julliot                 package:ade4                 R Documentation

_S_e_e_d _d_i_s_p_e_r_s_a_l

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     data(julliot)

_F_o_r_m_a_t:

     'julliot' is a list containing the 3 following objects : 

     _t_a_b is a data frame with 160 rows (quadrats) and 7 variables
          (species). 

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

     _a_r_e_a 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_.

_R_e_f_e_r_e_n_c_e_s:

     Julliot, C. (1992) Utilisation des ressources alimentaires par le
     singe hurleur roux,  _Alouatta seniculus_ (Atelidae, Primates), en
     Guyane :  impact de la dissmination des graines sur la
     rgnration forestire. Thse de troisime 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.

_E_x_a_m_p_l_e_s:

     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)

