An ecological-network-analysis based perspective on the biological control of algal blooms in Ulansuhai Lake, China

The underappreciated role of biotic factors in controlling the bloom ecology of potentially harmful microalgae in the Hudson-Raritan Bay

Despite widespread distribution of harmful algal blooms (HABs) and new and improved methods for detecting and quantifying them, no unifying ecological explanation has been found. Improved understanding depends upon local, ecological studies that include analysis of phytoplankton species data in relation to both abiotic and biotic parameters. Ecological network analysis was used to detect co-occurrence patterns among abiotic and biotic parameters in a long-term monitoring dataset (i.e., 2010-2021) from the eutrophic Hudson-Raritan Estuary (HRE) between the states of New York and New Jersey. The regular co-occurrence of potentially harmful bloom-forming species with companion species observed through microscopy was supported by the results of ecological network analysis, which showed that there were far more associations between HAB species and biotic parameters (∼95%) than abiotic parameters (∼5%). Temperature was the environmental variable that was most associated with HAB species throughout the estuary. The numerous network associations of HAB species with one another and with diatoms, dinoflagellates, and zooplankton highlight the complexity of planktonic food webs and interactions. Results also suggest that some taxa may play a central role in structuring the HRE plankton communities. These findings demonstrate that biotic associations play an underappreciated role in plankton structure and the value of examining the ecology of HAB species within the breadth of their biological communities. While network analysis does not fully explain and confirm complex associations among species, it does provide fresh insights and testable hypotheses to strengthen understanding and improve prediction.

PSR-BP Neural Network-Based Health Assessment of the Huangshui Plateau Urban Wetlands in China

Wetland health assessment provides important basis for wetland restoration and management. However, it is quite tricky to select proper indicators from multiple assessment indicators that can truly reflect the health state of urban wetlands. In an attempt to address these problems, a pressure-state-response (PSR) and back propagation artificial neural network (BP) conjoined model was established for health assessment of several plateau urban wetlands in Xining City, China. The model was driven and verified through field monitoring and social questionnaire data for 4 consecutive years from 2016 to 2019. Results indicate that: (1) Eight health evaluation indexes, including population density, eutrophication level, increasing humidity, carbon dioxide absorption, air purifying, recreation, wetland management level and investment in ecological construction and protection were selected from 45 input indexes. (2) The health index of Huangshui National Wetland Park has been increasing year by year, with an average of comprehensive health score of 0.746, 0.790, 0.884, and 0.877, respectively. The indicators that contributed the most to the restoration effect were leisure and entertainment service value (2016), carbon dioxide absorption service value (2017), eutrophication (2018), and wetland management level (2019), respecially. (3) Compared with the single PSR method, the advantages of this method include; There are fewer evaluation indicators, more accurate results (excluding the interference of some highly variable indicators) and more sensitive to environmental changes. The current study proposed a novel method that may provide additional accurate and refined indicators for urban wetland health assessment.

Comprehensive modelling and cost-benefit optimization for joint regulation of algae in urban water system

Algal blooms in urban water system is an international concern, which especially in China, have become a major obstacle to the urban water environment improvement since the preliminary achievements were made in the treatment of black and odorous water bodies. The complex blooming mechanisms require a joint regulation plan. This study established a framework that consisted of three steps, i.e., simulation, optimization, and verification, to build an optimal joint regulation plan. By taking the urban river network in Suzhou Pingjiang Xincheng as a case study, the cost-benefits of six alternative regulation measures were assessed using an algal bloom mechanism model and the discounted cash flow model based on 70 regulation scenarios. The joint regulation plan was optimized using the marginal-cost-based greedy strategy on the basis of the cost-benefits of different measures. The optimized joint plans, which were verified to be global optima, were more cost-effective than the designed regulation scenarios, and reduced the average chlorophyll-a concentrations by 55.3%-60.1% compared with the status quo. Applying the optimized cost allocation ratios of each measure to adjust the existing regulation scheme of another similar case verified that the optimization results had great generalizability.

Biogeographic distribution patterns of algal community in different urban lakes in China: Insights into the dynamics and co-existence

Urban lake ecosystems are significant for social development, but currently we know little about the geographical distribution of algal community in urban lakes at a large-scale. In this study, we investigated the algal community structure in different areas of urban lakes in China and evaluated the influence of water quality parameters and geographical location on the algal community. The results showed that obvious differences in water quality and algal communities were observed among urban lakes in different geographical areas. Chlorophyta was the dominant phylum, followed by cyanobacteria in all areas. The network analysis indicated that algal community composition in urban lakes of the western and southern area showed more variations than the eastern and northern areas, respectively. Redundancy analysis and structural equation model revealed that nutrients and pH were dominant environmental factors that affected the algal community, and they showed higher influence than that of iron, manganese and COD _Mn concentration. Importantly, algal community and density exhibited longitude and latitude relationship. In general, these results provided an ecological insight into large-scale geographical distributions of algal community in urban lakes, thereby having potential applications for management of the lakes.

Spatiotemporal variability of phytoplankton functional groups in a shallow eutrophic lake from cold, arid regions

In the shallow eutrophic lakes in cold, arid regions, the phytoplankton functional groups and the factors that drive their spatiotemporal variabilities remain unclear. Samples were collected from Lake Ulansuhai in April, August, and October 2017 (wet season) and January 2018 (dry season). Based on the functional group classification method, 23 phytoplankton functional groups with 5 major ones were identified. During the wet season, high amounts of nutrients, elevated temperatures, and heavy rainfall produced spatiotemporal variabilities in phytoplankton communities, whereas during the dry season, the frozen period was the critical factor that determined the spatiotemporal variabilities in the phytoplankton communities. Through redundancy analyses, total nitrogen and total phosphorus concentrations were observed to directly affect the phytoplankton growth; algal growth affected the chemical oxygen demand, and pH and environmental factors interacted with the phytoplankton growth. These results highlight the complex feedbacks of shallow eutrophic lake ecosystems in arid regions. Group TC (represented by Lyngbya) was correlated with Huangtai algae. In August, a Huangtai algal bloom resulted in a relatively stable water column, which was conducive to group TC growth. Therefore, the presence of certain phytoplankton functional groups can indicate the current lake conditions by identifying the coverage of Huangtai algae, which provides a scientific basis for an early warning of a potential algal bloom.

Characterizing ecological interaction networks to support risk assessment in classical biological control of weeds

A key element in weed biological control is the selection of a biological control agent that minimizes the risks of non-target attack and indirect effects on the recipient community. Network ecology is a promising approach that could help decipher tritrophic interactions in both the native and the invaded ranges, to complement quarantine-based host-specificity tests and gain insights on potential interactions of biological control agents. This review highlights practical questions addressed by networks, including 1) biological control agent selection, based on specialization indices, 2) risk assessment of biological control agent release into a novel environment, via particular patterns of association such as apparent competition between agent(s) and native herbivore(s), 3) network comparisons through structural metrics, 4) potential of network modelling and 5) limits of network construction methods.

Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District

Research on carbon emissions of complex interactive activities in urban agglomerations is one of the hotspots of global climate change research. A comprehensive analysis of the urban agglomeration system’s carbon emissions is essential to reveal strategies for reduction and support sustainable development. The objective of this research is to develop an integrated carbon emission network model to explore the impact of different energy types on the Northeast Industrial District (NID), China. Four representative energy groups are considered. Specifically, at the aggregated sector-level, this research quantified the relative contributions of socioeconomic factors to carbon emission changes using structural decomposition analysis and examined the system efficiency and redundancy through robustness analysis. At the disaggregated level, the research investigated carbon emissions of different sectors from production-based, consumption-based, and income-based viewpoints. Moreover, emissions from specific categories of final demand and primary input were quantified. It was found that the increase of final demand level will proceed to push up the carbon emissions of the NID. Changing the production structure contributes to reducing emissions. The carbon emissions system has a high redundancy and low efficiency, illustrating that there are many emission pathways within the system. In addition, the use of crude oil significantly increases system redundancy and inhibits system efficiency. However, the major limitation of the model is that the long-term changes of the system are not considered. Moreover, considering the actual policies, emission reduction simulations could be added in the future.

Temporal and Spatial Variation Trends in Water Quality Based on the WPI Index in the Shallow Lake of an Arid Area: A Case Study of Lake Ulansuhai, China

Ulansuhai, the largest shallow lake of the Yellow River of China, is an important component of the Hetao region irrigation system. Many concerns have concentrated on its water quality, which affects the local water security and sustainable economic development. In this study, the water pollution index (WPI), an effective water quality evaluation method, was used to compare the pollution levels among pollution indicators and to determine the major pollution indicators. The regime shift index (RSI) approach was employed to identify the water quality trends. Cluster analysis and Daniel trend test methods were employed to analyse the inner-annual and inter-annual spatio-temporal trends of the typical water quality indicators (e.g., total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), and chemical oxygen demand (COD)) in Lake Ulansuhai. The results show that the water quality of Ulansuhai improved from 1998 to 2017; spatial variations in the WPITN, WPITP, and WPIDO followed the order of inlet > centre and outlet, while spatial variations in the WPICOD showed the order of outlet > inlet > centre. TN was the critical pollution indicator throughout the year. In 2017, the dry season and wet season were determined using cluster analysis. The WPICOD was higher than the WPITN, WPITP, and WPIDO in the dry season, while the WPITN, WPITP, and WPIDO were higher than the WPICOD in the wet season. WPI was grouped into three clusters: highly polluted regions, moderately polluted regions, and less polluted regions, However, there is a discrepancy between the three polluted regions that were divided into the dry season and the wet season. The WPICOD was highest among all pollution indicators in 2017. Major sources of pollution that contribute to the deterioration of water quality include inner-annual or inter-annual pollution, agricultural non-point pollution, point source pollution, and internal pollution. This study provides useful information for authorities to effectively manage water quality and control water pollution.