Browsing by Subject "community structure"

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Exploring Biological Network Structure with Clustered Random Networks
(2009-12) Bansal, Shweta; Khandelwal, Shashank; Meyers, Lauren Ancel; Meyers, Lauren Ancel
Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.
Interspecific Dominance Via Vocal Interactions Mediates Altitudinal Zonation In Neotropical Singing Mice
(2013-11) Pasch, Bret; Bolker, Benjamin M.; Phelps, Steven M.; Pasch, Bret; Phelps, Steven M.
Interspecific aggression between ecologically similar species may influence geographic limits by mediating competitive exclusion at the range edge. Advertisement signals that mediate competitive interactions within species may also provide social information that contributes to behavioral dominance and spatial segregation among species. We studied the mechanisms underlying altitudinal range limits in Neotropical singing mice (Scotinomys), a genus of muroid rodent in which males vocalize to repel rivals and attract mates. We first delineated replacement zones and described temperature regimes on three mountains in Costa Rica and Panama where Chiriquii singing mice (S. xerampelinus) abruptly replace Alston''s singing mice (S. teguina). Next, we conducted interspecific behavioral trials and reciprocal removal experiments to examine if interspecific aggression mediated species replacement. Finally, we performed reciprocal playback experiments to investigate whether response to song matched competitive interactions. Behavioral trials and removal experiments suggest that S. xerampelinus is behaviorally dominant and excludes S. teguina from higher, cooler altitudes. Playback experiments indicate that subordinate S. teguina is silenced and repelled by heterospecific song, whereas S. xerampelinus responded to heterospecifics with approach and song rates comparable to responses to conspecifics. Thus, interspecific communication reflects underlying dominance and suggests that acoustic signaling contributes to altitudinal zonation of ecologically similar congeners. Our findings implicate the use of social information in structuring spatial distributions of animal communities across landscapes and provide insight into how large-scale patterns are generated by individual interactions.