Frontiers in Marine Science (Sep 2024)
Dynamics of phytoplankton communities in the Baltic Sea: insights from a multi-dimensional analysis of pigment and spectral data—part I, pigment dataset
Abstract
This study aims to investigate the seasonal and spatial distribution of surface phytoplankton communities in the Baltic Sea, using pigment analysis and hydrological parameters. Data were collected during six oceanographic campaigns between 2005 and 2008, including high-performance liquid chromatography (HPLC) pigment characterization and hydrological measurements. The first part of this comprehensive study was focused on the HPLC phytoplankton pigment dataset in relation to hydrological conditions. The research highlighted the importance of high-quality input data for accurate taxonomic analysis. Several unsupervised machine learning approaches, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and network-based community detection analysis (NCA), were used to analyze the data and identify phytoplankton communities based on biomarker pigments. Five main phytoplankton communities were identified: diatoms, dinoflagellates, cryptophytes, green algae, and cyanobacteria. The results evidenced distinct seasonal patterns, with diatom blooms dominating in spring, cyanobacterial blooms in mid-summer, and haptophyte and dinoflagellate peaks occurring in late summer and autumn. While PCA and NCA provided consistent insights into community structure, HCA offered less clarity in distinguishing between groups. The results of the statistical analysis were then compared with those of traditional approaches such as CHEMTAX and region-specific bio-optical algorithms, providing new perspectives on the taxonomic composition of phytoplankton groups. This study provides valuable insights into phytoplankton dynamics in the Baltic Sea and the effectiveness of different analytical approaches in understanding community structure, providing metrics that can enhance current and future advancements in remote sensing, including support for hyperspectral ocean color remote sensors.
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