Summary

This script takes the SAUP 2021 catch data and creates 1 data layer:

An average non-industrial catch dataset used to weight B/Bmsy values in the fisheries model. For this dataset, the catch is assigned to FAO and OHI regions.

IMPORTANT NOTE: the fisheries subgoal data preps will need to be run first before this can be completed. That is where the RAM stock status dataprep and the fisheries catch download occurs.

Updates from previous assessment

This is a new layer for the artisanal opportunities goal. And a new fisheries catch dataset.

Data Source

Reference: Pauly D., Zeller D., Palomares M.L.D. (Editors), 2020. Sea Around Us Concepts, Design and Data (seaaroundus.org).

Downloaded: August 24, 2021

Description: Tons per year and SAUP region with information on sector type, industry type, fishing entitity, reporting status and taxonomic information.

Time range: 1950 - 2018

Format: CSV

Additional Information: Methods

Setup

Note: the same data was used to prepare fisheries subgoal scores. We will be using the prepped annual catch csv file prepared in the mazu fis/ folder. This means that fis will need to be run before this.

knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'figs/',message = FALSE, warning = FALSE, echo = TRUE, eval=FALSE)

## Libraries
library(readr)
library(dplyr)
library(raster)
library(parallel)
library(purrr)
library(stringr)
library(tidyr)
library(foreach)
library(here)
library(sf)
library(tidyverse)
library(maps)
library(readxl)

setwd(here::here("globalprep/ao/v2021"))
source('../../../workflow/R/common.R')

## Paths for data
fis_path = file.path(dir_M,"git-annex/globalprep/fis/v2021/int") # filepath to fisheries path

Load Data

Non-Industrial Catch

The SAUP data is separated into commercial, recreational, subsistence, and artisanal fishing. We will only grab the artisanal and subsistence catch for Artisanal Opportunities.

Look at catch data

## read in the catch data
catch <- read.csv(file.path(fis_path, "stock_catch_by_rgn_taxa.csv"))
sort(unique(catch$rgn_id))

Aggregate Nonindustrial catch

Aggregate catch per OHI region and FAO area. This catch will be used twice.

The catch is used to weight scores per region. For this we need to use catch records, including those not reported at the species level. See note below.
The catch data at species level is used to calculate stock status (BBmsy) per stock (remember that our definition of a stock is a species caught within a single FAO area).

Note: Save IUU and Reported only (CatchTotal) as the catch sum.

NonIndustrial Catch

output_df <- catch %>% 
    dplyr::filter(discard_id != 1) %>% # filter out discards
    dplyr::filter(other_id != 1) %>% # filter out catch not used for humans
    dplyr::select(year, rgn_id, fao_rgn, TaxonName, CommonName, human_use_id, fishing_sector, tons, stock_id, TaxonKey) %>%
    dplyr::filter(fishing_sector %in% c("Subsistence", "Artisanal")) %>% 
    dplyr::group_by(year, rgn_id, fao_rgn, TaxonName, CommonName, stock_id, TaxonKey) %>%
    dplyr::summarise(tons = sum(tons)) %>% 
    dplyr::ungroup()

write.csv(output_df, file = file.path(dir_M,'git-annex/globalprep/ao/v2021/int/ao_stock_catch_by_rgn_taxa.csv'), row.names=FALSE)

test <- output_df %>%
  group_by(year) %>%
  summarise(sum = sum(tons)) # looks good

Data Check

Take a look at catch data with missing ohi and fao regions in stock_catch_by_rgn_taxa. These have taxon key matches, but no ohi or fao regions assigned to them.

region_data()

df <- read_csv(file.path(dir_M,'git-annex/globalprep/ao/v2021/int/ao_stock_catch_by_rgn_taxa.csv')) %>%
  left_join(rgns_eez)

# they all have ohi or fao regions; however there are only 197 regions with artisanal or subsistence catch in the SAUP data..

Prep data for mean catch

Wrangle

Mean catch data is used to weight the B/Bmsy values in the fishery subgoal.

file <- file.path(dir_M,'git-annex/globalprep/ao/v2021/int/ao_stock_catch_by_rgn_taxa.csv')

catch <- read_csv(file) %>%
  rename(common = CommonName, fao_id = fao_rgn, species=TaxonName)

summary(catch)


## filter out non ohi eez regions 
catch <- catch %>%
  filter(!is.na(rgn_id)) %>%
  filter(!is.na(fao_id)) %>%
  filter(rgn_id <= 250) %>%
  filter(rgn_id != 213)


## calculate total annual catch for each stock
catch <- catch %>%
  dplyr::select(year, rgn_id, fao_id, stock_id, TaxonKey, tons) %>%
  group_by(rgn_id, fao_id, TaxonKey, stock_id, year) %>%
  summarize(catch = sum(tons)) %>%
  ungroup()

 
# missing <- c("Belgium", "Latvia", "Estonia", "Kerguelen Islands", "Faeroe Islands", "Iceland", "Finland", "Poland", "Lithuania", "Sweden")
# 
# test <- catch %>%
#   left_join(rgns_eez) %>%
#   filter(rgn_name %in% missing)
# sort(unique(test$rgn_name))
# sort(missing)

Take a look at a few stocks.

data.frame(dplyr::filter(catch, stock_id == "Marine_fishes_not_identified-57" & rgn_id==1))

Fill in Zeros

For years with no reported catch, add zero values (after first reported catch)

## these data have no zero catch values, so add years with no reported catch to data table:
catch_zeros <- catch %>%
  spread(year, catch) %>%
  data.frame() %>%
  gather("year", "catch", num_range("X", min(catch$year):max(catch$year))) %>%
  mutate(year = as.numeric(gsub("X", "", year))) %>%
  mutate(catch = ifelse(is.na(catch), 0, catch))

## this part eliminates the zero catch values prior to the first reported non-zero catch   
catch_zeros <- catch_zeros %>%
  group_by(fao_id, TaxonKey, stock_id, rgn_id) %>%
  arrange(year) %>%
  mutate(cum_catch = cumsum(catch)) %>%
  filter(cum_catch > 0) %>%
  dplyr::select(-cum_catch) %>%
  ungroup()

Calculate Mean Catch

Calculate mean catch for ohi regions (using data from 1980 onward). These data are used to weight the RAM b/bmsy values.

mean_catch <- catch_zeros %>%
  filter(year >= 1980) %>%
  group_by(rgn_id, fao_id, TaxonKey, stock_id) %>%
  mutate(mean_catch = mean(catch, na.rm=TRUE)) %>% # mean catch for each stock (in a specific ohi-fao region)
  filter(mean_catch != 0)  %>%      ## some stocks have no reported catch for time period
  ungroup()

Check out the data

data.frame(filter(mean_catch, stock_id == "Marine_fishes_not_identified-57" & rgn_id==1)) # includes finfishes (100139) and other marine fishes (100039)

Toolbox formatting and save

options(scipen = 999) # to prevent taxonkey from turning into scientific notation

mean_catch_toolbox <- mean_catch %>%
  mutate(stock_id_taxonkey = paste(stock_id, TaxonKey, sep="_")) %>%
  dplyr::select(rgn_id, stock_id_taxonkey, year, mean_catch) %>%
  filter(year >= 2001) %>%  # filter to include only analysis years
  data.frame()

write.csv(mean_catch_toolbox, "intermediate/mean_catch.csv", row.names=FALSE) ## save the total mean catch csv for reference if needed


length(unique(mean_catch_toolbox$rgn_id)) # only 196 regions... I suspect we will gapfill the missing regions...

old <- read.csv("intermediate/mean_catch_watson.csv")

length(unique(old$rgn_id)) # only 210 regions

OHI 2021: Artisanal Opportunities, Preparing non-industrial catch data for BBmsy calculations

Compiled on Fri Nov 12 14:34:39 2021 by sgclawson