Wetland Research and Training Centre, Chilika Development Authority, Balugaon 752030, Odisha, India.
Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba 305-8506, Japan.
Sci Total Environ. 2021 Aug 20;783:146873. doi: 10.1016/j.scitotenv.2021.146873. Epub 2021 Apr 1.
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 PO 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.
浮游植物群落的空间和季节性异质性受许多生物和非生物驱动因素的控制。然而，在热带咸水沿海泻湖，对非生物驱动因素的长期空间和时间趋势的识别及其与浮游植物群落结构的相互依存关系的研究还很不足。我们从印度奇利卡泻湖的一个 5 年数据集（n = 780）中检查了浮游植物群落的时空动态，该数据集来自泻湖的 13 个采样站，盐度梯度定义了环境变量的空间模式。广义加性模型显示，泻湖中浮游植物生物量、pH 值和溶解 PO 呈下降趋势。物种群落的层次模型显示，盐度（44.48 ± 28.19%）、水温和季节（4.27 ± 0.96%）分别解释了浮游植物组成变化的最大变异。硅藻门（指示值（IV）：0.74）和甲藻门（IV：0.72）是高盐度区的主要指示生物，而蓝藻门（IV：0.81）、眼虫门（IV：0.79）和绿藻门（IV：0.75）是低盐度区的强指示生物。甲藻门和金藻门对环境驱动因素的反应要复杂得多，因为随机效应解释了它们丰度变化的 70-75%。微小原甲藻（IV：0.52）、夜光藻（IV：0.52）和亚历山大藻（IV：0.51）是磷限制的潜在指示物。海洋硅藻双菱藻（IV：0.43）是氮限制的潜在指示物。层次模型显示，蓝藻门、绿藻门和眼虫门之间存在正相关关系，而甲藻门和金藻门与蓝藻门、绿藻门和眼虫门之间存在负相关关系。陆地卫星 8-Operational Land Imager 卫星模型预测北部和南部区域的蓝藻门丰度最高和最低，这与泻湖的现场测量结果一致。这项研究通过在季风调节的热带沿海泻湖中分离栖息地过滤和生物相互作用的信号，突出了浮游植物群落的动态及其与环境驱动因素的关系。
