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Investigating the differences in driving mechanisms for phytoplankton community composition under various human disturbances in cold regions.
- Source :
-
Journal of Cleaner Production . Jul2024, Vol. 461, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- In river ecosystems, phytoplankton are essential components, and this study delves into their response to critical environmental indicators. We analyzed the spatiotemporal variations of phytoplankton diversity in cold regions, employing techniques such as Principal Component Analysis (PCA), Spearman correlation coefficient, and Generalized Additive Model (GAM) to unveil the critical environmental factors influencing phytoplankton diversity and habitat suitability under varying degrees of human interference. With 37 monitoring stations across two cold region rivers, phytoplankton and water bodies were monitored in spring, summer, and autumn, resulting in the collection and analysis of 111 phytoplankton samples and 13 environmental indicators at each station. By identifying critical water quality indicators and exploring nonlinear relationships, the study revealed intricate interactions between phytoplankton diversity and environmental factors. Our findings suggest that overall phytoplankton species diversity is lower in rivers with high human interference, with turbidity, NH 4 +-N, chlorophyll-a, and total phosphorus (TP) deemed critical factors. Conversely, chlorophyll-a, NH 4 +-N, and electrical conductivity (EC) were identified as important factors in rivers with lower human interference, indicating differences in phytoplankton driving mechanisms between the two rivers. These insights provide valuable references for the sustainable development and effective assessment of cold region river ecosystems in the Anthropocene. [Display omitted] • Under varying degrees of interference, differences exist in the composition of phytoplankton communities. • Under different types of interference, key environmental factors exhibit similarities, yet differences persist. • Building a GAM and adding interaction terms can improve model accuracy. • The driving mechanisms of phytoplankton vary between rivers with different hydrological conditions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09596526
- Volume :
- 461
- Database :
- Academic Search Index
- Journal :
- Journal of Cleaner Production
- Publication Type :
- Academic Journal
- Accession number :
- 177600772
- Full Text :
- https://doi.org/10.1016/j.jclepro.2024.142686