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

Sun, Xiuming; Wu, Naicheng; Hörmann, Georg; Faber, Claas; Messyasz, Beata; Qu, Yueming; Fohrer, Nicola. 2022 Using integrated models to analyze and predict the variance of diatom community composition in an agricultural area. Science of the Total Environment, 803, 149894. 10, pp. https://doi.org/10.1016/j.scitotenv.2021.149894

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Official URL: http://dx.doi.org/10.1016/j.scitotenv.2021.149894

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.scitotenv.2021.149894
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	0048-9697
Additional Keywords:	daily dataset, diatoms, lowland river, integrated modeling, prediction
NORA Subject Terms:	Ecology and Environment
Date made live:	08 Apr 2022 10:30 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532454

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.scitotenv.2021.149894

UKCEH and CEH Sections/Science Areas:

Pollution (Science Area 2017-)

ISSN:

0048-9697

Additional Keywords:

daily dataset, diatoms, lowland river, integrated modeling, prediction

NORA Subject Terms:

Ecology and Environment