Phytoplankton community, structure and succession delineated by partial least square regression in Daya Bay, South China Sea

Wu, Mei-Lin; Wang, Yu-Tu; Cheng, Hao; Sun, Fu-Lin; Jiao, Fei; Sun, Cheng; Yin, Jingdong; Zhao, Hui; Wang, You‐Shao

doi:10.1007/s10646-020-02188-2

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…The results of the present study indicated that diatoms are the major phytoplankton species in the waters around Macau, which is consistent with data from the inshore waters of the northern South China Sea [22][23][24]35,36]. The phytoplankton species that can thrive under normal and warm temperature conditions were dominant and showed obvious tropical and subtropical characteristics [22,23].…”

Section: Discussionsupporting

confidence: 89%

Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau

Luo

et al. 2022

IJERPH

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An investigation of the waters around Macau collected 43 phytoplankton species belonging to 29 genera and 5 phyla, including 32 species from 22 genera of Bacillariophyta, 7 species from 3 genera of Pyrrophyta, 2 species from 2 genera of Cyanophyta, and 1 genus and 1 species from both Euglenophyta and Chromophyta. The dominant phytoplankton species in the study areas were Skeletonema costatum (Greville) Cleve, Aulacoseira granulata (Ehrenberg) Simonsen, Thalassiothrix frauenfeidii Grunow, and Thalassionema nitzschioides Grunow. The phytoplankton abundance in the waters around Macau was between 46,607.14 and 1,355,000 cells/m3, with the highest abundance noted in station S8. Diatoms were the main contributor to phytoplankton abundance in station S8, accounting for 96.2% of the total abundance. Station S4 exhibited the lowest phytoplankton abundance of 46,607.1 cells/m3, with diatoms and Chromophytaaccounting for 58.6% and 29.9% of the total phytoplankton abundance, respectively. Biodiversity analysis results showed that the phytoplankton richness index was 1.18–3.61, the uniformity index was 0.24–0.78, and the Shannon–Wiener index was 0.94–3.41. Correlation analysis revealed that ammonia nitrogen was significantly negatively correlated with the phytoplankton richness, uniformity, and Shannon–Wiener indices. Nitrite nitrogen, nitrate nitrogen, inorganic nitrogen, salinity, turbidity, and pH were positively correlated with the phytoplankton evenness index and Shannon–Wiener index. Cluster and non-metric multidimensional scaling analyses demonstrated that the phytoplankton community structure in the waters around Macau could be divided into three groups, with A. granulata, S. costatum, T. frauenfeidii, T. nitzschioides, Chaetoceros curvisetus Cleve, and Chaetoceros diadema (Ehrenberg) Gran being predominant in different grouping communities (contribution% > 10%). Biota-Environment Stepwise Analysis (BIOENV) showed a significant correlation between the phytoplankton community and nitrite nitrogen content in the waters around Macau (correlation: 0.5544, Mantel test: statistic 0.4196, p = 0.009), which was consistent with the results of the canonical correspondence analysis.

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Section: Discussionsupporting

confidence: 89%

Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau

Luo

et al. 2022

IJERPH

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“…Many studies have shown that salinity can be a crucial factor driving the growth and succession of marine phytoplankton communities [34]. We found similar results: as the salinity increased, the abundance of phytoplankton also increased.…”

Section: Vital Environmental Factors Induced Differences In the Phyto...supporting

confidence: 85%

The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait

Xu,

Ma,

Zhang

et al. 2023

Water

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The distribution and variation in environmental factors and the phytoplankton community in the Qiongzhou Strait were investigated in autumn 2015 and spring 2016. The environmental factors were different in the two seasons, as seen when using one-way ANOVA testing, principal component analysis, and cluster analysis. The temperature and levels of dissolved oxygen and soluble solids were higher in autumn, and the ammonia (NH4-N) level was much higher in spring (4.66 ± 0.97 µmol/L), which led to a higher concentration of dissolved inorganic nitrogen in the northeast area. A total of 118 species of phytoplankton were found, with 47 common species in both seasons and more species in spring. Bacillariophyta (diatom) contributed to over 80% of the relative abundance in both seasons. The species of Chrysophyta and Xanthophyta were endemic species in autumn and spring, respectively. Skeletonema costatum and Chaetoceros curvisetus were the common dominant species, with nine dominant species in both seasons. Higher abundance and biodiversity of phytoplankton appeared in spring, and their differences were mostly determined by NH4-N level, hydrodynamic conditions and artificial activities in the coastal area in the Qiongzhou Strait.

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“…An additional equilibration of 1ns under NVT and NPT conditions was carried out, while the constant temperature was 300 K and the constant pressure was 1 bar, respectively (Sun et al, 2020). Finally, the system was performed with running 5 ns MD simulation and coordinates were written every 10ps, energies every 1ps.…”

Section: Simulationsmentioning

confidence: 99%

“…PLS calculates a group of latent variables in connection with the output maximally and determines the relationship between the input and output data (Foodeh et al, 2020). It is a stretch of the multiple linear regression models and is widely used in many domains (Wu et al, 2020). Unlike multiple linear regression (MLR), it can handle the data with noisy, strongly collinear, and X-variables (Dong et al, 2018).…”

Section: Partial Least Square Regressionmentioning

confidence: 99%

Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds

Ding

Nan

et al. 2022

Computers in Biology and Medicine

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Phytoplankton community, structure and succession delineated by partial least square regression in Daya Bay, South China Sea

Cited by 9 publications

References 31 publications

Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau

Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau

The Impact of Environmental Factors on the Spatiotemporal Heterogeneity of Phytoplankton Community Structure and Biodiversity in the Qiongzhou Strait

Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds

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