Towards Machine Learning-based Fish Stock Assessment
This work addresses the need for more accurate fish stock assessments for sustainable fisheries management, particularly in changing ecosystems, but it is incremental as it builds on existing methods.
The paper tackled the problem of low forecast performance in fish stock assessment models by proposing a hybrid model that combines classical statistical models with gradient boosted trees, resulting in improved forecast accuracy for recruitment and spawning stock biomass in most of the five stocks tested.
The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of relevant stock parameters like recruitment or spawning stock biomass, especially in ecosystems that are changing due to global warming and other anthropogenic stressors. In this paper, we investigate the use of machine learning models to improve the estimation and forecast of such stock parameters. We propose a hybrid model that combines classical statistical stock assessment models with supervised ML, specifically gradient boosted trees. Our hybrid model leverages the initial estimate provided by the classical model and uses the ML model to make a post-hoc correction to improve accuracy. We experiment with five different stocks and find that the forecast accuracy of recruitment and spawning stock biomass improves considerably in most cases.