Biodiversity maintenance is a main goal in ecology. Hence, phytoplankton diversity and biomass were analyzed in a coastal bay (Cumã Bay) of the Amazon Macrotidal Mangrove Coast, which has been designated as an international hotspot for conservation (Ramsar site) with high biological productivity and diversity that provides crucial ecosystem services and elevated fish production. An ecohydrology-based approach was applied to identify the main factors that drive the patterns of phytoplankton diversity and biomass, considering spatio-temporal analyses of physical, chemical, and biological variables from May 2019 to June 2020. Phytoplankton dynamics were investigated using multivariate analyses, correlations, and generalized additive models. Seven indices were tested to select the most efficient biodiversity metric. The hydrological conditions of Cumã Bay were governed primarily by elevated precipitation and macrotidal dynamics, resulting in two different functional zones based on environmental variability: the freshwater influence zone and marine influence zone. Seasonally, the maximum freshwater discharge, low salinity and light availability promoted cell abundance and biomass increase, with blooms of Skeletonema costatum, which reduced the taxonomic diversity of the community in the rainy season. During the dry season, turbid waters resulting from macrotidal dynamics and wind speed limited light penetration and phytoplankton photosynthesis, leading to a higher uniformity in the species distribution. Shannon index was the most sensitive biodiversity metric to environmental changes. This study found that deterministic processes governed the community, which rainfall on the Amazon coast, along with wind speed, salinity, light availability and nutrients were the main controlling factors for phytoplankton diversity and richness.

生物多样性维护是生态学的主要目标。因此，在亚马逊大潮红树林海岸的沿海海湾（库马湾）分析了浮游植物多样性和生物量，该海岸已被指定为国际保护热点（拉姆萨尔湿地），具有高生物生产力和多样性，可提供重要的生态系统服务和提高鱼类生产。考虑到 2019 年 5 月至 2020 年 6 月物理、化学和生物变量的时空分析，采用基于生态水文学的方法来确定驱动浮游植物多样性和生物量模式的主要因素。使用多变量分析研究浮游植物动力学，相关性和广义加性模型。测试了七个指数以选择最有效的生物多样性指标。库马湾的水文条件主要受降水增加和潮汐动力学的影响，导致基于环境变化的两个不同的功能区：淡水影响区和海洋影响区。季节性地，最大淡水排放、低盐度和光照可促进细胞丰度和生物量增加，水华Skeletonema costatum，在雨季降低了群落的分类多样性。在旱季，由于潮汐动力学和风速产生的浑水限制了光的穿透和浮游植物的光合作用，导致物种分布更加均匀。香农指数是对环境变化最敏感的生物多样性指标。这项研究发现，确定性过程支配着群落，亚马逊海岸的降雨以及风速、盐度、光照和养分是浮游植物多样性和丰富度的主要控制因素。