Novel sequential modeling framework improves phytoplankton biomass predictions in response to multiple environmental stressors

Abstract Understanding the impacts of multiple environmental stressors on phytoplankton biomass is crucial for predicting marine ecosystem responses under global climate change. This study employed a sequential modeling framework integrating principal component analysis, generalized additive models, and artificial neural networks to improve predictions of phytoplankton chlorophyll a concentrations in the Taiwan Strait. Analyzing a decadal dataset, we found that a 2°C rise in sea surface temperature and a 0.2 pH decline will each lead to an 11.3% reduction in chlorophyll a biomass, whereas nitrogen enrichment is expected to increase it by only 2.8%. The combined effects of these stressors will result in an 18.3% reduction, with the most significant declines occurring in high‐chlorophyll areas during algal blooms. Compared to simpler models, our approach improved accuracy by reducing overestimation biases, particularly under acidification scenarios, highlighting the need for advanced, multivariate models in forecasting phytoplankton dynamics under global changes.