DOI: 10.5061/dryad.f1vhhmgvt
title: Network analyses reveal the role of large snakes in connecting feeding guilds in a species-rich Amazonian snake community

description: In ecological communities, interactions between consumers and resources
 lead to the emergence of ecological networks and a fundamental problem to
 solve is to understand which factors shape network structure. Empirical
 and theoretical studies on ecological networks suggest predator body size
 is a key factor structuring patterns of interaction. Because larger
 predators consume a wider resource range, including the prey consumed by
 smaller predators, we hypothesized that variation in body size favors the
 rise of nestedness. In contrast, if resource consumption requires specific
 adaptations, predators are expected to consume distinct sets of resources,
 thus favoring modularity. We investigate these predictions by
 characterising the trophic network of a species-rich Amazonian snake
 community (62 species). Our results revealed an intricate network pattern
 resulting from larger species feeding on higher diversity of prey,
 promoting nestedness, and specific lifestyles feeding on distinct
 resources, promoting modularity. Species removal simulations indicated
 that the nested structure is favored mainly by the presence of five
 species of the family Boidae, which because of their body size and
 generalist lifestyles connect modules in the network. Our study highlights
 the particular ways traits affect the structure of interactions among
 consumers and resources at the community level.
description: <i>The network structure of
 interactions between snakes and their food resources</i>
 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 <b>A</b>
 in which if a snake <i>i</i> feeds on a given resource
 <i>j</i> and zero otherwise. The matrix <b>A</b>
 defines a bipartite network in which one set of nodes is represented by
 snake species, the other set of nodes by resource types and the links
 describe interactions between snake species and food resource types. Our
 food resources are not described at species level but at coarse categories
 such as small mammals, medium mammals and big mammals (see details in
 Supplementary material - dataset). 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)<i>. </i>
 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 (<i>C</i>), 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 (<i>M</i>), 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 (<i>N</i>), 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 <i>Q<sub>B</sub></i> 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 the
 <i>Q<sub>B</sub></i> 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). In order to highlight the unique
 inferences provided by the network approach, we compared the results of
 the network analysis with the results of a multivariate analysis.
 Multivariate analysis methods are widely used in ecology due to their
 ability to analyse complex systems registered in an interaction matrix
 (Prado et al. 2002). Among the several types of multivariate analyses, we
 chose correspondence analysis (CA) because of its ability to reveal
 reciprocal relationships between two sets of equal interest (Greenacre
 1984; Lewinsohn and Prado 2008), in our case, snakes and their food
 resources.   <i>The role of snake species in
 network structure</i> If the network of interactions analysed
 follows the organisation pattern structured by body mass (i.e. presenting
 higher nestedness ​​than expected by the null model 2), we hypothesized
 snake average body mass to be positively correlated with the number of
 resources consumed by the snake species. To explore this prediction, we
 investigate the association between average body mass and the role of
 species in the network structure. We recorded the estimates of the average
 body mass of each snake species in our network (data available in Feldman
 et al. 2016). Average body mass was log-transformed prior to analysis
 (Supplementary material Appendix 2 Table A1). In
 order to understand the individual contribution of each species of snake
 to nestedness, we used a jackknife resampling approach in which we removed
 a snake species and recomputing the degree of nestedness. We repeated the
 procedure for all snake species in the network and then we computed a
 change in nestedness: <i>ΔN<sub>i</sub> = N -
 N<sub>i</sub>,</i> in which <i>N</i> is the
 degree of nestedness of the complete network and
 <i>N<sub>i</sub> </i>is the degree of nestedness
 after the removal of a snake species <i>i</i>. If body size is
 shaping the contribution to nestedness, we should expect that the
 <i>ΔN<sub>i</sub></i> will assume increasingly
 positive values as larger snakes are removed from the network, indicating
 that nestedness is higher in the presence of these larger snake
 species.   <i>The relationship between
 lifestyles and network structure</i> Because dietary specialisation in
 snakes can be related to habitat occupation (see Martins et al. 2002,
 Alencar et al. 2017), we expect snake lifestyles to affect the degree of
 dietary specialisation (e.g., an aquatic snake would rely upon aquatic
 prey). If this is true, the distribution of lifestyles in the different
 modules will not be random. We evaluated this prediction using two
 analyses. First, we analysed the frequency of snake lifestyles in
 different modules. We estimated the probability of the observed number of
 species of a given lifestyle in a given module be reproduced by randomly
 assigning species across modules, but preserving the number of snake
 species in each lifestyle and the number of snake species in each module
 (n = 1000 randomisations). Then, we analysed the dissimilarity on
 lifestyles between modules. To do so, we used the Bray-Curtis index,
 available in the vegan package in R (Oksanen et al. 2018) (see detailed
 description in Supplementary material Appendix 1). Dissimilarity between a
 pair of modules range from 0 (modules are identical in the composition of
 lifestyles) to 1 (no lifestyle occurs in both modules).   <i>Sensitivity analyses
 </i><i>focuses on the level of resource
 resolution</i> Sampling effects may affect the
 description of network patterns. Therefore, we performed a sensitivity
 analysis to explore how robust is the description of network patterns to
 changes in our dataset. We add information to the use of resources by
 snakes by using data from other Amazonian regions, based on evidence that
 there is no significant intraspecific variation on the snake's diet
 across different localities in Amazonia (Martins and Oliveira, 1998;
 Supplementary material Appendix 1 and Appendix 2 Table A1).
 Snake diet often include food
 resources that are mainly consumed and resources that are only eventually
 consumed. We performed a sensitivity analysis to check if the patterns
 reported in our study are robust enough when considering the presence or
 absence of secondary resources in the snake diet. We described two
 matrices of interactions: (1) a matrix in which only main resources were
 considered; (2) and a matrix in which both main and secondary resources
 were considered. We defined if a resource is main or secondary according
 to information about snake diet preferences available in Martins and
 Oliveira (1998). Then, we calculate the nestedness and modularity values
 in the presence and absence of secondary resources. The nestedness values
 of the two networks were compared with a null model generated with 5,000
 random removals of food resources from each of the analysed networks.
 Finally, we calculated whether there was a significant difference between
 the nestedness of the network in the presence and absence of secondary
 resources. Because taxonomic resolution
 might influence the detection of patterns in the network (Rezende et al.
 2009), we performed another sensitivity analysis to check if the type of
 resource categorization could affect the network patterns. Thus, we
 described two other matrices of interaction with different degrees in the
 resources taxonomic resolution: less specific network (Supplementary
 material Appendix 1 Figure A2 and Appendix 3 Table A1) and more specific
 network (Supplementary material Appendix 1 Figure A3 and Appendix 4 Table
 A1).
description:  Additional information that may be needed is available in the
 supplementary material captions.