recruitment               package:CTFS               R Documentation

_A_n_n_u_a_l _R_e_c_r_u_i_t_m_e_n_t _R_a_t_e _b_y _C_a_t_e_g_o_r_i_e_s (_U_s_e_r _d_e_f_i_n_e_d _g_r_o_u_p_s)

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

     Computes annual recruitment rate for all trees or any user defined
     categorization of trees.  Any two census datasets can be provided
     in order of date of census.  The annual recruitment rate and other
     statistics are computed for user defined categories.

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

     recruitment(census1, census2, mindbh=10, alivecode = c("A", "AB", "AS"), 
             split1 = NULL, split2 = NULL)

_A_r_g_u_m_e_n_t_s:

 census1: name of census datafile for first census, must be a
          *dataframe*, must be of same length as 'census2'

 census2: name of census datafile for second census, must be a
          *dataframe*, must be of same length as 'census1'

  mindbh: minimum DBH in 'census1' for inclusion in computation.

alivecode: character, codes of the variable 'status'  that indicate the
          tree is alive.  The most general valid categories are: "A"
          and  "AB" and "AS".

  split1: a *vector* of categorical values of the same  length as
          'census' which groups trees into classes of  interest for
          which mortality values are computed.  This vector can be 
          composed of characters or numbers.

  split2: a second *vector* of categorical values of the same  length
          as 'census' which groups trees into classes of  interest for
          which mortality values are computed.  This vector can be 
          composed of characters or numbers.

_D_e_t_a_i_l_s:

     See 'CTFS.recruitment' for details on the computation of 
     recruitment rates and associated functions.

     Any two censuses on a datafile must be used. They do not have to
     be  sequential, only that 'census1' has to be before 'census2'.

     The vectors 'split1' and 'split2' must be of the same length as
     'census1' and 'census2' but can contain     NA. 

     Take care when creating a split vector based on dbh.  The only
     valid dbh for a recruit is in the second census of any given
     interval.  Recruits do not have a valid dbh for the first census
     of an interval as they are not large enough yet to enter the
     census.  If the first census is used, then no recruits will be
     identified.  So a dbh vector should be based on the dbh of the
     tree in the second census.  The inverse is true for mortality.
     Therefore the same dbh split vector CANNOT be used for
     computations of recruitment and mortality.  Use a dbh vector based
     on the census1 for mortality and on  census2 for recruitment.

     The results of 'recruitment' can be organized into *dataframes*
     with the use of 'assemble.demography'.

_V_a_l_u_e:

     'recruit' returns a *list of arrays* with the values of 'split1'
     as the first dimension and the values of 'split2' as the second
     dimension of the array.  The array contains the following named
     components:

     $N1: the number of living trees at the second census

      $R: the number of trees that were recruited between the first and
           second census

   $rate: the recruitment rate in %/year

  $lower: the lower 95% confidence

  $upper: the upper 95% confidence

   $time: mean number of years between census for trees used in 
          recruitment rate computation.

  $date0: mean date of first census

  $date1: mean date of second census


     If the vector(s) 'split1' and 'split2' are provided by the user,
     then recruitment rates and associated statistics are computed for
     each value of the vectors.  The vectors are nested so that
     recruitment rates is computed for each category of 'split2' within
     each category of 'split1'.  recruitment values are returned for
     all levels of each vector and if no value can be computed then 0
     or NA is returned as appropriate.

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

     Rick Condit and Pamela Hall

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

     'CTFS.recruitment'

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

     ## Not run: 
     1. Default use of recruit()
     rec.out <- recruitment(tst.bci90.full,tst.bci95.full)
     rec.out

     2. Create a vector of habitat for each tree based on the quadrate 
     location of tree

     habitat.vct <- sep.quadinfo(tst.bci90.full,bciquad.info)
     rec.hab.out <- recruitment(tst.bci90.full,tst.bci95.full,split1=habitat.vct)

     ## End(Not run)

