ArticleScientific reports2026
Integrated analytical framework for identifying factors related to the ecological degradation of lakes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.