ArticleBiology2025
Niche Differentiation Characteristics of Phytoplankton Functional Groups in Arid Regions of Northwest China Based on Machine Learning.
Article in Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigates the distribution patterns, interspecific relationships, and community stability mechanisms of phytoplankton functional groups, aiming to elucidate the ecological processes that drive phytoplankton communities in aquatic ecosystems of arid regions. We conducted seasonal sampling from 2023 to 2024 at four auxiliary reservoirs in the Tarim River Basin, namely Shangyou Reservoir (SY), Shengli Reservoir (SL), Duolang Reservoir (DL), and Xinjingzi Reservoir (XJZ). In recent years, researchers have grouped phytoplankton into functional groups based on their shared morphological, physiological, and ecological characteristics-with these three types of traits serving as the core criteria for distinguishing different functional groups. A total of 18 functional groups were identified from the phytoplankton collected across four seasons, among which eight (A, D, H1, L0, M, MP, P, and S1) are dominant. Redundancy Analysis (RDA) indicated that environmental factors such as pH, electrical conductivity (COND), and dissolved oxygen (DO) are key driving factors affecting phytoplankton functional groups. Interspecific association analysis showed that the phytoplankton communities in DL, SL, and XJZ reservoirs were dominated by positive associations, reflecting stable community structures that are less prone to drastic fluctuations under stable environmental conditions. In contrast, the SY Reservoir was dominated by negative associations, indicating that it is in the early stage of succession with an unstable community. This may be related to intense human disturbance to the reservoir and its role in replenishing the Tarim River, which leads to significant water level fluctuations. The results of the Chi-square test and Pearson correlation analysis showed consistent trends but also differences: constrained by the requirement for continuous normal distribution, Pearson correlation analysis identified more pairs of negative associations, reflecting its limitations in analysing clumped-distributed species. Random forest models further indicated that functional groups M, MP, L0, and S1 are the main positive drivers of interspecific relationships. Among them, the increase in S1 can promote the growth of functional groups dominated by
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