A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network

Eastern Boundary Upwelling Systems (EBUS) are highly productive ocean regions. Yet, substantial differences in net primary production (NPP) exist within and between these systems for reasons that are still not fully understood. Here, we explore the leading physical processes and environmental factors controlling NPP in EBUS through a comparative study of the California, Canary, Benguela, and Humboldt Current systems. The NPP drivers are identified with the aid of an artificial neural network analysis based on self-organizing-maps (SOM). Our results suggest that in addition to the expected NPP enhancing effect of stronger equatorward alongshore wind, three factors have an inhibiting effect: (1) strong eddy activity, (2) narrow continental shelf, and (3) deep mixed layer. The co-variability of these 4 drivers defines in the context of the SOM a continuum of 100 patterns of NPP regimes in EBUS. These are grouped into 4 distinct classes using a Hierarchical Agglomerative Clustering (HAC) method. Our objective classification of EBUS reveals important variations of NPP regimes within each of the four EBUS, particularly in the Canary and Benguela Current systems. Our results show that the Atlantic EBUS are generally more productive and more sensitive to upwelling favorable winds because of weaker factors inhibiting NPP. Perturbations of alongshore winds associated with climate change may therefore lead to contrasting biological responses in the Atlantic and the Pacific EBUS.