nam {SyNet}R Documentation

Sympatry Network Analysis

Description

Performs the analysis of a sympatry network oriented to detect groups of species cohesively sympatric (units of co-occurrence) via iterative removal of nodes with high betweenness score (intermediary species).

Usage

 
  nam(msym)

Arguments

msym Object of classes 'dotinference', 'gridinference' or 'hullinference'.

Details

The input argument msym contains a sympatry matrix S of order n (= number of species). Each entry Sij is 1 if there is a sympatric link between species i and j, while Sij is 0 if allopatry is suggested. The diagonal elements are 1 because sympatry is reflexive. Furthermore, sympatry matrix is symmetric due to reciprocal nature of this relationship.

Sympatry matrices are adjacency matrices, and finding the connection patterns in the associated network is the scope of nam function. In a given sympatry network, nodes represent species and edges sympatric links. nam is an iterative process of node removal to isolate subsets of nodes with within-group sympatry and between-group allopatry (units of co-occurrence). Units of co-occurrence are generally embedded in the global network due to intermediary nodes connecting them.

nam identifies and removes nodes with the highest intermediacy. Intermediacy is evaluated with the betweenness measure (Freeman, 1977). After the node removal, a sub-network is generated and the process is repeated until it arrives to a sub-network with all nodes with zero betweenness. In this way, nam produces a serie of sub-networks, one of them holding the units of co-occurrence.

As units of co-occurrence are being segregated, the overall clustering performance (OCP) increases. This index reflects the change in clustering performance along all nodes of a sub-network with respect to the basal condition (Dos Santos et al., 2008). The sub-network maximizing OCP is selected. Here, the components with 3 or more nodes represent the units of co-occurrence we are interested on.

Value

An object of class nam, which is a list with components:

LastNet Integer vector indicating the last network (or sub-network) where a given node was found. Zero for the basal network and >0 for succesive sub-networks.
Betweenness Highest betweenness value recorded at the respective instance of removal process.
OCPtrajectory Serie of OCP values recorded along the removal process.
Selected Index of sub-network selected.
Categories Data frame that arranges species into four categories of NAM Status. See Note below.


Simultaneously, the function displays a graph showing the evolution of OCP throughout the removal process (y-axis: index value; x-axis: sub-network considered).

Note

Betweenness measure was calculated with Newman's algorithm (Newman, 2001).

NAM classifies species into four categories: Intermediary species, Isolated species, Diad and Unit of Co-occurrence(UC).

Author(s)

Daniel A. Dos Santos

References

Freeman, L. C. 1977. A set of measures of centrality based on betweenness. Sociometry 40:35-41.

Newman, M. E. J. 2001. Scientific collaboration networks. II. Shortest paths, weighted networks and centrality. Phys. Rev. E 64:016132.

Dos Santos, D. A., Fernandez, H. R., Cuezzo, M. G., Dominguez, E. 2008. Sympatry Inference and Network Analysis in Biogeography. Systematic Biology (in press).

See Also

Objects of class 'dotinference' are produced with function dotinfer.
Objects of class 'gridinference' are produced with function gridinfer.
Objects of class 'hullinference' are produced with function hullinfer.
Previous to NAM analysis, is necessary to test the network adequacy to be segregated into units of co-occurrence:partition.
Concept of clustering performance is summarized in Details section of partition documentation.

Examples

 data(epiphragmophora)
 nam(dotinfer(epiphragmophora))

[Package SyNet version 1.0 Index]