Towards Machine Learning-based Fish Stock Assessment
Stefan Lüdtke Maria E. Pierce
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.