vegdist {vegan} R Documentation

## Dissimilarity Indices for Community Ecologists

### Description

The function computes dissimilarity indices that are useful for or popular with community ecologists. All indices use quantitative data, although they would be named by the corresponding binary index, but you can calculate the binary index using an appropriate argument. If you do not find your favourite index here, you can see if it can be implemented using designdist. Gower, Bray–Curtis, Jaccard and Kulczynski indices are good in detecting underlying ecological gradients (Faith et al. 1987). Morisita, Horn–Morisita, Binomial, Cao and Chao indices should be able to handle different sample sizes (Wolda 1981, Krebs 1999, Anderson & Millar 2004), and Mountford (1962) and Raup-Crick indices for presence–absence data should be able to handle unknown (and variable) sample sizes. Most of these indices are discussed by Krebs (1999) and Legendre & Legendre (2012), and their properties further compared by Wolda (1981) and Legendre & De Cáceres (2012). Aitchison (1986) distance is equivalent to Euclidean distance between CLR-transformed samples ("clr") and deals with positive compositional data. Robust Aitchison distance by Martino et al. (2019) uses robust CLR ("rlcr"), making it applicable to non-negative data including zeroes (unlike the standard Aitchison).

### Usage

vegdist(x, method="bray", binary=FALSE, diag=FALSE, upper=FALSE,
na.rm = FALSE, ...) 

### Arguments

 x Community data matrix. method Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "chisq", "chord", "aitchison", or "robust.aitchison". binary Perform presence/absence standardization before analysis using decostand. diag Compute diagonals. upper Return only the upper diagonal. na.rm Pairwise deletion of missing observations when computing dissimilarities. ... Other parameters. These are ignored, except in method ="gower" which accepts range.global parameter of decostand, and in method="aitchison", which accepts pseudocount parameter of decostand used in the clr transformation.

### Details

Jaccard ("jaccard"), Mountford ("mountford"), Raup–Crick ("raup"), Binomial and Chao indices are discussed later in this section. The function also finds indices for presence/ absence data by setting binary = TRUE. The following overview gives first the quantitative version, where x_{ij} x_{ik} refer to the quantity on species (column) i and sites (rows) j and k. In binary versions A and B are the numbers of species on compared sites, and J is the number of species that occur on both compared sites similarly as in designdist (many indices produce identical binary versions):

 euclidean d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2} binary: \sqrt{A+B-2J} manhattan d_{jk}=\sum_i |x_{ij}-x_{ik}| binary: A+B-2J gower d_{jk} = (1/M) \sum_i \frac{|x_{ij}-x_{ik}|}{\max x_i-\min x_i} binary: (A+B-2J)/M where M is the number of columns (excluding missing values) altGower d_{jk} = (1/NZ) \sum_i |x_{ij} - x_{ik}| where NZ is the number of non-zero columns excluding double-zeros (Anderson et al. 2006). binary: \frac{A+B-2J}{A+B-J} canberra d_{jk}=\frac{1}{NZ} \sum_i \frac{|x_{ij}-x_{ik}|}{|x_{ij}|+|x_{ik}|} where NZ is the number of non-zero entries. binary: \frac{A+B-2J}{A+B-J} clark d_{jk}=\sqrt{\frac{1}{NZ} \sum_i (\frac{x_{ij}-x_{ik}}{x_{ij}+x_{ik}})^2} where NZ is the number of non-zero entries. binary: \frac{A+B-2J}{A+B-J} bray d_{jk} = \frac{\sum_i |x_{ij}-x_{ik}|}{\sum_i (x_{ij}+x_{ik})} binary: \frac{A+B-2J}{A+B} kulczynski d_{jk} = 1-0.5(\frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ij}} + \frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ik}} ) binary: 1-(J/A + J/B)/2 morisita d_{jk} = 1 - \frac{2 \sum_i x_{ij} x_{ik}}{(\lambda_j + \lambda_k) \sum_i x_{ij} \sum_i x_{ik}}, where \lambda_j = \frac{\sum_i x_{ij} (x_{ij} - 1)}{\sum_i x_{ij} \sum_i (x_{ij} - 1)} binary: cannot be calculated horn Like morisita, but \lambda_j = \sum_i x_{ij}^2/(\sum_i x_{ij})^2 binary: \frac{A+B-2J}{A+B} binomial d_{jk} = \sum_i [x_{ij} \log (\frac{x_{ij}}{n_i}) + x_{ik} \log (\frac{x_{ik}}{n_i}) - n_i \log(\frac{1}{2})]/n_i, where n_i = x_{ij} + x_{ik} binary: \log(2) \times (A+B-2J) cao d_{jk} = \frac{1}{S} \sum_i \log \left(\frac{n_i}{2}\right) - (x_{ij} \log(x_{ik}) + x_{ik} \log(x_{ij}))/n_i, where S is the number of species in compared sites and n_i = x_{ij}+x_{ik}

Jaccard index is computed as 2B/(1+B), where B is Bray–Curtis dissimilarity.

Binomial index is derived from Binomial deviance under null hypothesis that the two compared communities are equal. It should be able to handle variable sample sizes. The index does not have a fixed upper limit, but can vary among sites with no shared species. For further discussion, see Anderson & Millar (2004).

Cao index or CYd index (Cao et al. 1997) was suggested as a minimally biased index for high beta diversity and variable sampling intensity. Cao index does not have a fixed upper limit, but can vary among sites with no shared species. The index is intended for count (integer) data, and it is undefined for zero abundances; these are replaced with arbitrary value 0.1 following Cao et al. (1997). Cao et al. (1997) used \log_{10}, but the current function uses natural logarithms so that the values are approximately 2.30 times higher than with 10-based logarithms. Anderson & Thompson (2004) give an alternative formulation of Cao index to highlight its relationship with Binomial index (above).

Mountford index is defined as M = 1/\alpha where \alpha is the parameter of Fisher's logseries assuming that the compared communities are samples from the same community (cf. fisherfit, fisher.alpha). The index M is found as the positive root of equation \exp(aM) + \exp(bM) = 1 + \exp[(a+b-j)M], where j is the number of species occurring in both communities, and a and b are the number of species in each separate community (so the index uses presence–absence information). Mountford index is usually misrepresented in the literature: indeed Mountford (1962) suggested an approximation to be used as starting value in iterations, but the proper index is defined as the root of the equation above. The function vegdist solves M with the Newton method. Please note that if either a or b are equal to j, one of the communities could be a subset of other, and the dissimilarity is 0 meaning that non-identical objects may be regarded as similar and the index is non-metric. The Mountford index is in the range 0 \dots \log(2), but the dissimilarities are divided by \log(2) so that the results will be in the conventional range 0 \dots 1.

Raup–Crick dissimilarity (method = "raup") is a probabilistic index based on presence/absence data. It is defined as 1 - prob(j), or based on the probability of observing at least j species in shared in compared communities. The current function uses analytic result from hypergeometric distribution (phyper) to find the probabilities. This probability (and the index) is dependent on the number of species missing in both sites, and adding all-zero species to the data or removing missing species from the data will influence the index. The probability (and the index) may be almost zero or almost one for a wide range of parameter values. The index is nonmetric: two communities with no shared species may have a dissimilarity slightly below one, and two identical communities may have dissimilarity slightly above zero. The index uses equal occurrence probabilities for all species, but Raup and Crick originally suggested that sampling probabilities should be proportional to species frequencies (Chase et al. 2011). A simulation approach with unequal species sampling probabilities is implemented in raupcrick function following Chase et al. (2011). The index can be also used for transposed data to give a probabilistic dissimilarity index of species co-occurrence (identical to Veech 2013).

Chao index tries to take into account the number of unseen species pairs, similarly as in method = "chao" in specpool. Function vegdist implements a Jaccard, index defined as 1-\frac{U \times V}{U + V - U \times V}; other types can be defined with function chaodist. In Chao equation, U = C_j/N_j + (N_k - 1)/N_k \times a_1/(2 a_2) \times S_j/N_j, and V is similar except for site index k. C_j is the total number of individuals in the species of site j that are shared with site k, N_j is the total number of individuals at site j, a_1 (and a_2) are the number of species occurring in site j that have only one (or two) individuals in site k, and S_j is the total number of individuals in the species present at site j that occur with only one individual in site k (Chao et al. 2005).

Morisita index can be used with genuine count data (integers) only. Its Horn–Morisita variant is able to handle any abundance data.

Mahalanobis distances are Euclidean distances of a matrix where columns are centred, have unit variance, and are uncorrelated. The index is not commonly used for community data, but it is sometimes used for environmental variables. The calculation is based on transforming data matrix and then using Euclidean distances following Mardia et al. (1979). The Mahalanobis transformation usually fails when the number of columns is larger than the number of rows (sampling units). When the transformation fails, the distances are nearly constant except for small numeric noise. Users must check that the returned Mahalanobis distances are meaningful.

Euclidean and Manhattan dissimilarities are not good in gradient separation without proper standardization but are still included for comparison and special needs.

Chi-square distances ("chisq") are Euclidean distances of Chi-square transformed data (see decostand). This is the internal standardization used in correspondence analysis (cca, decorana). Weighted principal coordinates analysis of these distances with row sums as weights is equal to correspondence analysis (see the Example in wcmdscale). Chi-square distance is intended for non-negative data, such as typical community data. However, it can be calculated as long as all margin sums are positive, but warning is issued on negative data entries.

Chord distances ("chord") are Euclidean distance of a matrix where rows are standardized to unit norm (their sums of squares are 1) using decostand. Geometrically this standardization moves row points to a surface of multidimensional unit sphere, and distances are the chords across the hypersphere. Community data are non-negative, and all points will be on the positive quadrant of the hypersphere, and the distances have upper limit \sqrt{2}. If data can have negative values, the upper limit is 2.

Bray–Curtis and Jaccard indices are rank-order similar, and some other indices become identical or rank-order similar after some standardizations, especially with presence/absence transformation of equalizing site totals with decostand. Jaccard index is metric, and probably should be preferred instead of the default Bray-Curtis which is semimetric.

Aitchison distance (1986) and robust Aitchison distance (Martino et al. 2019) are metrics that deal with compositional data. Aitchison distance has been said to outperform Jensen-Shannon divergence and Bray-Curtis dissimilarity, due to a better stability to subsetting and aggregation, and it being a proper distance (Aitchison et al., 2000).

The naming conventions vary. The one adopted here is traditional rather than truthful to priority. The function finds either quantitative or binary variants of the indices under the same name, which correctly may refer only to one of these alternatives For instance, the Bray index is known also as Steinhaus, Czekanowski and Sørensen index. The quantitative version of Jaccard should probably called Ružička index. The abbreviation "horn" for the Horn–Morisita index is misleading, since there is a separate Horn index. The abbreviation will be changed if that index is implemented in vegan.

### Value

Should provide a drop-in replacement for dist and return a distance object of the same type.

### Note

The function is an alternative to dist adding some ecologically meaningful indices. Both methods should produce similar types of objects which can be interchanged in any method accepting either. Manhattan and Euclidean dissimilarities should be identical in both methods. Canberra index is divided by the number of variables in vegdist, but not in dist. So these differ by a constant multiplier, and the alternative in vegdist is in range (0,1). Function daisy (package cluster) provides alternative implementation of Gower index that also can handle mixed data of numeric and class variables. There are two versions of Gower distance ("gower", "altGower") which differ in scaling: "gower" divides all distances by the number of observations (rows) and scales each column to unit range, but "altGower" omits double-zeros and divides by the number of pairs with at least one above-zero value, and does not scale columns (Anderson et al. 2006). You can use decostand to add range standardization to "altGower" (see Examples). Gower (1971) suggested omitting double zeros for presences, but it is often taken as the general feature of the Gower distances. See Examples for implementing the Anderson et al. (2006) variant of the Gower index.

Most dissimilarity indices in vegdist are designed for community data, and they will give misleading values if there are negative data entries. The results may also be misleading or NA or NaN if there are empty sites. In principle, you cannot study species composition without species and you should remove empty sites from community data.

### Author(s)

Jari Oksanen, with contributions from Tyler Smith (Gower index), Michael Bedward (Raup–Crick index), and Leo Lahti (Aitchison and robust Aitchison distance).

### References

Aitchison, J. The Statistical Analysis of Compositional Data (1986). London, UK: Chapman & Hall.

Aitchison, J., Barceló-Vidal, C., Martín-Fernández, J.A., Pawlowsky-Glahn, V. (2000). Logratio analysis and compositional distance. Math. Geol. 32, 271–275.

Anderson, M.J. and Millar, R.B. (2004). Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. Journal of Experimental Marine Biology and Ecology 305, 191–221.

Anderson, M.J., Ellingsen, K.E. & McArdle, B.H. (2006). Multivariate dispersion as a measure of beta diversity. Ecology Letters 9, 683–693.

Anderson, M.J & Thompson, A.A. (2004). Multivariate control charts for ecological and environmental monitoring. Ecological Applications 14, 1921–1935.

Cao, Y., Williams, W.P. & Bark, A.W. (1997). Similarity measure bias in river benthic Auswuchs community analysis. Water Environment Research 69, 95–106.

Chao, A., Chazdon, R. L., Colwell, R. K. and Shen, T. (2005). A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecology Letters 8, 148–159.

Chase, J.M., Kraft, N.J.B., Smith, K.G., Vellend, M. and Inouye, B.D. (2011). Using null models to disentangle variation in community dissimilarity from variation in \alpha-diversity. Ecosphere 2:art24 doi: 10.1890/ES10-00117.1

Faith, D. P, Minchin, P. R. and Belbin, L. (1987). Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57–68.

Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics 27, 623–637.

Krebs, C. J. (1999). Ecological Methodology. Addison Wesley Longman.

Legendre, P. & De Cáceres, M. (2012). Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecology Letters 16, 951–963. doi: 10.1111/ele.12141

Legendre, P. and Legendre, L. (2012) Numerical Ecology. 3rd English ed. Elsevier.

Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979). Multivariate analysis. Academic Press.

Martino, C., Morton, J.T., Marotz, C.A., Thompson, L.R., Tripathi, A., Knight, R. & Zengler, K. (2019) A novel sparse compositional technique reveals microbial perturbations. mSystems 4, 1.

Mountford, M. D. (1962). An index of similarity and its application to classification problems. In: P.W.Murphy (ed.), Progress in Soil Zoology, 43–50. Butterworths.

Veech, J. A. (2013). A probabilistic model for analysing species co-occurrence. Global Ecology and Biogeography 22, 252–260.

Wolda, H. (1981). Similarity indices, sample size and diversity. Oecologia 50, 296–302.

Function designdist can be used for defining your own dissimilarity index. Function betadiver provides indices intended for the analysis of beta diversity.

### Examples

data(varespec)
vare.dist <- vegdist(varespec)
# Orlóci's Chord distance: range 0 .. sqrt(2)
vare.dist <- vegdist(decostand(varespec, "norm"), "euclidean")
# Anderson et al.  (2006) version of Gower
vare.dist <- vegdist(decostand(varespec, "log"), "altGower")
# Range standardization with "altGower" (that excludes double-zeros)
vare.dist <- vegdist(decostand(varespec, "range"), "altGower")


[Package vegan version 2.6-2 Index]