DOI: 10.5281/zenodo.13757369
title: Global monthly catch of tuna, tuna-like and shark species (1950-2021) by 1° or 5° squares (IRD level 2)

description: This dataset lists global catches of tuna, tuna-like, and shark species from 1950 to 2021. Catches are stratified by month, species, gear type, fishing fleet, fishing mode (i.e., type of school used), area (1° or 5° squares), and unit of catch (weight or number). This dataset was computed using public domain catch datasets released by FIRMS.

IRD Level 2 refers to the processes applied by the French National Research Institute for Sustainable Development (IRD) to primary datasets, generating this refined dataset.

This work aims not only to provide a biomass dataset for scientific purposes but also to identify and address data inconsistencies, improving the overall process. All code, materials, and packages used are available on the GitHub repository, firms-gta/geoflow-tunaatlas, along with detailed documentation on the impact of each specific treatment.

Warning: This dataset is designed to enhance the understanding of fish counts at level 0. It is not suitable for accurately georeferencing data by country or fishing fleet and should not be used for studies on fishing zone legality or quota management. While it offers a georeferenced footprint of captures to reflect reported biomass more closely, significant uncertainty remains regarding the precise locations of the catches.

Global level 2 processing includes the conversion and raising of georeferenced catch data to match nominal dataset values.

Appendix work:



The github repository (DOI:10.5281/zenodo.14039665 Allowing to reproduce this dataset)

A Shiny app has been created to easily visualize catch data in CWP format: ghcr.io/firms-gta/tunaatlas_pie_map_shiny_cwp_database:latest. The docker image is based on this DOI dataset and allows to explore it.

Data paper incoming.


If you are interested in creating a customized version of this Global Tuna Atlas with specific filters or adjustments based on particular issues, please feel free to reach out to us. 

 
description: Major differences from previous work:

For level 2: 



Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy.

Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures.

Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations.

Strata for which catches in tons are raised to match nominal data have had their numbers removed.

Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting.

Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets.

The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R")


Level 0 dataset has been modified creating differences in this new version notably : 



The species retained are different; only 32 major species are kept.

Mappings have been somewhat modified based on new standards implemented by FIRMS.

New rules have been applied for overlapping areas.

Data is only displayed in 1 degrees square area and 5 degrees square areas.


The data is enriched with "Species group", "Gear labels" using the fdiwg standards.

Recommendations:

To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs.

In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix.

Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets.