Past and potential future effects of habitat fragmentation on structure and stability of plant–pollinator and host–parasitoid networks

Bees are the most important pollinator taxon; therefore, understanding the scale at which they forage has important ecological implications and conservation applications. The foraging ranges for most bee species are unknown. Foraging distance information is critical for understanding the scale at which bee populations respond to the landscape, assessing the role of bee pollinators in affecting plant population structure, planning conservation strategies for plants, and designing bee habitat refugia that maintain pollination function for wild and crop plants. We used data from 96 records of 62 bee species to determine whether body size predicts foraging distance. We regressed maximum and typical foraging distances on body size and found highly significant and explanatory nonlinear relationships. We used a second data set to: (1) compare observed reports of foraging distance to the distances predicted by our regression equations and (2) assess the biases inherent to the different techniques that have been used to assess foraging distance. The equations we present can be used to predict foraging distances for many bee species, based on a simple measurement of body size.

The main theories of biodiversity either neglect species interactions or assume that species interact randomly with each other. However, recent empirical work has revealed that ecological networks are highly structured, and the lack of a theory that takes into account the structure of interactions precludes further assessment of the implications of such network patterns for biodiversity. Here we use a combination of analytical and empirical approaches to quantify the influence of network architecture on the number of coexisting species. As a case study we consider mutualistic networks between plants and their animal pollinators or seed dispersers. These networks have been found to be highly nested, with the more specialist species interacting only with proper subsets of the species that interact with the more generalist. We show that nestedness reduces effective interspecific competition and enhances the number of coexisting species. Furthermore, we show that a nested network will naturally emerge if new species are more likely to enter the community where they have minimal competitive load. Nested networks seem to occur in many biological and social contexts, suggesting that our results are relevant in a wide range of fields.