Spatial Catchability

library(marlin)

library(tidyverse)
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#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(marlin)

library(tidyverse)

library(gganimate)

library(ggridges)

years <- 20

resolution <-  10

patches <- resolution^2

seasons <- 8

steps <- years * seasons

time_step <-  1 / seasons

spatial_catchability<-  expand_grid(x = 1:resolution, y = 1:resolution) %>%
  mutate(habitat =  dnorm(x, resolution / 2, .5 *resolution) *  dnorm(y, resolution / 2, .5 * resolution)) %>% 
  mutate(habitat = habitat * (x >= 4)) 

bigeye_catchability <- spatial_catchability %>% 
  pivot_wider(names_from = y, values_from = habitat) %>% 
  select(-x) %>% 
  as.matrix()


fauna <- 
  list(
    "bigeye" = create_critter(
      scientific_name = "thunnus obesus",
      adult_diffusion = 10, # standard deviation of the number of patches moved by adults
      recruit_diffusion = 5,
      density_dependence = "global_ssb", 
      seasons = seasons,
      init_explt =  1,
      explt_type = "f",
      spawning_seasons = c(7),
      sigma_r = 0,
      rec_ac = 0,
      linf = 100,
      vbk = 0.2,
      age_mature = 4,
      m = 0.2,
      weight_a = 1e-4,
      weight_b = 3,
      max_age = 15,
      query_fishlife = FALSE,
      ssb0 = 42
    )
  )


fleets <- list(
  "longline" = create_fleet(
    list("bigeye" = Metier$new(
      critter = fauna$bigeye,
      price = 10,
      sel_form = "logistic",
      sel_start = 1,
      sel_delta = .01,
      catchability = 1e-3,
      p_explt = 1,
      spatial_catchability = bigeye_catchability
    )
    ),
    base_effort = 0,
    resolution = resolution
  )
)


# fleets <- tune_fleets(fauna, fleets) 


spatial_q_sim <- simmar(fauna = fauna,
               fleets = fleets,
               years = years)



test<- process_marlin(sim = spatial_q_sim, time_step = time_step)


test$fauna |> 
  filter(step  < 5) |> 
  group_by(step, age) |> 
  summarise(n = sum(n)) |> 
  ggplot() +
  geom_col(aes(age, n, fill = age == min(age))) +
  facet_wrap(~step) + 
  theme_minimal()
#> `summarise()` has grouped output by 'step'. You can override using the
#> `.groups` argument.

spatial_catchability |> 
  ggplot(aes(x,y,fill = habitat)) + 
  geom_tile()

plot_marlin(test)

plot_marlin(test, plot_type = "space")
#> Warning in plot_marlin(test, plot_type = "space"): Can only plot one time step
#> for spatial plots, defaulting to last of the supplied steps

plot_marlin(test, plot_type = "length", fauna = fauna, plot_var = "n")
#> Warning in plot_marlin(test, plot_type = "length", fauna = fauna, plot_var =
#> "n"): trying to plot too many steps at once, cutting down to 10
#> dropping recruits from plot since drop_recruits = TRUE