OHI 2023: Food Provision

1 Summary

This script combines the fisheries catch data with the mariculture production data to create the weights for how much of each score will affect the entire food provision score.

2 Updates from previous assessment

• Mariculture data was updated since last scenario year but fisheries wasn’t
• Updated scenario year input/some file paths to be more programmatic
• Updated labeling of the graphs

3 Methods

## load libraries
library(dplyr)
library(tidyr)
library(here)

scen_year <- 2023 # change to latest year
prev_scen_year <- scen_year - 1

setwd(here::here("globalprep","fp", paste0("v", scen_year)))

## Load FAO-specific user-defined functions
source('http://ohi-science.org/ohiprep_v2021/workflow/R/common.R')

4 Import Output Data: Mariculture data

Mariculture production in tonnes.

mar <- read.csv(file.path("..", "..", "mar", paste0("v", scen_year), "output", "MAR_FP_data.csv")) # see metadata in its prep

Fisheries data.

fis <- read.csv(file.path("..", "..", "fis", paste0("v", scen_year), "output", "FP_fis_catch.csv")) %>%
  dplyr::select(rgn_id, year, fis_t = fis_catch) # see metadata in its prep

5 Wrangle

5.1 Tidy MAR data

mar_tidy <- mar %>%
  group_by(rgn_id, year) %>%
  summarize(mar_t = sum(value, na.rm=TRUE)) %>%
  dplyr::select(rgn_id, year, mar_t) %>%
  ungroup()

# this one is turning to NA in FP
filter(mar_tidy, rgn_id == 95) # ok, this makes sense

5.2 Tidy FIS data

fis_tidy <- fis %>%
  mutate(fis_t = ifelse(fis_t == 0, NA, fis_t)) %>%  # 11 NA values is correct (there were 0 in v2023)
  group_by(rgn_id) %>%
  arrange(year) %>%
  fill(fis_t) %>% 
  ungroup()

5.3 Combine MAR and FIS

Adjust years so they are equivalent.

adjust <- max(mar_tidy$year) - max(fis_tidy$year)

mar_adjust <- mar_tidy %>%
  mutate(year = year - adjust)

tmp <- full_join(fis_tidy, mar_adjust, by = c('rgn_id', 'year')) # v2023: removed all = TRUE because was not a valid argument, did not see an equivalent

## If NA, turn it into a 0 before weighting
tmp_weights <- tmp %>%
  mutate(fis_t = ifelse(is.na(fis_t), 0, fis_t)) %>%
  mutate(mar_t = ifelse(is.na(mar_t), 0, mar_t)) %>%
  mutate(w_fis = fis_t/(fis_t + mar_t)) %>%
  mutate(w_fis = ifelse(mar_t == 0 & fis_t == 0, NA, w_fis)) %>%
  filter(year >= 2005) %>%
  dplyr::select(rgn_id, year, w_fis) 

hist(tmp_weights$w_fis,
     main = "Weights",
     xlab = "Value")

6 Data check

Compare to previous year data

compare <- read.csv(paste0("../../fp/v", prev_scen_year, "/output/wildcaught_weight.csv")) %>%
  rename(w_fis_old = w_fis) %>%
  left_join(tmp_weights, by = c('rgn_id', 'year'))
plot(compare$w_fis_old, compare$w_fis,
     xlab = paste0("Old data (v", prev_scen_year, ")"),
     ylab = paste0("New data (v", scen_year, ")"))
abline(0, 1, col="red")

7 Save data

write.csv(tmp_weights, 'output/wildcaught_weight.csv', row.names=FALSE)

## add gf file (no gapfilling)
tmp_gf <- tmp_weights %>%
  mutate(w_fis = 0) %>%
  dplyr::select(rgn_id, year, gapfilled = w_fis)

write.csv(tmp_gf, 'output/wildcaught_weight_gf.csv', row.names=FALSE)