Abstract
Most of the studies in Ecology have been devoted to analyzing the effects the environment has on individuals, populations, and communities, thus neglecting the effects of biotic interactions on the system dynamics. In the present work we study the structure of bacterial communities in the oligotrophic shallow water system of Churince, Cuatro Cienegas, Mexico. Since the physicochemical conditions of this water system are homogeneous and quite stable in time, it is an excellent candidate to study how biotic factors influence the structure of bacterial communities. In a previous study, the binary antagonistic interactions of 78 bacterial strains, isolated from Churince, were experimentally determined. We employ these data to develop a computer algorithm to simulate growth experiments in a cellular grid representing the pond. Remarkably, in our model, the dynamics of all the simulated bacterial populations is determined solely by antagonistic interactions. Our results indicate that all bacterial strains (even those that are antagonized by many other bacteria) survive in the long term, and that the underlying mechanism is the formation of bacterial community patches. Patches corresponding to less antagonistic and highly susceptible strains are consistently isolated from the highly-antagonistic bacterial colonies by patches of neutral strains. These results concur with the observed features of the bacterial community structure previously reported. Finally, we study how our findings depend on factors like initial population size, differential population growth rates, homogeneous population death rates, and enhanced bacterial diffusion.
Highlights
Microbial ecosystems have proved to be excellent frameworks to understanding ecological systems (Prosser et al, 2007)
Computational Algorithm In order to simulate the evolution of a bacterial community interacting according to the antagonism matrix reported in Pérez-Gutiérrez et al (2013), we developed a computational algorithm as follows: TABLE 1 | ID numbers, labels, and aggressiveness indexes of the investigated bacterial strains
Bacteria To investigate whether, as claimed by Pérez-Gutiérrez et al (2013), antagonistic interactions have a lead role in the spatial distribution of bacterial communities, as well as in the diversity differences found across sites, we took the antagonism matrix reported therein, and used it to implement the algorithm described in the Section 2
Summary
Microbial ecosystems have proved to be excellent frameworks to understanding ecological systems (Prosser et al, 2007). Much of that progress has been achieved by means of simplified theoretical models (Momeni et al, 2011). Most of these models account for the interaction of only a few microbial. Antagonism explains self-assemblage of bacterial communities populations. This is an advantage, because simple models can be more studied both numerically and analytically, and a limitation, because such models oversimplify biological reality. Thanks to the rapid increase of computer power, it is possible to investigate the dynamics of larger sets of interacting populations (Costello et al, 2012)
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