Global Fishing Effort Model Data and Shiny App

Fishing exerts significant pressure on marine ecosystems, influencing fish stock dynamics, biodiversity, and food security on a global scale. Effective fisheries management and marine conservation efforts rely on accurate assessments of fishing effort—the amount of time and resources expended in fishing activities—at appropriate spatial and temporal scales. However, existing datasets on global fishing effort are fraught with limitations, including a lack of spatial resolution in country-level statistics and incomplete coverage in vessel tracking systems. This study aims to address these gaps by developing a novel approach to remapping global fishing effort, integrating FAO country-level fishing effort data, national logbooks, AIS-derived fishing patterns from the Global Fishing Watch project, and vessel detections derived from satellite imagery into a statistically driven spatial allocation framework.

We employ a statistical remapping approach similar to that of McDonald et al. 2024 that spatially allocates fishing effort based on multiple data sources, including country-level effort data from Rousseau et al. (2019), AIS data from Global Fishing Watch, vessel detections from satellite imagery, and environmental and geographical factors (e.g., sea surface temperature, chlorophyll-a concentration, bathymetry, distance to productive fishing grounds) as well as governance variables (e.g., EEZ boundaries, FAO Major Fishing Areas, etc.) from different sources. Our approach ensures that the spatial allocation of fishing effort reflects real-world constraints and ecological drivers and enables us to allocate country-level fishing effort data across the global ocean in a way that aligns with observed vessel behavior, known ecological preferences, and regulatory constraints. The resulting dataset offers a 1 degree resolution for industrial fishing and 0.5 degree resolution for artisanal fishing. 

This repository contains all code developed to produce the shiny application. This app provides an interactive platform for exploring and downloading mapped global industrial fishing effort data. Users can filter by year, country, sector, gear type (industrial only), vessel length, Exclusive Economic Zone (EEZ), and FAO statistical area using the selection sidebar in each tab.

This latest version of our mapping methodology integrates country-level fishing effort estimates ( Rousseau et al. 2019 ) with a statistical spatial allocation model. 

For each fishing country, we trained a two-stage hurdle random forest model to predict the spatial distribution of fishing effort:

• The first stage predicts whether fishing occurs in each grid cell globally from 1950-2017.
• The second stage estimates the intensity of fishing effort in each cell globally from 1950-2017.

By multiplying the predictions from both stages, we obtain the estimated fishing intensity (the proportion of a country's total fishing effort ) in each cell where fishing is predicted to occur. These estimates are then scaled to kW days of fishing effort using total fishing effort values from Rousseau et al. 2019.

Mapped effort estimates are provided as nominal fishing effort (kilowatt days) or effective fishing effort (kilowatt days), with a spatial resolution of 1° cell (0.5° for artisanal fishing), spanning the years 1950-2017. To estimate effective effort, we have assumed a year-on-year increase in technical efficiency of 3.5%, as in Rousseau et al. 2019.

The Global Fishing Effort Explorer Shiny app was created, and is under continuous development by Gage Clawson, Camilla Novaglio & Julia Blanchard from the Institute for Marine & Antarctic Studies (IMAS), University of Tasmania.

Caveats and limitations

Users should be aware that historical predictions (1950-2014) may not capture:

• Technological changes in fishing capabilities
• Evolution of fishing strategies and practices
• Changes in management regulations
• Shifts in target species or fishing grounds due to socio-economic factors

If you have any ideas on how to improve this app or if you found any issues, you can “create an issue” in our GitHub repository. For general enquiries contact Julia Blanchard at julia.blanchard@utas.edu.au

The development of this app was funded by the Food and Agriculture Organization of the United Nation (FAO). We would also like to acknowledge the use of computing facilities provided by Digital Research Services, IT Services at the University of Tasmania.