Fisheries model options

Hi @MijuKim,

I wanted to follow up with your questions about fisheries and how to model them. You said that you have landing data from 1990- 2016 for over 100 species, and you have spatial information for where it was sold. You wanted to know if you could calculate B/Bmsy from this data, and if we had code that could help you do it.

We would recommend you NOT using your landings to calculate B/Bmsy. There are models that you could use if you had CATCH instead of landings, but those models make a lot of assumptions so that would not be the first choice.

Here are the options we suggest:

Option 1: find new B/Bmsy data and keep the FIS goal model the same. You look to see if someone has already calculated B/Bmsy data within Korea. This would be the best option, but it might not be possible or you would have that data already...

Option 2: use B/Bmsy data from global assessments and keep the FIS goal model the same.

We calculate B/Bmsy scores for South Korea using data from the Ram Legacy Database and you can see how we do it here. But before you look at how we did it, have a look at the species that you could use. There are only 8 species that would have B/Bmsy data, and for all of South Korea (not for each of your 11 regions)

library(tidyverse)

## read in processed data
ram_bbmsy <- readr::read_csv('https://raw.githubusercontent.com/OHI-Science/ohiprep/master/globalprep/fis/v2017/int/ram_bmsy.csv') 

## filter only South Korea, global rgn_id 20
ram_bbmsy_kor <- ram_bbmsy %>%
  filter(rgn_id == 20) 

ram_bbmsy_kor %>% distinct(stock_id)
#> # A tibble: 8 x 1
#>                stock_id
#>                   <chr>
#> 1   Thunnus_alalunga-61
#> 2  Makaira_nigricans-61
#> 3 Thunnus_orientalis-61
#> 4      Kajikia_audax-61
#> 5    Xiphias_gladius-61
#> 6     Thunnus_obesus-61
#> 7 Katsuwonus_pelamis-61
#> 8  Thunnus_albacares-61

Your model here could be these 8 species global bbmsy (sustainability portion) and you could weight them when you combine them by where it is purchased from your landings data, if that data matched up. The scoring would reflect how sustainable fish is in each of the 11 regions. So Option 2 is probably better than Option 1, but you would have very few species, at a coarse spatial scale.

Option 3: redefine/redesign the FIS model using landing data. The philosophy behind the FIS goal is that it should capture the food that the ocean provides to people. When we use B/Bmsy, we are saying that the status of the fish stocks, with a target of MSY, represents the food sustainably provided to people.

But, your landings data are more about markets. What if your model was more about nutrition? If you knew how much fish every person per region needed, then you could set a "target fish consumption value", and calculate the pounds of fish being sold per region and how much people need? This "target fish consumption value" could be some kind of nutritional requirement that the government sets or from the literature, or some kind historical amount of fish that South Koreans used to eat that you'd like to get back to as a target.

Assumptions here would be that landings = total fish sold (and not sold in external markets)

Those are the options that came to us with what we know about your data; we can keep talking about it here and if there is more data available!

Cheers, Julie

OHI-Science / kor

Fisheries model options #5