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Uncovering the rules of microbial community invasions

Abstract

Understanding the ecological and evolutionary processes determining the outcome of biological invasions has been the subject of decades of research with most work focusing on macro-organisms. In the context of microbes, invasions remain poorly understood despite being increasingly recognized as important. To shed light on the factors affecting the success of microbial community invasions, we perform simulations using an individual-based nearly neutral model that combines ecological and evolutionary processes. Our simulations qualitatively recreate many empirical patterns and lead to a description of five general rules of invasion: (1) larger communities evolve better invaders and better defenders; (2) where invader and resident fitness difference is large, invasion success is essentially deterministic; (3) propagule pressure contributes to invasion success, if and only if, invaders and residents are competitively similar; (4) increasing the diversity of invaders has a similar effect to increasing the number of invaders; and (5) more diverse communities more successfully resist invasion.

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Fig. 1: Diagrammatic representation of the model and simulation experiments.
Fig. 2: One hundred independent invasion experiments for each of three unique invader community sizes.
Fig. 3: Adaptive divergence of resident and invader community determines invasion success.
Fig. 4: Propagule pressure and invader genotype richness can increase invasion success.
Fig. 5: Larger and/or more diverse communities better resist invasions.
Fig. 6: Model simulations are consistent with published experimental results.

Data availability

All data presented in this paper has been deposited in a public repository and can be accessed at https://github.com/vilacelestin/vilaetal2019.

Code availability

All code presented in this paper has been deposited in a public repository and can be accessed at https://github.com/vilacelestin/vilaetal2019.

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Acknowledgements

J.V. and M.P. were postgraduate students on the Computational Methods in Ecology and Evolution course at Imperial College, London. J.R. was funded by fellowships from the Natural Environment Research Council (NE/I021179, NE/L011611/1). T.B. was supported by a Royal Society University Research Fellowship. We thank members of the A. Sanchez and R. Chisholm Laboratories for useful discussions about the work. We thank N. Kristensen for comments on the manuscript. We thank A. Jousset and D. Hambright for providing us with access to data for re-plotting in Fig. 6. Our simulations were performed using the high-performance computing facility at Imperial College, London. This study is a contribution to Imperial College’s Grand Challenges in Ecosystems and the Environment initiative.

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All authors contributed to designing the study. J.C.C.V. performed the analyses with input from J.R. and with reference to code written by M.P. J.C.CV. and J.R. wrote the paper. All authors revised the paper. J.C.C.V. and M.P. were postgraduate students in the research group of J.R. M.L.J. was a PhD student in the group of T.B.

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Correspondence to Jean C. C. Vila.

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Supplementary methods, Table 1 and Figs. 1–7.

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Vila, J.C.C., Jones, M.L., Patel, M. et al. Uncovering the rules of microbial community invasions. Nat Ecol Evol 3, 1162–1171 (2019). https://doi.org/10.1038/s41559-019-0952-9

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