The non‐random assembly of network motifs in plant–pollinator networks

Abstract

Ecological processes leave distinct structural imprints on the species interactions that shape the topology of animal–plant mutualistic networks. Detecting how direct and indirect interactions between animals and plants are organised is not trivial since they go beyond pairwise interactions, but may get blurred when considering global network descriptors. Recent work has shown that the meso‐scale, the intermediate level of network complexity between the species and the global network, can capture this important information. The meso‐scale describes network subgraphs representing patterns of direct and indirect interactions between a small number of species, and when these network subgraphs differ statistically from a benchmark, they are often referred to as ‘network motifs’. Although motifs can capture relevant ecological information of species interactions, they remain overlooked in natural plant–pollinator networks. By exploring 60 empirical plant–pollinator networks from 18 different studies with wide geographical coverage, we show that some network subgraphs are consistently under‐ or over‐represented, suggesting the presence of worldwide network motifs in plant–pollinator networks. In addition, we found a higher proportion of densely connected network subgraphs that, based on previous findings, could reflect that species relative abundances are the main driver shaping the structure of the meso‐scale on plant–pollinator communities. Moreover, we found that distinct subgraph positions describing species ecological roles (e.g. generalisation and number of indirect interactions) are occupied by different groups of animal and plant species representing their main life‐history strategies (i.e. functional groups). For instance, we found that the functional group of ‘bees’ was over‐represented in subgraph positions with a lower number of indirect interactions in contrast to the rest of floral visitors groups. Finally, we show that the observed functional group combinations within a subgraph cannot be retrieved from their expected probabilities (i.e. joint probability distributions), indicating that plant and floral visitor associations within subgraphs are not random either. Our results highlight the presence of common network motifs in plant–pollinator communities that are formed by a non‐random association of plants and floral visitors functional groups.

Multilayer diffusion networks as a tool to assess the structure and functioning of fine grain sub-specific plant-pollinator networks

“…We considered subgraph-level processes because it has been recently suggested that they may be particularly important for analyzing the functional consequences of community structure: at that scale, the local patterns of realized direct interactions can reveal the different mechanisms through which individual- and species-based nodes can indirectly influence each other (Simmons et al ., 2018) and it has been shown that some sub-graphs are over-represented in plant-pollinator networks (i.e. form distinctive motifs) (Lanuza et al ., 2023). For the network-level structure, we calculated (i) the modular partitions that better describe network information flows (subsection “Modularity”).…”

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

“…(2019) highlighted the limits of macro‐scale metrics for capturing structural differences between bipartite networks and showed the usefulness of motifs (a small subset of species interactions exhibiting varying topologies), which capture the mesoscale structure (Milo et al., 2002). Motifs have been informative in various contexts, like the assessment of network stability, the resistance to biological invasions (Losapio et al., 2021; Milo et al., 2002; Stouffer & Bascompte, 2010; Vitali et al., 2022), or the characterization of the singular positions of functional groups (Lanuza et al., 2023). Another way to characterize the structure of interaction networks is through the spectral densities of the Laplacian graph representing each interaction network.…”

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

“…Simmons, Cirtwill, et al, (2019) highlighted the limits of macro-scale metrics for capturing structural differences between bipartite networks and showed the usefulness of motifs (a small subset of species interactions exhibiting varying topologies), which capture the mesoscale structure (Milo et al, 2002). Motifs have been informative in various contexts, like the assessment of network stability, the resistance to biological invasions (Losapio et al, 2021; Milo et al, 2002; Stouffer & Bascompte, 2010; Vitali et al, 2022), or the characterization of the singular positions of functional groups (Lanuza et al, 2023). Another way to characterize the structure of interaction networks is through the spectral densities of the Laplacian graph representing each interaction network.…”