Long-time-scale Investigation on the Seasonal Variation of Phytoplankton Size Class Distribution Patterns in the Arabian Sea

This study deals with the evaluation of phytoplankton size classes (PSCs) and their spatio-temporal variability in the Arabian Sea (AS) which primarily focuses on the community shift, behavioral pattern affected by the seasonal changes. The Arabian Sea is selected for this purpose is inherently dynamic subjected to extreme seasonal changes, with corresponding variability in phytoplankton production. Concisely, the chlorophyll-a (chl-a) concentration is not only considered as a proxy of phytoplankton biomass but also an essential factor for identifying phytoplankton community structure. Therefore, investigation on the synoptic relationship between -a concentration and phytoplankton size classes (micro is >20 μm, nano is 2 – 20 μm and picoplankton is < 2 μm) is essential to understand the variability of phytoplankton communities in the long-time-scales. To start with, 16 years of MODIS-Aqua satellite derived chl-a imageries (eight-day composite) were aggregated for this study. The inconsistency in the data due to cloud cover was resolved by reconstructing the chl-a data using empirical orthogonal function: DINEOF method. Thereby, the OC-PFT model was run on reconstructed chl-a data to retrieve the PSCs for 16 years. Using the time-series PSCs, a dominant peak, community shift, and the seasonal pattern were determined using phenology metrics: FFT and wavelet analysis. The overall results indicate the intensive response of microplankton towards the seasonal pattern especially during winter cooling (December – March) and summer upwelling (June-September) periods with relative abundance especially in Northwest AS and Southwest AS due to high nutrient and light availability. Conversely, nanoplankton encounters ubiquitous nature throughout the AS whereas picoplankton appeared to be dominant in lower latitudes of AS (i.e. Southeast AS) because of its survival capability (nutrient and light-independent) in oligotrophic conditions. Thus, the gap-filling process helps to efficiently handle the long-time-scale chl-a data, and PSC’s estimation using reconstructed gap-free chlorophyll-a concentration provides the better understanding of PSC’s shift and its dynamics on seasonal and temporal scales in the AS.