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Vegetation in Trois-Fontaines

vegtf.Rd

This data set contains abundance values (Braun-Blanquet scale) of 80 plant species for 337 sites. Data have been collected by Sonia Said and Francois Debias.

Usage

data(vegtf)

Format

vegtf is a list with the following components:

veg

a data.frame with the abundance values of 80 species (columns) in 337 sites (rows)

xy

a data.frame with the spatial coordinates of the sites

area

a data.frame (area) which define the boundaries of the study site

sp.names

a vector containing the species latin names

nb

a neighborhood object (class nb defined in package spdep)

Spatial

an object of the class SpatialPolygons of sp, containing the map

Source

Dray, S., Said, S. and Debias, F. (2008) Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation. Journal of vegetation science, 19, 45--56.

Examples

if(requireNamespace("spdep", quietly = TRUE) & requireNamespace("adespatial", quietly = TRUE)) {
  data(vegtf)
  coa1 <- dudi.coa(vegtf$veg, scannf = FALSE)
  ms.coa1 <- adespatial::multispati(coa1, listw = spdep::nb2listw(vegtf$nb), nfposi = 2, 
    nfnega = 0, scannf = FALSE)
  summary(ms.coa1)
  plot(ms.coa1)
  
  if(adegraphicsLoaded()) {
    g1 <- s.value(vegtf$xy, coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE)
    g2 <- s.value(vegtf$xy, ms.coa1$li[, 1], Sp = vegtf$Spatial, pSp.col = "white", plot = FALSE)
    g3 <- s.label(coa1$c1, plot = FALSE)
    g4 <- s.label(ms.coa1$c1, plot = FALSE)
    G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
  } else {
    par(mfrow = c(2, 2))
    s.value(vegtf$xy, coa1$li[, 1], area = vegtf$area, include.origin = FALSE)
    s.value(vegtf$xy, ms.coa1$li[, 1], area = vegtf$area, include.origin = FALSE)
    s.label(coa1$c1)
    s.label(ms.coa1$c1)
  }
}
#> 
#> Multivariate Spatial Analysis
#> Call: adespatial::multispati(dudi = coa1, listw = spdep::nb2listw(vegtf$nb), 
#>     scannf = FALSE, nfposi = 2, nfnega = 0)
#> 
#> Scores from the initial duality diagram:
#>           var       cum      ratio      moran
#> RS1 0.5237982 0.5237982 0.04018794 0.08653081
#> RS2 0.4852328 1.0090310 0.07741699 0.08012944
#> 
#> Multispati eigenvalues decomposition:
#>           eig       var    moran
#> CS1 0.1689640 0.4047914 0.417410
#> CS2 0.1306298 0.3131971 0.417085
#> Error in s.match(dfxy1 = ms.coa1$li, dfxy2 = ms.coa1$ls, xax = 1, yax = 2,     plot = FALSE, storeData = TRUE, pos = -3, psub = list(text = "Scores and lag scores")): non convenient selection for dfxy1 or dfxy2 (can not be converted to dataframe)