Beta diversity computed as Var(Y)

Source: R/beta.div.R

Compute estimates of total beta diversity as the total variance in a community data matrix Y, as well as derived SCBD and LCBD statistics, for 19 dissimilarity coefficients or the raw data table. Computing beta diversity as Var(Y) for raw, untransformed community composition data is not recommended. Tests of significance of the LCBD indices are also produced.

beta.div(
  Y,
  method = "hellinger",
  sqrt.D = FALSE,
  samp = TRUE,
  nperm = 999,
  adj = TRUE,
  save.D = FALSE,
  clock = FALSE
)

Arguments

Y

Community composition data. The object class can be either data.frame or matrix.

method

One of the 19 dissimilarity coefficients available in the function: "hellinger", "chord", "log.chord", "chisquare", "profiles", "percentdiff", "ruzicka", "divergence", "canberra", "whittaker", "wishart", "kulczynski", "jaccard", "sorensen", "ochiai", "ab.jaccard", "ab.sorensen", "ab.ochiai", "ab.simpson", "euclidean". See Details. Names can be abbreviated to a non-ambiguous set of first letters. Default: method="hellinger".

sqrt.D

If sqrt.D=TRUE, the dissimilarities in matrix D are square-rooted before computation of SStotal, BDtotal and LCBD. This transformation may be useful for methods "manhattan", "whittaker", "divergence", "canberra", "percentdiff", "ruzicka", "wishart" since square-root transformation of the dissimilarities makes these D matrices Euclidean.

  • Note 2 - The logical value given to parameter sqrt.D has no incidence on calculations through methods "euclidean", "profiles", "hellinger", "log.chord", "chord", "chisquare" since no D matrix is computed in those cases.

  • Note 3 - For methods "jaccard", "sorensen", "ochiai", that function produces the dissimilarity matrix in the form sqrt(D), which is Euclidean.

samp

If samp=TRUE, the abundance-based distances (ab.jaccard, ab.sorensen, ab.ochiai, ab.simpson) are computed for sample data. If samp=FALSE, they are computed for true population data.

nperm

Number of permutations for the tests of significance of LCBD indices.

adj

Compute adjusted p-values using the Holm method. Default: adj=TRUE

save.D

If save.D=TRUE, the distance matrix will appear in the output list.

clock

If clock=TRUE, the computation time is printed. Useful when nperm is large.

Value

A list containing the following results:

  • beta: Total sum of squares and total beta diversity [= Var(Y)] of the data matrix. BDtotal statistics computed with the same D index are comparable among data sets having the same or different numbers of sampling units (n), provided that they are of the same size or represent the same sampling effort.

  • SCBD: Vector of Species contributions to beta diversity (SCBD), if computed.

  • LCBD: Vector of Local contributions to beta diversity (LCBD) for the sites.

  • p.LCBD: P-values associated with the LCBD indices.

  • p.adj: Corrected P-values for the LCBD indices, Holm correction.

  • method: Method selected.

  • note: Notes indicate whether the selected coefficient is Euclidean or not.

  • D: The distance matrix if save.D=TRUE.

When all sites contain a different set of species with no species in common, the maximum value that BDtotal can take depends on the method used in the calculation.

  • With methods "hellinger", "chord", "profiles", which have maximum values of sqrt(2), BDtotal produces an index in the range [0, 1] with a maximum value of 1.

  • For dissimilarity indices with maximum values of 1, BDtotal has a maximum value of 0.5.

  • Dissimilarity indices that do not have maximum values of 1 or sqrt(2) produce BDtotal values that do not have an upper bound; hence they cannot be compared across taxonomic groups or among study sites. This group includes the chi-square distance.

See Legendre & De Caceres (2013, p. 957-958), Table 2 and section Maximum value of BD.
For two sites only, the LCBD results are not interesting. With all coefficients, the two LCBD indices are equal to 0.5. The two associated p-values are 1 because LCBD is 0.5 for all columnwise permutations of the data.
The calculation is aborted when Y only contains two identical rows of data. In that case, SStotal and BDtotal are 0 and the LCBD indices cannot be computed (value NaN).

Details

Calculations may be carried out in two ways, depending on the selected method.

  • For untransformed or transformed raw data, the total sum of squares (SStotal) is first computed, then the total beta diversity (BDtotal), which is SStotal divided by (n - 1), is calculated. This algorithm is used for methods "euclidean", "profiles", "hellinger", "chord", "log.chord", "chisquare". No transformation of the data is computed when the method is "euclidean". For methods "profiles", "hellinger", "chord", "log.chord", "chisquare", the algorithm begins with computation of the same-name transformation of the community data (Legendre and Gallagher 2001; Legendre and Legendre 2012, Section 7.7; Legendre and Borcard 2018); SStotal and BDtotal are then computed for the transformed data, followed by calculation of the SCBD and LCBD indices.

  • Calculations of BDtotal can also be conducted from a dissimilarity matrix. SStotal is computed by summing the squared dissimilarities in the lower triangular dissimilarity matrix and dividing by n. Then, total beta diversity (BDtotal) is obtained by dividing SStotal by (n-1). With option sqrt.D = TRUE, the computation of SStotal is equivalent to summing the distances instead of the squared distances. Choices are: "whittaker", "divergence", "canberra", "percentdiff", "ruzicka", "wishart", "kulczynski", "ab.jaccard", "ab.sorensen", "ab.ochiai", "ab.simpson", "jaccard", "sorensen", "ochiai". Equations for these dissimilarities are presented in Table 1 of Legendre and De Cáceres (2013). The Ružička index is described in Legendre (2014); this coefficient is suitable for beta diversity studies. See Chao et al. (2006) for details about the abundance-based (ab) coefficients.

Community composition data can be log-transformed prior to analysis with the chord distance; see Legendre and Borcard (2018). The log(y+1) transformation (log1p function of base) reduces the asymmetry of the species distributions. The chord-log distance, readily available among the methods of the beta.div function, is the chord distance computed on log(y+1)-transformed data. This combined transformation is meaningful for community composition data because the log is one of the transformations in the Box-Cox series, corresponding to exponent 0; see Legendre and Legendre (2012, Section 1.5.6). Exponent 1 (no transformation of the data) followed by the chord transformation and calculation of the Euclidean distance would simply produce the chord distance. Exponent 0.5 (square root) followed by the chord transformation and the Euclidean distance would produce the Hellinger distance. The chord, Hellinger and log-chord distances represent a series where the data are increasingly transformed to reduce the asymmetry of the distributions. Note that it is meaningless to subject log-transformed community compostion data to the "profiles", "hellinger", or "chisquare" distances available in this function.

The Jaccard, Sørensen and Ochiai coefficients are the binary forms of 10 of the 12 dissimilarity coefficients (including the Ružička index) that are suitable for beta diversity assessment. The equivalences are described in Legendre and De Cáceres (2013, Table 1). These popular coefficients can be computed directly using function beta.div without going to the trouble of applying the quantitative forms of these coefficients to data reduced to presence-absence form. beta.div produces the dissimilarity matrix in the form sqrt(D), which is Euclidean. Hence for these three coefficients, function beta.div should be used with option sqrt.D=FALSE.

Species contributions to beta diversity (SCBD indices for the species) are computed for untransformed or transformed raw data, but they cannot be computed from dissimilarity matrices.

Local contributions to beta diversity (LCBD indices) represent the degree of uniqueness of the sites in terms of their species compositions. They can be computed in all cases: raw (not recommended) or transformed data, as well as dissimilarity matrices. See Legendre and De Cáceres (2013) for details. LCBD indices are tested for significance by random, independent permutations within the columns of Y. This permutation method tests H0 that the species are distributed at random, independently of one another, among the sites, while preserving the species abundance distributions in the observed data. See Legendre and De Cáceres (2013) for discussion.

This version of beta.div calls computer code written in C to speed up computation, especially for the permutation tests of the LCBD indices.

References

Chao, A., R. L. Chazdon, R. K. Colwell and T. J. Shen. 2006. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62: 361-371.

Legendre, P. 2014. Interpreting the replacement and richness difference components of beta diversity. Global Ecology and Biogeography 23: 1324-1334.

Legendre, P. and D. Borcard. 2018. Box-Cox-chord transformations for community composition data prior to beta diversity analysis. Ecography 41: 1820-1824.

Legendre, P. and M. De Cáceres. 2013. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecology Letters 16: 951-963.

Legendre, P. and E. D. Gallagher, E.D. 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129: 271-280.

Legendre, P. and Legendre, L. 2012. Numerical Ecology. 3rd English edition. Elsevier Science BV, Amsterdam.

Author

Pierre Legendre pierre.legendre@umontreal.ca

Examples


if(require("vegan", quietly = TRUE) & require("adegraphics", quietly = TRUE)){
data(mite)
res = beta.div(mite, "hellinger", nperm=999)

# Plot a map of the LCBD indices using the Cartesian coordinates
data(mite.xy)
s.value(mite.xy, res$LCBD, symbol = "circle", col = c("white", "brown"), main="Map of mite LCBD")

### Example using the mite abundance data and the percentage difference dissimilarity
res = beta.div(mite, "percentdiff", nperm=999, clock=TRUE)

# Plot a map of the LCBD indices
signif = which(res$p.LCBD <= 0.05)  # Which are the significant LCBD indices?
nonsignif = which(res$p.LCBD > 0.05)  # Which are the non-significant LCBD indices?
g1 <- s.value(mite.xy[signif,], res$LCBD[signif], ppoint.alpha = 0.5, plegend.drawKey = FALSE,
 symbol = "circle", col = c("white", "red"), main="Map of mite LCBD (red = significant indices)")
g2 <- s.value(mite.xy[nonsignif,], res$LCBD[nonsignif], ppoint.alpha = 0.5,
symbol = "circle", col = c("white", "blue"))
g2+g1
}

#> Time for computation = 0.814000  sec