Damien R Finn
email: damien.finn@thuenen.de

Benoît Bergk-Pinto

Christina Hazard

Graeme W Nicol

Christoph C Tebbe

Timothy M Vogel

Functional trait relationships demonstrate life strategies in terrestrial prokaryotes

Keywords: 19 Theoretical ecology, niche differentiation, copiotroph-oligotroph, Random Forest 20 modelling

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Phylogenetic distance (P) of each taxonomic group increased with decreasing 376 taxonomic rank, and was highest in Proteobacteria, Actinobacteria and Firmicutes 377 Families (Supplementary Figure 2b). There was a strong positive linear relationship 378 between P and C (y = 0.86x -0.25, R 2 = 0.39, p < 0.001) supporting the result that 379 taxonomic groups of closer related taxa tended to share more similar compositions of 380 traits. 386 ranged from 28 -65% per genome, being particularly high in the Archaea. On average, 387 47% of traits per genome were Uncharacterised with a standard deviation of 9.5%.

388

Of the 260 BRITE 3 categories, the 60 most important traits in separating all Phyla 389 and Families are ranked by importance measured as Mean Decrease in Accuracy 390 (MDA) of the Random Forest models (Figure 3). This is a measure of the average 391 increase in classification error during permutation of trees (n = 300 -400) when that 392 particular trait is missing from the tree. For example, the accuracy of classifying 393 Families was most improved by inclusion of the ABC transporters trait. Based on the 394 identified traits, the Phylum model was capable of successfully classifying 81.14% of 395 individual taxa. The Family model was capable of successfully classifying 71.43% of 396 individual taxa. Confusion matrices for both models are presented as Supplementary 397 Tables 234, and show that classification was particularly difficult for Chloroflexi and 398 Planctomycetes (classification error > 80%) in the Phylum model and for 399 Cellulomonadaceae and the divergent Acidobacteria Lineage (classification error > 400 80%) in the Family model. Random Forest models were robust against variation in P 401 within Families, for example the nine families with all taxa perfectly classified ranged 402 in P from the lowest (0.68) to highest (0.9).

403

The important traits in classifying the taxonomic groups were involved in: a) et al., 2013) and, in the absence of other primary producers, allow 546 biological soil crust forming Cyanobacterial taxa to establish (Garcia-Pichel et al., 547 2001). Anoxic wetland and rice paddy environments support diverse communities of 548 anaerobic Firmicutes, Chloroflexi and methanogenic Archaea (He et al., 2019, Finn et 549 al., 2020b). These trends were noted prior to bioinformatic advances of metagenome 550 assembled genomes (MAGs) that allow for the specific comparison of individual traits 551 between uncultured environmental prokaryote genomes (Hug et al., 2013). The 552 generation of MAGs has emerged as a useful tool for identifying traits necessary for 553 life in such environments, and particularly for expanding knowledge of severely under-554 represented, difficult to culture taxonomic groups. For example, the recent 555 reconstruction of 52 515 MAGs from a wide range of host-associated and 556 environmental metagenomes was able to increase genomic information of 557 Planctomycetes and Verrucomicrobia by 79% and 68%, respectively (Nayfach et al., 558 2021). Importantly, both 16S rRNA gene surveys and MAGs demonstrate that some 559 functional traits that facilitate life under certain environmental conditions are 560 intrinsically linked to taxonomy.

404

561

The ecological coherence observed in Figure 2 does not imply that closely related 562 taxa (e.g. Bacillus velezensis LS69 and Bacillus amyloliquefaciens plantarum FZB42)

563 have identical phenotypes. Close relatives possess a combination of core and 564 accessory genes (traits) and the presence of even a single accessory gene is sufficient 565 to dramatically alter a strain's phenotype (van Rossum et al., 2020). Rather, our results 566 (Figure 2) demonstrate that the composition of core and accessory traits in 567 alphaproteobacterial Beijerinckiaceae are most similar to each other, relative to 568 alphaproteobacterial Rhizobiaceae or to Actinobacteria, Firmicutes etc. Finally, flagella assembly and chemotaxis traits identified Alphaproteobacteria, 614 Betaproteobacteria and Acidobacteria as Phyla that were particularly enriched with this 615 mechanism of motility, while bacterial Actinobacteria, Bacteroidetes, Cyanobacteria, 616 Chloroflexi and Verrucomicrobia were depleted (Supplementary Table 9). The 617 Proteobacteria, Firmicutes and Under-represented models were better suited for 618 identifying specific Families homogenously enriched or depleted in bacterial flagella 619 and chemotaxis (Supplementary Tables 10 and11). Enriched Families included the 620 Rhodospirillaceae, Nitrosomonadaceae, Neisseriales lineage, divergent Acidobacteria 621 lineage, Sporomusaceae, Bacillaceae, Clostridiaceae and Planctomycetes. Other 622 forms of motility such as twitching and gliding have been noted in the 623 Pseudomonadaceae, Myxococcaceae and Cyanobacteria (McBride, 2001) but these 624 traits were not identified by the Random Forest as being homogenously enriched in 625 any Families. Furthermore, while Thaumarchaeota and Methanogens were both 626 identified as being depleted in bacterial flagella assembly and chemotaxis traits 627 (Supplementary Table 9 and11), Archaea possess a structurally distinct flagellum 628 more similar to the Type IV bacterial pilus (Jarrell and Albers, 2012). These taxa were 629 not enriched with Type IV pilus, either, and it is possible that archaeal flagella may 630 have failed proper characterisation by KEGG. Thus, while some Families were 631 relatively enriched/depleted in bacterial flagella and chemotaxis traits, specific taxa 632 depleted in these are not necessarily non-motile.

633

In summary, while the Random Forests may overlook certain traits in individual taxa, 634 the models were highly robust in detecting conserved, shared traits within a 635 Phylum/Family. Here the 'depth' of shared traits is limited by the number of taxa that 636 could be considered as Phylum or Family. In future, if five (or more) taxa belonging to 637 the same Genus or even Species could be compared, unique traits would be observed 638 to explain how these subgroups have evolved from their respective Families to occupy 639 distinct niches. Ideally the selection of individual taxa within groups for such future 640 comparative analyses would also be standardised based on phylogenetic distance, 641 either with P or a similar method, that would improve the robustness of trait-based 642 comparisons at such a fine taxonomic level. 661 Chthoniobacter flavus (Verrucomicrobia) as having particularly large genomes with 662 extensive repertoires for cellulose, hemicellulose, pectin and lignin degradation 663 (Bentley et al., 2002, Sangwan et al., 2004, Schneiker et al., 2007). Comparative 664 genomics analyses have also identified Actinobacteria, Acidobacteria and 665 Verrucomicrobia as being enriched in numerous enzymes for cellulose, hemicellulose 666 and starch catabolism (Trivedi et al., 2013). In situ these Families likely play a critical 667 role in making organic carbon bioavailable as di-and monosaccharides for the 668 microbial community.

669

The Families equipped with many copies of high-affinity sugar uptake 670 'Phosphotransferase systems' (3 -14 copies) did not necessarily correspond to those 671 enriched with CAZy -only the Myxococcaceae and Verrucomicrobia were enriched in 672 both. Pseudomonadaceae, Rhodospirillaceae, Neisseriales lineage and 673 Clostridiaceae were only enriched in PTS. Despite being enriched in CAZy, the 674 Frankiaceae were simultaneously depleted in PTS. The complex interplay between 675 taxa capable of producing extracellular CAZy and competitors that rapidly scavenge 676 available di-and monosaccharides has been well described by models that predict 677 such competitive interactions exert important controls on the growth rate of the 678 community as a whole (Freilich et al., 2011) and may even act to aid terrestrial carbon 679 storage and limit carbon dioxide emissions from microbial respiration (Kaiser et 1, Supplementary Table 11).

682

ABC transporters facilitate the ATP-dependent uptake of soluble compounds across 683 membranes or export waste metabolites, extracellular enzymes and toxins (Young & 684 Holland, 1999, Higgins, 2001). This means of active transport allows microorganisms 685 to acquire nutrients with high affinity at concentrations of 5 -500 µg carbon L -1 versus 686 the less efficient diffusion of nutrients across membranes, dependent on extracellular 687 concentrations of 0.5 -5 mg carbon L -1 (Kuznetsov et al., 1979). In the spatially 688 heterogenous soil environment where the concentration of bioavailable carbon 689 substrate often limits growth (Blagodatsky & Richter, 1998), possession of high affinity 690 transporters likely provides a competitive advantage. The rhizosphere-associated 691 Rhodospirillaceae, Rhizobiaceae and Burkholderiaceae tended to have the highest 692 trait copies of ABC transporters (100 -350 copies per genome, Supplementary Figure 693 4). The diverse, non-rhizospheric Deltaproteobacteria, Actinobacteria, Firmicutes, 694 Cyanobacteria and Verrucomicrobia all had greater than 50 copies per genome, 695 highlighting the importance of these traits in soil. The particularly high gene copy 696 number in rhizosphere-associated taxa from presumably nutrient-rich environments 697 contrasts the assumption that ABC transporters are considered to play a greater role 698 in nutrient-poor environments (Lauro et al., 2009). Comparative genomics analyses of 699 soil bacteria have also found putatively copiotrophic Proteobacteria and Firmicutes to 700 be particularly enriched in PTS and ABC transporters (Trivedi et al., 2013). In this 701 study, the transporters enriched in rhizosphere-associated taxa were primarily aimed 715 Three Proteobacterial Families, Rhodospirillaceae, Bradyrhizobiaceae and 716 Burkholderiaceae, were particularly enriched in these traits. Genomic and culture-717 dependent analyses show these Families to be free-living or symbiotic diazotrophs in 718 soil and freshwater environments (Madigan et al., 1984, Itakura et al., 2009, de los 719 Santos et al., 2018). Given their significantly greater copies of nitrogen-fixing genes, 720 these Families may be a particularly important source of organic nitrogen for soil 721 communities. Saprotrophic Mycobacteriaceae genomes, also identified as nitrogen 722 cyclers, tend to have many copies of genes involved in ammonia uptake and glutamate 723 synthesis (Amon et al., 2010). This taxon may play an alternative role in converting 724 mineral nitrogen to biomass where organic nitrogen as protein in excreted products or 725 necromass can undergo proteolysis and uptake between other community members.

726 The identification of Sporomusaceae as enriched in 'Nitrogen metabolism' traits is 727 unusual as these obligate anaerobic fermenters cannot use nitrate as an electron 728 acceptor (Möller et al., 1984). Nor were the Sporomusaceae enriched in ammonia 729 uptake or glutamate synthesis genes (data not shown), and so it is uncertain what role 730 this Family plays in nitrogen cycling. Thaumarchaeota and Nitrosomonadaceae, known 731 ammonia oxidisers, were not enriched in 'Nitrogen metabolism' traits relative to other 732 Families (Supplementary Figure 5) despite Nitrosomonadaceae possessing multiple 733 copies of the operon responsible for ammonia oxidation (Klotz & Norton, 1998).

734 Specific traits may be overlooked here if the BRITE category includes many diverse 735 KOs (e.g. ammonia oxidation, nitrogen fixation, glutamate synthesis etc).

736

Another specialised metabolic pathway of interest involves 'Methane metabolism' 737 that includes production and oxidation of a potent greenhouse gas. Unsurprisingly, the et al., 2009) the above proteobacterial representatives act as the primary 742 terrestrial methane sink (Dunfield, 2007, Conrad, 2009). (Shimada et al., 2017). The 757 genomes of these taxa are also heavily enriched in regulatory genes for complex 758 developmental stages, fruiting bodies and/or filamentous branching growth in soils 759 (Bentley et al., 2002, Gao et al., 2006, Schneiker et al., 2007). Thus, certain taxa may 760 respond primarily to extracellular cues while others strictly monitor and respond to 761 changes in cell homeostasis. This trend has been noted previously -in 167 genomes 762 across various Bacteria and Archaea, Proteobacteria had a higher ratio of sensors for 763 external versus internal stimuli and were considered 'extroverts', while Cyanobacteria 764 were considered strong 'introverts' focussed on responding to internal stimuli 765 (Galperin, 2005).

766

As mentioned above, Archaea exhibited unique traits in basal transcription and 767 protein regulation via proteasome. These transcription factors were primarily involved 768 in identifying DNA damage and excision repair: TFII-B, TFII-D, ERCC-2 and ERCC-3.

769 DNA repair differs markedly between Bacteria and Archaea/eukaryotes. Specifically, 770 Bacteria excise 12 nucleotides around a damaged site with a 3 polypeptide system 771 whereas Archaea excise 24 -32 nucleotides with a 13 -16 polypeptide system 772 (Sancar, 1996). The use of ubiquitin-labelling and proteasome degradation of 773 misfolded or 'old' proteins is arguably a more efficient system for recycling amino acids 774 and regulating the 'lifespan' of a protein in eukaryotes, however, Bacteria are still fully 775 capable of regulating protein misfolding or proteolysis with RpoH (and others) induced 776 upon environmental stress (Goldberg, 2003). From an ecological perspective, it is 777 difficult to discern if these eukaryote-like traits confer any sort of competitive advantage 778 to Archaea. They may simply be examples of convergent evolution for dealing with 779 environmental stress. 1)

796 and Acidobacteria, Planctomycetes and Verrucomicrobia (Supplementary Table 11).

797 These are highly specialised exporters that deliver DNA and/or toxins directly to other 798 bacterial or plant cells, however, their role in ecology is poorly understood beyond root 799 galls induced by Agrobacterium tumefaciens (Christie & Vogel, 2000). These taxa 800 should be explored for whether they utilise these traits for horizontal gene transfer or 801 to inject toxins directly into other prokaryotes, and thus potentially provide a selective 802 advantage for colonisation and competition.

803

Another archetypal trait for interactions between community members are 804 production of antimicrobials and antimicrobial resistance genes. Penicillin and 805 cephalosporin synthesis were enriched in the Sporomusaceae and Bacillaceae relative 806 to other Firmicutes. Polyketide and Type II polyketide syntheses were important for 807 separating Frankiaceae and Streptomycetaceae from other Actinobacteria 808 (Supplementary Table 10). The Streptomycetaceae have a long history of use in 809 biotechnology as prolific antimicrobial producers (Bentley et al., 2002). Bacillaceae (in 810 particular Bacillus subtilis species) are also well known producers of a wide variety of 811 antimicrobials (Caulier et al., 2019), but we noted that Sporomusaceae have an even 812 greater number of these traits (Supplementary Figure 9). To the authors' knowledge, 813 antibiotic production in Sporomusaceae has not been investigated thoroughly and this 814 may be a consequence of its obligate anaerobic nature and difficulties in culturing. In 9 and10). 825 oligotroph species or in mixed communities as described previously (Lauro et al., 2009, 826 Vieira-Silva & Rocha, 2010, Roller & Schmidt, 2015, Pascual-Garcia & Bell, 2020).

827 Rhizosphere-associated Gamma-, Alpha-and Betaproteobacteria in Clade I fit the 828 assumptions of a copiotrophic niche that invests in high metabolic rate -these taxa 829 were uniquely enriched in competing for nutrient uptake via high-affinity ABC 830 transporters, and energy generation from pyruvate, fatty acids, benzoate and 831 glyoxylate carbon sources. Clade I was also enriched in glutathione metabolism, which 832 acts as the major antioxidant for reducing intracellular free radicals produced during 833 central carbon metabolism (Smirnova & Oktyabrsky, 2005). Antioxidants have been 834 hypothesised as an essential function for copiotrophs to survive their high metabolic 835 rates (Koch, 2001). All five Clade I Families were enriched in oxidative phosphorylation.

836 The oxidative phosphorylation traits encompass a wide variety of electron transport 837 chain proteins (oxidoreductases, dehydrogenases, cytochromes and ATPases) and 838 are crucial for efficient energy production (Brochier-Armanet et al., 2009). All five 839 Families were also enriched in nitrogen metabolism, which included both nitrogen 840 fixation and glutamate (i.e. protein) synthesis. Nitrogen fixation is an energy intensive 841 process requiring 20 -30 ATP per reduced N 2 (Burris & Roberts, 1993) and may be 842 intrinsically linked to taxa with high oxidative phosphorylation. Finally, Clade I also 843 shared motility and chemotaxis, which are also energy intensive traits. Clade II 844 consisted of the remaining Gamma-, Alpha-and Betaproteobacteria, yet these were 845 relatively less enriched in Clade I 'copiotroph' traits. These particular taxa may be 846 responsible for the lack of a consistent copiotrophic response upon nutrient addition in 847 Proteobacteria (Ho et al., 2017).

848

Clade III was comprised of taxa generally considered as oligotrophs (Ho et al., 2017) 849 with the exception of Bacteroidetes (Fierer et al., 2007). These taxa possessed high 851 through biofilm and capsule/slime production (Flemming & Wingender, 2010), beta-852 Lactam resistance, penicillin biosynthesis and several members had high pentose 853 phosphate pathway for efficient carbon metabolism under starvation (Hodgson, 2000).

854 Clade III also possessed high CAZy traits, which Clade I largely lacked, and is 855 consistent with observations of oligotrophs being primarily responsible for catabolising 856 relatively recalcitrant plant material (Goldfarb et al., 2011) (Goldfarb et al., 2011, Leff et al., 2015) (Gause, 1932, Hutchinson, 1957, 913 Leibold, 1995). A more meaningful perspective would be to consider the additional role 914 of 'resource acquisition' that incorporates multiple axes for the life strategies identified 915 via hierarchical clustering of traits. Figure 5b 932 5b, which does not imply either copiotrophic or oligotrophic resource investment, is an 933 oversimplification and with improved understanding of traits in these taxa it may be 934 possible to fracture and further separate them into more detailed groups. This would 935 prove particularly beneficial for the poorly characterised Archaea.

936

Moving beyond a one-dimensional r-K spectrum to accommodate additional trait-937 driven life strategies has been proposed in plant ecology (Grime, 1977). Specifically, This paper aims at identifying copiotrophic vs oligotrophic strategies among 170 species of bacteria and archea by comparing their similarity in their traits (based on clustering of functionally similar amino acid sequences). Instead of finding two clear cluster reflecting copiotrophic-oligotrophic framework, they identify 5 clusters. Only one of these clusters can be fit as copiotrophic. All other ones, reflect, as the author suggest, unique life-history strategies on their own. I really like the paper and I think it is worth publication for two reasons: 1) It challenges simplistic views that entire kingdoms (or other higher taxa) of microorganisms can placed along a unique axis of energy use. 2) the method they use will surely be used by other researchers. I also send my kudos to the authors to develop a step-by-step repo explaining their method. Doing so, not only ensures reproducibility but also encourage other scientist to test similar questions with their approach.

938
I like it as it is. However, I want to acknowledge that I cannot judge on the information of the natural history and ecology of many of the taxa included.

Comment:

My only major suggestion is to make more explicit the way they test hypothesis 3: "copiotrophic and oligotrophic groups would emerge based on trait enrichment". (L149.150) From what I understood in the method section (283-287) that hypothesis was tested by subsetting 60 traits that emerge from their random forest clustering that are "associated with copiotroph-oligotroph growth strategies". This association is based "previous studies". Since this is a major hypothesis in the paper, I suggest to summarize the traits as a table (at least in the supplementary) where it is indicated which ones are expected to belong in copiotroph-oligotroph axis. Even better, it would be great to have them grouped as a figure. A way to simplify this large number of traits in a figure would be to group them according the Brite categories. Ultimately, I am talking about some conceptual figure that shows the idealized clustering of copiotropholigotroph based exactly on the 60 traits you use.

Response:

F o r P e e r R e v i e

w

The authors would like to thank the reviewer for their incredibly generous comments and the suggestion to create a conceptual figure summarising the Brite category clustering per Family. Indeed, by trying to consider the best way to communicate our results it has helped us to further synthesise them. Figure 5 is now a conceptual diagram that attempts to show how the five identified clades overlap based on their trait composition. This is either as a) a one-dimensional axis of 'resource investment', analogous to the classical dichotomy of copiotroph-oligotroph, and b) a multidimensional space where clades are further separated based on traits for both 'resource investment' and different means of 'resource acquisition'. By considering both investment and acquisition together, we hope to show that the clades show less overlap in space, which would theoretically allow them to occupy separate niches and co-exist. We do not include all 60 Brite categories in this figure, but rather have chosen some select traits (14 in total) that may be useful in demonstrating our point e.g. if a taxon possesses many glycosyltransferases in addition to spore formation or antimicrobial resistance genes it could be considered an 'oligotrophic degrader'. The 14 traits were partially chosen based on a comment from Reviewer 2, which suggested to name major functional markers that could be used to predict a taxon's niche. Section 4.7 has been updated to incorporate and explain Figure 5 in the context of our results.

We are acutely aware that this conceptual figure may be met with criticism from the scientific community, and we are more than happy to modify, adjust or otherwise take on board any further constructive comments from the reviewer.

Minor comment L40-42. I think you can be bolder. I think your study shows that the copio-oligotroph view is to simplistic and that more life-history strategies emerge once you compare species based on their traits and not pre-conceived ideas of how microorganisms should behave. I think that stating that the study "warrants a more nuanced view of life-histories" is too soft and vague.

Response:

The concluding sentences of the abstract have been amended to emphasise that the copiotroph-oligotroph framework is insufficient to describe prokaryote niche, and that we suggest considering traits involved in growth and resource acquisition in tandem when predicting niche: "However, the formation of five clades suggested a more nuanced view to describe niche differentiation in terrestrial systems is necessary. We suggest considering traits involved both in resource investment and acquisition when predicting niche."

Reviewer: 2

Comments to the Author I reviewed the manuscript "Functional trait relationships demonstrate generalisable F o r P e e r R e v i e w life strategies in terrestrial prokaryotes" by Finn et al. The objective of the manuscript is to identify the traits within soil bacterial and archaeal species that can distinguish their lifestyle and niche occupancy. Genome sequence of 175 taxa from 11 Phyla and 35 Families were used for analysis. The findings strongly suggested a nonrandom distribution of traits across phylogenetic lineages. Random forest analysis further identified traits that were directly related to niche colonization, substrate use efficiency and metabolism within phylogenetic lineages. I enjoyed reading the manuscript. The manuscript fits very well within the scope of journal. Proper statistical tests have been performed to reach the conclusions. Manuscript is very well written and presented. I have few suggestions that will further strengthen the manuscript.

Comment:

1.

There has been some work that has explored the genomic basis of tropic lifestyle in soil bacteria. This work should be cited and discussed appropriately. e.g. Trivedi et al. 2013. Trends in Microbiology.

Response:

The authors would like to thank the reviewer for their generous comments. In regard to this specific comment, references and discussions of Trivedi et al., 2013 have been incorporated several times throughout the manuscript, for example: Ln 121: "A genomic basis for traits associated with soils dominated by putative copiotrophs and oligotrophs has been expertly reviewed elsewhere, and interested readers are referred to Trivedi et al., 2013 and references therein." Ln 639: "Comparative genomics analyses have also identified Actinobacteria, Acidobacteria and Verrucomicrobia as being enriched in numerous enzymes for cellulose, hemicellulose and starch catabolism (Trivedi et al., 2013)." And Ln 674: "Comparative genomics analyses of soil bacteria have also found putatively copiotrophic Proteobacteria and Firmicutes to be particularly enriched in PTS and ABC transporters (Trivedi et al., 2013)." Furthermore, the study by Barbéran et al., 2012 to define 'generalist' and 'specialist' soil taxa based on co-occurrence has been incorporated in the Introducion: Ln 86: "… identifying habitat generalists and specialists based on taxon co-occurrence patterns (Barbéran et al., 2012) …"

Comment: 2.
Only five strains from family level classification were selected. Authors have mentioned multiple times that some families will have a significant phylogenetic diversity within strains, while for others, it will be restricted. How do these differences impact overall results? What was the criteria of selecting only 5 and not more?

Response: A minimum of five genomes per taxonomic group were selected to accommodate Thaumarchaeota, Verrucomicrobia Chloroflexi and some others, which had the least number of publicly available sequenced genomes of soil taxa. We specifically wanted to avoid generating a dataset which favoured frequently isolated and sequenced soil organisms (e.g. Bacilli, Rhizobia) and thus five genomes per group were used to balance statistical analyses. This is mentioned at line 177 in the Methods section, under Collection of terrestrial prokaryote genomes.

Questions 2 and 3 from the reviewer have prompted additional analyses to quantify the phylogenetic (dis)similarity of taxonomic groups based on full length 16S rRNA genes, and to compare this to trait similarity (C). The 16S genes were aligned with MUSCLE, simple Neighbour Joining trees were constructed in R with ape/phangorn packages, and phylogenetic distance (P) calculated as per Equation 1, excepting that P is in units of gene sequence similarity as opposed to Bray-Curtis distance for trait similarity (C). Important considerations between P and C are as follows:

1) Taxa were originally chosen in the hope of maximising P (and presumably C) per Family by choosing different Genera where possible. This was not always possible, and ultimately choices were constrained by what genomes were publicly available. However, looking at values of P per Phylum and Family (Supplementary Figure 2), there does not appear to be a consistent relationship between Families where different Genera were included and P. For example, Lactobacillaceae consisted of 5 Lactobacillus spp. (P of 0.772) whereas the Beijerinckiaceae consisted of 4 different genera that appear to be actually more similar to each other in regard to the 16S rRNA gene (P of 0.852). In retrospect this lack of consistent variation in phylogenetic distance between taxa based on pre-existing nomenclature should not be a surprise at it is the reason for the efforts of the researchers involved with the Genome Taxonomy Database who seek to standardise the current state of microbial taxonomy.

2) There is a very nice linear relationship between P and C when considering taxa at both the Phyla and Family levels (Supplementary Figure 3). As implied by the reviewer, this will mean any Families that consist of more closely related taxa (i.e. high P) will be more likely to be similar in regard to their traits.

3) The importance of this underlying relationship between the variation in phylogenetic distance between taxa in a group and the results of the Random Forest models is, however, uncertain. Supplementary Table 3 shows the Confusion Matrix for the Family level Random Forest. Some Families with the poorest predictive capacity (< 50% taxa correctly predicted) had quite high P (> 0.8), e.g. Bradyrhizobiaceae, Cellulomonadaceae. Some Families with low P (< 0.7) had poor predictive capacity, F o r P e e r R e v i e w e.g. Divergent Acidobacteria. Conversely, Families that had the highest predictive capacity (100%) ranged in P from 0.68 -0.9. It is the opinion of the authors that the presence of specific, unique and consistent traits in a taxonomic group is far more important for the RF results than the underlying relationship between phylogenetic distance and trait composition. For example, the five Methanogen taxa had very low phylogenetic distance (0.68) yet the RF was perfectly accurate in classifying them, probably due to their methanogen metabolism and Eukaryote-like genetic traits.

Nevertheless, we feel the reviewer raises a very important point when considering taxon selection for these types of comparative genomic analyses, and in future studies we shall place emphasis on standardising phylogenetic distance within groups. The following has been added to the manuscript to highlight this:

In the Methods section 2.3 discussing UPGMA of trait similarity analysis: Ln 291: "Furthermore, the full length 16S rRNA gene of each taxon was collated from NCBI. Genes were aligned with MUSCLE (Edgar, 2004) and a Neighbour-Joining phylogenetic tree was constructed with the 'phangorn' package in R. Phylogenetic distance present in taxonomic groups (P) was measured as per Equation 1., excepting that branch length was in units of DNA sequence similarity as opposed to Bray-Curtis dissimilarity. Finally, simple linear regression was used to test a relationship between P and C."

In the Results section discussing the UPGMA trait tree and RF models: Ln 364: "A Neighbour-Joining tree of full length 16S rRNA genes showed that all taxa clustered preferentially based on their taxonomic nomenclature at high taxonomic rank, including the Chloroflexi, indicating that the discrepancies in the UPGMA were not due to misclassification of the individual taxa (Supplementary Figure 2a)."

Ln 388: "Phylogenetic distance (P) of each taxonomic group increased with decreasing taxonomic rank, and was highest in Proteobacteria, Actinobacteria and Firmicutes Families (Supplementary Figure 2b). There was a strong positive linear relationship between P and C (y = 0.86x -0.25, R 2 = 0.39, p < 0.001) demonstrating that taxonomic groups of closer related taxa tended to share more similar compositions of traits." Ln 412: "Random Forest models were robust against variation in P within Families, for example the nine families with 0% classification error ranged in P from the lowest (0.68) to highest (0.9)."

And finally in the Discussion section in regard to the robustness and interpretation of the Random Forest approach: Ln 643: "Ideally the selection of individual taxa within groups for such future comparative analyses would also be standardised based on phylogenetic distance, What were the criteria of selection of 5 species within a family e.g., phylogenetic distance?

Response: As mentioned above, the selection criteria of the five taxa per Family was based primarily on public availability of genomes. However, the authors acknowledge and fully agree with the reviewers point that phylogenetic distance should be considered as a means to standardise selection of taxa. While we are confident that our methods are robust to the underlying relationship between P and C in the coarse Phyla and Family taxonomic groups analysed here, we will incorporate a method for standardising taxon selection in future studies that consider a finer detail of niche differentiation between closely related Species and Strains.

Comment: 4.

Lines 165-166: I like the idea of choosing strains that perform particular functions. However, these functions are performed by specific bacterial lineages. My question is whether these biased selections for traits inflate the closeness within a group. Or in other words, if the other members within the families of the selected functional groups were selected, do the results would have been the same?

Response: This is an interesting question, and fortunately our results have several functional groups that can be compared to address it. For example, to our knowledge, all characterised members of Nitrosomonadaceae are chemolithoautotrophic nitrifiers while the Cyanobacteria perform photosynthesis. Thus, the relatively high value of C measured here for these groups that share common functions is not surprising. However, trait variation certainly exists within other functional groups, for example the five Methanogens (isolated from diverse anaerobic habitats including rice rhizosphere, tropical and boreal wetlands) had an intermediate value of C. Perhaps the best example is that the inclusion of functionally distinct methanotrophs, methylotrophs and heterotrophs within the Beijerinckiaceae did not have a negative effect on its C, which yielded one of the highest values (discussed at Ln 524). Ultimately trait composition will be influenced by a taxon's functional group (e.g. ammonia oxidiser, heterotroph, etc), its evolutionary life-history (as a very general example Bacteria, Archaea) and the environment from where the taxon was isolated.

We have included a statement this effect in the Discussion, which is an interesting and important concept to consider: Ln 569: "The differing values of C for Methanogen and photosynthetic Cyanobacteria functional groups (0.33 and 0.44, respectively) is also worthy of note. Despite all five taxa in each group performing the same core role in a community, the individual isolates came from varying environments. The methanogens were isolated from a range of geographically separate wetlands, rice paddy soil and farm slurry and, while the Cyanobacteria were also isolated from geographically separate environments, they were all from sandy deserts or other nutrient poor, arid soils (Supplementary Table 1 and references therein). Ultimately a taxon's trait composition will be affected by its functional role in a community, its evolutionary life-history (e.g. Beijerinckiaceae described above) and its local environment." Comment: 5.

Like Lauro et al. 2009, is there a way to do a self-organizing map or a constrained analysis showing the separation of different phylogenetic groups according to the traits?

Response:

The constrained analyses of Lauro et al., 2009 revolved around the a priori classification of one genome as 'copiotrophic' and one genome as 'oligotrophic'. Unknown genomes were then placed somewhere between these two based on similarity of shared traits. In this study we specifically sought to avoid any preconceived a priori assumptions of 'taxon A is a copiotroph', and instead opted for unconstrained, exploratory cluster-based statistical techniques (e.g. dendrograms, Random Forest) that would let the data speak for itself. We hoped that the UPGMA dendrogram (Figure 2a) would be the simplest and most flexible way to demonstrate relationships between different phylogenetic groups and their trait composition, while the clustering of taxonomic groups based on the counts of specific copiotropholigotroph traits would validate whether certain taxa could be considered copiotrophic (Figure 4).

Comment: 6.

Based on the results, can you substantiate the phylogenetic grouping of copiotroph/oligotroph groups? I think yes, there is much information, but I would like to have a strong statement suggesting that based on the results, the inclusion of X, Y, and Z phylum as copiotroph was validated. Then can you resolve the debate about the inclusion of groups such as Alpha and betaproteobacteria as copiotroph/oligotroph (Lines 104-105). See 5 as the analysis suggested there can help you to address this question. This information will help the researchers that want to link microbial community composition to soil processes, particularly soil C cycling. A definitive statement outlining which groups were confirmed as copiotrophs and oligotrophs has been included in the Discussion. This also explicitly links to how these results can give insight into soil processes such as C cycling:

Ln 872: "These results support previous observations that Rhodospirillaceae, Bradyrhizobiaceae, Burkholderiaceae, Pseudomonadaceae and Rhizobiaceae are copiotrophic, while Planctomycetes, Verrucomicrobia, Myxococcaceae, Polyangiaceae and Acidobacteria are oligotrophic (Ho et al., 2017 and references therein). As has been proposed previously, the dominance of these groups in certain soils can provide inferences for ecosystem processes in that system, for example soils dominated by Verrucomicrobia, Planctomycetes and Acidobacteria will have greater capacity to degrade complex plant material while retaining most catabolised carbon in biomass (i.e. high growth or carbon use efficiency) or excreted byproducts that assist in soil aggregation (e.g. high LPS production) (Trivedi et al., 2013). Conversely, soils dominated by copiotrophic Proteobacteria Families will be systems primarily dependent on labile di-and monosaccharides that demonstrate low carbon use efficiency."

Resolving the outstanding question of taxonomic groups that do not fit into the classical copiotroph-oligotroph framework is addressed further below as a conceptual figure (Comment 11). Comment: 7.

One important trait that distinguish copiotrophs/oligotrophs is the carbon use efficiency (CUE). There have been some efforts to incorporate microbial CUE to parameterize models for soil carbon. While CUE's topic is touched briefly in Introduction, it has not been explicitly discussed in later sections.

Response:

Carbon use efficiency has now been mentioned in regard to the specific copiotrophic/oligotrophic taxonomic groups that will have a high CUE (please see the above response to Comment 6).

Comment: 8.

Authors talk about MAGs in the discussion section. It will be worth discussing how MAGs will help to elaborate similar studies that is reported. Where we are and what needs to be done further?

Response:

The authors agree that this is worth expanding upon. The following two statements have been added to the Discussion to better incorporate the value of MAGs and current gaps for trait-based analyses: Ln 560: "The generation of MAGs has emerged as a useful tool for identifying traits necessary for life in such environments, and particularly for expanding knowledge of severely under-represented, difficult to culture taxonomic groups. For example, the recent reconstruction of 52, 515 MAGs from a wide range of host-associated and environmental metagenomes was able to increase genomic information of Planctomycetes and Verrucomicrobia by 79% and 68%, respectively (Nayfach et al., 2021). Importantly, both 16S rRNA gene surveys and MAGs demonstrate that some functional traits that facilitate life under certain environmental conditions are intrinsically linked to taxonomy." Ln 970: "However, to truly unravel differentiated niches and general microbial life strategies, two limitations must be overcome. Firstly, a better understanding of the many 'Uncharacterised' traits in environmental isolates is required. For example, the recent large-scale MAG study by Nayfach et al., (2021) identified 5.8 million protein clusters (traits), of which over 75% could not be annotated meaningfully by current protein databases. Secondly, robust trait-based analyses down to the finer scale of distinct genomes will likely be necessary to consider how individual members of a community have either differentiated in order to co-exist or are in the throes of competition that will ultimately exclude one of the competitors."

Comment: 9.

Based on the results, where will you fit archaea (copiotrophs or oligotrophs)?

Response:

The authors are hesitant to place Archaea as either copiotrophs or oligotrophs as we consider the one-dimensional framework to be insufficient for describing niche. Please see further below for a detailed response (Comment 11).

Comment: 10. How this type of data will be used to distill the complexity within the soil microbiome functions? Can we use the functional signatures to determine the metabolic potential of microbial communities from metagenome data? How can this information help us to incorporate microbial activities into process models? I suggest touching on these in conclusions.

Response:

Again, this will be addressed further in Comment 11. Comment: 11.

One of the hallmarks of impactful papers is to move beyond information to Response:

The authors particularly appreciated this comment, and in particular the expression 'moving beyond information to knowledge'! Indeed, our attempts to consider a conceptual figure that conveys our results in a simple manner has been extremely helpful in further synthesising them.

Figure 5 is now a conceptual diagram that attempts to show how the five identified clades overlap based on their trait composition. This is either as a) a one-dimensional axis of 'resource investment', analogous to the classical dichotomy of copiotropholigotroph, and b) a multi-dimensional space where clades are further separated based on traits for both 'resource investment' and different means of 'resource acquisition'. By considering both investment and acquisition together, we hope to show that the clades show less overlap in space, which would theoretically allow them to occupy separate niches and co-exist. The final section of the Discussion, from Ln 916, has been expanded to discuss this figure and its significance for interpreting microbial niche.

In regard to Comment 10, which asks how our results may provide signatures for process-based models or linking to community function, we have chosen the BRITE categories in Figure 5 to act as signatures for defining, for example, Rhizobiaceae as 'copiotrophic competitors'. This is explicitly stated:

Ln 929: "Figure 5b is a conceptual diagram of niche space where clades have been further separated along additional axes based on their enrichment of traits involved in competition, degradation or specialised metabolic pathways. The BRITE categories listed on the various axes are chosen to be useful markers in predicting the niche of a taxon. This expanded niche space would suggest taxa in Clade I are well equipped for nutrient acquisition (primarily, but not limited to, di-and monosaccharides), rapid growth and oxidative stress regulation as 'copiotrophic competitors'. …"

And finally, specifically in regard to whether Archaea should be copiotrophs or oligotrophs:

Ln 956: "The large space conceptualised for Clade V in Figure 5b, which does not imply either copiotrophic or oligotrophic resource investment, is an oversimplification and with improved understanding of traits in these taxa it may be possible to fracture and further separate them into more detailed groups. This would prove particularly beneficial for the poorly characterised Archaea."

F

82

A consistent trend noted in macroecology is that traits linked to how carbon and 83 energy is processed and allocated to biomass can describe separate niches (Brown et 84 al., 2004). The canonical example are r and K strategists, where carbon and energy 85 are primarily invested in reproduction, or alternatively invested in tolerating biotic 86 and/or abiotic stressors, respectively (Grime, 1977). These dichotomous strategies 87 have been observed in microbial ecology: copiotrophs are considered as 88 microorganisms with relatively high growth rates that have relatively poor growth 89 efficiency (as carbon incorporated to biomass per unit resource), relatively high cell 90 maintenance energy costs, dependence on relatively high concentrations of organic 91 carbon in their environment, demonstrate rapid population blooms upon the addition of 92 organic matter and are not overly tolerant of abiotic stress (Semenov, 1991, Koch, 93 2001, Roller & Schmidt, 2015, Ho et al., 2017). Oligotrophs are considered as the 94 inverse -low growth rate, high growth efficiency, low cell maintenance energy 95 requirements, high substrate uptake affinity, slow growth at a slow yet at a consistent 96 rate and are resilient to abiotic stress. Although the niche concept in macroecology has 97 a formalized definition founded on where a taxon can maintain a stable population 98 within multi-dimensional environmental space (Leibold, 1995), in this study, niche is 99 used simply to distinguish between prokaryotes being relatively more copiotrophic 100 versus oligotrophic. 3). This is a measure of the average 395 increase in classification error during permutation of trees (n = 300 -400) when that 396 particular trait is missing from the tree. For example, the accuracy of classifying et al., 2002, Sangwan et al., 2004, Schneiker et al., 2007). Comparative 670 genomics analyses have also identified Actinobacteria, Acidobacteria and 671 Verrucomicrobia as being enriched in numerous enzymes for cellulose, hemicellulose 672 and starch catabolism (Trivedi et al., 2013). In situ these Families likely play a critical 673 role in making organic carbon bioavailable as di-and monosaccharides for the 674 microbial community.

675

The Families equipped with many copies of high-affinity sugar uptake 676 'Phosphotransferase systems' (3 -14 copies) et al., 2009) the above proteobacterial representatives act as the primary 748 terrestrial methane sink (Dunfield, 2007, Conrad, 2009). (Shimada et al., 2017). The 763 genomes of these taxa are also heavily enriched in regulatory genes for complex 764 developmental stages, fruiting bodies and/or filamentous branching growth in soils 765 (Bentley et al., 2002, Gao et al., 2006, Schneiker et al., 2007). Thus, certain taxa may 766 respond primarily to extracellular cues while others strictly monitor and respond to 767 changes in cell homeostasis. This trend has been noted previously -in 167 genomes 768 across various Bacteria and Archaea, Proteobacteria had a higher ratio of sensors for 769 external versus internal stimuli and were considered 'extroverts', while Cyanobacteria 770 were considered strong 'introverts' focussed on responding to internal stimuli 771 (Galperin, 2005).

772

As mentioned above, Archaea exhibited unique traits in basal transcription and 773 protein regulation via proteasome. These transcription factors were primarily involved 774 in identifying DNA damage and excision repair: TFII-B, TFII-D, ERCC-2 and ERCC-3.

775 DNA repair differs markedly between Bacteria and Archaea/eukaryotes. Specifically, 776 Bacteria excise 12 nucleotides around a damaged site with a 3 polypeptide system 777 whereas Archaea excise 24 -32 nucleotides with a 13 -16 polypeptide system 778 (Sancar, 1996). The use of ubiquitin-labelling and proteasome degradation of 779 misfolded or 'old' proteins is arguably a more efficient system for recycling amino acids 782 upon environmental stress (Goldberg, 2003). From an ecological perspective, it is 783 difficult to discern if these eukaryote-like traits confer any sort of competitive advantage 784 to Archaea. They may simply be examples of convergent evolution for dealing with 785 environmental stress.

786

Finally, most microbial cells likely exist within complex biofilms and/or assemblages 787 adhered to surfaces with excreted exopolysaccharides, DNA and protein that serve to 788 protect from adverse environmental factors (Flemming & Wingender, 2010) 1)

802 and Acidobacteria, Planctomycetes and Verrucomicrobia (Supplementary Table 11).

803 These are highly specialised exporters that deliver DNA and/or toxins directly to other 804 bacterial or plant cells, however, their role in ecology is poorly understood beyond root 805 galls induced by Agrobacterium tumefaciens (Christie & Vogel, 2000). These taxa 806 should be explored for whether they utilise these traits for horizontal gene transfer or 807 to inject toxins directly into other prokaryotes, and thus potentially provide a selective 808 advantage for colonisation and competition.

809

Another archetypal trait for interactions between community members are 810 production of antimicrobials and antimicrobial resistance genes. Penicillin and (Goldfarb et al., 2011, Leff et al., 2015) and their enriched ABC transporters, 890 pyruvate, glyoxylate, benzoate and fatty acid metabolism all likely contribute to 891 generating energy for complex lifecycles. Simultaneously, their enrichment of Clade III 892 oligotroph traits for CAZy metabolism in addition to many traits for producing and 893 resisting antimicrobials indicate a unique niche for these Actinobacteria that does not 894 necessarily fall within the classical copiotroph-oligotroph framework.

811

895

Clade V differed markedly from all other clades and mostly consisted of 'specialist'

896 metabolic functional groups involved in photosynthesis, ammonia oxidation, 897 methanogenesis, lactic acid production and other fermentation. Similar to Clade III, 898 these taxa would also be expected to have relatively low metabolic rates due to 899 depletion of copiotroph traits associated with rapid metabolism and energy generation.

900 Unlike Clade III, these taxa seemed to lack consistent mechanisms for stress 901 tolerance. Thus, while certain taxa did invest in traits for rapid metabolic rate (Clades I 902 and IV) and others primarily in stress tolerance (Clades III and IV), some taxa lacked 903 these approaches altogether and pursued entirely distinct niches (Clade V). As an 904 average of 47% of traits within each genome were Uncharacterised, Clade V is an 905 over-simplification and that if novel, currently uncharacterised proteins and the traits 906 they fulfil were incorporated into hierarchical clustering, this clade would separate more 907 meaningfully. 917 between Clades would suggest taxa from each group could not co-exist if 'resource 918 investment' was the only important consideration (Gause, 1932, Hutchinson, 1957, 919 Leibold, 1995). A more meaningful perspective would be to consider the additional role 920 of 'resource acquisition' that incorporates multiple axes for the life strategies identified 921 via hierarchical clustering of traits. Figure 5b

947

Moving beyond a one-dimensional r-K spectrum to accommodate additional trait-948 driven life strategies has been proposed in plant ecology (Grime, 1977). Specifically, 949 Grime argued that plant taxa fall within a multi-dimensional space defined by extremes

  Forest trait identification384 The KEGG annotated traits belonged to 260 different BRITE 3 categories. The 385 percentage of traits that could not be annotated (and were termed 'Uncharacterised')

2

 2 What can the Random Forests tell us?572 The Random Forest works by identifying the traits that are most reliable in classifying 573 individual Phyla and Families. It selects traits that tend to be: a) of equal copy number 574 per genome within a taxonomic group; and b) that differ markedly in copy number 575 between taxonomic groups, since distinct separation of copies will maximise 576 successful classification. A clear example of this is the consistent identification of 577 eukaryote-like basal transcription factors, proteasome and RNA transport present in 578 the Thaumarchaeota and Euryarchaeota, since they are absent from the majority of

  strategies emerge from differentially enriched traits 822 We hypothesised that taxa would emerge as being inherently copiotrophic or 823 oligotrophic based on trends in their enriched traits. Traits were chosen based on 824 identification via Random Forest and identification as associated with copiotroph-

  sphingolipid synthesis that can defend against desiccation and antimicrobials

2 Figure 1 .6Figure 3 .Figure 4 .34

 2134 Figure 1 268x157mm (300 x 300 DPI)

  with P or a similar method, which would improve the robustness of trait-based comparisons at such a fine taxonomic level." Comment: 3.

  you think of making a conceptual figure showing different genes and how they are responsible for different lifestyles or niche colonization? This is just a thought as I understand how difficult it can be.

  , responding and tolerating the environment 752 A particularly interesting divergence of traits were involved in how taxa detect and Bradyrhizobiaceae and Myxococcaceae were enriched in two-755 component systems. These membrane-bound histidine kinases respond rapidly to 756 extracellular stimuli(Galperin, 2005) and these traits were primarily involved in 757 nitrogen, potassium, initiating chemotaxis and C 4 -dicarboxylate responses. Families 758 enriched in transcription factors were the Myxococcaceae, Polyangiaceae, 759 Streptomycetaceae, Mycobacteriaceae and Frankiaceae. These factors regulate 760 transcription in response to intracellular cues and here these factors were primarily 761 rpoD (housekeeping), rpoH (heat-shock/protein damage), rpoE (extra-cellular 762 cytoplasmic stress) and rpoS (starvation) responses

  the 'lifespan' of a protein in eukaryotes, however, Bacteria are still fully 781 capable of regulating protein misfolding or proteolysis with RpoH (and others) induced

908916

  If one were to consider taxa within the one-dimensional copiotroph-oligotroph 909 spectrum, Clade I would represent one extreme, Clades III and V another, with Clades 910 II and IV falling in between. Figure5ais a conceptual diagram where these Clades 911 have been placed on a singular axis of 'resource investment', with the niche space of 912 Clades enriched in traits associated with rapid growth (e.g. glutathione) toward the 913 'copiotrophy' pole while Clades enriched in stress tolerance traits (e.g. LPS production) 914 are placed toward the 'oligotrophy' pole. However, this approach overlooks the diverse 915 functional potentials (and distinct niches) for carbon and energy metabolism in Clade Vassociated with the various Clades. Furthermore, the large overlaps in niche space

  is a conceptual diagram of niche space 922 where clades have been further separated along additional dimensions based on their 923 enrichment of traits involved in competition, degradation or specialised metabolic 924 pathways. The BRITE categories listed on the various axes are chosen to be useful 925 markers in predicting the niche of a taxon. This expanded niche space would suggest 926 hierarchical clustering in Figure4with each clade as its own 'life strategy'. The strategy 927 of taxa in Clade I follow that of classical 'copiotrophs' andI are well equipped for nutrient 928 acquisition (primarily, but not limited to, di-and monosaccharides), rapid growth and 929 oxidative stress regulation as 'copiotrophic competitors'. Clade II, which may not be 930 capable of competing directly with Clade I for carbon and energy, and perhaps the 931 evolution ofmay thus occupy the niche space of specialist Proteobacterial 932 methylotrophs, methane and ammonia oxidisers may be a responseso as to avoid 933 competition over saccharidesbe 'copiotrophic metabolic specialists'. The strategy of 934 Clade III is would be to specialise in decomposingdecompose plant material via diverse 935 CAZy and possess a variety of environmental stress tolerance traits as 'oligotrophic 936 degraders'. Clade IV, which include for example are unique Actinobacteria that share 937 traits for competition, degradation and oligotrophy, could thus occupy niche space 938 between Clades I and III traits, yet likely fill a distinct niche due to complex 939 developmental cycles and filamentous growth. Finally, the strategy of Clade V is would 940 be to fill highly specialised, unrelated metabolic niches reliant on completely distinct 941 carbon sources to other taxa. The large space conceptualised for Clade V in Figure9425b, which does not imply either copiotrophic or oligotrophic resource investment, is an 943 oversimplification and with improved understanding of traits in these taxa it may be 944 possible to fracture and further separate them into more detailed groups. This would 945 prove particularly beneficial for the poorly characterised Archaea. and are unified by 946 an absence of classical copiotroph and oligotroph traits.

  Secondly, we hypothesised that the traits differentially enriched between taxonomic 647 groups would largely reflect those associated with copiotrophs or oligotrophs, namely 648 metabolism, nutrient acquisition and environmental stress response and tolerance. Of 649 fundamental interest to soil microbiologists is the decomposition of plant biomass. This 650 is the primary source of organic carbon to non-arid terrestrial systems (Kögel-Knabner, 651 2002) and the transformation of plant material to substrates bioavailable for 652 microorganisms is essential for community growth and activity. The traits involved in 653 plant material catabolism belonged to the BRITE categories 'Starch and Sucrose 654 Metabolism' (e.g. extracellular cellobiosidases, endoglucanases, glucosidases, 655 trehalases, amylases) and 'Glycosyltransferases', all of which are carbohydrate 656 activated enzymes (CAZy). The Families particularly enriched in these traits were the

	646 657 Polyangiaceae,	F r o Myxococcaceae,	Rhizobiaceae,	Streptomycetaceae,
		P
		e e r
		R e v
			i e w
	643
	644
	645 4.3 Plant-derived carbon metabolism and nutrient acquisition
		20

658 Mycobacteriaceae, Frankiaceae and Verrucomicrobia (Table 1, Supplementary Table 659 11). Genomic and culture-dependent analyses support Sorangium cellulosum 660 (Polyangiaceae), Streptomyces coelicolor A3(2) (Streptomycetaceae) and

  Here, we identified the 'specialist' Families enriched in CAZy traits versus the 681 'opportunists' scavenging for sugars via PTS (Table

	680 2015).
	F
	o r
	P
	e e r
	R e v
	i e w
	al.,
	21

  Bradyrhizobiaceae and Myxococcaceae were enriched in two-749 component systems. These membrane-bound histidine kinases respond rapidly to 750 extracellular stimuli(Galperin, 2005) and these traits were primarily involved in 751 nitrogen, potassium, initiating chemotaxis and C 4 -dicarboxylate responses. Families 752 enriched in transcription factors were the Myxococcaceae, Polyangiaceae, 753 Streptomycetaceae, Mycobacteriaceae and Frankiaceae. These factors regulate 754 transcription in response to intracellular cues and here these factors were primarily 755 rpoD (housekeeping), rpoH (heat-shock/protein damage), rpoE (extra-cellular 756 cytoplasmic stress) and rpoS (starvation) responses

	748 Rhodospirillaceae,
			F
			o r
			P
				e e r
				R e v
					i e w
	747 respond	to	environmental	stimuli.	Gram-negative	Pseudomonadaceae,
				23

743 744 745 4.5 Sensing, responding and tolerating the environment 746 A particularly interesting divergence of traits were involved in how taxa detect and

  The trait-based analyses here were effective in identifying general 979 trends in potential function of terrestrial microbial taxa at the Phylum and Family level.980 Further investigation will be necessary to identify traits that give rise to niche 981 differentiation at lower taxonomic ranks and, ultimately, the importance of this for 982 ecosystem processes of interest. The authors wish to thank the Australian Government Endeavour research program 987 ID: 6123_2017 for funding D.R. Finn. Funding support for B. Bergk-Pinto came from 988 the European Union's Horizon 2020 research and innovation program under the Marie 989 Sklodowska-Curie ITN 675546-MicroArctic. The authors also wish to thank the 990 microbiologists involved in the isolation and genome sequencing of the taxa studied 991 here; it is hoped the majority of those involved are cited within the Supplementary 992 Material. Without this diligent culture-dependent work by the scientific community, the 993 theoretical ecology performed here would not be possible. The authors declare no conflict of interest regarding this study or its outcomes.

	952 'Uncharacterised' traits in environmental isolates is required. For example, the recent 953 large-scale MAG study by Nayfach et al., (2021) identified 5.8 million protein clusters 954 (traits), of which over 75% could not be annotated meaningfully by current protein 955 databases. Secondly, robust trait-based analyses down to the finer scale of distinct 956 genomes will likely be necessary to consider how individual taxonomic members of a 957 community have either differentiated in order to co-exist or are in the throes of 958 competition that will ultimately exclude one of the competitors. 959 960 986 994
	961 5. Conclusion 995 Conflict of Interest
	F o r P e e r 962 In a collection of 175 terrestrial prokaryotes that possess 220 664 traits shared 963 between at least two taxa, concepts in niche differentiation were explored. Non-random 964 trait distributions were shown as preferential clustering of related taxa within most F o r 996 997 998 References F o r F o r F o r F o r F o r F o r 965 Phyla with a general trend of highest similarity at the level of Family. This strongly 966 supported ecological coherence of shared traits within close relatives. Random Forest 967 models successfully identified BRITE 3 categories that best explained differing traits 968 between taxonomic groups. These traits were involved in a wide range of biological P e e r P e e r P e e r P e e r P e e r P e e P e e r r
	Grime argued that plant taxa fall within a multi-dimensional space defined by extremes 939 on three axes: 'competitors' that acquire nutrients, light, water etc. more effectively 940 than neighbouring taxa in the same environment, 'stress tolerators' that are long-lived, R e v i e w 969 functions, including core physiological traits used historically to categorise taxa. Many 970 traits were also involved in functions often associated with copiotrophs and oligotrophs, 971 namely metabolism, nutrient acquisition and environmental stress tolerance. 972 Hierarchical clustering of differential traits formed five distinct clusters, with Clade I 973 representing the classical copiotrophic niche, Clades III and V as oligotrophic, and 974 Clades II and IV in between. A more refined perspective would be to consider each R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R R e v i e w e v i e w
	941 slow growing taxa that resist desiccation, alkaline soils etc., and 'ruderals' that have 975 Clade as its own life strategy in a niche space that considers both resource investment
	942 very brief lifecycles between periods of disturbance and invest in environmentally 976 and acquisition simultaneously; for example, the strategy of Clade I is to invest in
	943 hardy seeds. Despite these varied strategies for resource investment, plants are 977 competition and rapid growth, while Clade V pursue highly distinct, specialised
	944 unified in that photosynthesis is their primary form of acquiring carbon and energy. The 945 diversity of microbial strategies for acquiring carbon and energy enables them to 946 explore a greater range of potential niche space, and in addition to growth traits that 947 allow for a relatively more copiotrophic or oligotrophic investment of those resources, 948 likely contributes to the high diversity of co-existing taxa observed in soil microbial 949 communities. 978 metabolic functions. 983
	950 984	However, to truly unravel differentiated niches and general microbial life strategies,
	951 two limitations must be overcome. Firstly, a better understanding of the many 985 Acknowledgements
		29 30 37

Table 1 :

 1 Summary of traits significantly enriched and depleted in the Family Random Forest model.

	BRITE 1	BRITE 3	Significantly enriched	Significantly depleted
		00540 LPS	Pseudomonadaceae, Burkholderiaceae, Chitinophagaceae,	Leuconostocaceae, Clostridiaceae, Lactobacillaceae, Bacillaceae, all
	Metabolism	biosynthesis	Acidobacteria Lineage, Verrucomicrobia	Actinobacteria, Chloroflexi, Thaumarchaeota, Methanogen Lineage
		01040 Biosynthesis
		of unsaturated fatty		Leuconostocaceae, Clostridiaceae, Lactobacillaceae, Bacillaceae, Cyanobacteria,
		acids	Mycobacteriaceae, Frankiaceae, Pseudomonadaceae	Chloroflexi, Thaumarchaeota, Methanogen lineage
		00561 Glycerolipid
		metabolism	Myxococcaceae, Mycobacteriaceae, Frankiaceae	Thaumarchaeota, Methanogen Lineage
		00571 LAM
		biosynthesis	Streptomycetaceae, Mycobacteriaceae, Frankiaceae	All other Families
				All Gammaproteobacteria, Rhodospirillaceae, Bradyrhizobiaceae, Neisseriales
				Lineage, Nitrosomonadaceae, Sporomusaceae, Leuconostocaceae,
		00600 Sphingolipid	Myxococcaceae, Chitinophagaceae, Planctomycetaceae,	Lactobacillaceae, Cellulomonadaceae, Chloroflexi, Thaumarchaeota, Methanogen
		metabolism	Verrucomicrobia	Lineage
		00500 Starch and	Polyangiaceae, Streptomycetaceae, Mycobacteriaceae,
		sucrose metabolism	Frankiaceae, Verrucomicrobia	Moraxcellaceae, Thaumarchaeota, Methanogen Lineage
		04714	Methylocystaceae, Rhodospirillaceae, Bradyrhizobiaceae,
		Thermogenesis	Beijerinckaceae, Mycobacteriaceae	All other Families
		00640 Propanoate
		metabolism	Burkholderiaceae, Mycobacteriaceae, Frankiaceae	Methylococcaceae, Leuconostocaceae, Lactobacillaceae, Thaumarchaeota
		00910 Nitrogen	Rhodospirillaceae, Bradyrhizobiaceae, Burkholderiaceae,
		metabolism	Sporomusaceae, Mycobacteriaceae	Leuconostocaceae, Lactobacillaceae, Chloroflexi
		00680 Methane
		metabolism	Methylococcaceae, Methylocystaceae, Methanogen Lineage	Leuconostocaceae, Lactobacillacaceae
		99982 Unclassified	Rhodospirillaceae, Beijerinckiaceae, Mycobacteriaceae,	Leuconostocaceae, Lactobacillaceae, Microbacteriaceae, Promicromonosporaceae,
		energy metabolism	Frankiaceae	Cellulomonadaceae, Chloroflexi, Planctomycetales, Thaumarchaeota
		09113 Global maps
		only (unclassified		Methylococcaceae, Beijerinckiaceae, Nitrosomonadaceae, Leuconostocaceae,
		metabolism)	Pseudomonadaceae, Mycobacteriaceae	Bacillaceae, Cytophagaceae, Acidobacteriaceae, Cyanobacteria, Verrucomicrobia
		00480 Glutathione	Rhizobiaceae, Bradyrhizobiaceae, Myxococcaceae,	All Firmicutes, Acidobacteriaceae, Chloroflexi, Planctomycetales, Verrucomicrobia,
		metabolism	Polyangiaceae, Burkholderiaceae	Thaumarchaeota, Methanogen Lineage
				Methylococcaceae, Pseudomonadaceae, Rhizobiaceae, Beijerinckiaceae,
				Neisseriales Lineage, Nitrosomonadaceae, all Firmicutes, Streptomycetaceae,
				Promicromonosporaceae, Cellulomonadaceae, Acidobacteriaceae, Cyanobacteria,
		00281 Geraniol		Chloroflexi, Planctomycetaceae, Verrucomicrobia, Thaumarchaeota, Methanogen
		degradation	Moraxellaceae, Myxococcaceae, Mycobacteriaceae	Lineage
	Environmental
	Information	01001 Protein
	Processing	kinases	Myxococcaceae, Polyangiaceae, Frankiaceae	Lactobacillaceae
		02010 ABC
		transporters	Rhodospirillaceae, Rhizobiaceae, Burkholderiaceae	Acidobacteriaceae, Thaumarchaeota, Methanogen Lineage
		02020 Two	Pseudomonadaceae, Rhodospirillaceae, Bradyrhizobiaceae,	Leuconostocaceae, Lactobacillaceae, Bacillaceae, Promicromonosporaceae,
		component system	Myxococcaceae	Cellulomonadaceae, Chloroflexi, Thaumarchaeota
		02060
		Phosphotransferase	Pseudomonadaceae, Rhodospirillaceae, Myxococcaceae,	Methylococcaceae, Frankiaceae, Acidobacteria Lineage, Thaumarchaeota,
		system (PTS)	Neisseriales lineage, Clostridiaceae, Verrucomicrobia	Methanogen Lineage

Moraxellaceae, Methylococcaceae, Methylocystaceae, Rhodospirillaceae, Bradyrhizobiaceae, Beijerinckiaceae, all Deltaproteobacteria, all Betaproteobacteria, Sporomusaceae, Clostridiaceae, Streptomycetaceae, Frankiaceae, all Bacteroidetes, all Acidobacteria, Chloroflexi, Planctomycetes, Thaumarchaeota, Methanogen Lineage Page 47 of 147 ScholarOne Support 1-434/964-4100 FEMS Microbiology Ecology F o r P e e r R e v i e w

Table 1 :

 1 List of microorganisms utilised in this study.

									Ecosystem
	ID	Phylum	Class	Order	Family	Genus	Species	Strain	Process a	Reference
	Ga0074846
	_101	Firmicutes	Bacilli	Bacillales	Bacillaceae	Terribacillus	saccharophilus	DSM21619	ND	(An et al 2007b)
	Ga0067102							Plantarum
	_11	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	amyloliquefaciens	FZB42	Rhizosphere	(Chen et al 2007)
	Ga0077930									(Helgason et al
	_11	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	anthracis	BA1015	Pathogen	2000)
	Ga0175433									(Graham and Istock
	_11	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	subtilis	KH2	ND	1978)
	CP015911	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus	velezensis	LS69	Rhizosphere	(Liu et al 2017)
	LAC_LBA_D
	RAFT__cont
	ig00001	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	plantarum		Rhizosphere	(Chen et al 2005)
	Ga0106160									(Endo and Okada
	_101	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	composti	DSM18527	Rhizosphere	2007)
	Ga0106062									(Kawasaki et al
	_101	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	floricola	DSM23037	Rhizosphere	2011)
	1423745	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	florum	F91	Rhizosphere	(Endo et al 2010)
	1670446	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	herbarum		ND	(Mao et al 2015)
	Ga0126468
	_101	Firmicutes	Bacilli	Lactobacillales	Leuconostocaceae	Leuconostoc	mesenteroides	213MO	Rhizosphere	(Chen et al 2005)
	Ga0106620
	_101	Firmicutes	Bacilli	Lactobacillales	Leuconostocaceae	Weissella	confusa	DSM20196	Rhizosphere	(Chen et al 2005)
	Ga0114202
	_101	Firmicutes	Bacilli	Lactobacillales	Leuconostocaceae	Fructobacillus	sp	EFBN1	Decomposition	(Endo et al 2009)
										(Magnusson et al
	155866	Firmicutes	Bacilli	Lactobacillales	Leuconostocaceae	Weissella	soli		Rhizosphere	2002)
										(Barrangou et al
	907931	Firmicutes	Bacilli	Lactobacillales	Leuconostocaceae	Leuconostoc	fallax	KCTC3537	Rhizosphere	2002)
	BO10DRAF
	T_scf71800
	00000004_q
	uiver.1	Firmicutes	Clostridia	Clostridiales	Clostridiaceae	Clostridium	phytofermentans	KNHs212	Decomposition	(Warnick et al 2002)
	CLOBI_conti
	g000001	Firmicutes	Clostridia	Clostridiales	Clostridiaceae	Clostridium	beijerinckii	A21	Rhizosphere	(Johnson et al 1997)

Table 2 :

 2 Confusion matrix of Phylum level predictions based on Random Forest selection of important traits.

	Successful classifications

Table 3 :

 3 Confusion matrix of Family level predictions.

	Page 61 of 147									FEMS Microbiology Ecology
																							Success
	Phylum	Family	Mor.	Methyloc.	Pseu.	Methylocy.	Rhod.	Rhiz.	Brad.	Beij.	Myxo.	Poly.	Neis.	Nitr.	Burk.	Spor.	Leuc.	Clos.	Lact.	Bac.	Micrococ.	Microbac.	Rate (%)
	Gammaproteob
	acteria	Moraxellaceae	5																				100
		Methylococcac
		eae		4																			80
		Pseudomonada
		ceae			4									1									80
	Alphaproteobac	Methylocystace
	teria	ae				3				2													60
		Rhodospirillace
		ae				1	4																80
		Rhizobiaceae						4	1														80
		Bradyrhizobiac
		eae				2		1	2														40
		Beijerinckiacea
		e				1				4													80
	Deltaproteobact	Myxococcacea
	eria	e									5												100
		Polyangiaceae									1	3			1								60
	Betaproteobact	Neisseriales
	eria	Lineage											5										100
		Nitrosomonada
		ceae			1									4									80
		Burkholderiace
		ae			1								1		3								60
		Sporomusacea
	Firmicutes	e														2							40
		Leuconostocac
		eae															5						100
		Clostridiaceae																4		1			80
		Lactobacillacea
		e															2		3				60
		Bacillaceae																1		4			80
		Micrococcacea
	Actinobacteria	e																			3		60
		Microbacteriace
		ae						1														4	80
		Streptomycetac
		eae																			1		60
		Mycobacteriace
		ae																					100
		Frankiaceae																					60
		Promicromonos
		poraceae																					100
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Table 4 :

 4 Confusion matrix of Family level predictions. Supplementary Table 9: Summary of traits significantly enriched and depleted in the Phylum Random Forest model. Supplementary Table 10: Summary of traits significantly enriched and depleted in Firmicutes and Actinobacteria models. Supplementary Table 11: Summary of traits significantly enriched and depleted in Proteobacteria and Others models.

	Success
	Rate (%)

Phylum

Family Strep. Myco. Fran. Prom. Cell. Cyto. Chitin. Acido.Lin. Acido. Cyano. Chloro. Planc. Verru. Thaum. Methan. Supplementary Figure 8: Important predictor traits in classifying subgroups of Families as selected by the Proteobacteria, Actinobacteria, Firmicutes and Others model. Traits are ranked in order of decreasing Mean Decrease in Accuracy (MDA) of the four models.

  Damien R. Finn1,2,3 , Benoît Bergk-Pinto 2 , Christina Hazard 2 , Graeme W. Nicol 2 ,Keddy, 1992). In contrast, trait-based approaches to explain microbial ecology 70 have only been performed in few instances, such as conceptualizing niches of 71 methanotrophs based on abundance in high versus low methane environments or 72 disturbed versus undisturbed soils(Ho et al., 2013), correlating increasing growth rate 73 with increasing ribosomal gene and ribosome-associated gene copy number(Vieira- 74 Silva & Rocha, 2010), deterministic modelling of nitrification rate based on ammonia 75 and oxygen uptake rate, temperature sensitivity and growth rate(Bouskill et al., 2012), 76 defining distinct niches of 32 marine microorganisms based on clustering of genome-77 encoded functional proteins(Lauro et al., 2009), identifying habitat generalists and 78 specialists based on taxon co-occurrence patterns(Barbéran et al., 2012) and recently 79 comparisons of 23 'core' traits (e.g. motility, carbon metabolism, optimal pH for growth) 80 across 15 000 diverse host-associated and environmental genomes(Madin et al.,

	o r P e e r 12. I appreciate the authors of acknowledging the tedious work of other researchers Comment: on culturing. This is very important. Response: Of course we are happy to do so, as it is essential work. F o r P e e r 1 Functional trait relationships demonstrate generalisable life strategies in terrestrial 2 prokaryotes. 3 4 5 Christoph C. Tebbe 3 , Timothy M. Vogel 2 6 7 1 School of Agriculture and Food Sciences, University of Queensland, Brisbane, 8 Australia 4072; 9 2 Environmental Microbial Genomics, Laboratoire Ampère, École Centrale de Lyon, 10 Université de Lyon, Écully, France 69134; 11 3 Thünen Institut für Biodiversität, Johann Heinrich von Thünen Institut, 12 Braunschweig, Germany 38116. 13 16 Damien Finn; current address: Thünen Institut für Biodiversität, Braunschweig, 17 Germany 38116; email address: damien.finn@thuenen.de e e r r e e 15 Author correspondence: P P 14 F o r F o r 69 1979, 81 2020).
	R e v i e w R e v i e w 20 Theoretical ecology, niche differentiation, copiotroph-oligotroph, Random Forest 18 19 Keywords: R e v R e v 21 modelling 22 23 i e w i e w
	24 Abstract
	25 Functional, physiological traits are the underlying drivers of niche differentiation. A
	26 common framework related to niches occupied by terrestrial prokaryotes is based on
	27 copiotrophy or oligotrophy, where genomic resource investment is primarily in either
	28 metabolism rapid growth or stress tolerance, respectively. A quantitative trait-based
	29 approach was employed to demonstrate quantitativesought relationships between
	30 taxa, traits and niche in terrestrial prokaryotes. With 175 taxa from 11 Phyla and 35
	31 Families (n = 5 per Family), traits were considered as discrete counts of shared
	32 genome-encoded proteins. The composition of 220 664 traitsTrait composition strongly
	33 supported non-random trait functional distributions as preferential clustering of related
	34 taxa via unweighted pair-group method with arithmetic mean. Trait similarity between
	1 3

  Kingdoms; b) the selecting prokaryotes selected are all involved in terrestrial 139 ecosystem processes of interest, including organic matter decomposition, nitrogen 140 fixation, nitrification, denitrification, methane oxidation, plant-growth promotion, 141 bioremediation of pollutants, pathogenesis and methanogenesis; c) selected selecting 142 taxa were isolated from a wide range of terrestrial environments, such as nutrient rich 143 decomposing plant material and rhizosphere, submerged wetland and rice paddy soils, 144 polluted soils, and nutrient poor hot and cold arid environments; and d) avoiding the 145 inclusion of multiple subspecies and/or strains of a single species was specifically 146 avoided to prevent biases in analyses where highly over-represented species are 147 compared with species that have fewer cultured representatives. The taxonomic 148 system used here is from the NCBI, which is built upon a historical array of culture-149 dependent, physiological observations and genetic similarity to cultured isolates as 150 average nucleotide identity, DNA-DNA hybridisation or 16S rRNA gene homology 151 (ncbi.nlm.nih.gov/Taxonomy/Browser). The use of this classification system and 152 comparison to others is discussed further below. Bacteroidetes and Firmicutes. Thus, the UPGMA demonstrated that taxonomic 376 relatives at the Phylum level tended to cluster with each other preferentially based on 377 trait composition, and secondly that while similarity was broadly highest at low 378 taxonomic rank, some Families were more coherent than others.

	F o r P e e r F o r P e e r 137 terrestrial prokaryote genomes from 35 Families (n = 5 each), from 11 Phyla and two We hypothesised that: 1) traits are non-randomly distributed, with relatively closely e e r e e r e e r 239 from Levins' Overlap (Finn et al., 2020a), which measures pairwise similarity in 240 distributions of taxa, as the following: 241 242 C = 1 -( ) Equation 1. e e r e e r e e r e e r 375 the 379 Phylogenetic distance (P) of each taxonomic group increased with decreasing 380 taxonomic rank, and was highest in Proteobacteria, Actinobacteria and Firmicutes 381 Families (Supplementary Figure 2b). There was a strong positive linear relationship 382 between P and C (y = 0.86x -0.25, R 2 = 0.39, p < 0.001) supporting the result that 383 taxonomic groups of closer related taxa tended to share more similar compositions of 384 traits. 138 153 F o r P F o r P F o r P F o r P F o r P F o r P 385 F 386 o 387 3.2 Random Forest trait identification r P 388 The KEGG annotated traits belonged to 260 different BRITE 3 categories. The
	R e v i e w R e v i e w 154 related taxa demonstrating greater similarity than unrelated taxa (ecological 155 coherence); 2) traits that are differentially enriched between taxonomic groups would 156 primarily be involved in metabolism, nutrient acquisition and/or tolerating R e v R e v R e v R e v R e v R e v R e v 157 environmental stress; and 3) copiotrophic and oligotrophic taxonomic groups would 158 emerge based on collective trait enrichment. 159 i e w i e w i e w i e w i e w i e w i e w
	160
	161 2. Methodology
	162 2.1 Collection of terrestrial prokaryote genomes
	163 A collection of 175 sequenced and annotated genomes was collated (Supplementary
	164 Table 1). Listed are the genome ID, phylogenetic lineage, role in an ecosystem process
	165 if known, and isolation or genome sequencing reference. These genomes were
	166 sourced from the National Centre for Biotechnology Information (NCBI) and Joint
	167 Genome Institute (JGI) databases. Genomes were chosen based on several criteria:
	5 8 12

101

These distinct niches became associated with specific terrestrial taxa at high 102 taxonomic rank based on recent molecular analyses. In complex microbial 168 a) five isolates per Family were chosen to have an equal minimum sample size per 169 group, with this sample size being constrained by sequenced genomes of under-170 represented groups in public databases; b) only a single subspecies/strain per species 389 percentage of traits that could not be annotated (and were termed 'Uncharacterised')

390 ranged from 28 -65% per genome, being particularly high in the Archaea. On average, 391 47% of traits per genome were Uncharacterised with a standard deviation of 9.5%.

392

Of the 260 BRITE 3 categories, the 60 most important traits in separating all Phyla 393 and Families are ranked by importance measured as Mean Decrease in Accuracy 394 (MDA) of the Random Forest models (Figure

397

  Families was most improved by inclusion of the ABC transporters trait. Based on the 398 identified traits, the Phylum model was capable of successfully classifying 81.14% of 399 individual taxa. The Family model was capable of successfully classifying 71.43% of 400 individual taxa. Confusion matrices for both models are presented as Supplementary 401 Tables2 -4, and show that classification was particularly difficult for Chloroflexi and Ideally the selection of individual taxa within groups for such future 646 comparative analyses would also be standardised based on phylogenetic distance, 647 either with P or a similar method, that would improve the robustness of trait-based 648 comparisons at such a fine taxonomic level. Secondly, we hypothesised that the traits differentially enriched between taxonomic 653 groups would largely reflect those associated with copiotrophs or oligotrophs, namely 654 metabolism, nutrient acquisition and environmental stress response and tolerance. Of 655 fundamental interest to soil microbiologists is the decomposition of plant biomass. This 656 is the primary source of organic carbon to non-arid terrestrial systems (Kögel-Knabner, 657 2002) and the transformation of plant material to substrates bioavailable for 658 microorganisms is essential for community growth and activity. The traits involved in

	645 distinct niches. 649
	650
	651 4.3 Plant-derived carbon metabolism and nutrient acquisition
	F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R Myxococcaceae, Rhizobiaceae, e 664 Mycobacteriaceae, Frankiaceae and Verrucomicrobia (Table 1, Supplementary Table 652 659 663 Polyangiaceae, Streptomycetaceae, v 665 11). Genomic and culture-dependent analyses support Sorangium cellulosum i 666 (Polyangiaceae), Streptomyces coelicolor A3(2) (Streptomycetaceae) and e w 667 Chthoniobacter flavus (Verrucomicrobia) as having particularly large genomes with
	668 extensive repertoires for cellulose, hemicellulose, pectin and lignin degradation
	669 (Bentley
	402 Planctomycetes (classification error > 80%) in the Phylum model and for
	403 Cellulomonadaceae and the divergent Acidobacteria Lineage (classification error >
	404 80%) in the Family model. Random Forest models were robust against variation in P
	405 within Families, for example the nine families with all taxa perfectly classified ranged
	406 in P from the lowest (0.68) to highest (0.9).
	407	The important traits in classifying the taxonomic groups were involved in: a)
	408 metabolism and nutrient acquisition (oxidative phosphorylation, tricarboxylic acid
		20

plant material catabolism belonged to the BRITE categories 'Starch and Sucrose 660 Metabolism' (e.g. extracellular cellobiosidases, endoglucanases, glucosidases, 661 trehalases, amylases) and 'Glycosyltransferases', all of which are carbohydrate 662 activated enzymes (CAZy). The Families particularly enriched in these traits were the

  did not necessarily correspond to those 677 enriched with CAZy -only the Myxococcaceae and Verrucomicrobia were enriched in can be present in some taxa from the Verrucomicrobia (Op den 747 Camp

	746 methane oxidation
		F F F
		o r o r o r
		P P P
			e e r e e r e e r
			R e v R e v R e v
			i e w i e w i e w
	678 both.	Pseudomonadaceae,	Rhodospirillaceae,	Neisseriales	lineage	and
			23

  . Families 789 with high copy numbers of exopolysaccharide biosynthesis and secretion systems may 790 act as integral members of soil communities by predominantly contributing to 791 biofilm/aggregate formation. The 'LPS biosynthesis' and 'Starch and sucrose 792 metabolism' BRITE categories can synthesise N-acetyl glucosamine-based and 793 cellulose-based exopolysaccharides, respectively. Taxa enriched in both these 794 categories and secretion systems were the Polyangiaceae and Burkholderiaceae 795 (Table1), and in the refined 'Under-represented' model, Acidobacteria and 796 Verrucomicrobia (Supplementary Table11). Type IV secretion systems were another important trait identified in the Random Forest 801 models. These were enriched in Pseudomonadaceae and Myxococcaceae (Table

	797
	798
	799 4.6 Direct cell-cell interactions
	800

  cephalosporin synthesis were enriched in the Sporomusaceae and Bacillaceae relative 812 to other Firmicutes. Polyketide and Type II polyketide syntheses were important for 813 separating Frankiaceae and Streptomycetaceae from other Actinobacteria copiotrophic Proteobacteria Families will be systems primarily 883 dependent on labile di-and monosaccharides that demonstrate low carbon use 884 efficiency. 885 Streptomycetaceae, Mycobacteriaceae and Frankiaceae in Clade IV shared 886 enrichment of several Clade I copiotroph traits. As mentioned above in Section 4.5, 887 these Actinobacteria invest carbon and energy into complex filamentous growth and 888 developmental cycles. They demonstrate classic copiotrophic responses to nutrient 889 addition

	882 dominated by
	F F F
	o r o r o r
	P P P
	e e r e e r e e r
	R e v R e v R e v
	i e w i e w i e w
	27

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F o r P e e r R e v i e w

Supplementary Figure 1: Box and whisker plot comparing amino acid sequence similarity (%) within traits versus between traits, as determined by MCL. Amino acid sequences grouped together as traits were significantly more similar than those in different traits (Student's T test, p = 2 x 10 -16 ).

ScholarOne Support 1-434/964-4100 FEMS Microbiology Ecology F o r P e e r R e v i e w Supplementary Figure 2: a) Neighbour joining tree of full length 16S rRNA genes of taxa analysed in this study. b) Phylogenetic distance (P) of each taxonomic group, ranging between 0.6 (dissimilar) and 0.9 (similar) determined from branch lengths of 16S rRNA gene analysis in a). P was calculated as ecological coherence (Eq 1.) excepting that branch lengths are in units of gene sequence similarity and not Bray-Curtis distance (C).

Supplementary Figure 3: a) Linear correlation between Phylogenetic Distance (P) derived from 16S rRNA genes and ecological coherence (C) of functional traits present in Phyla analysed in this study. b) Linear correlation at the level of Family.