Assessing the impact of river connectivity on fish biodiversity in the Yangtze River Basin using a multi-index evaluation framework.

The Yangtze River Basin, the world's third-largest river basin and a hot spot for global biodiversity conservation, is facing biodiversity crisis caused by reduced river connectivity. The deterioration arises from four dimensions: longitudinal, lateral, vertical and temporal. However, limited research has quantified the spatiotemporal connectivity of the Yangtze River Basin and further evaluated the consequent impact on fish biodiversity. In our study, a multi-index evaluation framework was developed to assess the variations in the four-dimensional connectivity of the Yangtze River Basin from 1980 to 2020, and fish biodiversity affected by reduced connectivity was detected by environmental DNA metabarcoding. Our results showed that the Yangtze River Basin suffers from a pronounced connectivity reduction, with 67% of assessed rivers experiencing deteriorated connectivity in recent years. The lost fish biodiversity along the river reaches with the worst connectivity was likely attributed to the construction of hydropower plants. The headwaters and the downstreams of most hydropower plants had a higher fish biodiversity compared with reservoirs. The free-flowing reaches in the downstream of the lowest hydropower station, had higher lotic fish abundance compared with that in the upstream. As for the entire Yangtze River Basin, 67% of threatened fish species, with 70% endemic species, were threatened by reduced river connectivity. Our result indicates that the massive loss of river connectivity changes the spatiotemporal patterns of fish community and threatens protected fish. More effective measures to restore the populations of affected fish in rivers with reduced river connectivity are required.

Estimating the temporal and spatial distribution and threats of bisphenol A in temperate lakes using machine learning models.

Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.