“Neutral community dynamics and the evolution of species interactions”

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Marco Túlio P. Coelho and Thiago F. Rangel (Apr 2018)

The DOI will be http://dx.doi.org/10.1086/696216

The study integrates neutral community dynamics with species interactions

The mutual beneficial (mutualistic) interactions between species have sparked the interest of many naturalists over the centuries. Those interactions frequently involve dozens, or even hundreds of species, forming a complex network of interdependences. Ecologists have described recurrent natural features in the structure of these networks. For example, (i) few species interact with many species, while most species interact only with few species; (ii) specialist species interact only with small subgroup of species; (iii) the number of realized interactions is low compared to the total number of potential interactions; and, (iv) closely related species tend to interact with the same subgroup of partners. Although the features of mutualistic networks are well known, ecologists still do not fully understand why these patterns exist.

Marco Túlio P. Coelho and Thiago F. Rangel from the Universidade Federal de Goiás, Brazil, have developed a computer simulation model to study the causes of patterns in mutualistic networks. In their highly simplified virtual world, all individuals are identical, regardless of their species, and interact with one another by chance alone. Surprisingly, the four most recurring features of mutualistic networks emerge even in such a highly simplified virtual world. By studying the properties and rules that govern their virtual world, the researchers show that features of mutualistic networks are an emergent consequence of where the individuals are located and how they disperse over space, but not of their species identity.


A contemporary goal in ecology is to determine the ecological and evolutionary processes that generate recurring structural patterns in mutualistic networks. One of the great challenges is testing the capacity of neutral processes to replicate observed patterns in ecological networks, since the original formulation of the neutral theory lacks trophic interactions. Here, we developed a stochastic simulation neutral model adding trophic interactions to the neutral theory of biodiversity. Without invoking ecological differences among individuals of different species, and assuming that ecological interactions emerge randomly, here we demonstrated that a spatially explicit multitrophic neutral model is able to capture the recurrent structural patterns of mutualistic networks (i.e., degree distribution, connectance, nestedness and phylogenetic signal of species interactions). Non-random species distribution, caused by probabilistic events of migration and speciation, create non-random network patterns. These findings have broad implications for the interpretation of niche-based processes as drivers of ecological networks, as well as the integration of network structures with demographic stochasticity.