The Yellow River is a valuable resource in the Ningxia Hui Autonomous Region and plays a vital role in local human activities and biodiversity. Bacteria are a crucial component of river ecosystems, but the driving factors and assembly mechanisms of bacterial community structure in this region remain unclear. Herein, we documented the bacterial community composition, determinants, co-occurrence pattern, and assembly mechanism for surface water and sediment. In comparison to sediment, the bacterioplankton community showed significant seasonal variation, as well as less diversity and abundance. The network topology parameters indicated that the sediment bacterial network was more stable than water, but the bacterioplankton network had higher connectivity. In this lotic ecosystem, COD_Mn, Chl a, and pH affected the structure of the bacterioplankton community, while TP was the primary factor influencing the structure of the sediment bacterial community. The combined results of the neutral community model and the phylogenetic null model indicate that Bacterial communities in both habitats were mainly affected by stochastic processes, with ecological processes dominated by ecological drift for bacterioplankton and dispersal limitation for sediment bacteria. These results provide essential insights into future research on microbial ecology, environmental monitoring, and classified management in the Ningxia section of the Yellow River.

Research on the spatiotemporal evolution and trade-offs of ecosystem services (ESs) is important for optimizing the ecological security barrier system and promoting coordinated socio-economic development. Natural factors, e.g., climate change, and human factors, e.g., unreasonable land use, have impacted and damaged ecosystem structure and function, leading to challenges with ES trade-offs and the spatial identification of priority protected areas. Here, the spatiotemporal evolution characteristics of five ESs (water yield, nitrogen export, soil retention, carbon storage, and habitat quality) in Anhui Province, China, from 2000–2020 were analyzed based on the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. The trade-offs and spatial patterns among different ESs were explored using Pearson correlation and hotspot analyses; the dynamics of natural growth, cultivated land protection, and ecological protection scenarios for ESs in 2030 were simulated and analyzed by coupling InVEST with the patch-generating land use simulation (PLUS) model. The results reveal the following. (1) From 2000–2020, increases in water yield and soil retention occurred, with concurrent declines in the other services; the total nitrogen high-value area was mainly concentrated in the plain, with the other services’ high-value areas mainly concentrated in the Dabieshan and Southern Anhui Mountains, with each ES showing similar spatial distributions across years. (2) The ESs were mainly synergistic, with trade-offs mainly between nitrogen export and other services. (3) Hotspot overlap between water yield and the other ESs was relatively low; no more than 6.53% of ecosystems per unit area provided five ESs simultaneously. (4) Other than water yield, the ecological protection scenario was more conducive to improving ecosystem functions. This study’s results indicate inadequate synergy among ESs in Anhui Province; competition among land types must be further balanced in the future. This study provides a basic reference for implementing ecological projects and constructing ecological security patterns.

This paper aims to preliminarily understand the structure and diversity of the bacterial community in the sediments of the Qingshui River, and analyze the differences of dominant bacteria in different river reaches, and identify the influence degree of environmental factors. In this study, surface sediments of the main stream of the Qingshui River were selected to analyze both bacterial community composition and a diversity index using the high-throughput sequencing analysis of bacterial 16S rDNA, further exploring their relationships with environmental factors. Results showed that 16,855 OTUs in the surface sediments belonged to 66 phyla, 164 classes, 274 orders, 317 families, and 501 genera of bacteria, while carbon/nitrogen-fixing bacteria were dominant at the class and genus level. There was a significant (p < 0.05) spatial difference between bacterial species composition and the diversity index in surface sediments. Proteobacteria was the most abundant phylum in the sediments of the main stream of the Qingshui River, with an average abundance of 48.15%, followed by Bacteroidetes (21.74%) and Firmicutes (5.71%). The abundance of Alphaproteobacteria in Proteobacteria was the highest (15.38%) and followed by Flavobacteriia in Bacteroidetes (11.57%). The most dominant bacteria genera were different at different areas. The most dominant genera were Phyllobacterium in Kaicheng, Qiying, Liwang, Tongxin and Changshantou, with relative abundances of 4.27%, 4.67%, 5.88%, 4.15% and 6.22%, respectively. Flavobacterium was the most dominant genus in both Dongjiao and Sanying, with a relative abundance of 5.03% and 5.84%, respectively. Rhodobacter was the most dominant genus in Hexi, with a relative abundance of 8.29%. Gillisia was the most dominant genus in Quanyanshan, with a relative abundance of 5.51%. Pearson correlation analysis further indicated that NH₄⁺, pH, and Cr were the main factors affecting the bacterial community structure and diversity in surface sediments. Therefore, our findings suggest that both nutrient elements (i.e., N) and toxic heavy metalloids affect the abundance and diversity of bacteria in surface sediments from the main stream of the Qingshui River. Areas of the river sampled in this study provide the biggest microbial sampling coverage to date. The results provide a preliminary understanding of bacterial communities in sediments of different reaches of the Qingshui River, and provide a reference for further research on the application of functional bacteria in pollution control of the Qingshui River.

To understand the characteristics of the water’s environmental carrying capacity in the Aiyi River in Yinchuan, Ninxia, China, a BP artificial neural network analysis based on water environmental factors and aquatic biodiversity metrics were conducted. Twelve indicators were used to construct an evaluation index system to examine the water environment carrying capacity of the river, namely ratio of water supply and water requirements; ratios of water environmental capacity to pollutant emissions of COD_Mn, BOD₅, TN, NH_3-N, and TP; self-purification rate; biodiversity indices of phytoplankton, zooplankton zoobenthos, and aquatic macrophytes; and a comprehensive nutrition state index. A model of water environmental carrying capacity was constructed according to the BP artificial neural network. The model, which quantitatively analyzed the water environmental carrying capacity of the Aiyi River, found it to be in the basic carrying state.

Prymnesium parvum is an environmentally harmful algae and well known for its toxic effects to the fish culture. However, there is a dearth of studies on the growth behavior of P. parvum and information on how the availability of nutrients and environmental factors affect their growth rate. To address this knowledge gap, we used a uniform design approach to quantify the effects of major nutrients (N, P, Si and Fe) and environmental factors (water temperature, pH and salinity) on the biomass density of P. parvum. We also generated the growth model for P. parvum as affected by each of these nutrients and environmental factors to estimate optimum conditions of growth. Results showed that P. parvum can reach its maximum growth rate of 0.789, when the water temperature, pH and salinity is 18.11 °C, 8.39, and 1.23‰, respectively. Moreover, maximum growth rate (0.895–0.896) of P. parvum reached when the concentration of nitrogen, phosphorous, silicon and iron reach 3.41, 1.05, 0.69 and 0.53 mg/l, respectively. The order of the effects of the environmental factors impacting the biomass density of P. parvum was pH > salinity > water temperature, while the order of the effects of nutrients impacting the biomass density of P. parvum was nitrogen > phosphorous > iron > silicon. These findings may assist to implement control measures of the population of P. parvum where this harmful alga threatens aquaculture industry in the waterbodies such as Ningxia region in China.

To investigate the health of the Diannong River water ecosystem, we collected and analyzed phytoplankton, zooplankton, and microorganisms from the Diannong River in April, July, and October 2021. We also analyzed the physical and chemical factors of the water environment and analyzed the habitat quality. The reference points were determined by the habitat composite index and water quality score. Phytoplankton index of biotic integrity (P-IBI), Zooplankton index of biotic integrity (Z-IBI), and microbial index of biotic integrity (M-IBI) which evaluated the health status of Diannong River were constructed by distribution range analysis, discriminatory ability analysis and correlation analysis of candidate biological indicators. Stepwise regression analysis and path coefficient analysis were conducted to determine the environmental factors driving the changes in aquatic IBI. The results showed that the indicators of P-IBI were the number of Cyanobacteria taxonomic units %, the number of Green Algae taxonomic units%, the relative abundance of Euglena, the relative abundance of Green Algae, and the relative abundance of toxic-producing algae. The indicators of Z-IBI were the total number of zooplankton taxonomic units, the relative abundance of Copepods, the relative abundance of the top 3 dominant species, and the Simpson index; the indicators of M-IBI were the Observed species, the relative abundance of Chloroflexi, the relative abundance of Proteobacteria, the relative abundance of the highest dominant taxonomic unit, the relative abundance of the top 5 dominant taxonomic units, the relative abundance of pollution intermediate genus, and the Ace index. The results of the IBI evaluation for three aquatic organisms showed that most of the sites in the upper reaches of the Diannnong River were at healthy or healthier levels; most of the sites in the middle reaches of the Diannnong River and the Yuehai Lake area were at mediocre or poor levels; and most of the sites in the downstream reaches of the Diannong River were average or mediocre levels. the main water environment factors driving the changes in P-IBI were water temperature (WT) and pH. The main water environment factors driving the changes in Z-IBI were total dissolved solids (TDS), WT and total nitrogen (TN); the main water environment factors driving the changes of M-IBI were fluoride ion (F⁻) and electrical conductivity (EC). This study provides the scientific reference for the application of the index of biotic integrity (IBI) for a variety of aquatic organisms in the river and lake waters and a basis for the management and optimization of the Diannong River aquatic ecosystem.