Patterns and drivers of phytoplankton phenology off SW Iberia: A phenoregion based perspective

Phytoplanktonpatterns, tightly linked to the dynamics of the ocean surface layer and its atmospheric forcing, have major impacts on ecosystem functioning and are valuable indicators of its response to environmental variability and change. Phytoplankton phenologyand its underlying drivers are spatially variable, and the study of its patterns, particularly over heterogeneous regions, benefits from a delineation of regions with specific phenologicalproperties, or phenoregions. The area Southwest off the Iberian Peninsula (SWIP, NE Atlantic) integrates a highly complex set of coastal and ocean domains that collectively challenge the understanding of regional phytoplankton phenologyand related forcing mechanisms. This study aims to evaluate phytoplankton phenologypatterns over the SWIP area, during an 18-year period (September 1997 – August 2015), using an objective, unsupervised partition strategy (Hierarchical Agglomerative Clustering – HAC) based on phenologicalindices derived from satellite ocean colour data. The partition is then used to describe region-specific phytoplankton phenologicalpatterns related to bloommagnitude, frequency, duration and timing. Region-specificvariability patterns in phenologicalindices and their linkages with environmental determinants, including local ocean physical-chemical variables, hydrodynamic variables and large scale climate indices, were explored using Generalized Additive Models(GAM). HAC analyses identified five coherent phenoregions over SWIP, with distinctive phytoplankton phenologicalproperties: two open ocean and three coastal regions. Over the open ocean, a single, low magnitude and long bloomevent per year, was regularly observed. Coastal phenoregions exhibited up to six short bloomevents per year, and higher intra-annual and variability. GAM models explained 50–90% of the variance of all phenologicalindices except bloominitiation timing, and revealed that interannual patterns in phytoplankton phenologyand their environmental drivers varied markedly among the five phenoregions. Over the oceanic phenoregions, large-scale climate indices (Eastern Atlantic Pattern, Atlantic Meridional Oscillation), mixed layer depth (MLD) and nitrate concentration preceding primary bloomevents were influential predictors, reflecting the relevance of nutrient limitation. For the Coastal-Slope, a relatively more light-limited phenoregion, North Atlantic Oscillationand wind speed were more relevant, and bloommagnitude was also positively influenced by riverine discharge. This variable was a significant predictor of bloomfrequency, magnitude and duration over the Riverine-influenced region. Over the Upwelling-influenced region, upwelling intensity and mean annual MLD showed stronger partial effects on phytoplankton phenology. Overall, our phenology-based unsupervised approach produced a biologically-relevant SWIP partition, providing an evaluation of the complexity of interactions between phytoplanktonand multiple environmental forcing, particularly over coastal areas.