@article{TEXTUAL,
      recid = {10228},
      author = {Baskerville, Edward B. and Dobson, Andy P. and Bedford,  Trevor and Allesina, Stefano and Anderson, T. Michael and  Pascual, Mercedes},
      title = {Spatial Guilds in the Serengeti Food Web Revealed by a  Bayesian Group Model},
      journal = {PLOS Computational Biology},
      address = {2011-12-29},
      number = {TEXTUAL},
      abstract = {<p>Food webs, networks of feeding relationships in an  ecosystem, provide fundamental insights into mechanisms  that determine ecosystem stability and persistence. A  standard approach in food-web analysis, and network  analysis in general, has been to identify compartments, or  modules, defined by many links within compartments and few  links between them. This approach can identify large  habitat boundaries in the network but may fail to identify  other important structures. Empirical analyses of food webs  have been further limited by low-resolution data for  primary producers. In this paper, we present a Bayesian  computational method for identifying group structure using  a flexible definition that can describe both functional  trophic roles and standard compartments. We apply this  method to a newly compiled plant-mammal food web from the  Serengeti ecosystem that includes high taxonomic resolution  at the plant level, allowing a simultaneous examination of  the signature of both habitat and trophic roles in network  structure. We find that groups at the plant level reflect  habitat structure, coupled at higher trophic levels by  groups of herbivores, which are in turn coupled by  carnivore groups. Thus the group structure of the Serengeti  web represents a mixture of trophic guild structure and  spatial pattern, in contrast to the standard compartments  typically identified. The network topology supports recent  ideas on spatial coupling and energy channels in ecosystems  that have been proposed as important for persistence.  Furthermore, our Bayesian approach provides a powerful,  flexible framework for the study of network structure, and  we believe it will prove instrumental in a variety of  biological contexts.</p>},
      url = {http://knowledge.uchicago.edu/record/10228},
}