# The role of plant polyploidy in the structure of plant-pollinator communities

## TL;DR

Polyploidy in plants affects how plant-pollinator communities are structured, with more polyploids linked to more nested and less modular networks.

## Contribution

This study reveals how polyploidy influences plant-pollinator community structure through phenotypic and environmental factors.

## Key findings

- Higher polyploid frequency is linked to increased network nestedness and decreased modularity.
- Self-compatibility and flower shape mediate the relationship between polyploidy and network structure.
- Polyploid abundance has minimal impact on network connectance and resilience to extinction.

## Abstract

Polyploidization is a major macromutation, bearing notable genomic and ecological consequences. While the impact of polyploidy on plant abiotic niches is well studied, our understanding of its consequences on biotic interactions, and particularly pollination, is lacking and hardly includes its role in shaping plant-pollinator community structure. Here, we integrate hundreds of plant-pollinator networks, ploidy inferences, reproductive traits, and climatic attributes to ascertain whether a general pattern characterizes the link between polyploid frequency and network structure. We further examine whether environmental factors and plant traits known to be associated with polyploidy mediate this relationship. Our analysis reveals that an increased frequency of polyploid species within networks is positively associated with network nestedness while being negatively associated with modularity. Path analysis reveals that these associations are partially mediated via the frequency of self-compatible plants and by differences in flower shape. Despite these alterations in community structure, the heightened abundance of polyploids appears to have minimal impact on network connectance and resilience to extinction. Our findings indicate that unlike abiotic interactions, the relationships between polyploidy and biotic interactions are less predictable and reflect the combined contributions of phenotypic and environmental factors. However, we acknowledge that incomplete data sets limit a clear understanding of the causal relationship that may exist.