Marine ecosystems establish and function via interactions between diversified organisms and abiotic factors, connecting every species that live in a habitat within certain spatial scales. The metabolic activities of microorganisms, including phytoplankton and zooplankton (together also known as eukaryotes), as well as bacterioplankton can greatly influence the symbionts of the whole organisms, and biogeochemical cyclings. So, it is important to understand how microbiota interacts with environmental factors, and which factor is the main driving force that influence microbiota. The South China Sea (SCS) is characterized by a tropical and subtropical climate and is the largest marginal sea in the western tropical Pacific Ocean. Being the greatest terrestrial input source of the SCS, the Pearl River Estuary (PRE) is located in the subtropical area in southeast China, and adjacent to the northwestern coast of the SCS. The PRE delta region is one of the most economically developed areas in China. However, intensive anthropogenic activity has released tremendous amount of nutrients into the estuary, which results in algae blooms and hypoxic zones in recent years. As a result of the complex interactions between seawater and freshwater, PRE was generally characterized by strong physicochemical gradients of various abiotic factors (Niu et al., 2020), including salinity, temperature, nutrients (N, P, Si) and micro-nutrients (Fe), etc.
To investigate the variation in physicochemical properties and microbial assembly along the gradient from Pearl River to PRE to the northen SCS, surface water samples of 27 stations were collected from the PRE and coastal open water of northern SCS in September 2018. A salinity of 31 was applied to divide the estuarine zone of PRE and oceanic water zone of SCS (Dong et al., 2004). The environmental factors of PRE samples gradient encompassed a drastic variation (P<0.01, Wilcoxon test) with samples of SCS. Nutrients and Fe concentrations, as well as Chl a, in PRE were significantly higher than those in SCS. In PRE, in general the investigated factors with exception of Chl a were negatively correlated with salinity. In SCS, N was positively correlated with P, Fe was observed to corelate with P and Si positively.
For bacterial composition, the relative abundances of Alphaproteobacteria, Gammaproteo-bacteria and SAR406 significantly increased along transect of PRE to SCS. Conversely, the proportions of Actinobacteria, Planctomycetes and Verrucomicrobia decreased along the transect. For eukaryotic profile, rapid addition occurred in Dinoflagellata, while Chlorophyta, Cryptophyta and Fungi abruptly descend when the location of sample sites shifted from PRE to SCS. Particularly, the relative abundance of Ochrophyta presented hump-shaped distribution, peaking in the transition sites of two different zones. The bacterial composition had successive variation from PRE to SCS. Rather, polarization emerged between estuarine water and saltwater in the eukaryotic community, that estuarine sites had uniform proportion with more evenly distributed phyla, while oceanic sites had heterogeneous composition with Dinoflagellata dominant phylum accounting to a large proportion of 75.73%±12.73% in average.
Environmental factors coupled with spatial (distance) limitation were major drivers that modulated microbial configuration and the emergence of biogeography, owing to the parallel shifts of physicochemical parameters together with latitudinal, geological or land use differentiations across regions (Zhang et al., 2018). In our study, environmental parameters of the studied sites as well as geographical coordinates exerted significant influences on the diversity and structure of the microbiota (Table 1). The abiotic drivers including environmental parameters and geographical coordinates had significant correlations with certain microbial phyla’s relative abundance and their alpha-diversity indices. Through mantel and partial mantel test (controlled by geographic distance or environmental distance), it is clear that the Euclidean distances of environmental parameters as well as geographical distance significantly correlated with both bacterial and eukaryotic community dissimilarities (Table 1). The correlation coefficients of the environmental factor distance, especially the total environmental variation distance (bacteria: ρ
geo= 0.79, P≤ 0.001; eukaryotes: ρ
geo= 0.64, P≤ 0.001), however, had higher values than that of geographical distance (bacteria: ρ
env= 0.36, P< 0.01; eukaryotes: ρ
env= 0.38, P< 0.01).
Table 1 Results of Mantel and Partial Mantel tests demonstrated correlations of environmental factors (Euclidean distance), total environmental variability (Euclidean distance) and geographic distance with bacterial and eukaryotic beta-diversity dissimilarity (Bray-Curtis) using Pearson’s coefficient (n=27, with 999 permutations).
Variation
Source |
Bacteria |
Eukaryotes |
| ρ |
ρgeo |
ρenv |
ρ |
ρgeo |
ρenv |
| Salinity |
0.87*** |
0.85*** |
0.64*** |
0.80*** |
0.75*** |
0.59*** |
| Temperature |
0.46*** |
0.43*** |
NS |
0.38*** |
0.34*** |
NS |
| Nitrate and Nitrite |
0.84*** |
0.81*** |
0.45*** |
0.76*** |
0.71*** |
0.47*** |
| Phosphate |
0.85*** |
0.81*** |
0.51*** |
0.77*** |
0.71*** |
0.51*** |
| Silicate |
0.84*** |
0.82*** |
0.51*** |
0.75*** |
0.70*** |
0.47*** |
| Dissolved Iron |
0.50*** |
0.50*** |
NS |
0.37*** |
0.34*** |
NS |
| Suspended Particulate Matter |
0.71*** |
0.70*** |
0.16* |
0.61*** |
0.57*** |
NS |
| Chlorophyll a |
0.50*** |
0.51*** |
NS |
0.36** |
0.35*** |
NS |
| Total Environmental Variation |
0.82*** |
0.79*** |
- |
0.69*** |
0.64*** |
- |
| Geographic Distance |
0.47*** |
- |
0.36** |
0.49*** |
- |
0.38** |
Total Environmental Variation referred to the Euclidean distance of all environmental variables. ρ: correlation coefficients between abiotic drivers and microbial dissimilarity derived from Mantel testes. ρ
geo: correlation coefficients between abiotic drivers and microbial dissimilarity controlled by geographic distance derived from Partial Mantel testes. ρ
env: correlation coefficients between abiotic drivers and microbial dissimilarity controlled by the Euclidean distance of the environmental factors (for the calculation of the ρ
env of each environmental factor, the studied factor itself was eliminated and the rest of the environmental variables were used) derived from Partial Mantel testes. NS: not significant,
*: P < 0.05,
**: P < 0.01,
***: P ≤ 0.001.
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