DOI: 10.5061/dryad.00000005p
title: Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network

description: 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.
description: <strong>Flowering
 phenology and pollinators activity</strong>
 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. <strong>Floral trait
 similarity</strong> 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. <strong>Flowering
 overlap</strong> 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.
 <strong>Plant–pollinator networks structure, nestedness, and
 modularity contributions</strong>
 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. <strong>Pollinator
 sharing</strong> 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.
description: 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.