Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Modulation of microbial community dynamics by spatial partitioning

## Abstract

Microbial communities inhabit spatial architectures that divide a global environment into isolated or semi-isolated local environments, which leads to the partitioning of a microbial community into a collection of local communities. Despite its ubiquity and great interest in related processes, how and to what extent spatial partitioning affects the structures and dynamics of microbial communities are poorly understood. Using modeling and quantitative experiments with simple and complex microbial communities, we demonstrate that spatial partitioning modulates the community dynamics by altering the local interaction types and global interaction strength. Partitioning promotes the persistence of populations with negative interactions but suppresses those with positive interactions. For a community consisting of populations with both positive and negative interactions, an intermediate level of partitioning maximizes the overall diversity of the community. Our results reveal a general mechanism underlying the maintenance of microbial diversity and have implications for natural and engineered communities.

This is a preview of subscription content

## Access options

\$32.00

All prices are NET prices.

## Data availability

Experimental data generated for this manuscript are deposited at GitHub at https://github.com/youlab/partitioning_NCB2021. Source data are provided with this paper.

## Code availability

The simulation and data analysis code used in this study are deposited at GitHub at https://github.com/youlab/partitioning_NCB2021.

## References

1. Schimel, J. P. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).

2. Althani, A. A. et al. Human microbiome and its association with health and diseases. J. Cell. Physiol. 231, 1688–1694 (2016).

3. Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Animal behavior and the microbiome. Science 338, 198–199 (2012).

4. Cydzik-Kwiatkowska, A. & Zielinska, M. Bacterial communities in full-scale wastewater treatment systems. World J. Microbiol. Biotechnol. 32, 66 (2016).

5. Che, S. & Men, Y. Synthetic microbial consortia for biosynthesis and biodegradation: promises and challenges. J. Ind. Microbiol. Biotechnol. 46, 1343–1358 (2019).

6. Fuhrman, J. A. Microbial community structure and its functional implications. Nature 459, 193–199 (2009).

7. Widder, S. et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 10, 2557–2568 (2016).

8. Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

9. Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

10. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

11. Wu, F. et al. A unifying framework for interpreting and predicting mutualistic systems. Nat. Commun. 10, 242 (2019).

12. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evolution 1, 109 (2017).

13. Lopatkin, A. J. et al. Persistence and reversal of plasmid-mediated antibiotic resistance. Nat. Commun. 8, 1689 (2017).

14. Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).

15. Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).

16. Song, H., Payne, S., Gray, M. & You, L. C. Spatiotemporal modulation of biodiversity in a synthetic chemical-mediated ecosystem. Nat. Chem. Biol. 5, 929–935 (2009).

17. Tei, M., Perkins, M. L., Hsia, J., Arcak, M. & Arkin, A. P. Designing spatially distributed gene regulatory networks to elicit contrasting patterns. ACS Synth. Biol. 8, 119–126 (2019).

18. Reichenbach, T., Mobilia, M. & Frey, E. Mobility promotes and jeopardizes biodiversity in rock–paper–scissors games. Nature 448, 1046–1049 (2007).

19. Welch, J. L. M., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl Acad. Sci. USA 114, E9105–E9114 (2017).

20. Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911–E920 (2015).

21. Bickel, S. & Or, D. Soil bacterial diversity mediated by microscale aqueous-phase processes across biomes. Nat. Commun. 11, 116 (2020).

22. Dal Co, A., van Vliet, S., Kiviet, D. J., Schlegel, S. & Ackermann, M. Short-range interactions govern the dynamics and functions of microbial communities. Nat. Ecol. Evolution 4, 366–375 (2020).

23. Hsu, R. H. et al. Microbial interaction network inference in microfluidic droplets. Cell Syst. 9, 229–242 (2019).

24. Leibold, M. A. & Chase J. M. Metacommunity Ecology, Vol. 59 (Princeton University Press, 2018).

25. Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

26. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

27. Stephens, W. Z. et al. Identification of population bottlenecks and colonization factors during assembly of bacterial communities within the zebrafish intestine. mBio 6, e01163-15 (2015).

28. Houghteling, P. D. & Walker, W. A. Why is initial bacterial colonization of the intestine important to infants’ and children’s health? J. Pediatr. Gastroenterol. Nutr. 60, 294–307 (2015).

29. Wolfe, B. E., Button, J. E., Santarelli, M. & Dutton, R. J. Cheese rind communities provide tractable systems for in situ and in vitro studies of microbial diversity. Cell 158, 422–433 (2014).

30. Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).

31. Handa, I. T. et al. Consequences of biodiversity loss for litter decomposition across biomes. Nature 509, 218–221 (2014).

32. Schloter, M., Lebuhn, M., Heulin, T. & Hartmann, A. Ecology and evolution of bacterial microdiversity. FEMS Microbiol. Rev. 24, 647–660 (2000).

33. Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).

34. Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl Acad. Sci. USA 111, E2149–E2156 (2014).

35. Kong, W., Meldgin, D. R., Collins, J. J. & Lu, T. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14, 821–829 (2018).

36. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

37. Justice, N. B., Sczesnak, A., Hazen, T. C. & Arkin, A. P. Environmental selection, dispersal, and organism interactions shape community assembly in high-throughput enrichment culturing. Appl. Environ. Microbiol. 83, e01253-17 (2017).

38. Ha, J. et al. Poro-elasto-capillary wicking of cellulose sponges. Sci. Adv. 4, eaao7051 (2018).

39. Fierer, N. & Lennon, J. T. The generation and maintenance of diversity in microbial communities. Am. J. Bot. 98, 439–448 (2011).

40. Dai, Z. et al. Versatile biomanufacturing through stimulus-responsive cell–material feedback. Nat. Chem. Biol. 15, 1017–1024 (2019).

41. Cao, Y. et al. Collective space-sensing coordinates pattern scaling in engineered bacteria. Cell 165, 620–630 (2016).

42. MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 1967).

43. Wilcox, B. A. & Murphy, D. D. Conservation strategy—the effects of fragmentation on extinction. Am. Nat. 125, 879–887 (1985).

44. Simberloff, D. & Abele, L. G. Refuge design and island biogeographic theory—effects of fragmentation. Am. Nat. 120, 41–50 (1982).

45. Weiher, E. et al. Advances, challenges and a developing synthesis of ecological community assembly theory. Philos. Trans. R. Soc. B Biol. Sci. 366, 2403–2413 (2011).

46. Brown, B. L., Sokol, E. R., Skelton, J. & Tornwall, B. Making sense of metacommunities: dispelling the mythology of a metacommunity typology. Oecologia 183, 643–652 (2017).

47. Wright, D. H. & Reeves, J. H. On the meaning and measurement of nestedness of species assemblages. Oecologia 92, 416–428 (1992).

48. Burkey, T. V. Extinction in nature reserves—the effect of fragmentation and the importance of migration between reserve fragments. Oikos 55, 75–81 (1989).

49. Storch, D. The theory of the nested species–area relationship: geometric foundations of biodiversity scaling. J. Vegetation Sci. 27, 880–891 (2016).

50. Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).

51. Huang, S. et al. Coupling spatial segregation with synthetic circuits to control bacterial survival. Mol. Syst. Biol. 12, 859 (2016).

52. Balagadde, F. K. et al. A synthetic Escherichia coli predator–prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).

53. Chung, C. T. & Miller, R. H. Preparation and storage of competent Escherichia coli cells. Methods Enzymol. 218, 621–627 (1993).

54. Blankenberg, D. et al. Manipulation of FASTQ data with Galaxy. Bioinformatics 26, 1783–1785 (2010).

55. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

56. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

57. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

58. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

## Acknowledgements

This work is partially supported by grants from the US National Institutes of Health (L.Y.: R01GM098642, R01GM110494), National Science Foundation (L.Y.: MCB-1412459, MCB-1937259; C.T.L.: DEB 1257882), Office of Naval Research (L.Y.: N00014-12-1-0631) and Army Research Office (L.Y.: W911NF-14-1-0490) and a David and Lucile Packard Fellowship (L.Y.).

## Author information

Authors

### Contributions

F.W. conceived the research, designed and performed modeling and experiments, interpreted the results and wrote the manuscript. Y.H. assisted with experimental design and execution and manuscript revisions. A.W. constructed the barcoded Keio communities and assisted with the experimental design, data collection and analysis of the barcoded Keio communities and manuscript revisions. M.W. assisted with ideation, experimental design and execution, analyses of previously published data and manuscript revisions. J.L. assisted with experimental design, data collection and analysis of the gut microbiome and manuscript revisions. S.W. assisted with liquid handling robotics and manuscript revisions. N.L. assisted with inkjet printing and manuscript revisions. S.H. assisted with design and manufacturing of the microfluidics device. C.T.L. assisted with data interpretation, establishing the general relevance in ecology and manuscript revisions. L.A.D. assisted with experimental design, data interpretation and manuscript revisions. L.Y. conceived the research, assisted in research design and data interpretation and wrote the manuscript.

### Corresponding author

Correspondence to Lingchong You.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

## Peer review

### Peer review information

Nature Chemical Biology thanks Hyun Youk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data

### Extended Data Fig. 1 Intuition of the dependence of community diversity on partitioning level.

a. Local community behaviors for one-directional negative interaction. Low partitioning allows the interaction between the two populations, leading to the collapse of the green population. High partitioning separates the two populations and enables the growth of the green population. b. Local community behaviors for one-directional positive interaction. Low partitioning levels enable the growth of the green population, whereas the green population cannot grow well at high partitioning levels in absence of the other population. c. The same principle applies to 12-member communities with all negative interactions (red) or all positive interactions (blue). Increasing partitioning increases the diversity of communities with negative interactions but decreases the diversity of communities with positive interactions. Data are represented as mean values + /- SD, with n = 10, error bar represents standard deviation. d. Steady state is not required to generate biphasic response. The simulation results of a fully connected 15-member community at different tf values: 50 (dark grey), 100 (grey), and 200 (light grey). The community has 50% of negative interactions and 50% of positive interactions, with a maximum δ of 1.5, a maximum γ value of 1, and a maximum β of 5. e. Biphasic dependence of diversity of local communities, which is quantified as the count of local communities that have unique combinations of members. The plot is generated with a 10-member community; each dot represents one randomization of the initial seeding. The solid trace represents the average number + /- SD (error bars) and n = 10 and the same as panel f. f. Biphasic dependence of diversity of local communities containing a population. The plot is generated with the same 10-member community in panel e, with one set of initial seeding. Each dot represents the diversity of local communities containing a population at a partitioning level. The solid trace represents the average number across 10 members. g. More unique types of local communities lead to higher diversity. For communities with both positive and negative interactions, types of local communities correlate with final community diversity. The lighter the trace, the more members there are in a community.

### Extended Data Fig. 2 Distribution of the biodiversity of local communities.

The patterns of the distribution of local patch biodiversity are similar regardless of the nature of interactions. Intermediate partitioning levels lead to bimodal distributions. The black lines indicate the biodiversity of the global community; the light purple lines show the average of local community biodiversity and error bars show the standard deviation of the local community diversity with N = # of partitions. The histogram shows the distribution of local community biodiversity corresponding to each portioning level. Normalized count is the count of local communities in each bin normalized by the total number of local communities in the partitioning level. a. 20-species communities with all negative interactions. The interaction networks have 100% connectedness. δ is generated by a uniform distribution with minimum equals to 0 and maximum equals to 2. β is generated by a uniform distribution with minimum equals to 0 and maximum equals to 3. γ is generated by a uniform distribution with minimum equals to 0 and maximum equals to 0.8. b. 20-species communities with 1:1 count of negative to positive interactions. All parameter settings are the same as panel a, except for the split between negative and positive interactions. c. 20-species communities with all positive interactions. Intermediate partitioning levels lead to bimodal distributions. All parameter settings are the same as panel a, except for the split between negative and positive interactions.

### Extended Data Fig. 3 Characterizations of the synthetic strains.

a.-c. Circuit diagrams of strains 1, 2, and 3. d. Monoculture response to inducers and QS signals. Growth of strain 1 was not strongly impacted by inducers and QS signals. Growth of strain 2 was inhibited by 3OC6HSL, as well as the induction of circuit by aTc and IPTG. The growth of strain 3 was inhibited by IPTG that induces CcdB but rescued by addition of 3OC6HSL. The experiments were done with 1000-fold dilution of overnight monocultures. The strains were cultured in M9 medium at 30°C in a plate reader. “-” indicates no inducers or QS signals were added. “a” indicates the addition of [aTc] of 10 nM. “I” indicates the addition of [IPTG] of 1 mM. “C6” indicates the addition of [3OC6HSL] of 10 nM. e. Response of strain 2 and 3 to supernatant of 1. The supernatant of strain 1 (introduced in the center) inhibited growth of strain 2 (initially spread on the entire plate) around the center. The rescue strain 3 by strain 1 was confirmed by the elevated growth at the center of the plate. The agar plates were made with LB medium, 1.5% agar. 1 mM IPTG and 100 nM of aTc were also added to induce circuit functions. The overnight culture of strain 1 was induced by 1 mM of IPTG and 100 nM of aTc to produce 3OC6HSL. f. Community structure of the pair with negative interaction. Strain 2 had an increased relative abundance with increasing partitioning level. The community structures were measured by selective plating. Data are represented as mean values + /- SD and n = 16. g. Community structure of the pair with positive interaction. Strain 3 has a reduced relative abundance with increasing partitioning level. The slight reduction of biodiversity at the low partitioning was due to the slight decrease of relative abundance of strain 3 possibly driven by background competition between the two strains. Data are represented as mean values + /- SD and n = 16.

### Extended Data Fig. 4 Spatial partitioning by controlled seeding.

a. Partitioning based on controlled seeding. Number of species in local communities (group size) decreases with increasing partitioning. All combinations of the same group size were tested and pooled after growth to determine the richness of the pooled community–the total number of populations. Due to the exponential nature of combination, controlled seeding is difficult to implement in an exhaustive manner for communities with large number of populations. b. Simulations of all 6 major types of interactions in monocultures and cocultures. A check (cross) mark indicates that a population can (cannot) survive by itself. Overall, partitioning promotes coexistence for negative interaction-dominated pairs and impedes that for positive interaction-dominated pairs. The predator-prey interaction shows both trends, depending on whether the populations survive by themselves. Previously published data are consistent with these simulation results. c. Spatial partitioning by controlled seeding in large communities with only negative or positive interactions. Increasing partitioning increases diversity for communities with only negative interactions and decreases for communities with only positive interactions. Each dot represents a randomly generated interaction network, and 10 networks are generated for negative interaction (red trace) and positive interaction (blue trace) networks. The open circles represent the mean across the 10 random interaction networks and error bars represent the standard deviations. d. Robust biphasic dependence is observed with controlled seeding. Partitioning implemented by controlled seeding also generates robust biphasic dependence for communities with both negative and positive interactions. Simulations are done on 10 randomly generated interaction networks of 10 populations with 1:1 ratio of positive versus negative interactions (grey dots). Open circles represent the mean and error bars represent the standard deviation.

### Extended Data Fig. 5 Controlled seeding as an alternative implementation of spatial partitioning.

Local group size refers to the number of populations that are seeded into a local community at t0. Reducing group size is effectively increasing partitioning level. a. An 8-member community that is dominated by negative interactions reaches higher diversity with increasing. The previous study has collected eight soil bacterial species and found that the final community compositions are primarily driven by competitive exclusion. We have used the published data that are generated by co-culturing all possible combinations of a specific number of species to test our theory. b. A 14-member auxotroph community reaches maximum diversity at an intermediate partitioning level, with a sharp drop of diversity from local group size of 2 to 1. This previous publication investigated 14 E. coli auxotrophs where none can grow as monocultures but some pairs grow collectively in cocultures. When all 14 auxotrophs are cocultured, only a few coexist whereas others are competed out due to competition. c. The biodiversity of a 12-member synthetic human gut microbiome consortia that has both positive and negative interactions follow a biphasic dependence on partitioning. This previous study has cultured the single species, all pairwise assemblages, all single-species dropout communities, and all 12-member community. For each community, the abundance is measured using 16 S rRNA gene sequencing.

### Extended Data Fig. 6 Construction of barcoded Keio strains and sequencing quantification.

a. The backbone of barcoded plasmids. b. Calibration experiment. Samples prepared at known fixed concentrations were generated and prepared through the entire workflow of NGS library preparation, sequencing and data analysis, as described in the Methods. The image represents the layout of sample prepared at known ratios for sequencing where daker shading indicates higher relative strain concentrations in mixture. Samples were prepared based on 2-fold dilution between each group. c. Identifying outliers. A box and whisker plot of 94 samples sequences at equal ratio was used to identify barcodes that amplified poorly or over-amplified as compared to other barcode sequences. The center of the box and whisker plot is 762. Outlier barcodes were defined as those giving sequencing counts with a distance greater than 1.5 times below the 1st quartile’s interquartile range (distance between the 1st and 3rd quartile) or 1.5 times above the 3rd quartile’s interquartile range. Eight barcodes were removed from future analysis. d. Overall calibration results. Normalized relative abundance of sequencing counts obtain for each barcode plotted versus the expected sample ratio shows a good correlation between expected and actual barcode abundances. Each data point represents a single barcoded Keio strain. e. Correlation between NGS measurements of technical replicate experiments. Sequencing technical replicate of three different samples sequences independently. f. Correlation between replicate NGS measurements on the same biological samples. Sample preparation and sequencing replicate for three sample starting from the same template DNA that were processed independently through sample preparation and sequencing protocols.

### Extended Data Fig. 7 Characterizations of the Keio auxotrophic (a & b) and non-auxotrophic (c) communities.

a. Community growth response to amino acid concentrations and initial density. Collective growth demonstrates the presence of positive interactions among 47 Keio auxotroph strains. With no casamino acids added (the darkest trace) and 0.0002% of casamino acid, the auxotroph Keio community shows no detectable growth at 48 hours in M9 medium at 30oC despite having an initial density above 0. However, with higher initial cell densities, the community began to grow, which is a typical behavior of cooperative communities. The data are represented as mean with error bars representing standard deviations across 7 replicate wells (N = 7). From the darkest to lightest, the lines casamino acid concentrations of 0%, 0.0002%, 0.001%, 0.005%, 0.02%, and 0.1%. b. The distribution of OD600 of each well at different casamino acid concentrations and partitioning levels. As expected, there is systematic increase in OD600 with increasing casamino acid concentrations (labeled at each row). Based on the initial cell density and Poisson distribution, 96 and 384-well plates are almost always are seeded with at least one cell in each well. However, at low casamino concentrations (0.0002% and 0.001%), bimodal distributions occur at 96 and 384-well plates indicating that the initial community compositions and their interactions play a crucial role in the growth of a strain. At the 1536 partitioning level, high casamino acid concentrations (0.1% and 0.02%) also show bi-modal distributions, which is expected due to a probability of ~30% wells being empty at a $$\lambda = 1.2$$.c. Increased diversity with a 94-member community dominated by negative interactions. The strains were randomly selected from the Keio collection with auxotrophic strains excluded. Since all strains share the same genetic background, strong negative interactions are expected due to competition for nutrients and space.

### Extended Data Fig. 8 Deducing the effect of spatial partitioning on a groundwater community.

a. The experimental setup of the groundwater study. Partitioning increases with higher dilution due to the decrease of local community sizes and reduced number of interactions. Serial dilution introduces two additional variables: the decrease of initial cell densities and subsampling of the initial community that occurs with increasing levels of dilution. b. Negligible impact of initial cell density on final community diversity. 10 randomly generated 1000-member community were simulated with increasing dilution rate, which is equivalent to decreasing density of the initial community (grey dots). Data are represented as mean values+/- SD with N=10. c. Estimated global initial community structure. The population abundance distribution loosely follows power law, which approximates the distribution pooled final communities of different dilution levels, especially at 10X and 102X. The estimated abundance distribution of the original sample is based on power-law distribution and an estimate of 5000 total OTUs, which is feasible compared with 399 OTUs that are sequenced in the final communities that account for both 10X dilution and OTUs that were not able to grow. d. Comparison between the measured richness at each dilution level and the estimated richness of initial communities. The baseline is the mean sampled from the estimated community composition of the original sample by simulating serial dilution. The error bars of the baseline are the standard deviation across 10 simulated samplings (n=10). The # of OTU present was calculated from the published data. Dilution of 104 in anaerobic condition has no OTU present and it is thus not shown. e. Biphasic dependence of a groundwater community. The community was analyzed in both anaerobic (NO3) and aerobic (O2) culture conditions. Down sampling due to dilution was accounted for to estimate the number of OTUs sampled at t0. Y-axes indicate the percentage of OTUs that are present at tf (after culturing) out of the number of sampled OTUs at t0 (see SI for detailed method). Data are represented as mean values + /- SD with N=10.

### Extended Data Fig. 9 Response of members of different architypes to spatial partitioning.

a. Eight archetypes of species based on fitness, competition, and cooperation, using three metrics: fitness (1−δ), positive interaction it receives (β), and negative interaction it receives (γ). The color of each archetype is determined by the level of fitness, the strength of positive interaction received, and the strength of positive interaction received, which correspond to the level of blue, red, and green. Each archetype is named to reflect their characteristics. b. A 160-member community comprised of all eight archetypes. The same simulation framework described in Fig. 2 was used to simulate how each archetype responds to partitioning. 20 populations were defined for each archetype. The interaction matrix was randomized while constraining the strength of interactions each population receives according to its archetype. The size of each dot represents the final population density. c. Relative abundance of the eight archetypes. Each trace is the average relative abundance of all 20 species that belong to the same archetype. Higher partitioning level enriches species receiving negative interactions (traces with colors that have red components) whereas lower partitioning level enriches species receiving positive interactions (traces with colors that have red components). Instead of white, we plotted the relative abundance of “Drama Queens” in grey. Data are represented as mean values + /- SD with N=20. d. Mixed partitioning level provides a robust way to maintain community biodiversity. From the top left to bottom right panel, proportion of negative interaction change from 0 to 1.0 for a 40-species community. Even though the biodiversity response changes from negative monotonic dependence to biphasic dependence, and to positive monotonic dependence, mixed partitioning, which in this set of simulation is even volume mix across partitioning levels, creates a robust way to maintain biodiversity of the community (bar on the right). Data are represented as mean values + /- SD with N=10.

## Supplementary information

### Supplementary Information

Supplementary Notes 1–3, Figs. 1–7 and Tables 1–4.

## Source data

### Source Data Fig. 3

Statistical source data.

### Source Data Fig. 4

Statistical source data.

### Source Data Fig. 5

Statistical source data.

### Source Data Extended Data Fig. 3

Statistical source data.

### Source Data Extended Data Fig. 6

Statistical source data.

### Source Data Extended Data Fig. 7

Statistical source data.

## Rights and permissions

Reprints and Permissions

Wu, F., Ha, Y., Weiss, A. et al. Modulation of microbial community dynamics by spatial partitioning. Nat Chem Biol 18, 394–402 (2022). https://doi.org/10.1038/s41589-021-00961-w

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41589-021-00961-w