Microbial community dynamics and eco-evolutionary theory

Microbial dynamics provide an important and concrete setting for developing symbiosis theory. Microbes shape processes ranging from host health and disease to ecosystem functioning, agriculture, and conservation. For example, the human microbiome influences immunity, metabolism, and susceptibility to infection, while microbial evolution and horizontal gene transfer affect antibiotic resistance and therapeutic outcomes. In agriculture and conservation, interactions with microbial partners can influence plant productivity, stress tolerance, and the success of species reintroductions.

I am particularly interested in how interactions among microbes shape community dynamics and the evolution of microbial and host traits.  Microbes engage in an astonishing spectrum of behaviors, often in ways that depend on the broader community context: from metabolic facilitation and cooperative signaling to competitive hijacking and chemical warfare. Quorum sensing, for example, allows bacteria to coordinate collective behaviors, while other community members can alter or disrupt these communication systems. My work on mycorrhizal symbioses provides another example of how microbial interactions can shape host outcomes: mycorrhizal fungi and plants exchange resources in ways that can influence plant productivity, raising the possibility of using mycorrhizae as sustainable alternatives to conventional fertilizers and optimizing their use to improve crop yields. Together, these examples highlight the need to move beyond pairwise interactions toward a community-level understanding of microbial dynamics. 

Yet mathematical theory has only begun to incorporate the complexity of microbial communities and the ways in which their members interact and evolve within their environments. Thanks to advances in transcriptomic and metabolomic technologies we now have an enormous amount of compositional and correlational data, but comparatively few mechanistic insights. We can observe which microbes are present, how their abundances change, and which features are associated with particular outcomes, but we often lack theory that tells us why these patterns emerge or what to expect under new conditions. There is therefore a need for mathematical frameworks that can turn patterns into mechanisms, and mechanisms into predictions: theory that can tell us what to expect, when dynamics will change, how microbial communities may respond to a changing world, and how they might be steered toward desired outcomes.

One of the challenges in developing such a theory is that ecology and evolution have traditionally been treated as distinct disciplines. Ecology asks who interacts with whom; evolution asks which traits are inherited and how they change. Yet when ecological interactions influence transmission, selection, and inheritance, and evolutionary change feeds back to alter those interactions, the two processes become intrinsically tangled. Microbial systems make these eco-evolutionary feedbacks particularly evident. Microbial communities can evolve on ecological timescales, while ecological interactions can directly shape which organisms are transmitted, which traits are favored, and which partnerships persist. Developing an eco-evolutionary theory of microbial dynamics and host–microbe symbioses is therefore an open theoretical frontier to which my research contributes. 

Selected publications

Mutualism at the leading edge: Insights into the eco-evolutionary dynamics of host-symbiont communities during range expansion
M. M. Martignoni, R. C. Tyson, O. Kolodny, J. Garnier · Journal of Mathematical Biology, 88(2), 24 · 2023

Towards a theory of microbially-mediated invasion encompassing parasitism and mutualism
M. M. Martignoni, J. Garnier, R. C. Tyson, K. D. Harris, O. Kolodny · Biological Invasions, 27(12), 253 · 2025

Investigating the impact of the mycorrhizal inoculum on the resident fungal community and on plant growth
M. M. Martignoni, J. Garnier, M. M. Hart, R. C. Tyson · Ecological Modelling, 438, 109321 · 2020