() |>
group_by(RECFIN_YEAR) |>
- summarise(Dead_Catch = sum(SUM_TOTAL_MORTALITY_MT)) |>
+ summarise(Dead_Catch = sum(TOTAL_MORTALITY_MT)) |>
mutate(State = 'WA (mt)')
-wa_historical <- read.csv(here('Data/Raw_not_confidential/WA_historical_rec.csv')) |>
+wa_historical <- read.csv(here('Data/Raw_not_confidential/WA_historical_rec.csv')) |>
tibble::as_tibble() |>
filter(AREA %in% 1:4) |> # these are marine areas, per T. Tsou on 12/10/24 in meeting notes
group_by(RECFIN_YEAR) |>
@@ -247,7 +239,8 @@ ca_mrfss_catch <- read.csv(here('Data/Confidential/Rec/RecFIN_CA_MRFSS.csv')) |>
n_ca_catch_mt * weighted_ratio),
State = 'CA (mt)') |>
select(-n_ca_catch_mt, -weighted_ratio) |>
- rename(RECFIN_YEAR = YEAR)
+ rename(RECFIN_YEAR = YEAR) |>
+ filter(RECFIN_YEAR != 1980)
# Interpolate between 3-year average before and after MRFSS break
ca_mrfss_interpolate <- tibble(
@@ -298,7 +291,7 @@ For CA rec catch estimates for the assessment:
Initial length sample sizes after running `PacFIN.Utilities::cleanPacFIN()`:
```{r comm-bio, cache=TRUE}
-load(here('Data/Confidential/Commercial/pacfin_12-09-2024/PacFIN.YTRK.bds.09.Dec.2024.RData'))
+load(here('Data/Confidential/Commercial/pacfin_12-11-2024/PacFIN.YTRK.bds.11.Dec.2024.RData'))
bds_clean <- PacFIN.Utilities::cleanPacFIN(bds.pacfin, verbose = FALSE)
bds_clean |>
filter(state == 'WA' | state == 'OR' | PACFIN_GROUP_PORT_CODE == 'CCA' |
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diff --git a/docs/index.html b/docs/index.html
index 735e7c7..17c1b54 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,7 +6,7 @@
-
+
Data summary