Citizen Science Reveals Broad Plant–Pollinator Interaction Patterns but Diverges from Field-Sampled Network Structure

Citizen science has extended the spatial and temporal scope of biodiversity data, yet questions remain regarding how these data can elucidate ecological interaction networks. Plant–pollinator networks are a comprehensive framework for assessing community structure, specialization, and resilience, but they are sensitive to sampling methodology and network composition. Here, we analyzed ten years of citizen science observations to characterize plant–pollinator network structure across four major insect orders, including Hymenoptera, Lepidoptera, Diptera, and Coleoptera. Key network metrics, including connectance, nestedness, linkage density, interaction evenness, specialization (H2′), and modularity, were calculated to analyze network structure, including potential biases in the representation of specialist taxa (d′). To compare citizen science with standardized field observations, an Anthophila-only subset of the citizen science dataset was compared at the genus level with a traditionally sampled bee–flower network. Citizen science-based networks captured measurable specialization and connectance across four insect orders, exhibited significant spatial structure, and revealed that retained genera had significantly higher observed specialization than genera excluded by rare-taxon filtering, although the effect size was small. Within the Anthophila-only comparison, citizen science networks consistently exhibited higher linkage density and interaction evenness, whereas differences in connectance, nestedness, specialization, and modularity depended on whether complete, rarefied, or shared-genus networks were evaluated. These results indicate that inferred plant–pollinator network structure differed between sampling regimes and was sensitive to interaction count, taxonomic breadth, network dimensions, and spatial and temporal coverage. Citizen science captured broader spatial and taxonomic variation across a larger, temporally expansive dataset, whereas traditional sampling provided a more standardized snapshot of local bee–flower interaction structure.