A new model explaining the origin of different topologies in interaction networks

Abstract

Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, and others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS). Without any selection on stability, our model reproduced all commonly observed network topologies. Our simulations demonstrate that resource heterogeneity drives network topology. On the one hand, systems containing only homogeneous resources form generalized nested networks, in which generalist consumers have higher performance on each resource than specialists. On the other hand, heterogeneous systems tend to have a compound topology: modular with internally nested modules, in which generalists that divide their interactions between modules have low performance. Our results demonstrate that all real‐world topologies likely emerge through processes driving interactions between pairs of species. Additionally, our simulations suggest that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.

Bats and hawkmoths form mixed modules with flowering plants in a nocturnal interaction network

“…All those constraints and trait-matching processes might explain the structure of the studied network. We found evidence that it has a compound topology (sensu Pinheiro et al, 2019). In order words, it has a modular structure, but its modules seem to be internally nested.…”

“…Finally, we also tested for a compound topology (sensu Lewinsohn et al., 2006) in the studied network. A compound network has a modular structure, but its modules show a different kind of internal structure, such as nestedness (see Pinheiro et al., 2019). We used a set of customized functions written in R to run this analysis (see Felix et al., 2017; Pinheiro et al., 2019, and the supplement).…”

“…The computation of network metrics and the null model analysis were made using the package bipartite (Dormann et al., 2019), and the sampling completeness analysis was performed using the package vegan (Oksanen et al., 2013). The compound topology analysis was made using customized R functions (Felix et al., 2017; Mello et al., 2019; Pinheiro et al., 2019), which we made available in the supplement. The software Gephi 0.9.2 (Bastian et al., 2009) was used for graph drawing.…”

Uncovering structural features that underlie coexistence in an invaded woody plant community with interaction networks at multiple life stages

“…I measured the nestedness of the adult–seedling network including only competitive interactions and including only facilitative interactions. I used the weighted nestedness based on overlap and decreasing abundance metric (wNODA, Almeida‐Neto & Ulrich, 2011; Pinheiro, Felix, Dormann, & Mello, 2019), calculated using the r package bipartite (Dormann, 2011; Dormann, Frund, Bluthgen, & Gruber, 2009; Dormann, Gruber, & Fründ, 2008). wNODA can range from 0 (non‐nested) to 100 (perfectly nested).…”

“…).I measured the nestedness of the adult-seedling network including only competitive interactions and including only facilitative interactions. I used the weighted nestedness based on overlap and decreasing abundance metric (wNODA,Almeida-Neto & Ulrich, 2011;Pinheiro, Felix, Dormann, & Mello, 2019), calculated using the R package bipaRtite…”