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Context: Alterations of landscapes and riverscapes by humans have fundamentally altered patterns in freshwater biodiversity throughout the world's aquatic systems. Past research has demonstrated precipitous declines in small- and medium-sized streams, with much less attention given to patterns and drivers of fish biodiversity in larger riverine systems. Objectives: By examining alpha and beta diversity of fishes in the middle and lower reaches of the Yangtze River, China, we aimed to address the following question: What is the role of river–lake connectivity, wetland extent, and human stress factors (e.g., urbanization, fishing pressure, navigation, and shoreline modifications) in shaping fish alpha (species richness) and beta (species nestedness and turnover) diversity? Methods: We examined associations among three classifications of fish assemblage data (i.e., all species, nonmigratory species, and migratory species) sampled along gradients of river–lake connectivity (i.e., Dongting and Poyang lakes), wetland extent, and a suite of human stress factors (urbanization, fishing pressure, navigation, shoreline modification). We conducted redundancy analyses and distance-based redundancy analyses to facilitate mechanistic interpretations of the associations between the explanatory variables and fish alpha and beta diversity. Results: Longitudinally from the middle to the lower reaches of the Yangtze River, fish alpha diversity showed marked decreases while fish beta diversity increased. River segments with greater levels of human impact were generally associated with reduced fish species richness, while segments with higher wetland extent and greater river–lake connectivity tended to support greater species richness. River–lake connectivity, wetland extent, and fishing pressure largely influenced total fish beta diversity. Species nestedness was primarily associated with amount of wetlands, and exhibited the strongest associations with nonmigratory fish species. Turnover was primarily associated with river–lake connectivity (mainly Poyang Lake), especially for migratory species. Conclusions: Our analyses demonstrate the roles of river–lake connectivity, wetland extent, and human stress factors in shaping patterns of alpha and beta diversity for migratory and nonmigratory fishes in the middle and lower reaches of the Yangtze River. Fish diversity conservation in this large river ecosystem calls for protecting hydrological connectivity and wetland habitats, along with reducing fishing pressure. Results from this study will help better inform fish conservation efforts in the Yangtze River and have implications towards other large river systems of the world. [ABSTRACT FROM AUTHOR]

Background: The application of index of biotic integrity (IBI) to evaluate river health can be an essential method for river ecosystem management. However, these types of methods were developed in small, low-order streams, and are therefore, infrequently applied to large rivers. To that end, phytoplankton communities and environmental variables were monitored in 30 sampling segments of the middle and lower reaches of the Yangtze River, China during the wet (July–August) and dry (November–December) seasons in 2017–2018. We developed a phytoplankton-based index of biotic integrity (P-IBI) and used the index to assess the ecological health of the Yangtze River. Relationships among P-IBI, its component metrics, and environmental factors were analyzed across different seasons. Results: Results obtained from the P-IBI indicated that the phytoplankton-based ecological health of the Yangtze River was rated as "good" during both seasons, with an overall better condition in the dry season. During the wet season, there were scattered river segments with P-IBI ratings of "fair" or below. Water quality and land use appeared to shape the patterns of P-IBI. In the wet season, P-IBI negatively correlated with total phosphorus, nitrate, total suspended solids, turbidity, conductivity, and dissolved oxygen. In the dry season, P-IBI positively correlated with total nitrogen, ammonium, and nitrite, and negatively correlated with water temperature. Conclusions: The ecological health of the Yangtze River as reflected by the P-IBI exhibited spatial and temporal variability, with the effect of water quality being greater than that of local land use. This study indicated the importance of considering seasonal effects in detecting large river ecological health. These findings enhanced our understanding of the ecological health and characterized potential benchmarks for management of the Yangtze River. These findings also may be applicable to other large rivers elsewhere. [ABSTRACT FROM AUTHOR]

The rapid growth of the digital economy has driven economic development, but the massive demand for electricity from digital reforms, coupled with China's coal-based power generation, has created a significant CO 2 emission problem. Previous studies have assessed digital economy sectors with an incomplete scope and a lack of carbon emissions assessment at the intermediary-side. To fill these gaps, this study assessed CO 2 emissions using input–output modeling of the core industry sector of the digital economy and the industrial digitalization sector at the production, intermediary, and demand sides, and identified key CO 2 transmission centers. The results show the following: (1) Digital economy sectors had a high betweenness and were important CO 2 transmission centers in the economic system, transmitting more than 4.08 billion tonnes of betweenness-based CO 2 emissions; (2) specifically, the industrial digitalization sector transmitted the most CO 2 in the economic system, and the digital product manufacturing sector was the core industry sector with the highest betweenness and a strong transmission effect on the CO 2 emissions in the supply chain; (3) digital economy sectors had large CO 2 emissions on the production, intermediary, and demand sides, and transmitted CO 2 more through the demand-side and key transmission centers. These results suggest that digital economy sectors can decarbonize and reduce CO 2 emissions by (1) improving material use efficiency in the digital product manufacturing sector, (2) reducing the use of carbon-intensive energy and materials in the digital economy sectors, and (3) establishing CO 2 emission disclosure rules, incentives, and penalties. • The digital product manufacturing sector had high betweenness. • Industrial digitalization sector was the largest intermediary-side contributor. • Digital economy sectors had large CO2 emissions from three sides. • Digital economy sectors influenced upstream CO2 emissions more through demand. [ABSTRACT FROM AUTHOR]

Influences of multiple environmental factors on water quality patterns is less studied in large rivers. Landscape analysis, multiple statistical methods, and the water quality index (WQI) were used to detect water quality patterns and influencing factors in China's largest river, the Yangtze River. Compared with the dry season, the wet season had significantly higher total phosphorus (TP), chemical oxygen demand (COD), total suspended solids (TSS), and turbidity (TUR). The WQI indicated "Moderate" and "Good" water quality in the wet and dry seasons, respectively. Compared with other sites, the upper reach sites that immediately downstream of the Three Gorges Dam had lower TP, TN, TSS and TUR in both seasons, and had lower and higher water temperature in the wet and dry seasons, respectively. Water quality patterns were mainly driven by heterogeneity in land use (i.e., wetland, cropland, and urban land), hydrology (i.e., water flow, water level), and climate (i.e., rainfall, air temperature). Water quality in the wet season was primarily driven by land use while the joint effect of land use and hydrology primarily drove in the dry season. Decision-makers and regulators of large river basin management may need to develop programs that consider influences from both human and natural drivers for water quality conservation. • The WQI showed Moderate to Good water quality in China's largest river. • Dam impoundment reduced nutrients and TSS downstream. • Land use was the main driver for water quality in the wet season. • Land use and hydrology jointly drove water quality in the dry season. • Management may consider prioritizing the 5 km riparian zone for conservation. [ABSTRACT FROM AUTHOR]