The network structure of interactions between snakes and their food resources
We analysed the snake diet derived from a long-term study carried out in a Central Amazonia site on the natural history of forest snakes (Martins and Oliveira 1998). We described the resource use by snakes as an interaction matrix A in which if a snake i feeds on a given resource j and zero otherwise. The matrix Adefines a bipartite network in which snakes and resource types depict two distinct set of nodes and links describe interactions between snake species and food resources. Our food resources are not described at species level but at coarse categories such as small mammals, medium mammals and big mammals. Similar approach led to insights in the study of food webs (Cohen 1977) and individual-based networks (Araújo et al. 2008). In fact, there is no intrinsically correct level of description when characterizing an ecological network (Guimarães 2020). We opted for these coarse categories because they are in agreement with (i) the evidence that snakes are specialized in broad categories of resources, e.g., serpentiform organisms that include snakes, amphisbaenians, and caecilians (see Martins and Oliveira 1998); (ii) the level of detail available from the diet analyses of snakes. Having said that, to verify if our level of network description affects our analyses we performed a set of sensitivity analyses (details below).
We used four metrics to characterise the structure of the interactions network analysed: (i) degree distribution, which is the description on how the number of food resources a given snake can feed on (the degree) varies across snake species; (ii) connectance (C ), which is the proportion of all possible interactions actually recorded in the network. Connectance values range from 0 (non-connected network) to 1 (maximum connectance); (iii) modularity (M ), a measure of the extent to which the network is formed by groups (modules) of snake species in which snake within a module overlap in much of their resources, whereas snakes in different modules show no or weak resource use overlap; and (iv) nestedness (N ), which consists of an interaction pattern in which the specialists interact with sets of resources with which the generalists also interact. Detailed descriptions of the metrics are available in the Supplementary material Appendix 1.
We used QB metric, defined by Barber (2007), to characterise modularity, with values ranging from 0 (non-modular network) to 1 (completely modular). A simulated annealing algorithm (Guimerà & Amaral 2005) was used to optimise theQB value. Modularity analyses were performed using the Modular program (Marquitti et al. 2014). All the above and the following analyses were performed using R version 3.5.1 (R Core Team 2018), with the exception of modularity. We performed a set of sensitivity analyses to verify if our results are dependent on our approach to compute modularity (Supplementary material Appendix 1).
The NODF metric was used to characterise the nestedness degree (Almeida-Neto et al. 2008) and its values ranges from 0 (non-nested network) to 100 (perfect nestedness). The degree of nestedness and modularity were then compared with a theoretical benchmark provided by the null model 2 of Bascompte et al. (2003) (see detailed description in Supplementary material Appendix 1). We generated 1000 null model matrices to estimate nestedness and modularity. If a network shows a degree of nestedness or modularity larger than expected by the null model 2, then there is evidence of ecological or evolutionary processes acting on the network organisation that goes beyond those shaping the degree of specialisation of the snake species (e.g, Bascompte et al. 2003).