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Fungal communities as dual indicators of river biodiversity and water quality assessment
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Document Title
Fungal communities as dual indicators of river biodiversity and water quality assessment
Author
Siriarchawatana P. Harnpicharnchai P. Phithakrotchanakoon C. Kitikhun S. Mayteeworakoon S. Chunhametha S. H??ng V.T.L. Eurwilaichitr L. Jiang C. Cai L. Ingsriswang S.
Affiliations
Thailand Bioresource Research Center (TBRC) National Center for Genetic Engineering and Biotechnology (BIOTEC) National Science and Technology Development Agency (NSTDA) Pathumthani Thailand; University of Science Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam; National Energy Technology Center (ENTEC) National Science and Technology Development Agency (NSTDA) Pathumthani Thailand; Institute of Microbiology Chinese Academy of Sciences Beijing China
Type
Article
Source Title
Water Research
ISSN
431354
Year
2024
Volume
253
Open Access
All Open Access Hybrid Gold
Publisher
Elsevier Ltd
DOI
10.1016/j.watres.2024.121252
Abstract
Given their ecological importance bioindicators are used for the assessment of the health of river ecosystems. This study explored the fungal compositions and the potential of fungal taxa as bioindicators for indicating the water quality of the Mekong River as the use of fungal indicators of the Mekong River was not previously well characterized. The Mekong River exhibited dynamic variations in both physicochemical/hydrochemical properties and fungal communities according to seasons and locations. The results revealed the dominance of alkaline earth metal ions and weak acids in the water. The magnesium-bicarbonate water type was found in the dry season but the water became the chloride-calcium type or mixed type of magnesium-bicarbonate and chloride-calcium in the rainy season at downstream sites. Fungal composition analysis revealed the dominance of Chytridiomycota in the dry season and intermediate periods and Ascomycota and Basidiomycota in the rainy season. The fungal communities were influenced by stochastic and deterministic assembly processes mainly homogeneous selection heterogeneous selection and dispersal limitation. The extent of environmental filtering implied that some fungal taxa were affected by environmental conditions suggesting the possibility of identifying certain fungal taxa suitable for being bioindicators of water quality. Subsequently machine learning with recursive feature elimination identified specific fungal bins mostly consisting of Agaricomycetes (mainly Polyporales Agaricales and Auriculariales) Dothideomycetes (mainly Pleosporales) Saccharomycetes (mainly Saccharomycetales) Chytridiomycota and Rozellomycota as bioindicators that could predict ambient and irrigation water quality with high selectivity and sensitivity. These results thus promote the use of fungal indicators to assess the health of the river. ? 2024 The Author(s)
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY-NC-ND
Rights
Authors
Publication Source
WOS