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 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)
par(mfrow = c(3,3))
## Not run: 
for(k in 1:7)
    area.plot(julliot$area,val = log(julliot$tab[,k]+1),
     sub = names(julliot$tab)[k], csub = 2.5)

## End(Not run)

if (require(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)
}

## Not run: 
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)
}
## End(Not run)
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)

## Not run: 
if (require(spdep, quiet = TRUE)){
par(mfrow = c(1,1))
neig0 <- nb2neig(dnearneigh(as.matrix(julliot$xy), 1, 1.8))
s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE,
 addax = FALSE, grid = FALSE)

gearymoran(neig.util.LtoG(neig0), log(julliot$tab+1))
orthogram(log(julliot$tab[,3]+1), ortho = scores.neig(neig0),
 nrepet = 9999)}

## End(Not run)

Worked out examples

> library(ade4)
> ### Name: julliot
> ### Title: Seed dispersal
> ### Aliases: julliot
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(julliot)
> 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)

> 
> 
> if (require(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)
+ }

Spatial Point Pattern Analysis Code in S-Plus

 Version 2 - Spatial and Space-Time analysis

> 
> 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(3,3))
> for(k in 1:7)
+     s.value(julliot$xy,log(julliot$tab[,k]+1),
+      sub = names(julliot$tab)[k], csub = 2.5)

> 
> if (require(spdep, quiet = TRUE)){
+ par(mfrow = c(1,1))
+ neig0 <- nb2neig(dnearneigh(as.matrix(julliot$xy), 1, 1.8))
+ s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE,
+  addax = FALSE, grid = FALSE)

+ 
+ gearymoran(neig.util.LtoG(neig0), log(julliot$tab+1))
+ orthogram(log(julliot$tab[,3]+1), ortho = scores.neig(neig0),
+  nrepet = 9999)}
deldir 0.0-12 

     Please note: The process for determining duplicated points
     has changed from that used in version 0.0-9 (and previously).

class: krandtest 
Monte-Carlo tests
Call: orthogram(x = log(julliot$tab[, 3] + 1), orthobas = scores.neig(neig0), 
    nrepet = 9999)

Test number:   4 
Permutation number:   9999 
   Test         Obs   Std.Obs     Alter Pvalue
1 R2Max  0.05139172 -0.164973   greater 0.4898
2 SkR2k 65.94907607 -2.141132      less 0.0215
3  Dmax  0.17568397  2.206360 two-sided 0.0369
4   SCE  1.78430651  2.670545   greater 0.0265

other elements: NULL

> 
> 
> 
>