One of the most prominent problems related to biological invasions is the variation of local species composition, which often leads to ex novo interspecific interactions. Here, we explored and analysed the native species composition of gall inducers and their associated parasitoids and inquilines in Spanish areas invaded by Dryocosmus kuriphilus Yasumatsu 1951 (Hymenoptera: Cynipidae), an invasive pest of chestnut trees. After a quantitative description of these species' assemblages, we analysed through bipartite networks the level of the trophic specialisation of parasitoids and inquilines when considering either the host taxonomic identity, the host plant species or the host gall morphological type. We sampled galls of D. kuriphilus and native species of Cynipidae in different Spanish areas, including those where the exotic parasitoid Torymus sinensis Kamijo 1982 (Hymenoptera: Torymidae) had been released for D. kuriphilus biological control. The results indicate that the native parasitoids recruited by D. kuriphilus come almost exclusively from native communities on Quercus galls, except for one species from Rosa. Galls of D. kuriphilus had the second most diverse species composition; despite this species assemblage arose ex novo in less than a decade. The bipartite networks resulted more specialised when considering host plant taxa than when gall types and the host taxa were accounted. In such trophic webs, there were few parasitoid/inquiline specialist and many generalist species, which agrees with the rapid recruitment by D. kuriphilus. Higher parasitoid species richness in D. kuriphilus galls is likely due to their being a largely unexploited available resource for the native natural enemies of cynipid wasps.

Bipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.