bmotif: A package for motif analyses of bipartite networks

Abstract

1. Bipartite networks are widely used to represent a diverse range of species interactions, such as pollination, herbivory, parasitism and seed dispersal. The structure of these networks is usually characterised by calculating one or more indices that capture different aspects of network architecture. While these indices capture useful properties of networks, they are relatively insensitive to changes in network structure. Consequently, variation in ecologically-important interactions can be missed. Network motifs are a way to characterise network structure that is substantially more sensitive to changes in pairwise interactions and is gaining in popularity. However, there is no software available in R, the most popular programming language among ecologists, for conducting motif analyses in bipartite networks. Similarly, no mathematical formalisation of bipartite motifs has been developed.2. Here we introduce bmotif: a package for motif analyses of bipartite networks. Our code is primarily an r package, but we also provide matlab and Python code of the core functionality. The software is based on a mathematical framework where, for the first time, we derive formal expressions for motif frequencies and the frequencies with which species occur in different positions within motifs. This framework means that analyses with bmotif are fast, making motif methods compatible with the permutational approaches often used in network studies, such as null model analyses.3. We describe the package and demonstrate how it can be used to conduct ecological analyses, using two examples of plant-pollinator networks. We first use motifs to examine the assembly and disassembly of an Arctic plant-pollinator community and then use them to compare the roles of native and introduced plant species in an unrestored site in Mauritius.4. bmotif will enable motif analyses of a wide range of bipartite ecological networks, allowing future research to characterise these complex networks without discarding important meso-scale structural detail. K E Y W O R D Sbipartite networks, food web, matlab, motifs, pollination, Python, R, seed dispersal This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Influence of taxonomic resolution on mutualistic network properties

“…The R package bipartite (Dormann, Gruber, & Fründ, ) was used to calculate the normalized degree, robustness, modularity, and connectance of the network. Motifs were compared using the frequency of the 17 different motifs that involve up to five different taxa, which we calculated using the mcount function of the bmotif package (Simmons et al, ). The frequency of each motif was calculated as the number of times a given motif occurs in the network, divided by the number of motifs of the same size (involving the same number of taxa) that occurs in the network.…”

The disruption of a keystone interaction erodes pollination and seed dispersal networks

“…Each of these positions represents a different ecological pattern with direct and indirect interactions (Stoufferet al 2012;Baker et al 2015;Cirtwill & Stouffer 2015;Simmons et al 2019). Therefore, the frequency of positions that a particular species occupies defines its ecological importance in the community (Simmons et al 2019). Even if different species occupy the same position, the motif still conserves its ecological function.…”

“…The advantage of motifs is that they are significantly more sensitive to changes in network structure than the network indices commonly used (i.e. degree distribution, nestedness) (Simmons et al 2019). In addition, the meso-scale level motif analysis incorporates indirect interactions undetected by macro-scale network indices (i.e.…”

Network perspective on the efficiency of peace accords implementation

“…To perform this analysis, we compute efficiently the frequency of all 44 possible two-to six-node sub-graphs shown in Figure 3 using recently released software [37]. Within these 44 sub-graphs, individual nodes can occupy 148 unique positions, which represent different situations [31] and require appropriate counting.…”