Additional file 2 of Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Abstract

Additional  file 2: Figure S1. Ranking of importance weights ofenvironmental factors in different alpha-diversities predictive models. FigureS2. a. Comparison ofintra- and inter-group Bray-Curtis similarity between predicted and observedcommunities. b. Average prediction accuracy R21:1of microbial taxa at different taxonomic levels. Figure S3. Environmental factor importance weights andPearson’s correlation coefficients. FigureS4. Correlation ofcorrelation coefficients of environment factors with ASVs>10%subcommunity, skewness, and kurtosis of normalized environment variables withtheir Garson’s connection weights. Figure S5. a. Comparison of predictiveaccuracy R21:1 between low,medium, and high abundance taxa. b. Comparison of predictiveaccuracy R21:1 between low,medium, and high-frequency taxa. c. Correlation of relative abundance with the occurrencefrequency of ASVs. d. Correlation of the R21:1in test sets with the coefficient of variation of ASVs. Figure S6. Comparison ofaverage relative abundance and occurrencefrequency between above, neutral, and below partitions. Figure S7. Fit of theneutral community model (NCM) of above, neutral, and below partitions. Figure S8.The taxonomic composition, average relative abundance, occurrence frequency,and estimated migration rate of core and non-core taxa. Figure S9. Predictionof functional groups with 10 high-weight environmental factors. Figure S10. Fitof the neutral community model (NCM) of high abundance, medium abundance, andlow abundance subcommunities. Figure S11. Changes of mean square errors (MSE) andcoefficients of determination (R2) on the validation set with epochswhen training the model