Maximum entropy networks show that plant-arbuscular mycorrhizal fungal associations are anti-nested and modular

Abstract

Many applications of network theory to plant-mycorrhizal associations have used a bipartite description, in which one set of nodes is the plants, and the other set is the fungi. Most applications have relied on null models from algorithms that randomly rewire the observed connections to test for non-random patterns in the network. We used existing plant-arbuscular mycorrhizal (AM) fungal datasets to apply a new, well validated generation of network models relaxing the very limiting assumptions of traditional null models. We focused on nestedness and modularity, which have been related to the functioning and stability of communities. Given the existent literature, we expected nestedness and modularity to be prevalent. We modelled plant-AM fungal associations using maximum entropy networks with a degree sequence, soft constraint to generate null distributions for nestedness and modularity. Most plant-AM fungal associations were anti-nested and modular. This pattern was consistent across habitat types and multiple spatial scales. Anti-nestedness can easily emerge from modularity when network patterns are determined by the identity of the plant and AM fungal nodes. Future studies will have to test how the observed patterns determine the ability of the associations to adapt to environmental changes.