Documentation

How to cite:

DOI

Emily Rodriguez & Juan Carlos Villaseñor-Derbez. (2026). RFMO Tuna Catch and Effort Data (Version v0.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.21494710

Tuna RFMO Data User Guide

This document provides an overview for users utilizing the harmonized tuna RFMO datasets in this repository. It explains what data are available, how they are structured, and how to load data.

What these data are

This repository harmonizes publicly available catch and effort datasets from three major tuna Regional Fisheries Management Organizations (RFMOs):

IATTC
ICCAT
WCPFC

Each RFMO publishes data in different formats, resolutions, and naming conventions. This repository standardizes them into a unified format so users can work with a single, consistent set of catch and effort datasets.

Available datasets

Files located under data/output/

Dataset	Gear	Spatial	Temporal	Temporal Coverage	Total Catch (mt)	By Flag	RFMOs Included	Dataset	Formats
`allrfmo_month_1deg_purseseine.rds`	purse seine	1°×1°	month	1958-2024	64,848,155	no	IATTC, ICCAT, WCPFC	`allrfmo_month_1deg_purseseine`	`.csv`, `.rds`
`allrfmo_year_1deg_purseseine.rds`	purse seine	1°×1°	year	1958-2024	67,807,604	no	IATTC, ICCAT, WCPFC	`allrfmo_year_1deg_purseseine`	`.csv`, `.rds`
`allrfmo_year_1deg_purseseine_flag.rds`	purse seine	1°×1°	year	1958-2024	54,633,092	yes	IATTC, ICCAT, WCPFC	`allrfmo_year_1deg_purseseine_flag`	`.csv`, `.rds`
`allrfmo_month_5deg_longline.rds`	longline	5°×5°	month	1950-2024	14,527,054	no	IATTC, ICCAT, WCPFC	`allrfmo_month_5deg_longline`	`.csv`, `.rds`
`allrfmo_month_5deg_longline_flag.rds`	longline	5°×5°	month	1950-2024	13,113,173	yes	IATTC, ICCAT, WCPFC	`allrfmo_month_5deg_longline_flag`	`.csv`, `.rds`
`allrfmo_year_5deg_longline.rds`	longline	5°×5°	year	1951-2024	10,944,941	no	IATTC, ICCAT, WCPFC	`allrfmo_year_5deg_longline`	`.csv`, `.rds`
`allrfmo_year_5deg_longline_flag.rds`	longline	5°×5°	year	1951-2024	11,134,893	yes	IATTC, ICCAT, WCPFC	`allrfmo_year_5deg_longline_flag`	`.csv`, `.rds`

Additonal length data

Files located under data/output/

Dataset	Gear	Spatial	Temporal	Temporal Coverage	By Flag	RMFOs Included	Formats
`wcpfc_length_data`	multi	5°×5°	multi	1953-2025	no	WCPFC	`.rds`

Available variables for catch and effort data

Variables	Description
`rfmo`	Related RFMO
`flag`	Flag code in ISO 3166-1 alpha-3 (excluding `SUN`)
`lon`	Longitude of fishing activity
`lat`	Latitude of the fishing activity
`year`	Year of record
`month`	Month of record
`effort_set`	Number of fishing sets
`effort_day`	Number of fishing days
`effort_t_hooks`	Thousands of hooks
`catch_tot`	Total catch (mt)
`catch_tot_mt`	Total catch (mt) (for longline datasets)
`catch_tot_num`	Total catch (numbers of tuna caught) (for longline datasets)
`catch_skj`	Catch of skipjack tuna (mt)
`catch_alb`	Catch of albacore tuna (mt)
`catch_bet`	Catch of bigeye tuna (mt)
`catch_yft`	Catch of yellowfin tuna (mt)

Additional variables specific to length datasets

Variables	Description
`tstrat`	Temporal stratification
`gear`	Gear type
`species`	Tuna species
`length_cm`	Fish length in centimeters
`length_bin`	Length class/bin in cm based on interval (e.g., 1,2 cm bins)

Spatial coverage

Downloading `.rds` data in R

The harmonized tuna RFMO datasets can be loaded directly into R from this GitHub repository without cloning the repository. The datasets for R are stored as .rds files in the data/output/ directory.

To download a dataset:

Navigate to data/output/ in the GitHub repository.
Select the dataset you want to use.
Copy the Raw file URL (not the regular GitHub webpage URL).
Pass the URL to readRDS() as shown below.

For example:

raw_url <- "https://raw.githubusercontent.com/jcvdav/tuna_data/main/data/output/allrfmo_month_1deg_purseseine.rds"

tuna_data <- readRDS(url(raw_url))

head(tuna_data)

# A tibble: 6 × 12
  rfmo    lon   lat  year month effort_set effort_day catch_tot catch_skj
  <chr> <dbl> <dbl> <dbl> <dbl>      <dbl>      <dbl>     <dbl>     <dbl>
1 wcpfc  148. -42.5  1999     3         17       13.4       64        64 
2 wcpfc  172. -41.5  2012     3         20       20        804.      804.
3 wcpfc  172. -41.5  2012     4          7        7        346.      346.
4 wcpfc  172. -41.5  2014     4         21       21       1376.     1376.
5 wcpfc  148. -40.5  2000     3         13       10.2      122       122 
6 wcpfc  172. -40.5  2014     3         12       12        836       836 
# ℹ 3 more variables: catch_alb <dbl>, catch_bet <dbl>, catch_yft <dbl>

Downloading `.csv` Data

.rds files are recommended for R users, but .csv files are also available for Excel users.

To download a .csv dataset to your computer:

Navigate to the data/output/ directory in the GitHub repository.
Select the .csv file you would like to download.
Click the Download raw file button.
Save the file to your desired location on your computer.

Dataset overviews

Purse seine catch time series

Show code for creating catch time series

### Creating a purse seine catch time series ###################################

# Combine purse seine datasets into a single table
ps_ts <- bind_rows(
  
  # Aggregate each dataset's catch into annual catch totals
  
  # Monthly purse seine data
  month_1deg_ps |>
    
    # Organize observations into groups by year
    group_by(year) |>
    
    # Calculate the total catch each year
    summarise(catch = sum(catch_tot, na.rm = TRUE)) |>
    
    # Create a new column called "dataset" that identifies which source dataset produced each time series
    mutate(dataset = "Monthly"),
  
  # Yearly purse seine data
  year_1deg_ps |>
    group_by(year) |>
    summarise(catch = sum(catch_tot, na.rm = TRUE)) |>
    mutate(dataset = "Yearly"),

  # Yearly purse seine data, by flag
  year_1deg_ps_flag |>
    group_by(year) |>
    summarise(catch = sum(catch_tot, na.rm = TRUE)) |>
    mutate(dataset = "Yearly (flag)")
)

#### Plot the summarized data ##################################################

ggplot(ps_ts,
       aes(x = year, 
           y = catch, 
           color = dataset)) +
  
  # geom_line() adds a line connecting observations through time
  geom_line(linewidth = 1) +
  scale_color_manual(
    values = c(
      "Monthly" = "#005F73",
      "Yearly" = "#0A9396",
      "Yearly (flag)" = "#94D2BD"
    )) +
  theme_bw() +
  labs(
    x = "Year",
    y = "Total catch (mt)",
    color = NULL
  )

Longline catch time series