Background and aims: Many recent studies show that plant-pollinator interaction webs exhibit consistent structural features such as long-tailed distributions of the degree of generalization, nestedness of interactions and asymmetric interaction dependencies. Recognition of these shared features has led to a variety of mechanistic attempts at explanation. Here it is hypothesized that beside size thresholds and species abundances, the frequency distribution of sizes (nectar depths and proboscis lengths) will play a key role in determining observed interaction patterns.
Methods: To test the influence of size distributions, a new network parameter is introduced: the degree of size matching between nectar depth and proboscis length. The observed degree of size matching in a Spanish plant-pollinator web was compared with the expected degree based on joint probability distributions, integrating size thresholds and abundance, and taking the sampling method into account.
Key results: Nectar depths and proboscis lengths both exhibited right-skewed frequency distributions across species and individuals. Species-based size matching was equally close for plants, independent of nectar depth, but differed significantly for pollinators of dissimilar proboscis length. The observed patterns were predicted well by a model considering size distributions across species. Observed size matching was closer when relative abundances of species were included, especially for flowers with openly accessible nectar and pollinators with long proboscises, but was predicted somewhat less successfully by the model that included abundances.
Conclusions: The results suggest that in addition to size thresholds and species abundances, size distributions are important for understanding interaction patterns in plant-pollinator webs. It is likely that the understanding will be improved further by characterizing for entire communities how nectar production of flowers and energetic requirements of pollinators covary with size, and how sampling methods influence the observed interaction patterns.