Seed dispersal

Description

This data set gives the spatial distribution of seeds (quadrats counts) of seven species in the understorey of tropical rainforest.

Usage

data(julliot)

Format

julliot is a list containing the 3 following objects :

tab

is a data frame with 160 rows (quadrats) and 7 variables (species).

xy

is a data frame with the coordinates of the 160 quadrats (positioned by their centers).

area

is a data frame with 3 variables returning the boundary lines of each quadrat. The first variable is a factor. The levels of this one are the row.names of tab. The second and third variables return the coordinates (x,y) of the points of the boundary line.

Species names of julliot$tab are Pouteria torta, Minquartia guianensis, Quiina obovata, Chrysophyllum lucentifolium, Parahancornia fasciculata, Virola michelii, Pourouma spp.

References

Julliot, C. (1992) Utilisation des ressources alimentaires par le singe hurleur roux, Alouatta seniculus (Atelidae, Primates), en Guyane : impact de la diss?mination des graines sur la r?g?n?ration foresti?re. Th?se de troisi?me cycle, Universit? de Tours.

Julliot, C. (1997) Impact of seed dispersal by red howler monkeys Alouatta seniculus on the seedling population in the understorey of tropical rain forest. Journal of Ecology, 85, 431–440.

Examples

data(julliot)

## Not run: 
if(adegraphicsLoaded()) {
  if(requireNamespace("sp"", quiet = TRUE)) {
    obj1 <- sp::SpatialPolygonsDataFrame(Sr = julliot$Spatial, data = log(julliot$tab + 1))
    g1 <- s.Spatial(obj1)
    
    g2 <- s.value(julliot$xy, scalewt(log(julliot$tab + 1)), Sp = julliot$Spatial, 
      pSp.col = "white", pgrid.draw = F)
    
  }
} else {
  if(requireNamespace("splancs", quiet = TRUE)) {
    par(mfrow = c(3, 3))
    for(k in 1:7)
      area.plot(julliot$area, val = log(julliot$tab[, k] + 1),
        sub = names(julliot$tab)[k], csub = 2.5)
    par(mfrow = c(1, 1))
    
    par(mfrow = c(3, 3))
    for(k in 1:7) {
      area.plot(julliot$area)
      s.value(julliot$xy, scalewt(log(julliot$tab[, k] + 1)),
        sub = names(julliot$tab)[k],csub = 2.5, add.p = TRUE)
    }
    par(mfrow = c(1, 1))
  }
}
## End(Not run)


if(adegraphicsLoaded()) {
  if(requireNamespace("sp", quiet = TRUE)) {
    g3 <- s.image(julliot$xy, log(julliot$tab + 1), span = 0.25)
  }
  g4 <- s.value(julliot$xy, log(julliot$tab + 1))
  
} else {
  if(requireNamespace("splancs", quiet = TRUE)) {
    par(mfrow = c(3, 3))
    for(k in 1:7)
      s.image(julliot$xy, log(julliot$tab[, k] + 1), kgrid = 3, span = 0.25,
        sub = names(julliot$tab)[k], csub = 2.5)
    par(mfrow = c(1, 1))
    
    par(mfrow = c(3, 3))
    for(k in 1:7)
      s.value(julliot$xy, log(julliot$tab[, k] + 1),
        sub = names(julliot$tab)[k], csub = 2.5)
    par(mfrow = c(1, 1))    
  }
}
        
## Not run: 
if(requireNamespace("spdep", quiet = TRUE)) {
  neig0 <- nb2neig(dnearneigh(as.matrix(julliot$xy), 1, 1.8))
  if(adegraphicsLoaded()) {
    g5 <- s.label(julliot$xy, nb = dnearneigh(as.matrix(julliot$xy), 1, 1.8))
  
  } else {
    par(mfrow = c(1, 1))
    s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE,
     addax = FALSE, grid = FALSE)
  }
  gearymoran(ade4:::neig.util.LtoG(neig0), log(julliot$tab + 1))
  orthogram(log(julliot$tab[, 3] + 1), ortho = scores.neig(neig0),
   nrepet = 9999)
}
## End(Not run)