A clustering approach to determine biophysical provinces and physical drivers of productivity dynamics in a complex coastal sea

The balance between ocean mixing and stratification influences primary productivity through light limitation and nutrient supply in the euphotic ocean. Here, we apply a hierarchical clustering algorithm (Ward’s method) to four factors relating to stratification (wind energy, freshwater index, water-column-averaged vertical eddy diffusivity, and halocline depth), as well as to depth-integrated phytoplankton biomass, extracted from a biophysical ocean model of the Salish Sea. Running the clustering algorithm on 4 years of model output, we identify distinct regions of the model do main that exhibit contrasting wind and freshwater input dynamics, as well as regions of varying water-column-averaged
vertical eddy diffusivity and halocline depth regimes. The spatial regionalizations in physical variables are similar in all 4 analyzed years. We also find distinct interannually consistent biological zones. In the northern Strait of Georgia and
Juan de Fuca Strait, a deeper winter halocline and episodic summer mixing coincide with higher summer diatom abundance, while in the Fraser River stratified central Strait of Georgia, shallower haloclines and stronger summer stratification coincide with summer flagellate abundance. Cluster based model results and evaluation suggest that the Juan de Fuca Strait supports more biomass than previously thought.
Our approach elucidates probable physical mechanisms controlling phytoplankton abundance and composition. It also demonstrates a simple, powerful technique for finding structure in large datasets and determining boundaries of biophysical provinces