Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon

Sci Total Environ. 2021 Aug 20:783:146873. doi: 10.1016/j.scitotenv.2021.146873. Epub 2021 Apr 1.

Abstract

Spatial and seasonal heterogeneity in phytoplankton communities are governed by many biotic and abiotic drivers. However, the identification of long-term spatial and temporal trends in abiotic drivers, and their interdependencies with the phytoplankton communities' structure is understudied in tropical brackish coastal lagoons. We examined phytoplankton communities' spatiotemporal dynamics from a 5-year dataset (n = 780) collected from 13 sampling stations in Chilika Lagoon, India, where the salinity gradient defined the spatial patterns in environmental variables. Generalized additive models showed a declining trend in phytoplankton biomass, pH, and dissolved PO4 in the lagoon. Hierarchical modelling of species communities revealed that salinity (44.48 ± 28.19%), water temperature (4.37 ± 5.65%), and season (4.27 ± 0.96%) accounted for maximum variation in the phytoplankton composition. Bacillariophyta (Indicator Value (IV): 0.74) and Dinophyta (IV: 0.72) emerged as top indicators for polyhaline regime whereas, Cyanophyta (IV: 0.81), Euglenophyta (IV: 0.79), and Chlorophyta (IV: 0.75) were strong indicators for oligohaline regime. The responses of Dinophyta and Chrysophyta to environmental drivers were much more complex as random effects accounted for ~70-75% variation in their abundances. Prorocentrum minimum (IV: 0.52), Gonyaulax sp. (IV: 0.52), and Alexandrium sp. (IV: 0.51) were potential indicators of P-limitation. Diploneis weissflogii (IV: 0.43), a marine diatom, emerged as a potential indicator of N-limitation. Hierarchical modelling revealed the positive association between Cyanophyta, Chlorophyta, and Euglenophyta whereas, Dinophyta and Chrysophyta showed a negative association with Cyanophyta, Chlorophyta, and Euglenophyta. Landsat 8-Operational Land Imager satellite models predicted the highest and lowest Cyanophyta abundances in northern and southern sectors, respectively, which were in accordance with the near-coincident field-based measurements from the lagoon. This study highlighted the dynamics of phytoplankton communities and their relationships with environmental drivers by separating the signals of habitat filtering and biotic interactions in a monsoon-regulated tropical coastal lagoon.

Keywords: Chilika Lagoon; Cyanophyta modelling; Generalized additive models; Joint species distribution modelling; Remote sensing; Salinity.

MeSH terms

  • Cyanobacteria*
  • Diatoms*
  • Environmental Monitoring
  • India
  • Phytoplankton
  • Seasons