ade4: julliot – R documentation

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julliot

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 with the following components:

tab: a data frame with 160 rows (quadrats) and 7 variables (species)
xy: a data frame with the coordinates of the 160 quadrats (positioned by their centers)
area: 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.
Spatial: an object of the class SpatialPolygons of sp, containing the map

Details

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

References

Julliot, C. (1992). Utilisation des ressources alimentaires par le singe hurleur roux, Alouatta seniculus (Atelidae, Primates), en Guyane : impact de la dissémination des graines sur la régénération forestière. Thèse de troisième cycle, Université 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", quietly = 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$Spatial, 
      pSp.col = "white", pgrid.draw = FALSE)
  }
} else {
  if(requireNamespace("splancs", quietly = TRUE)) {
    par(mfrow = c(3, 3))
    for(k in 1:7)
      area.plot(julliot$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", quietly = 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", quietly = 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", quietly = TRUE)) {
  neig0 <- nb2neig(spdep::dnearneigh(as.matrix(julliot$xy), 1, 1.8))
  if(adegraphicsLoaded()) {
    g5 <- s.label(julliot$xy, nb = spdep::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))
  
  if (requireNamespace("adephylo", quietly = TRUE)) {
    adephylo::orthogram(log(julliot$tab[, 3] + 1), ortho = scores.neig(neig0))
  }
}
## End(Not run)

ade4

Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

v1.7-16

GPL (>= 2)

Authors

Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Borcard, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.

Initial release