Spatial guilds in the Serengeti food web revealed by a Bayesian group model.
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.
Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America. ebaskerv@umich.edu
Edward B
EB
Baskerville
Andy P
AP
Dobson
Trevor
T
Bedford
Stefano
S
Allesina
T Michael
TM
Anderson
Mercedes
M
Pascual
D000818:Animals; D001499:Bayes Theorem; D017753:Ecosystem; D020387:Food Chain; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D010944:Plants / Q000378:metabolism; D011336:Probability; D012984:Software; D013636:Tanzania