Specialization, Constraints, and Conflicting Interests in Mutualistic Networks

• DOI: 10.1016/j.cub.2006.12.039
• Publication Date: 2007-02-03T07:46:33Z
• Authors: Nico Blüthgen; Florian Menzel; Thomas Hovestadt; Brigitte Fiala; Nils Blüthgen
• Keywords: 0106 biological sciences; EVO_ECOL; Agricultural and Biological Sciences(all); Biochemistry, Genetics and Molecular Biology(all); Adaptation, Biological; Information Theory; bats; bat; Biodiversity; Models, Theoretical; Plants; 15. Life on land; Biological Evolution; 01 natural sciences; Chiroptera; Mammalia; Animals; Animalia; Symbiosis; Chordata; Ecosystem

(Uploaded by Plazi for the Bat Literature Project) The topology of ecological interaction webs holds important information for theories of coevolution, biodiversity, and ecosystem stability [1–6]. However, most previous network analyses solely counted the number of links and ignored variation in link strength. Because of this crude resolution, results vary with scale and sampling intensity, thus hampering a comparison of network patterns at different levels [7–9]. We applied a recently developed [10] quantitative and scale-independent analysis based on information theory to 51 mutualistic plant-animal networks, with interaction frequency as measure of link strength. Most networks were highly structured, deviating significantly from random associations. The degree of specialization was independent of network size. Pollination webs were significantly more specialized than seeddispersal webs, and obligate symbiotic ant-plant mutualisms were more specialized than nectar-mediated facultative ones. Across networks, the average specialization of animal and plants was correlated, but is constrained by the ratio of plant to animal species involved. In pollination webs, rarely visited plants were on average more specialized than frequently attended ones, whereas specialization of pollinators was positively correlated with their interaction frequency. We conclude that quantitative specialization in ecological communities mirrors evolutionary trade-offs and constraints of web architecture. This approach can be easily expanded to other types of biological interactions.