37 research outputs found
Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model
Food webs, networks of feeding relationships among organisms, provide
fundamental insights into mechanisms that determine ecosystem stability and
persistence. Despite long-standing interest in the compartmental structure of
food webs, past network analyses of food webs have been constrained by a
standard definition of compartments, or modules, that requires many links
within compartments and few links between them. Empirical analyses have been
further limited by low-resolution data for primary producers. In this paper, we
present a Bayesian computational method for identifying group structure in food
webs using a flexible definition of a group that can describe both functional
roles and standard compartments. The Serengeti ecosystem provides an
opportunity to examine structure in a newly compiled food web that includes
species-level resolution among plants, allowing us to address whether groups in
the food web correspond to tightly-connected compartments or functional groups,
and whether network structure reflects spatial or trophic organization, or a
combination of the two. We have compiled the major mammalian and plant
components of the Serengeti food web from published literature, and we infer
its group structure using our method. We find that network structure
corresponds to spatially distinct plant groups 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 patterns, in contrast to the standard compartments
typically identified in ecological networks. From data consisting only of nodes
and links, the group structure that emerges supports recent ideas on spatial
coupling and energy channels in ecosystems that have been proposed as important
for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting
Triad-Based Role Discovery for Large Social Systems
The social role of a participant in a social system conceptualizes the circumstances under which she chooses to interact with others, making their discovery and analysis important for theoretical and practical purposes. In this paper, we propose a methodology to detect such roles by utilizing the conditional triad censuses of ego-networks. These censuses are a promising tool for social role extraction because they capture the degree to which basic social forces push upon a user to interact with others in a system. Clusters of triad censuses, inferred from network samples that preserve local structural properties, define the social roles. The approach is demonstrated on two large online interaction networks