Using integrated models to analyze and predict the variance of diatom community composition in an agricultural area

Xiuming Sun; Naicheng Wu; Georg Hörmann; Claas Faber; Beata Messyasz; Yueming Qu; Nicola Fohrer

doi:10.1016/j.scitotenv.2021.149894

Using integrated models to analyze and predict the variance of diatom community composition in an agricultural area

Sci Total Environ. 2022 Jan 10:803:149894. doi: 10.1016/j.scitotenv.2021.149894. Epub 2021 Aug 26.

Authors

Xiuming Sun¹, Naicheng Wu², Georg Hörmann³, Claas Faber³, Beata Messyasz⁴, Yueming Qu⁵, Nicola Fohrer³

Affiliations

¹ Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, 24118 Kiel, Germany. Electronic address: xsun@hydrology.uni-kiel.de.
² Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, 24118 Kiel, Germany; Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China. Electronic address: nwu@hydrology.uni-kiel.de.
³ Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, 24118 Kiel, Germany.
⁴ Department of Hydrobiology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland.
⁵ Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, 24118 Kiel, Germany; UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK.

PMID: 34525756
DOI: 10.1016/j.scitotenv.2021.149894

Abstract

With the growing demand of assessing the ecological status, there is the need to fully understand the relationship between the planktic diversity and the environmental factors. Species richness and Shannon index have been widely used to describe the biodiversity of a community. Besides, we introduced the first ordination value from non-metric multidimensional scaling (NMDS) as a new index to represent the community similarity variance. In this study, we hypothesized that the variation of diatom community in rivers in an agricultural area was influenced by hydro-chemical variables. We collected daily mixed water samples using ISCO auto water samplers for diatoms and for water-chemistry analysis at the outlet of a lowland river for a consecutive year. An integrated modeling was adopted including random forest (RF) to decide the importance of the environmental factors influencing diatoms, generalized linear models (GLMs) combined with 10-folder cross validation to analyze and predict the diatom variation. The hierarchical analysis highlighted antecedent precipitation index (API) as the controlling hydrological variable while water temperature, Si²⁺ and PO₄-P as the main chemical controlling factors in our study area. The generalized linear models performed better prediction for Shannon index (R² = 0.44) and NMDS (R² = 0.51) than diatom abundance (R² = 0.25) and species richness (R² = 0.25). Our findings confirmed that Shannon index and the NMDS as an index showed good performance in explaining the relationship between stream biota and its environmental factors and in predicting the diatom community development based on the hydro-chemical predictors. Our study showed and highlighted the important hydro-chemical factors in the agricultural rivers, which could contribute to the further understanding of predicting diatom community development and could be implemented in the future water management protocol.

Keywords: Daily dataset; Diatoms; Integrated modeling; Lowland river; Prediction.

MeSH terms

Biodiversity
Diatoms*
Environmental Monitoring
Hydrology
Rivers