tortoise               package:popbio               R Documentation

_T _a_n_d _F _m_a_t_r_i_c_e_s _f_o_r _d_e_s_e_r_t _t_o_r_t_o_i_s_e

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

     Transition matrix and fertility estimates for the desert tortoise
     _Gopherus agassizii_

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

     data(tortoise)

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

     A list of 1 average transition matrix (T) and 4 fertility matrix
     estimates (low, medium low, medium high, and high)

_S_o_u_r_c_e:

     Table 5 in Doak et al (1994).  Used by Caswell (2001) in chapter 9
     on sensitivity analysis.

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

     Caswell, H. 2001. Matrix population models: construction,
     analysis, and interpretation, Second edition. Sinauer, Sunderland,
     Massachusetts, USA.

     Doak, D., P. Kareiva, and B. Kleptetka. 1994. Modeling population
     viability for the desert tortoise in the Western Mojave Desert.
     Ecological Applications 4:446-460.

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

     data(tortoise)
     A<-tortoise$T + tortoise$F.med.high
     A

     tor<-eigen.analysis(A, zero=FALSE)
     tor$lambda

     ## All 4 growth rates (first add T to all four F matrices)
     tor4<-lapply(tortoise[2:5], "+", tortoise$T)
     sapply(tor4,  function(A){Re(eigen(A)$values[1])}   )

     ## Summed elasticities, example 9.4.  Growth on subdiagonal
     elas<-tor$elasticities
     elas
     el<-c(F=sum(elas[1,]), P=sum(diag(elas)), G=sum(elas[row(elas)==col(elas)+1]))
     el

     ## Triangle plot like figure 9.11 in Caswell.  
     ## Nicer triangle plots are found in many different packages.
     plot(c(0, 1, 2, 0), c(0, sqrt(3), 0, 0), type = "l", lwd = 2, 
      xlab = "", ylab = "", axes = FALSE, sub="Summed elasticities")
     text(c(0, 2, 1), c(0,0, sqrt(3)), names(el), cex = 1.5, pos=c(1,1,3), xpd=TRUE)
     points(2 - 2 * el[1] - el[3], el[3] * sqrt(3), cex=1.5)

     ## Senstivity plot like figure 9.3 in Caswell 
     ## use text to add labels closer to x-axis

     sens<-tor$sensitivities
     op<- par(mfrow=c(3,1), mar = c(1.5, 4.5, 1, 2) ,  oma=c(1.5,0,1.5,0), cex=1.2 )

     ## F in top row
     ep<-barplot(sens[1,], ylim=c(0,.4), col="white", las=1,  
        ylab=expression(paste("to ", italic(F[i]))), names="")
     box()
      text(ep, -.05,  1:8, xpd = TRUE)

     ## P on diagonal
     ep<-barplot(diag(sens), ylim=c(0,.4), col="white", las=1, 
        ylab=expression(paste("to ", italic(P[i]))), names="")
     box()
      text(ep, -.05,  1:8, xpd = TRUE)

     # G on subdiagonal
     barplot(c(sens[row(sens)==col(sens)+1],0), ylim=c(0,.4), col="white", las=1, 
        ylab=expression(paste("to ", italic(G[i]))) )
     box()
      text(ep, -.05,  c(1:7, NA), xpd = TRUE)

     mtext(expression(paste("Sensitivity of ", lambda, "...")), 3, outer=TRUE, cex=1.4)
     mtext(expression(paste("Size class ", italic(i))), 1, outer=TRUE, cex=1.2)

     par(op)

