Zoogrowth          package:marelacTeaching          R Documentation

_a _z_o_o_p_l_a_n_k_t_o_n _g_r_o_w_t_h _d_a_t_a_s_e_t

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

     This literature dataset, compiled by Hansen et al. (1997) contains
     84 measurements of maximal growth rates as a function of organism
     volume and temperature for various species of zooplankton. The
     maximal growth rates were obtained from laboratory experiments.

     It is used in the book to demonstrate how one can obtain
     order-of-magnitude estimates of model parameters (i.c. maximal
     growth) via allometric relations, i.e. by performing log-log
     regression of organism rates versus size.

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

     Zoogrowth

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

     a dataframe with 84 rows, and with following columns:

     _V_o_l_u_m_e the volume in [um 3].

     _M_u_m_a_x the maximal growht rate, [/hour].

     _S_p_e_c_i_e_s the name of reared zooplankton species.

     _T_e_m_p the rearing temperature, [dg C].

     _G_r_o_u_p the systematic group to which the organism belongs, one of
          Nanoflagellate, Dinoflagellate, Ciliate, Rotifer,
          Meroplankton, Copepod.

_A_u_t_h_o_r(_s):

     Karline Soetaert <k.soetaert@nioo.knaw.nl>

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

     Hansen PJ, Bjornsen PK, Hansen BW, 1997. Zooplankton grazing and
     growth: Scaling within the 2-2,000-mu m body size range. Limnology
     and Oceanograpy 42: 687-704.

_S_e_e _A_l_s_o:

     'SCOC', a dataset containing sediment community oxygen consumption
     rates

     See the paper of Hansen et al. 1997 for a description of the
     original literature sources of this dataset.

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

     plot(Zoogrowth$Volume,Zoogrowth$Mumax,log="xy",
           xlab="zooplankton volume, micrometer ^ 3",
           ylab="maximal growth rate, /hr",main="Zoogrowth",cex=2,
           pch=(15:20)[Zoogrowth$Group],col=(1:6)[Zoogrowth$Group])
     legend("topright",legend=levels(Zoogrowth$Group),col=1:6,pch=15:20)

     ll <- lm(log(Zoogrowth[,2])~ log(Zoogrowth[,1]))
     rr <- summary(ll)$r.squared
     A  <- exp(coef(ll)[1])
     B  <- (coef(ll)[2])
     curve(A*x^B,add=TRUE,lwd=2)
     AA <- round(A*100)/100
     BB <- round(B*100)/100
     expr <- substitute(y==A*x^B,list(A=AA,B=BB))
     text(100,.0035,expr,adj=0)
     expr2 <- substitute(r^2==rr,list(rr=round(rr*100)/100))
     text(100,0.002,expr2, adj=0)

