Environmental and Climatic Drivers of Phytoplankton Communities in Central Asia DOI Creative Commons
Fangze Zi, Tianjian Song, Jiaxuan Liu

et al.

Biology, Journal Year: 2024, Volume and Issue: 13(9), P. 717 - 717

Published: Sept. 12, 2024

Artificial water bodies in Central Asia offer unique environments which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies region challenge our understanding. In this study, we systematically investigated environment parameters phytoplankton community structure surveying 14 artificial waters on southern side Altai Mountains northern sides Tianshan Xinjiang region. The survey covered physical nutrient indicators, results showed noticeable spatial differences between different regions. temperature, dissolved oxygen, total nitrogen, phosphorus vary greatly. contrast, have more consistent indicators. identification that communities regions are somewhat different, with diatom species being dominant taxon. cluster analysis non-metric multidimensional scaling (NMDS) also confirmed variability areas. variance partitioning (VPA) climatic environmental factors can explain some observed data. Nevertheless, residual values indicated presence other unmeasured or influence stochasticity. This provides a scientific basis for regional resource management protection.

Language: Английский

Environmental and Climatic Drivers of Phytoplankton Communities in Central Asia DOI Creative Commons
Fangze Zi, Tianjian Song, Jiaxuan Liu

et al.

Biology, Journal Year: 2024, Volume and Issue: 13(9), P. 717 - 717

Published: Sept. 12, 2024

Artificial water bodies in Central Asia offer unique environments which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies region challenge our understanding. In this study, we systematically investigated environment parameters phytoplankton community structure surveying 14 artificial waters on southern side Altai Mountains northern sides Tianshan Xinjiang region. The survey covered physical nutrient indicators, results showed noticeable spatial differences between different regions. temperature, dissolved oxygen, total nitrogen, phosphorus vary greatly. contrast, have more consistent indicators. identification that communities regions are somewhat different, with diatom species being dominant taxon. cluster analysis non-metric multidimensional scaling (NMDS) also confirmed variability areas. variance partitioning (VPA) climatic environmental factors can explain some observed data. Nevertheless, residual values indicated presence other unmeasured or influence stochasticity. This provides a scientific basis for regional resource management protection.

Language: Английский

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