Mutualistic interactions, those that are beneficial for both interacting species, are recurrently found in ecosystems, the typical example being the plant-pollinator communities. Observations of natural systems showed that, if we draw mutualistic relationships as binary links between species, the resulting bipartite network of interactions displays a widespread particular ordering called nestedness. In a nested network the set of neighbours of a vertex of a given degree is a subset of the neighbours of all the vertices of larger degree.

The ubiquity of nestedness  observed in mutualistic networks of  very different latitudes, climates and involving very different species, which can go from big animals to insects, has triggered a large amount of works which address the problem of quantifying nestedness on one hand, and on the other, aim at understanding the origins of nestedness as well as the role of nested structures on a number of relevant features of mutualistic communities like structural stability and biodiversity persistence.

In particular, questions about how nestedness emerges and what are its determinants, are still open challenges subject to strong debates.

In this talk we will show how the question of the origin of nestedness can be solved by a statistical physics approach applied to the analysis of 167 real mutualistic networks. Building a grand canonical ensemble of networks that keeps on average the observed degree sequences we show that nestedness is not an irreducible emergent property, but the entropic consequence of the double heterogeneity of the degree sequences (number of mutualistic interactions of each species). Remarkably, we find that an outstanding majority of the analyzed networks does not show statistical significant nestedness. These findings point to the need of revising previous claims about the role of nestedness and might contribute to expand our understanding of how evolution shapes mutualistic interactions and communities by placing the focus on the node-dependent properties rather than on global quantities.