Cheating interactions favor modularity in mutualistic networks

A fundamental fact about mutualisms is that these mutually beneficial interactions often harbor cheaters that benefit from the use of resources and services without providing any positive feedback to the other players. The role of cheaters on the evolutionary dynamics of mutualisms has long been recognized, yet their broader consequences to the community level, and beyond species they interact with, is still poorly understood. Because mutualisms form networks that often involve dozens to hundreds of species, indirect effects generated by cheaters may cascade through the whole community, reshaping trait evolution. Here, we study how harboring cheating interactions can influence coevolution in mutualistic networks. We combine a coevolutionary model, empirical data on animal-plant mutualistic networks, and numerical simulations and show that a higher frequency of cheating interactions in a network can lead to the formation of groups of species phenotypically similar to each other and distinct from other groups, generating higher trait disparity. The resulting clustered trait patterns, in turn, change the patterns of interaction in simulated networks, fostering the formation of modules of interacting species. Our results indicate that cheaters contribute to generate phenotypic clusters in mutualistic networks, counteracting selection for convergence imposed by mutualists, and favoring the emergence of modules of interacting species. Based on these results, we suggest that cheaters might be a fundamental element for our understanding of the evolution of mutualistic networks.