Abstract
The causal relationships between external driving forces and the ecological degradation of lakes are characterized as complex and multidimensional, with multiple inputs and outputs, nonlinearity, and many interactions. Conventional parametric statistical methods such as correlation analysis and multiple linear regression cannot handle these characteristics simultaneously. Thus, we developed an integrated analytical framework to screen, identify, and predict the factors related to the ecological degradation of lakes based on redundancy analysis (RDA), variance partitioning analysis (VPA), and principal component analysis-based generalized additive models (PCA-based GAM). The RDA and VPA methods were employed to identify and rank the driving factors that explained the decrease in species richness (specifically of key aquatic organisms, including phytoplankton, submerged plants, zooplankton, benthic animals, and fish), which is a critical ecological indicator closely associated with lake ecological degradation. PCA-based GAM was used to explore the patterns associated with driving forces. The driving forces related to the changes in species richness during the 35 years from 1986 to 2020 were investigated in Baiyangdian (BYD) Lake, China. Three categories of driving forces were identified: anthropogenic pollution, climate change, and hydrological conditions. Significant detrimental changes in species richness were detected in the first decade, followed by relative stability in the next decade, and favorable changes since 2015. Anthropogenic pollution, climate change, and hydrological conditions explained 41%, 18%, and 13% of the total variance, respectively. The best predictive model structures included the water level (WL), air temperature (AT), total phosphorus (TP), and (WL*TP) interaction, and they explained 98.4% of the total data variance. The proposed method offers actionable solutions for lake management, including real-time ecological health monitoring, adaptive strategies and indicating ecological degradation.