Universal Dynamics of Microbial Communities in Full-Scale Textile Wastewater Treatment Plants and System Prediction by Machine Learning

The
performance of full-scale biological wastewater treatment
plants
(WWTPs) depends on the operational and environmental conditions of
treatment systems. However, we do not know how much these conditions
affect microbial community structures and dynamics across systems
over time and predictability of the treatment performance. For over
a year, the microbial communities of four full-scale WWTPs processing
textile wastewater were monitored. During temporal succession, the
environmental conditions and system treatment performance were the
main drivers, which explained up to 51% of community variations within
and between all plants based on the multiple regression models. We
identified the universality of community dynamics in all systems using
the dissimilarity-overlap curve method, with the significant negative
slopes suggesting that the communities containing the same taxa from
different plants over time exhibited a similar composition dynamic.
The Hubbell neutral theory and the covariance neutrality test indicated
that all systems had a dominant niche-based assembly mechanism, supporting
that the communities had a similar composition dynamic. Phylogenetically
diverse biomarkers for the system conditions and treatment performance
were identified by machine learning. Most of the biomarkers (83%)
were classified as generalist taxa, and the phylogenetically related
biomarkers responded similarly to the system conditions. Many biomarkers
for treatment performance perform functions that are crucial for wastewater
treatment processes (e.g., carbon and nutrient removal). This study
clarifies the relationships between community composition and environmental
conditions in full-scale WWTPs over time.