In ecoquants/bbnj: Biodiversity Beyond National Jurisdiction

knitr::opts_chunk$set(echo=T, message=F)

library(prioritizr) # devtools::load_all("~/github/prioritizr")
#library(bbnj)       # 
devtools::load_all() # setwd(here()); devtools::install_local(force=T) 
# devtools::install_github("ecoquants/bbnj")
library(raster)
library(sf)
library(dplyr)
library(readr)
library(stringr)
library(glue)
library(here)
library(fs)
library(knitr)
library(formattable)
area   = raster::area
select = dplyr::select

#message("library done")

if (interactive()){
  wd <- file.path(here::here(), "inst/app/www/scenarios/")
  setwd(wd) # getwd()
  if (!exists("rmd")){
    #rmd <- file.path(getwd(), "s00a.bio.30pct.gl.mol50km.Rmd")
    rmd <- file.choose()
  }
} else {
  rmd <- knitr::current_input(dir = T)  
}

pfx <- rmd %>% path_ext_remove()
tif <- glue("{pfx}_sol.tif")

`r basename(pfx)`

Projection: Mol 50km
Planning unit cost: set to 1
Objective function:
- add_min_set_objective(): Minimize the cost of the solution whilst ensuring that all targets are met
Feature targets:
- 30% of each input feature amount
- **Biodiversity:
  - bio_vgpm: Vertically Generalized Production Model
  - nspp_*: number of species by taxonomic group from AquaMaps [future]
  - rls_*: Red List sum of extinction risk by taxonomic group from AquaMaps [future]
- Physical:
  - phys_vents: hydrothermal vent count
  - phys_seamounts: seamounts count (3 depth classes)
  - phys_scapes_hetero: benthic heterogeneity
Regionalization:
- none, ie global

Note: Does not include maximum catch potential data.

# variables ----
rel_target <- 0.3
prjres     <- "_mol50km" # prjres in: View(projections_tbl)
redo       <- T

# problem & solution ----
P <- projections_tbl %>% filter(prjres == !!prjres)
if (!file.exists(tif) | redo){

# planning unit: ----

  r_pu_id <- get_d_prjres("r_pu_id", prjres) # plot(r_pu_id)
  r_pu <- setValues(r_pu_id, 1) %>% 
    mask(r_pu_id) # plot(r_pu)

# biodiversity: 2100 ----
  s_bio_gmbi <- get_d_prjres("s_bio_gmbi", prjres)
  lyrs_bio_future <- str_subset(names(s_bio_gmbi), "2100$") %>% 
  setdiff(c("nspp_all_2100", "nspp_na_2100", "rls_all_2100", "rls_na_2100"))
  s_bio_future <- subset(s_bio_gmbi, lyrs_bio_future)

# features ----
  s_features <- stack(
    get_d_prjres("r_vgpm", prjres),
    s_bio_future,
    #raster(s_fish_gfw, "mean_scaled_profits_with_subsidies") %>%
    #    gap_fill_raster() %>%
    #    rescale_raster(inverse=T),
    #raster(s_fish_ubc, "mcp_2004"),
    get_d_prjres("s_phys_seamounts",prjres),
    get_d_prjres("r_phys_vents",prjres),
    get_d_prjres("r_phys_scapes_hetero",prjres))

  names(s_features) <- c(
    "bio_vgpm",
    names(s_bio_future),
    #"fish_profit.subs"
    #"fish_mcp.2004",
    sprintf("phys_seamounts_%sm", c("0to200","gt200to800","gt800")),
    "phys_vents",
    "scapes_hetero")

# problem ----
  p <- problem(r_pu, s_features) %>%
    add_min_set_objective() %>% 
    add_relative_targets(rel_target)

# solve ----
  tif <- solve_log(p, pfx, redo=redo)
}