Biotropica · 2014 · 173 citations · 38 references
Life SciencesEcologyZoologyEcological NetworksOrnithologyPlant EcologyBird EcologyCommunity StructureInteraction NetworksMutualistic NetworksSpecies Interaction NetworksSpecies InteractionsBipartite NetworksEcological Community NetworksEcological Network StructureInteraction Network StructureModularity In Ecological NetworksNectar AvailabilityNectar Resource AvailabilitySpecies Interaction Frequency
Abstract Complex networks of species interactions might be determined by species traits but also by simple chance meetings governed by species abundances. Although the idea that species traits structure mutualistic networks is appealing, most studies have found abundance to be a major structuring mechanism underlying interaction frequencies. With a well‐resolved plant–hummingbird interaction network from the Neotropical savanna in Brazil, we asked whether species morphology, phenology, nectar availability and habitat occupancy and/or abundance best predicted the frequency of interactions. For this, we constructed interaction probability matrices and compared them to the observed plant‐hummingbird matrix through a likelihood approach. Furthermore, a recently proposed modularity algorithm for weighted bipartite networks was employed to evaluate whether these factors also scale‐up to the formation of modules in the network. Interaction frequencies were best predicted by species morphology, phenology and habitat occupancy, while species abundances and nectar availability performed poorly. The plant–hummingbird network was modular, and modules were associated to morphological specialization and habitat occupancy. Our findings highlight the importance of traits as determinants of interaction frequencies and network structure, corroborating the results of a previous study on a plant–hummingbird network from the Brazilian Atlantic Forest. Thus, we propose that traits matter more in tropical plant–hummingbird networks than in less specialized systems. To test the generality of this hypothesis, future research could employ geographic or taxonomic cross‐system comparisons contrasting networks with known differences in level of specialization.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
The modularity of pollination networks
Jens M. Olesen, Jordi Bascompte, Yoko Luise Dupont et al. · Proceedings of the National Academy of Sciences · 2007 · 2.6K citations · Full text
Life Sciences, Biology, Ecology +15
Sara R. Morris · The Wilson Bulletin · 2001 · 1.8K citations