Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning

To elucidate the complex dynamics of zooplankton grazing and its impact on the organic carbon pump, we leveraged machine learning algorithms to analyze extensive datasets encompassing zooplankton behavior, environmental variables, and carbon flux measurements. Specifically, we employed regression models to establish predictive relationships between zooplankton grazing rates and key environmental factors, such as Potential Temperature, Sea Ice extension and iron availability.

The results demonstrate the potential of machine learning in discerning patterns and nonlinear relationships within the data, offering insights into the factors influencing zooplankton grazing dynamics. Additionally, the models provide a predictive framework to estimate the contribution of zooplankton to the organic carbon pump under varying environmental conditions. We have further analyzed the results by using two explainable AI methods, the Layer Wise Relevance Propagation and Integrated Gradients that informs which physical variables contribute to the prediction.

This research contributes to our understanding of the intricate processes governing carbon sequestration in the ocean, with implications for climate change mitigation and marine ecosystem management. Machine learning techniques assists to unravel the complexities of zooplankton-mediated carbon flux, to unravel the complexities of zooplankton-mediated carbon flux, paving the way for more accurate predictions and proactive conservation strategies in the face of global environmental changes.