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• W3201315067 endingPage "150401" @default.
• W3201315067 startingPage "150401" @default.
• W3201315067 abstract "Microeukaryotes play a significant role in biogeochemical cycling and can serve as bioindicators of water quality in freshwater ecosystems. However, there is a knowledge gap on how freshwater microeukaryotic communities are assembled, especially that how terrestrial microeukaryotes influence freshwater microeukaryotic assemblages. Here, we used a combination of 18S rRNA gene amplicon sequencing and community-based microbial source tracking (MST) approaches (i.e., SourceTracker and FEAST) to assess the contribution of microeukaryotes from surrounding environments (i.e., soils, river sediments, swine wastewater, influents and effluents of decentralized wastewater treatment plants) to planktonic microeukaryotes in the main channel, tributaries and reservoir of a peri-urban watershed, China in wet and dry seasons. The results indicated that SAR (~ 49% of the total communities), Opithokonta (~ 34%), Archaeplastida (~ 9%), and Amoebozoa (~ 2%) were dominant taxa in the watershed. The community-based MST analysis revealed that sewage effluents (7.96 – 21.84%), influents (2.23 – 13.97%), and river sediments (2.56 – 11.71%) were the major exogenous sources of riverine microeukaryotes. At the spatial scale, the downstream of the watershed (i.e., main channel and tributaries) received higher proportions of exogenous microeukaryotic OTUs compared to the upstream reservoirs, while at the seasonal scale, the sewage effluents and influents contributed higher exogenous microeukaryotes to river water in wet season than in dry season. Moreover, the swine and domestic wastewater led to the presence of Apicomplexa in wet season only, implying rainfall runoff may enhance the spread of parasitic microeukaryotes. Taken together, our study provides novel insights into the immigration patterns of microeukaryotes and their dominant supergroups between terrestrial and riverine habitats." @default.
• W3201315067 created "2021-09-27" @default.
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• W3201315067 date "2022-02-01" @default.
• W3201315067 modified "2023-10-18" @default.
• W3201315067 title "Tracking microeukaryotic footprint in a peri-urban watershed, China through machine-learning approaches" @default.
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• W3201315067 doi "https://doi.org/10.1016/j.scitotenv.2021.150401" @default.
• W3201315067 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34562761" @default.
• W3201315067 hasPublicationYear "2022" @default.
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