SyNet-package: Inference and Analysis of Sympatry Networks

In SyNet: Inference and Analysis of Sympatry Networks

Description

Historical Biogeography focuses basically on sympatry patterns among species. SyNet extracts hypotheses of sympatry from available distributional evidence and integrates them into weighted and binary sympatry networks. SyNet tests the adequacy of networks to be segregated into groups of species cohesively sympatric (units of co-occurrence). It identifies units of co-occurrence from the more inclusive network where they are embedded. NAM algorithm iteratively removes intermediary species that are linked to those units otherwise disconnected. Here, you can do among other things: i) to analyze the spatial affinity between sets of records and ii) to explore dynamically the cleavogram associated to the analysis of sympatry networks. You can track many new ideas on numerical classification throughout the examples of functions. Remarkably, geographical data associated to any network partitioning can be exported to KML files which can be opened via Google Earth.

Details

Package:	SyNet
Type:	Package
Version:	2.0
Date:	2011-11-11
License:	GPL

Author(s)

Daniel A. Dos Santos <[e-mail]>

References

Dos Santos D.A., Fernandez H.R., Cuezzo M.G., Dominguez E. 2008. Sympatry Inference and Network Analysis in Biogeography. Systematic Biology 57:432-448.

Dos Santos D.A., Cuezzo M.G., Reynaga M.C., Dominguez E. 2011. Sympatry Inference and Network Analysis in Biogeography. Systematic Biology (in press)

Examples

  data(sciobius2x2)
  # Derive a sympatry matrix from distributional table.
  # Entries equal to 1 mean co-extensive sympatry.
  infer <- gridinfer(dntable = sciobius2x2)  
  # Run NAM method on the previously inferred sympatry network                                           
  outnam <- nam(infer) 
  # Extract the sympatry network with elements optimally rearranged
  # according to the NAM sequence of splitting events. 
  sm <- outnam$mt 
  # Plot the original network
  forgraph <- seq(0, by = 2*pi/47, length = 47)
  xcoord <- cos(forgraph)
  ycoord <- sin(forgraph)
  plot(xcoord, ycoord, main = "SCIOBIUS Sympatry Network", 
       xlab = "", ylab = "", pch = 19, col = 2, cex = 1.3, axes = FALSE)
  text (xcoord*1.05, ycoord*1.05, outnam$leaves)
  for (i in 1:46) 
    for (j in (i+1):47)
      if(sm[i,j] > 0) lines (c(xcoord[i], xcoord[j]), c(ycoord[i], ycoord[j]))
  mtext("Subnetwork 0 (Original network)", side = 1, line = 2, col = 4)
  # Next, we define a control window to go from initial network to the last sub-network.
  # Here, you can visualize the intrinsic dynamic associated to the iterative removal
  # of intermediary species. 
  subnet <- 0
  onClick <- function(action) {
    if(action == 1) subnet <<- 0 
    if(action == 2) subnet <<- pmax(0, subnet - 1) 
    if(action == 3) subnet <<- pmin(outnam$nsub - 1, subnet + 1) 
    if(action == 4) subnet <<- outnam$nsub - 1
    notremoved <- outnam$LastNet >= subnet
    plot(xcoord, ycoord, main = "SCIOBIUS Sympatry Network", 
         xlab = "", ylab = "", type = "n", axes = FALSE)
    points(xcoord[notremoved], ycoord[notremoved], pch = 19, col = 2)
    text (xcoord[notremoved]*1.05, ycoord[notremoved]*1.05, outnam$leaves[notremoved])
    for (i in 1:(outnam$nsp - 1)){ 
       if(!notremoved[i]) next
       for (j in (i+1):outnam$nsp) {
          if(!notremoved[j]) next
          if(sm[i,j] > 0) lines (c(xcoord[i], xcoord[j]), c(ycoord[i], ycoord[j]))
       }
    }
    mtext(paste("Subnetwork", subnet), side = 1, line = 2, col = 4)
  }
  ## Not run: 
  tt <- tktoplevel()
  tkwm.title(tt, "Evolution of NAM ...") 
  but1 <- tkbutton(tt, text = "<<", command = function(...) onClick(1), padx = 20)
  but2 <- tkbutton(tt, text = "<", command = function(...) onClick(2), padx = 20)
  but3 <- tkbutton(tt, text = ">", command = function(...) onClick(3), padx = 20)
  but4 <- tkbutton(tt, text = ">>", command = function(...) onClick(4), padx = 20)  
  tkgrid(tklabel(tt, text = "*** Change Sub-Network ***", font = "Times 16", foreground = "blue"),
         columnspan = 4)
  tkgrid(but1, but2, but3, but4)
  #Finally, type the following command to see the cleavogram called 'outnam':
  cleavogram()
  
## End(Not run)

SyNet documentation built on May 2, 2019, 1:10 p.m.