Fine sediment and flow velocity impact bacterial community and functional profile more than nutrient enrichment

Different effects of seasonal impoundment and land use change on microbiome in a tributary sediment of the three gorgers reservoir

Anthropogenic activities significantly impact river ecosystem nutrient fluxes and microbial metabolism. Here, we examined the seasonal and spatial variation of sediments physicochemical parameters and the associated microbiome in the Pengxi river, a representative tributary of Three Gorges Reservoir, in response to seasonal impoundment and land use change by human activities. Results revealed that seasonal impoundment and land use change enhanced total organic carbon (TOC), total nitrogen (TN) and ammonium nitrogen (NH4+-N) concentration in the sediment, but have different effects on sediment microbiome. Sediment microbiota showed higher similarity during the seasonal high-water level (HWL) in consecutive two years. The abundant phyla Acidobacteria, Gemmatimonadetes, Cyanobacteria, Actinobacteria and Planctomycetes significantly increased as water level increased. Along the changes in bacterial taxa, we also observed changes in predicted carbon fixation functions and nitrogen-related functions, including the significantly higher levels of Calvin cycle, 4HB/3HP cycle, 3HP cycle and assimilatory nitrate reduction, while significantly lower level of denitrification. Though land use change significantly increased TOC, TN and NH4+-N concentration, its effects on spatial variation of bacterial community composition and predicted functions was not significant. The finding indicates that TGR hydrologic changes and land use change have different influences on the carbon and nitrogen fluxes and their associated microbiome in TGR sediments. A focus of future research will be on assessing on carbon and nitrogen flux balance and the associated carbon and nitrogen microbial cycling in TGR sediment.

Transmission mechanism of antibiotic resistance genes and their differences between water and sediment in the Weihe River Basin

Antibiotic resistance genes (ARGs) are emerging microbial pollutants that are regulated by many factors and pose potential threats to aquatic environments. In this study, we used network analysis, correlation analysis, and constructed models based on metagenomic sequencing results to explore the spatial patterns, impact mechanisms, transmission risks and differences in ARGs in the water and sediment of the Weihe River Basin. The findings revealed notable disparities in ARGs, mobile genetic elements (MGEs), and bacterial communities. In the sediment, the abundance of ARGs was considerably greater than that in water. Moreover, the percentage of ARGs shared by the two components reached a value of 85.8%. Through network analysis, it was determined that the presence of 16 MGEs and 20 bacterial phyla was strongly associated with ARGs (R2 > 0.7, P < 0.05). The Mantel test showed that abiotic factors including DO, pH, nutrients, and heavy metals played important roles in the distribution of ARGs (P < 0.05). A structural equation model revealed that the key factors influencing the distribution of ARGs in water were bacterial diversity and environmental parameters (standardized effects of -0.730 and -0.667), and those in sediment were bacterial diversity and MGEs (standardized effects of -0.751 and 0.851). Neutral modeling indicated that deterministic processes played an important role in the assembly of ARGs in the water of the Weihe River Basin, and stochastic processes were dominant in the sediment. There was a highly significant positive linear correlation between ARGs and pathogens, and there was more complex co-occurrence in the water than in the sediment (R2 > 0.9, P < 0.05), with stronger migration and transmission occurring. Exploring ARGs in large-scale watersheds is immensely important for elucidating their traits and transmission mechanisms and consequently paving the way for the formulation of efficient strategies to mitigate resistance threats.

Effects of flow reduction and artificial light at night (ALAN) on litter decomposition and invertebrate communities in streams: A flume experiment

River ecosystems are heavily impacted by multiple stressors, where effects can cascade downstream of point sources. However, a spatial approach to assess the effects of multiple stressors is missing. We assessed the local and downstream effects on litter decomposition, and associated invertebrate communities of two stressors: flow reduction and artificial light at night (ALAN). We used an 18-flow-through mesocosm system consisting of two tributaries, where we applied the stressors, merging in a downstream section. We assessed the changes in decomposition rate and invertebrate community structure in leaf bags. We found no effect of ALAN or its interaction with flow reduction on the litter decomposition or the invertebrate community in the tributaries. Flow reduction alone led to a 14.8 % reduction in decomposition rate in the tributaries. We recorded no effect of flow reduction on the macroinvertebrates community composition in the litter bags. We also observed no effects of the spatial arrangement of the stressors on the litter decomposition and macroinvertebrate community structure downstream. Overall, our results suggest the impact of stressors on litter decomposition and macroinvertebrate communities remained local in our experiment. Our work thus calls for further studies to identify the mechanisms and the conditions under which spatial effects dominate over local processes.

Wastewater treatment plant effluents exert different impacts on antibiotic resistome in water and sediment of the receiving river: Metagenomic analysis and risk assessment

Wastewater treatment plants (WWTPs) are considered as hotspots for the spread of antibiotic resistome into the environment. However, the differential contributions of WWTPs to the antibiotic resistome in the receiving river water and sediment are poorly understood. Here, based on metagenomic analysis, we found that the WWTP effluents significantly elevated the diversities and abundances of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in the receiving river water from the Qinghai-Tibet Plateau, but showed less interference with the antibiotic resistome in sediment. Estimated by SourceTracker, WWTPs contributed 60.691.8% of ARGs in downstream river water, much higher than those for sediment (7.7568.0%). A holistic comparison of ARG risks based on analysis of ARG combination, mobility risk, ARG hosts and ARG-carrying pathogens further revealed the great impacts of WWTP effluents on downstream river water rather than sediment. Among various MGEs, tnpA exhibited the greatest potential for the dissemination of ARGs, and displayed highest co-occurrence frequency with multiple ARGs. P. aeruginosa, E. cloacae, and E. coli were identified as the critical-priority pathogens of ARG hosts. This study demonstrated the much greater impacts of WWTP effluents on the downstream water compared with sediment, which is significant for developing effective strategies to mitigate ARG risks.

Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions

Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria

Indicators based on nutrient-biota relationships in streams can inform water quality restoration and protection programs. Bacterial assemblages could be particularly useful indicators of nutrient effects because they are species-rich, important contributors to ecosystem processes in streams, and responsive to rapidly changing conditions. Here, we sampled 25 streams weekly (12-14 times each) and used 16S rRNA gene metabarcoding of periphyton-associated bacteria to quantify the effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis identified assemblage-level changes and amplicon sequence variants (ASVs) that increased or decreased with increasing TP and TN concentrations (i.e., low P, high P, low N, and high N ASVs). Boosted regression trees confirmed that relative abundances of gene sequence reads for these four indicator groups were associated with nutrient concentrations. Gradient forest analysis complemented these results by using multiple predictors and random forest models for each ASV to identify portions of TP and TN gradients at which the greatest changes in assemblage structure occurred. Synthesized statistical results showed bacterial assemblage structure began changing at 24 μg TP/L with the greatest changes occurring from 110 to 195 μg/L. Changes in the bacterial assemblages associated with TN gradually occurred from 275 to 855 μg/L. Taxonomic and phylogenetic analyses showed that low nutrient ASVs were commonly Firmicutes, Verrucomicrobiota, Flavobacteriales, and Caulobacterales, Pseudomonadales, and Rhodobacterales of Proteobacteria, whereas other groups, such as Chitinophagales of Bacteroidota, and Burkholderiales, Rhizobiales, Sphingomonadales, and Steroidobacterales of Proteobacteria comprised the high nutrient ASVs. Overall, the responses of bacterial ASV indicators in this study highlight the utility of metabarcoding periphyton-associated bacteria for quantifying biotic responses to nutrient inputs in streams.

Integrated Ecohydrological Models in Aquatic Ecosystems

As a critical component of the global environment, aquatic ecosystems support a wide range of organisms, including bacteria, fungi, algae, invertebrates, plants, and fish [...]