Abstract:

The interactions between plants and pollinators represent complex pollination networks. Recent improvements in social network analysis provide suitable tools for plant-pollinator interactions within an ecological context. Studies devoted to mutualism in the pollination network at community level have shed important insights into the structure and dynamic of these interactions and also floral evolution. The nested structure of pollination networks suggests that pollination service is redundant, and that relatively generalized species dominate these networks. Although these networks have high species composition turnover, they remain stable in terms of structure and species position, suggesting high interference resistant of pollination networks among seasons or years. Relatively little is known about the mechanisms behind these patterns. It has been suggested that network structure is largely controlled by morphology match between flowers, pollinator traits, and phylogenetic relationships. Meanwhile, community history and biodiversity have been used to link structure and species position of network. Ecologists and evolutionary biologists have become increasingly interested in these networks and recent studies of large-scale dynamics to facilitate the detection of mechanisms between different spatial and biodiverse scales in natural communities. However, there are many challenges of testing these networks. For example, previous visit-centered approaches provide insufficient information about pollen transfer among species, which is essential for plant reproductive success. Also, sample efforts have not been standardized and few studies have focused on zones of high biodiversity. Although debates will continue on the mechanisms behind these patterns, we suggest that factors relating to reproductive success should be considered in future studies, such as the impacts of pollen composition on pollinator condition or pollination efficiency.

Key words: pollination network, nested, social network analysis, stability, network dynamics, generalization and specialization