In pbs-assess/gfranges: Groundfish Ranges

knitr::opts_chunk$set(
  fig.keep = "all", fig.show = "asis", dpi = 600,
  fig.width = 6, fig.height = 5,
  echo = FALSE, warning = FALSE, message = FALSE
)
options(scipen = 999)
options(digits = 4)

library("gfranges")
library("dplyr")
library("ggplot2")

ggplot2::theme_set(gfplot::theme_pbs())

Set cell sizes and scale

scale_fac <- 2 # means that the raster is reprojected to _ X original grid (2 km)

d_all <- readRDS(paste0(
    "data/sablefish/predictions-sablefish-tv-depth-only-1n3n4n16-mature-biomass-prior-FALSE.rds"
  )) %>% filter(year >= 2008)

all_years <- unique(d_all$year)
length(all_years)
grid <- select(d_all, X,Y, depth, ssid) %>% unique()

grid_by_year <- data.frame(matrix(ncol=0, nrow= nrow(grid) * length(all_years)))
grid_by_year$ssid <- rep(grid$ssid, times = length(all_years))
grid_by_year$X <- rep(grid$X, times = length(all_years))
grid_by_year$Y <- rep(grid$Y, times = length(all_years))
grid_by_year$depth <- rep(grid$depth, times = length(all_years))
grid_by_year$year <- rep(all_years, each = nrow(grid))

Calculate mean and trend in log fishing pressure

fishing <- readRDS("data/_fishing_effort/fishing-effort-grid.rds")


d <- left_join(grid_by_year, fishing, by = c("X", "Y", "year"))

d$log_effort[is.na(d$log_effort)] <- -2.3
d$effort[is.na(d$effort)] <- 0
d$effort1[is.na(d$effort1)] <- 0
d$effort2[is.na(d$effort2)] <- 0
d$catch[is.na(d$catch)] <- 0
d$catch <- d$catch/1000
d$log_catch <- log(d$catch + 1)

### BOTH ODD YEAR ###
d1n3 <- d %>% filter(ssid %in% c(1,3))

catch1n3 <- vocc_gradient_calc(d1n3, "log_catch",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)

ssid1n3 <- vocc_gradient_calc(d1n3, "log_effort",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)

depth1n3 <- vocc_gradient_calc(d1n3, "depth",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)
ssid1n3$depth <- depth1n3$mean



# catch1n3$catch <- catch1n3$mean


### WCVI ###
d4 <- d %>% filter(ssid %in% c(4))

catch4 <- vocc_gradient_calc(d4, "log_catch",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)

ssid4 <- vocc_gradient_calc(d4, "log_effort",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)
depth4 <- vocc_gradient_calc(d4, "depth",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)
ssid4$depth <- depth4$mean



### WCHG ###
d16 <- d %>% filter(ssid %in% c(16))

catch16 <- vocc_gradient_calc(d16, "log_catch",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)

ssid16 <- vocc_gradient_calc(d16, "log_effort",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)
depth16 <- vocc_gradient_calc(d16, "depth",
  scale_fac = scale_fac,
  quantile_cutoff = 0.05
)
ssid16$depth <- depth16$mean

##### combine ssids
# fished <- rbind(ssid1n3, ssid4, ssid16)
# 
# fished <- fished %>%
#   mutate(
#     fishing_trend = trend,
#     mean_effort = exp(mean),
#     fishing_vel = velocity,
#     fishing_grad = gradient
#   ) %>%
#   dplyr::mutate(X = x, Y = y) %>%
#   gfplot:::utm2ll(., utm_zone = 9)

fished <- readRDS(file = paste0("data/fishing-effort-change-w-depth.rds"))

catch <- rbind(catch1n3, catch4, catch16)

catch <- catch %>%
  mutate(
    catch_trend = trend,
    mean_catch = exp(mean)-1,
    catch_vel = velocity,
    catch_grad = gradient
  ) %>%
  dplyr::mutate(X = x, Y = y) %>%
  gfplot:::utm2ll(., utm_zone = 9)

# fished$squashed_effort <- collapse_outliers(fished$mean_effort, c(0, 0.99))
# hist(fished$squashed_effort)

fished <- left_join(fished, catch)
fished <- fished %>%
  select(X, Y, x, y, fishing_trend, mean_effort, fishing_vel, fishing_grad, depth, catch_trend, mean_catch, catch_vel, catch_grad)

saveRDS(fished, file = paste0("data/fishing-effort-change-w-depth.rds"))