specaccum               package:vegan               R Documentation

_S_p_e_c_i_e_s _A_c_c_u_m_u_l_a_t_i_o_n _C_u_r_v_e_s

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

     Function 'specaccum' finds species accumulation curves or the
     number of species for a certain number of sampled sites or
     individuals.

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

     specaccum(comm, method = "exact", permutations = 100, ...)
     ## S3 method for class 'specaccum':
     plot(x, add = FALSE, ci = 2, ci.type = c("bar", "line", "polygon"), 
         col = par("fg"), ci.col = col, ci.lty = 1, xlab = "Sites", 
         ylab = x$method, ...)
     ## S3 method for class 'specaccum':
     boxplot(x, add = FALSE, ...)

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

    comm: Community data set.

  method: Species accumulation method (partial match). Method
          '"collector"' adds sites in the order they happen to be in
          the data, '"random"' adds sites in random order, '"exact"'
          finds the expected (mean) species richness, '"coleman"' finds
          the expected richness following Coleman et al. 1982, and
          '"rarefaction"' finds the mean when accumulating individuals
          instead of sites.  

permutations: Number of permutations with 'method = "random"'.

       x: A 'specaccum' result object

     add: Add to an existing graph.

      ci: Multiplier used to get confidence intervals from standard
          deviation (standard error of the estimate). Value 'ci = 0'
          suppresses drawing confidence intervals.

 ci.type: Type of confidence intervals in the graph: '"bar"' draws
          vertical bars, '"line"' draws lines, and '"polygon"' draws a
          shaded area.

     col: Colour for drawing lines.

  ci.col: Colour for drawing lines or filling the '"polygon"'.

  ci.lty: Line type for confidence intervals or border of the
          '"polygon"'.

xlab,ylab: Labels for 'x' and 'y' axis.

     ...: Other parameters to functions.

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

     Species accumulation curves (SAC) are used to compare diversity
     properties of community data sets using different accumulator
     functions. The classic method is '"random"' which finds the mean
     SAC and its standard deviation from random permutations of the
     data, or subsampling without replacement (Gotelli & Colwell 2001).
     The '"exact"' method finds the expected SAC using the method of
     Kindt (2003), and its standard deviation. Method '"coleman"' finds
     the expected SAC and its standard deviation following Coleman et
     al. (1982).  All these methods are based on sampling sites without
     replacement. In contrast, the 'method = "rarefaction"' finds the
     expected species richness and its standard deviation by sampling
     individuals instead of sites. It achieves this by applying
     function 'rarefy' with number of individuals corresponding to
     average number of individuals per site.

     The function has a 'plot' method. In addition, 'method = "random"'
     has 'summary' and 'boxplot' methods.

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

     The function returns an object of class '"specaccum"' with items: 

   call : Function call.

  method: Accumulator method.

   sites: Number of sites.  For 'method = "rarefaction"' this is the
          average number of sites corresponding to a certain number of
          individuals.

richness: The number of species corresponding to number of sites.  With
          'method = "collector"' this is the observed richness, for
          other methods the average or expected richness.

      sd: The standard deviation of SAC (or its standard error). This
          is 'NULL' in 'method = "collector"', and it is estimated from
          permutations in 'method = "random"', and from analytic
          equations in other methods.

    perm: Permutation results with 'method = "random"' and 'NULL' in
          other cases. Each column in 'perm' holds one permutation.

_N_o_t_e:

     The SAC with 'method = "exact"' was developed by Roeland Kindt,
     and its standard deviation by Jari Oksanen (both are unpublished).
     The 'method = "coleman"' underestimates the SAC because it does
     not handle properly sampling without replacement.  Further, its
     standard deviation does not take into account species
     correlations, and is generally too low.

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

     Roeland Kindt r.kindt@cgiar.org and Jari Oksanen.

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

     Coleman, B.D, Mares, M.A., Willis, M.R. & Hsieh, Y. (1982).
     Randomness, area and species richness. _Ecology_ 63: 1121-1133. 

     Gotellli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity:
     procedures and pitfalls in measurement and comparison of species
     richness. _Ecol. Lett._ 4, 379-391.

     Kindt, R. (2003). Exact species richness for sample-based
     accumulation curves. _Manuscript._

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

     'rarefy'. Underlying graphical functions are 'boxplot',
     'matlines', 'segments' and 'polygon'.

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

     data(BCI)
     sp1 <- specaccum(BCI)
     sp2 <- specaccum(BCI, "random")
     sp2
     summary(sp2)
     plot(sp1, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
     boxplot(sp2, col="yellow", add=TRUE, pch="+")

