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

Abstract

Phytoplankton patterns, 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 phenology and its underlying drivers are spatially variable, and the study of its patterns, particularly over heterogeneous regions, benefits from a delineation of regions with specific phenological properties, 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 phenology and related forcing mechanisms. This study aims to evaluate phytoplankton phenology patterns over the SWIP area, during an 18year period (September 1997-August 2015), using an objective, unsupervised partition strategy (Hierarchical Agglomerative Clustering-HAC) based on phenological indices derived from satellite ocean colour data. The partition is then used to describe region-specific phytoplankton phenological patterns related to bloom magnitude, frequency, duration and timing. Region-specific variability patterns in phenological indices 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 phenological properties: two open ocean and three coastal regions. Over the open ocean, a single, low magnitude and long bloom event per year, was regularly observed. Coastal phenoregions exhibited up to six short bloom events per year, and higher intra-annual and variability. GAM models explained 50 to 90% of the variance of all phenological indices except bloom initiation timing, and revealed that interannual patterns in phytoplankton phenology and 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 bloom events were influential predictors, reflecting the relevance of nutrient limitation. For the Coastal-Slope, a relatively more light-limited phenoregion, North Atlantic Oscillation and wind speed were more relevant, and bloom magnitude was also positively influenced by riverine discharge. This variable was a significant predictor of bloom frequency, 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 phytoplankton and multiple environmental forcing, particularly over coastal areas.

Sensitivity of Shelf Sea Marine Ecosystems to Temporal Resolution of Meteorological Forcing

“…However, Barnes et al (2015) predict that the peak amplitude of the spring microphytoplankton bloom at L4 is later in years when there is reduced wind although this tends to coincide with either warmer sea surface temperatures or low salinity. A similar trend for phytoplankton bloom initiation is also shown by Krug et al (2018) in the shelf slope system off the south west Iberian peninsula. Both studies hypothesized that reduced winds decreased the availability of winter nutrients for phytoplankton due to enhanced stratification and reduced mixing.…”

“…The phytoplankton bloom duration is typically defined as the time period when chlorophyll exceeds 5% of the annual median (Henson et al, 2009;Krug et al, 2018;Racault et al, 2012Racault et al, , 2017Sapiano et al, 2012;Siegel et al, 2002) with Siegel et al (2002), indicating that little difference occurs when the percentage is between 1% and 30%. Here, we define the start of the spring phytoplankton bloom as the first day of the year when depth-integrated chlorophyll is more than 10% of the annual median.…”

“…Changing wind conditions have also been shown to both advance and delay the onset of spring phytoplankton blooms (Follows & Dutkiewicz, 2002;Ruiz-Castillo et al, 2019;Sharples et al, 2006;Waniek, 2003). A decrease in wind stress is often correlated with an earlier phytoplankton bloom in open oceans such as in the Japan Sea (Kim et al, 2007;Yamada & Ishizaka, 2006), North Atlantic (González Taboada & Anadón, 2014;Henson et al, 2009;Ueyama & Monger, 2005), the open ocean off the South West Iberian peninsula (Krug et al, 2018), and shallower systems such as the North West European Shelf (González Taboada & Anadón, 2014) and Baltic Sea (Groetsch et al, 2016). However, in the shelf region off the South West Iberian peninsula and at Station L4 in the English Channel, an increase in wind speeds was linked to increased chlorophyll peaks and earlier bloom starts due to relief of nutrient stress (Barnes et al, 2015;Krug et al, 2018).…”

“…A decrease in wind stress is often correlated with an earlier phytoplankton bloom in open oceans such as in the Japan Sea (Kim et al, 2007;Yamada & Ishizaka, 2006), North Atlantic (González Taboada & Anadón, 2014;Henson et al, 2009;Ueyama & Monger, 2005), the open ocean off the South West Iberian peninsula (Krug et al, 2018), and shallower systems such as the North West European Shelf (González Taboada & Anadón, 2014) and Baltic Sea (Groetsch et al, 2016). However, in the shelf region off the South West Iberian peninsula and at Station L4 in the English Channel, an increase in wind speeds was linked to increased chlorophyll peaks and earlier bloom starts due to relief of nutrient stress (Barnes et al, 2015;Krug et al, 2018). Winds have also been highlighted as important in influencing the autumn bloom by breaking down stratification enabling nutrients to reach the surface (Kim et al, 2007;Wihsgott et al, 2019).…”

Disentangling Environmental Drivers of Phytoplankton Biomass off Western Iberia

“…During recent years, phytoplankton communities have been investigated in WIC, with several in situ and remote-sensing studies performed. These studies have contributed to a more indepth knowledge of local phytoplankton biomass variability and phenology (e.g., Navarro and Ruiz, 2006;Silva et al, 2009;Krug et al, 2018), community composition and structure (e.g., Lorenzo et al, 2005;Mendes et al, 2011;Goela et al, 2014), as well as its relationship with specific environmental processes, such as riverine discharges (e.g., Moita et al, 2003;Prieto et al, 2009;Guerreiro et al, 2013;Vaz et al, 2015) and coastal upwelling (e.g., Cravo et al, 2010;Pérez et al, 2010;Guerreiro et al, 2013;Vidal et al, 2017). Some investigations (e.g., Navarro and Ruiz, 2006;Krug et al, 2017) were also able to regionalize areas in CHLcoherent regions (i.e., areas with similar CHL variability patterns) to study the environmental drivers of phytoplankton.…”