A Bayesian network to simulate macroinvertebrate responses to multiple stressors in lowland streams

The bioaccumulation and ecotoxicity of co-exposure of per(poly)fluoroalkyl substances and polystyrene microplastics to Eichhornia crassipes

Gen X and F-53B have been popularized as alternatives to PFOA and PFOS, respectively. These per(poly)fluoroalkyl substances pervasively coexist with microplastics (MPs) in aquatic environments. However, there are knowledge gaps regarding their potential eco-environmental risks. In this study, a typical free-floating macrophyte, Eichhornia crassipes (E. crassipes), was selected for hydroponic simulation of a single exposure to PFOA, PFOS, Gen X, and F-53B, and co-exposure with polystyrene (PS) microspheres. F-53B exhibited the highest bioaccumulation followed by Gen X, PFOA, and PFOS. In the presence of PS MPs, the bioavailabilities of the four PFASs shifted and the whole plant bioconcentration factors improved. All four PFASs induced severe lipid peroxidation, which was exacerbated by PS MPs. The highest integrated biomarker response (IBR) was observed for E. crassipes (IBR of shoot: 30.01, IBR of root: 22.79, and IBR of whole plant: 34.96) co-exposed to PS MPs and F-53B. The effect addition index (EAI) model revealed that PS MPs showed antagonistic toxicity with PFOA and PFOS (EAI < 0) and synergistic toxicity with Gen X and F-53B (EAI > 0). These results are helpful to compare the eco-environmental impacts of legacy and alternative PFASs for renewal process of PFAS consumption and provide toxicological, botanical, and ecoengineering insights under co-contamination with MPs.

Four decades of region- and species-specific trends in lowland stream Ephemeroptera abundance

Lowland stream ecosystems are under threat from climate change, industrialization, urbanization, and intensive agriculture. Since the 1980s, improvements in water quality have led to an increase in lowland stream biodiversity. Despite restoration efforts, however, further recovery is often hampered by the presence of region-specific (combinations of) stressors, and species-specific stressor responses. Identification of these stressors may not be achieved by the analysis of abundance data over large areas for entire communities or species assemblages. Therefore, our study introduces an alternative in-depth approach, selecting Ephemeroptera as a model organism group and analyzing 41 years of species abundance data across distinct geographical regions. Our findings revealed that 15 Ephemeroptera species had already disappeared before 1985, emphasizing the importance of evaluating an extended historical period when analyzing biodiversity trends. While biodiversity was generally characterized by an initial recovery that stagnated over time, the analysis of the past 41 years of Ephemeroptera abundance data revealed strong differences in species' abundance trends between periods, regions, and species. Certain species were likely to have benefitted from local restoration measures in specific geographical regions, while others may have declined due to the presence of region-specific stressors. Our approach underscores the importance of studying the development of region- and species-specific stream biodiversity trends over time to aid the selection of the appropriate restoration measures to recover lowland stream biodiversity.

Decoupling the heterogeneity of sediment microbial communities along the urbanization gradients: A Bayesian-based approach

Comprehending the response of microbial communities in rivers along urbanization gradients to hydrologic characteristics and pollution sources is critical for effective watershed management. However, the effects of complex factors on riverine microbial communities remain poorly understood. Thus, we established a bacteria-based index of biotic integrity (Ba-IBI) to evaluate the microbial community heterogeneity of rivers along an urbanization gradient. To examine the response of Ba-IBI to multiple stressors, we employed a Bayesian network based on structural equation modeling (SEM-BN) and revealed the key control factors influencing Ba-IBI at different levels of urbanization. Our findings highlight that waterborne nutrients have the most significant direct impact on Ba-IBI (r = -0.563), with a particular emphasis on ammonia nitrogen, which emerged as the primary driver of microbial community heterogeneity in the Liuyang River basin. In addition, our study confirmed the substantial adverse effects of urbanization on river ecology, as urban land use had the greatest indirect effect on Ba-IBI (r = -0.460). Specifically, the discharge load from wastewater treatment plants (WWTP) was found to significantly negatively affect the Ba-IBI of the entire watershed. In the low urbanized watersheds, rice cultivation (RC) and concentrated animal feeding operations (CAFO) are key control factors, and an increase in their emissions can lead to a sharp decrease in Ba-IBI. In moderately urbanized watersheds, the Ba-IBI tended to decrease as the level of RC emissions increased, while in those with moderate RC emissions, an increase in point source emissions mitigated the negative impact of RC on Ba-IBI. In highly urbanized watersheds, Ba-IBI was not sensitive to changes in stressors. Overall, our study presents a novel approach by integrating Ba-IBI with multi-scenario analysis tools to assess the effects of multiple stressors on microbial communities in river sediments, providing valuable insights for more refined environmental decision-making.

Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network

Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.

Identifying spatial influence of urban elements on road-deposited sediment and the associated phosphorus by coupling Geodetector and Bayesian Networks

Elevated particles and phosphorus washed from road-deposited sediment (RDS) are noteworthy causes of eutrophication in urban water bodies. Identifying how urban elements (e.g., dwellings, roads) spatially influence RDS and the associated phosphorus can help pinpoint the primary management areas for RDS pollution and therefore effectively mitigate this problem. This study investigated spatial influence of urban elements on RDS build-up load and phosphorus load in Hanyang district of Wuhan city in central China. Bayesian Networks (BNs), combined with geographical detector (Geodetector) and correlation analysis, were applied to quantify spatial association between kernel density of urban elements, RDS build-up load and phosphorus load in RDS. Results showed that (1) areas with higher density of factories related elements usually had elevated level of RDS build-up load, aluminum-bound phosphorus (Al-P), occluded phosphorus (Oc-P), organophosphorus (Or-P). Higher load of RDS associated iron-bound phosphorus (Fe-P) and apatite phosphorus (Ca-P) usually occurred where dwellings, catering, and entertainment related elements were concentrated. (2) Urban elements mainly showed positive correlation with RDS build-up load, Fe-P, Ca-P, De-P (detrital apatite phosphorus), while they chiefly showed negative correlation with Ex-P (exchangeable phosphorus), Al-P, Oc-P, and Or-P. Bus stations, dwellings, and factories related elements had relatively strong determinant power over spatial stratified heterogeneity of RDS and RDS-associated phosphorus. (3) Geodetector and correlation analysis could boost factors filtering and construction of network structures in the process of developing BNs models. The developed BNs resulted in sound prediction of <150 μm RDS build-up load and phosphorus load, given that the prediction accuracy of models ranged from 0.532 to 0.657. These findings demonstrate that urban elements are useful spatial predictors of RDS pollution, and coupling Geodetector and BNs is promising in RDS pollution prediction and supporting urban nonpoint source pollution management.

The Relationship between Benthic Macroinvertebrate Assemblages and Water Quality Parameters in the Sanyati Basin, Lake Kariba, Zimbabwe

Biological monitoring of reservoirs is important in assessing aquatic health. This study aimed at assessing the structure of benthic macroinvertebrate communities in relation to physicochemical parameters along Sanyati basin shoreline in Lake Kariba, Zimbabwe. Six sites (S1 to S6) characterized by various human disturbances were sampled for physicochemical parameters and benthic macroinvertebrates from January to March 2018. We computed macroinvertebrate metrics and classified them into functional feeding groups (FFGs). A canonical correspondence analysis (CCA) triplot was constructed to assess species-physicochemical relations. Significant differences across the sampling sites were observed for pH, electrical conductivity, turbidity, nitrate-nitrogen (NO₃-N), ammonium nitrogen (NH₄-N), total nitrogen (TN), phosphate-phosphorus (PO₄-P), total phosphates (TP), and dissolved oxygen (DO). The results from CCA highlighted that S1 was generally associated with high pH, NH₄-N, and TN, and Oligochaeta, Syrphidae, and Hydrophilidae families. The highest percentage of EPT taxa (39.83%) was recorded at S6, while the lowest was recorded at S1. The taxa were made up of 50% predators, 26% collector-gatherers, 6% scrappers, 6% shredders, and 3% collector-filters with 3 taxa (Chironomidae, Hydropsychidae, and Leptoceridae) having more than two FFGs. Site S1 had a significantly higher mean abundance of collector-gatherers than the other sites. A high correlation between water parameters and SASS and ASPT scores was observed indicating their ability to detect environmental changes. These findings suggest that macroinvertebrate communities are good candidates for delineating the effects of industrial pollution on water quality.

A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes "small group" workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.