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Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network

Cite this dataset

Parra-Tabla, Víctor; Suárez-Mariño, Alexander; Arceo-Gómez, Gerardo; Albor, Cristopher (2022). Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network [Dataset]. Dryad.


Co-flowering communities are usually characterized by high plant generalization but knowledge of the underlying factors leading to high levels of generalization and pollinator sharing, and how these may contribute to network structure is still limited. Flowering phenology and floral trait similarity are considered among the most important factors determining plant generalization and pollinator sharing. However, these have been evaluated independently even though they can act in concert with each other. Moreover, the importance of flowering phenology and floral similarity, via their effects on plant generalization, in the structure of plant–pollinator networks have been scarcely studied. Here, we aim to evaluate the effect of flowering phenology and floral similarity in mediating the degree of pollinator sharing and plant generalization in two coastal communities and uncover their importance as drivers of plant–pollinator network structure.

We recorded flower production per species, as well as the identity and frequency of floral visitors along the entire flowering season. We estimated the degree of flowering overlap, the degree of floral similarity (using floral traits associated with size and color), and the degree of pollinator sharing among plant species within both communities.

Structural equation models (SEM) showed a positive effect of flowering overlap on pollinator sharing and plant generalization. Pollinator sharing and plant generalization positively affected network nestedness. Furthermore, SEM showed a direct positive effect of flowering overlap on network modularity. The SEM analyses also revealed a significant interaction effect of floral similarity and flowering overlap on pollinator sharing, with consequences for network nestedness in one community.

Our results highlight the importance of integrating multiple axes of differentiation such as flowering phenology and floral similarity into our understanding of the drivers of plant–pollinator network structure.


Flowering phenology and pollinators activity

In each community, 10 censuses on each plot were carried out recording the number of open flowers for each species. To record flower visitors’ identity and activity, two observation rounds (8:30 am and 10:30 am) were conducted within the plots during each visit to a community. Visits were only recorded when contact between the insect and the reproductive structures of the flowers was observed.

Floral trait similarity

We estimated species floral trait similarity in each community using the following traits: flower length, the diameter of the corolla, the opening of the corolla tube and flower color. The first three traits were measured with a caliper (± 0.1 mm) in 1-5 flowers per plant in at least five plants per species. We also measured floral reflectance spectra (300-700 nm) from the dominant corolla color in 1-3 flowers per species with a spectrophotometer (StellarNet INC) and a Tungsten Halogen lamp as an artificial light source.

Flowering overlap

To estimate flowering overlap between plant species pairs, we calculated the niche overlap index of Schoener (SI). We further calculated the Betweenness centrality value (BC hereafter) for each plant species within a co-flowering network where links represent the degree of co-flowering overlap between each plant species pair.

Plantpollinator networks structure, nestedness, and modularity contributions

We constructed plant–pollinator networks for each site and estimated network metrics for each plant community. For each plant–pollinator network, we evaluated the significance of nestedness and modularity using null model analysis. From the plant–pollinator interaction networks, the following species–level estimators were calculated: (a) plant generalization that was estimated using the species interaction strength; (b) nestedness contribution and (c) modularity contribution; for this we estimated the distribution of links within the modules (z values) which refers to the number of whitin module conections.

Pollinator sharing

The degree of pollinator sharing between pairs of plant species was estimated using the symmetric niche overlap index of Pianka. The pollinator sharing for each plant was obtained by averaging over each unique species pair. This index considers the identity of the different pollinator taxa, as well as their relative frequency of visits.

Usage notes

List of recorded species in sand dune and coastal scrubland plant communities in Telchac, Yucatan, Mexico. The code for each species is derived from the first two initials of gender and species epithet. Cs = Coastal scrubland, Sd = sand dune.

List of insect flower visitors recorded in each community in the study area. The number of visits and the percentage per community is shown.

Values by specie of betweenness centrality, pollinator sharing and interaction strength in coastal dune and coastal scrubland plant communities in Telchac, Yucatan, Mexico.


Consejo Nacional de Humanidades, Ciencias y Tecnologías, Award: 248406

Division of Environmental Biology, Award: 1931163