98%
921
2 minutes
20
The dynamic of a community of 20 bacterial strains isolated from river water was followed in R2 broth and in autoclaved river water medium for 27 days in batch experiments. At an early stage of incubation, a fast-growing specialist strain, Acinetobater sp., dominated the community in both media. Later on, the community composition in both media diverged but was highly reproducible across replicates. In R2, several strains previously reported to degrade multiple simple carbon sources prevailed. In autoclaved river water, the community was more even and became dominated by several strains growing faster or exclusively in that medium. Those strains have been reported in the literature to degrade complex compounds. Their growth rate in the community was 1.5- to 7-fold greater than that observed in monoculture. Furthermore, those strains developed simultaneously in the community. Together, our results suggest the existence of cooperative interactions within the community incubated in autoclaved river water.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00248-019-01322-w | DOI Listing |
PLoS One
September 2025
Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba, Japan.
The Tone River in Japan represents one of the southern limit distributions of chum salmon (Oncorhynchus keta) on the western side of the North Pacific, but the number of adult chum salmon observed here has declined dramatically since 2013 and reached zero in 2024. The factors behind the recent decline of the chum salmon population in the Tone River were investigated by using ocean reanalysis data and a 20-year particle-tracking simulation. Virtual chum salmon fry were released at the mouth of the Tone River in spring each year with six different swimming strategies to evaluate the effects of ocean currents on the population growth rate of salmon.
View Article and Find Full Text PDFMol Biol Rep
September 2025
Department of Biosciences, Integral University, Kursi Road, Lucknow, 226026, India.
Background: The river ecosystems provide habitats and source of water for a number of species including humans. The uncontrolled accumulation of pollutants in the aquatic environment enhances the development of antibiotic-resistant bacteria and genes.
Methods: Water samples were collected seasonally from different sites of Gomti and Ganga River.
Environ Pollut
September 2025
ECOSPHERE, Department of Biology, University of Antwerp, Belgium.
PER: and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants that accumulate in aquatic ecosystems, posing a threat to wildlife. This study examines the potential of Asian clams (Corbicula fluminea) as an active biomonitoring species for assessing PFAS contamination in the Scheldt River, Belgium. Clams were exposed in cages at six sites along the river for a six-week exposure period, with simultaneous collection of sediment and water samples at each site.
View Article and Find Full Text PDFJ Environ Manage
September 2025
University of Maryland Center for Environmental Science, Annapolis, MD, USA.
River water quality degradation is a prevailing problem in coastal China with intensifying human-nature interaction. However, the spatial and temporal dynamics of water quality and their drivers remain poorly understood. In this study, we developed an analytical framework integrating self-organizing mapping (SOM) with partial least squares structural equation models (PLS-SEMs) to analyze the patterns and drivers of river water quality at 49 stations from 2021 to 2023 in Fujian Province, a coastal region in southeastern China.
View Article and Find Full Text PDFJ Environ Manage
September 2025
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Dissolved oxygen (DO) is a key water quality indicator reflecting river health. Modeling and understanding the spatiotemporal dynamics of DO and its influencing factors are crucial for effective river management. Machine learning (ML) models have gained popularity in water quality prediction; however, their accuracy strongly depends on the predictor variables.
View Article and Find Full Text PDF