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Keywords: Microalgae, Wetlands, Physicochemical parameter, Canonical correspondence analysis
Microalgae play an important role in nutrient cycling and primary productivity in wetland ecosystems and are increasingly recognised for their potential in sustainable biotechnological applications. However, detailed understanding of relationships between microalgal diversity and environmental parameters in tropical Indian wetlands remains limited. In this study, we examined the diversity and distribution of microalgae across thirteen wetlands in West Bengal to explore species–environment relationships and identify potential indicator taxa. A total of 60 taxa were recorded, with Chlorophyceae (50%) as the dominant group, followed by Euglenophyceae (15%), Bacillariophyceae (12%), and Cyanophyceae (12%). Canonical Correspondence Analysis (CCA) was used to examine relationships between community composition and selected physicochemical variables. The analysis explained 29.2% of the total variance in species data and was not statistically significant, indicating that additional environmental factors may influence community patterns. The ordination suggested that Euglena polymorpha was associated with nutrient gradients, while Navicula lanceolata, Chlorella sp., and Desmodesmus sp. aligned with electrical conductivity and dissolved oxygen, whereas Scenedesmus and Pediastrum exhibited broader ecological tolerance. The occurrence of taxa such as Euglena, Phacus, Scenedesmus, Pediastrum, Navicula, and Nitzschia, interpreted using the Palmer Pollution Index, suggests their potential association with nutrient-enriched or organically influenced conditions, although these are considered indicative rather than definitive. Additionally, six microalgal strains were isolated, providing preliminary scope for further investigation. Overall, this study provides baseline information on microalgal diversity and their relationships with environmental variables in tropical wetlands. As the study was conducted during a single pre-monsoon period, further multi-seasonal investigations are required to better understand temporal dynamics and validate the observed patterns.
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