scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is a network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Ecological Networks ◽  
Random Networks ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is an ecological network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Random Networks ◽  
Ecological Network ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals When is an ecological network complex? Connectance drives degree distribution and emerging network properties

2013 ◽  
Author(s):  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Network Structure ◽  
Degree Distribution ◽  
Null Model ◽  
Random Networks ◽  
Ecological Network ◽  
Ecosystem Properties ◽  
Network Properties ◽  
Statistical Argument ◽  
Simple Network ◽  
Network Complex

Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.

scholarly journals Community structure and ecological specialization in plant–ant interactions

2015 ◽  
Vol 31 (4) ◽  
pp. 325-334 ◽  
Author(s):  
Paola A. Barriga ◽  
Carsten F. Dormann ◽  
Edward E. Gbur ◽  
Cynthia L Sagers
Keyword(s):  
Network Structure ◽  
Species Interactions ◽  
Null Model ◽  
Interaction Networks ◽  
Host Specialization ◽  
Random Networks ◽  
Ecological Specialization ◽  
Study Sites ◽  
Plant Extinction

Abstract:Environmental effects on species interactions can be studied by comparative analyses of network structure. For example, comparison of interaction networks among study sites can provide clues to geographic variation of host breadth. Obligate plant–ant interactions are ideal systems to explore these phenomena because they are long term and can be accurately sampled in the field. We tested two hypotheses: (1) network structure and host specialization do not vary among communities, and (2) the effects of plant extinction do not vary among communities. We sampled 10 or more plants for each of the 30 ant–plant species found in three Neotropical locations. We found that network specialization,H2′, was significantly higher than expected in random networks. The ant or plant specialization index,d′, distribution did not vary among localities, neither varied in link or asymmetry distribution. Plant extinction simulations showed that these interactions are vulnerable to plant loss, and the null model was more robust than the observed networks. This study provides a foundation on which plant and ant phylogenies can be added to explore compartment evolution.

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