Fig. 5: Alternatives to our proposed method.

A–C Linear model analysis. We use a linear model (code taken from⁴) that uses species presence/absence to predict degradation percent by using (A) all the data we generated as training and test sets, or (B) the mono- and pairwise co-culture data as training set and the rest of the data as test set, or (C) the larger communities as training set and the mono- and pairwise co-culture data as test set. Community richness is shown in color. Each dot is one degradation score measurement, such that biological replicates and technical replicates, if available, are all represented. R² shows Pearson’s correlation coefficient. D Proposing a new artificial selection method. Rather than disassembling communities, we propose to use the winning communities as templates to generate the offspring communities in the next round. These communities would then be seeded by taking the clonal ancestral species from the freezer, such that there would be no within-species evolution over rounds. Step 3 would be to select the top 10 communities, 4a to generate communities in proportion to their community scores and 4b to randomly choose 21/29 of the new communities (illustrated with 4) for either species removal or introduction (see white asterisks in step 4, 4a and 4b are shown in one step). Freezer icon created by SAM Designs from Noun Project.