Niche and neutral processes leave distinct structural imprints on indirect interactions in mutualistic networks

Abstract

Indirect interactions are central to ecological and evolutionary dynamics in pollination communities, yet we have little understanding about the processes determining patterns of indirect interactions, such as those between pollinators through shared flowering plants. Instead, research has concentrated on the processes responsible for direct interactions and whole‐network structures. This is partly due to a lack of appropriate tools for characterising indirect interaction structures, because traditional network metrics discard much of this information. The recent development of tools for counting motifs (subnetworks depicting interactions between a small number of species) in bipartite networks enables detailed analysis of indirect interaction patterns. Here we generate plant–hummingbird pollination networks based on three major assembly processes—neutral effects (species interacting in proportion to abundance), morphological matching and phenological overlap—and evaluate the motifs associated with each one. We find that different processes produce networks with significantly different patterns of indirect interactions. Neutral effects tend to produce densely connected motifs, with short indirect interaction chains, and motifs where many specialists interact indirectly through a single generalist. Conversely, niche‐based processes (morphology and phenology) produced motifs with a core of interacting generalists, supported by peripheral specialists. These results have important implications for understanding the processes determining indirect interaction structures. A free Plain Language Summary can be found within the Supporting Information of this article.

The non-random assembly of functional motifs in plant-pollinator networks

“…Following previous work (Simmons, Cirtwill, et al, 2019;Simmons et al, 2020), we broke down the plantpollinator networks into their constituent motifs that can capture both direct and indirect interactions. Prior to analyses, we turned the quantitative networks into qualitative (or binary) ones, where interactions are present or absent.…”

“…Prior to analyses, we turned the quantitative networks into qualitative (or binary) ones, where interactions are present or absent. This approach was preferred since quantitative motif analysis have been developed just recently and more work is needed to fully understand and interpret the information that they can convey (Simmons et al, 2020). All analyses were run considering singletons (species with only one interaction detected) but we conducted a second exploration without singletons (64.98% of interactions) to evaluate the effect of rare species on the observed patterns.…”

“…Note that the aim is not comparing different networks, which may differ in sampling effort, but correctly describing the structure of each individual network. Like Simmons et al (2020), we classified the different motifs by their average path length (mean number of links between all pairs of nodes) into four different groups: (i) complete or strong (x = 1.38), (ii) fan or medium-strong (x = 1.48), (iii) assymmetric complete or medium-weak (x = 1.60) and (iv) core-peripheral or weak (x = 1.85). In general terms, plant and floral visitor groups on more densely connected motifs (e.g., complete or fan) will experience lower number of indirect interactions in contrast to groups on less densely connected ones (e.g., medium-weak or weak).…”

“…Nonetheless, the emerging framework of network motifs (small sub-networks observed within a given network which are often referred as its building blocks; Milo et al, 2002) allows to consider both direct and indirect interactions (Simmons, Cirtwill, et al, 2019). The analysis of network motifs in plant-pollinator networks have revealed that the different ecological processes that govern species interactions (e.g., species abundances versus trait-matching) can lead to different patterns of indirect interactions (Simmons et al, 2020). Yet, the global patterns of indirect interactions in real plant-pollinator networks are still unknown (e.g., over-and under-represented motifs).…”

Network robustness and structure depend on the phenological characteristics of plants and pollinators

“…Matching of phenologies is essential for determining individual species interactions and thus networks as a whole. For example, flowering timing depends on pollination syndrome which effectively determines the species likely to pollinate a given plant (Cortés-Flores et al, 2017), and webs formed via phenological processes have a distinct structure from webs created by neutral processes (Simmons et al, 2020). Species phenology has been shown to affect network robustness (the ability to withstand perturbations) (Encinas-Viso et al, 2012, Ramos-Jiliberto et al, 2018, CaraDonna et al, 2014CaraDonna et al, 2017;Vizentin-Bugoni et al, 2020).…”