Estudio del paradigma complejidad-estabilidad de la ecología en ecosistemas microbianos mediante modelos basados en individuos

Ecology is the science that studies the ecosystems and the relationships that take place between their components. The relation between the complexity and the stability of microbial ecosystems is one of the most studied issues in this field because of its current social relevance. We can appreciate it in the human microbiota, a key factor for health care; in the soil microbiomes, with a vast application in agriculture; or in some microbial communities with great potential in environmental detoxification. The first studies of the complexity-stability relationship of ecosystems were performed by mathematicians like Robert May. They used population-level models to study the stability of the ecosystems by the local stability analysis. They analysed three complexity factors: the number of species, the connectivity (frequency of interaction per species) and the medium interaction strength. The May theorem indicates that these three factors reduce ecosystem stability when they increase. In the 80s, permanence studies began being applied to the complexity-stability debate. The permanence concept states that a stable ecosystem is the one in which none of the species dominates or tends to extinction. In Coyte et al. 2015, they used a cellular automaton to model microbial communities and performed a permanence analysis. A cellular automaton has a lattice of cells that can be empty or occupied by an individual. The individual can interact with other individuals in the proximal cells and grow by filling an empty cell. A new modelling tool was developed in the last decades. These are the individual based models (IBMs), which are the evolution of the cellular automatons. They use a continuous space where the individuals can grow. They simulate real biological processes in every individual such as nutrient uptake, substrate-dependent growth, signal processing, metabolite-mediated interaction, etc… Gro is the IBM used in this work to model and simulate bacterial communities in one-layer culture. We think that an IBM like Gro is able to model microbial ecosystems and to contribute to the study of the relationship between complexity and stability. The objective of this work is to deepen in the knowledge of this relationship studying, via permanence analysis, the effect of 4 complexity factors in the stability of simulated microbial communities: connectivity, interaction strength, interaction range and the positive-to-negative interaction ratio. The first group of experiments consist of simulations varying the level of the first 2 factors and in the second group varying the level of the other 2 factors. The simulations consist of 5 species communities interacting between them according to an interaction network. This network is randomly designed given a level of connectivity. The communities suffer a perturbation during the simulation process. From the simulation output data we estimated the population density per species and performed a permanence analysis to determine whether the microbial community is stable or not. We calculated the proportion of stable communities for each level of the studied factors. Our results indicate that the higher the interaction strength or the connectivity level here the less stable the microbial community. They suggest the same about the interaction range but not for the positive-negative interaction ratio. Regarding to the later factor, our results suggest that extreme ratios make the communities more stable. They also show the influence of the interaction-network topology on the stability. We conclude that an IBM like gro, used to study factors and parameters of the simulated microbial communities, can contribute to the knowledge of the complexity-stability relationship of the ecosystems