nam {SyNet} | R Documentation |
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).
nam(msym)
msym |
Object of classes 'dotinference', 'gridinference' or 'hullinference'. |
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.
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).
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).
Daniel A. Dos Santos
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).
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.
data(epiphragmophora) nam(dotinfer(epiphragmophora))