Microbial ecology and evolution

a) Marine microbes: I am currently involved in several projects related to the lower trophic levels of the marine food web; mostly, phytoplankton and/or marine viruses. I am interested in understanding how phytoplankton ability to react during ecological time to environmental changes (i.e. phenotypic plasticity) affects the species distribution in the short (ecological timescale) and the long term (evolutionary timescale). To that end, we have developed models that allow us to study the ecological and evolutionary implications of phytoplankton plastic responses to environmental changes by regulating nutrient-uptake capacity and rate, which also influences the cellular elemental ratio.

With Simon Levin from Princeton, Adam Martiny from U.C. Irvine, and Mike Lomas from Bigelow, we have recently showed that acclimation together with evolution can explain why the uptake strategies of the population in controlled environments are different from the taxon-level responses observed in the field, and the community-level ones (under review); Adam has also additional lab data, which we will try to replicate using our model, in order to gain some knowledge on how phytoplankton perform that regulation, and their stoichiometry, in different environments.

With Mick Follows and Oliver Jahn at M.I.T., Adam, and Simon, we are embedding our model for phytoplankton acclimation into the Darwin model, so that we can have a map of how this uptake regulation affects the global distribution and nutrient levels of the various marine phytoplankton species.

b) Bacterial spatio-temporal patterns: Ricardo Martinez-Garcia from Princeton, Carey Nadell from the Max Planck Institute for Terrestrial Microbiology, and I are using physics toy models to understand the ecological and evolutionary implications of the biofilm matrix (which allow cells to stick together) during the colonization of different environments. To that end, we use two identical strains of V. Cholerae, genetically modified to allow or supress their ability to synthesize the proteins to build the matrix. We modify the initial conditions, and try to replicate the observed colonization patterns with simple toy models. As in our previous work together, we will then map the toy model into a continuous equation, in order to identify the relevant biological mechanisms responsible for those patterns.

Catastrophic shifts and emergent patterns

In a clear example of how physics concepts can help understand better the ecology of an ecosystem, my collaborators and I use the theory of phase transitions to study whether real ecosystem undergo a catastrophic regime shift (a sudden irreversible change from one state to another, e.g. vegetation to desert), or a more avoidable and smoother continuous one.

A team from Princeton that includes Corina Tarnita, Efrat Sheffer, Robert Pringle and others, and I are studying how the presence of social insects affects the vegetation patterns observed in semi-arid environments. We use data we take in the field to inform theoretical models. We then use those models to study not only the patterns, but also how these insects affect the robustness and resilience of the whole ecosystem, which is reflected in the character of the transition towards the desertic state.

Miguel A. Muñoz and Paula Villa, and I study how different realistic ingredients such as demographic stochasticity of limited diffusion, when added to theoretical models, can precisely alter the character of the transition. This theoretical study can help design policies to manage natural resources to avoid sudden collapses, or design restoration strategies.

Marine food webs

Food webs are a graphic representation of the trophic relationships between species present in an ecosystem. Most of the time, food webs are modeled from a static perspective, which overlooks the high dynamic character of those trophic interactions. My collaborators and I aim to develop simple yet realistic dynamic models that improve our understanding of important ecological and evolutionary aspects of the marine food web.

James Watson from the Stockholm Resilience Center and I are working in devising models at an intermediate level of complexity, between the very simple but unreaslitic and unstable typical predator prey model, and the very complicated and extremely data-expensive physiologically-structured models.

In a closely-related project, Mia Eikeset from the Center for Ecological and Evolutionary Synthesis (Norway), Simon Levin, and I are developing a model to study how the most important commercial fish species in the Barents Sea will react to different future scenarios. Those scenarios include changes in fishery managment (quotas, etc), climate change, or catastrophic events like oil spills. Because there are many intertwined layers of temporal complexity, we are using an eco-evolutionary approach, in which we consider that ecology and evolution may happen at similar timescales and therefore can interact.

Both projects are framed into GreenMar, an international initiative which some of my collaborators and I are part of.

Interactions ecology-evolution

Common to all the themes above is the fact that ecology and evolution interact to shape ecosystems. In the case of microbes, their short generation time and vast offspring number makes it easy to observe mutations occurring at the ecological timescale. In the case of the marine food web, the fact that there are so many timescales present across trophic levels ensures that the evolution of some organisms of that network will influence the ecological relationship with others.

I am currently working with Nils Ch. Stenseth, and a team of experimentalists from the Center for Ecological and Evolutionary Synthesis (Norway) to develop a model able to replicate their lab observations. They use four genetically-modified E.Coli strains that feed on glucose, and one strain-specific additional sugar. Glucose concentration allows us to control the ecological interactions; state-of-the-art techniques to mark genetically the strains allow us to monitor evolution. We aim to use the model to inform experiments, and viceversa, with the hope of finding the conditions to, e.g. observe evolutionary Red Queen dynamics.