evodiss_family {adiv} | R Documentation |

## A Family of Indices Dedicated to Pair-wise Phylogenetic Dissimilarities between Communities

### Description

The function `evodiss_family`

was written thanks to function `dist.binary`

of package ade4. Function `dist.binary`

calculates specific compositional distances. The new function here replaces species with evolutionary units. It calculates Nipperess et al. (2010) parameters a, b, c, d (with incidence data), or A, B, C, D (with abundance data) and then use these parameters to compute pair-wise phylogenetic dissimilarities between communities.

The graphical function `evodiss_ternaryplot`

displays Nipperess et al. (2010) parameters a, b, c (with incidence data), or A, B, C (with abundance data) on a ternary plot (see Koleff et al. 2003).

### Usage

```
evodiss_family(phyl, comm, method = NULL, abundance = TRUE,
squareroot = TRUE, diag = FALSE, upper = FALSE, tol = 1e-08)
evodiss_ternaryplot(phyl, comm, abundance = TRUE,
tol = 1e-08, ...)
```

### Arguments

`phyl` |
an object inheriting the class |

`comm` |
a data frame typically with communities as rows, species as columns and presence/absence or an index of abundance as entries. Species should be labeled as in the phylogenetic tree where they are the tips. |

`method` |
either NULL or a number between 1 and 14. If |

`abundance` |
a logical indicating whether abundance data (if |

`squareroot` |
a logical. First a similarity index ( |

`diag` |
logical argument passed to function as.dist (R base). |

`upper` |
logical argument passed to function as.dist (R base). |

`tol` |
numeric tolerance threshold: values between - |

`...` |
other arguments can be added and passed to the function |

### Details

The function was written thanks to function `dist.binary`

of package ade4. Function `dist.binary`

calculates specific compositional distances. The new function here replaces species with evolutionary units and adds several indices. It calculates Nipperess et al. (2010) parameters a, b, c, d (with incidence data), or A, B, C, D (with abundance data). Then, the parameters are combined thanks to one out of 14 methods as defined below:

`method = 1`

: Jaccard index (1901); S3 coefficient of Gower and Legendre (1986) = a / (a+b+c).

`method = 2`

: Simple matching coefficient of Sokal and Michener (1958); S4 coefficient of Gower and Legendre (1986) = (a+d) / (a+b+c+d).

`method = 3`

: Sokal and Sneath(1963); S5 coefficient of Gower and Legendre (1986) = a / (a + 2(b + c)).

`method = 4`

: Rogers and Tanimoto (1960); S6 coefficient of Gower and Legendre (1986) = (a + d) / (a + 2(b + c) +d).

`method = 5`

: Dice (1945) or Sorensen (1948); S7 coefficient of Gower and Legendre (1986) = 2a / (2a + b + c).

`method = 6`

: Hamann coefficient; S9 index of Gower and Legendre (1986) = (a - (b + c) + d) / (a + b + c + d).

`method = 7`

: Ochiai (1957); S12 coefficient of Gower and Legendre (1986) = a / sqrt((a + b)(a + c)).

`method = 8`

: Sokal and Sneath (1963); S13 coefficient of Gower and Legendre (1986) = ad / sqrt((a + b)(a + c)(d + b)(d + c)).

`method = 9`

: Phi of Pearson; S14 coefficient of Gower and Legendre (1986) = (ad - bc) / sqrt((a + b)(a + c)(d + b)(d + c)).

`method = 10`

: S2 coefficient of Gower and Legendre (1986) = a / (a + b + c + d) (imposed unit self-similarity).

`method = 11`

: Kulczynski index; S10 coefficient of Gower and Legendre (1986) = 0.5 * (a/(a+b) + a/(a+c))

`method = 12`

: S11 coefficient of Gower and Legendre (1986) = 0.25 * (a/(a+b) + a/(a+c) + d/(b+d) + d/(c+d))

`method = 13`

: S8 coefficient of Gower and Legendre (1986) = (a+d)/(a+0.5*(b+c)+d)

`method = 14`

: Simpson coefficient = a/(a+min(b,c))

### Value

Function `evodiss_family`

returns an object of class `dist`

containing the PD-dissimilarities (phylogenetic dissimilarities) between communities.

Function `evodiss_ternaryplot`

returns a graph.

### Author(s)

Sandrine Pavoine sandrine.pavoine@mnhn.fr

### References

The methodologies are presented in
Pavoine, S. (2016) A guide through a family of phylogenetic dissimilarity measures among sites. *Oikos*, **125**, 1719–1732.

They gather in a common framework and extend earlier work introduced in
Koleff, P., Gaston, K.J., Lennon, J.J. (2003) Measuring beta diversity for presence-absence data. *Journal of Animal Ecology*, **72**, 367–382.

Nipperess, D.A., Faith, D.P., Barton, K.(2010) Resemblance in phylogenetic diversity among ecological assemblages. *Journal of Vegetation Science*, **21**, 809–820.

Dice, L.R. (1945) Measures of the amount of ecologic association between species. *Ecology*, **26**, 297–302.

Gower, J.C., Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. *Journal of Classification*, **3**, 5–48.

Jaccard, P. (1901) Etude comparative de la distribution florale dans une portion des Alpes et des Jura. *Bulletin de la Societe Vaudoise des Sciences Naturelles*, **37**, 547–579.

Ochiai, A. (1957) Zoogeographic studies on the soleoid fishes found in Japan and its neighbouring regions. *Bulletin of the Japanese Society of Scientific Fisheries*, **22**, 526–530.

Rogers, J.S. and Tanimoto, T.T. (1960) A computer program for classifying plants. *Science*, **132**, 1115–1118.

Sokal, R.R. and Michener, C.D. (1958) A Statistical Method for Evaluating Systematic Relationships. *The University of Kansas Science Bulletin*, **38**, 1409–1438.

Sokal, R.R. and Sneath, P.H.A. (1963) *Principles of numerical taxonomy*. San Francisco: W. H. Freeman.

Sorensen, T. (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. *Kongelige Danske Videnskabernes Selskabs Biologiske Skrifter*, **5**, 1–34.

### See Also

### Examples

```
## Not run:
if(require(ape)){
data(batcomm)
phy <- read.tree(text=batcomm$tre)
ab <- batcomm$ab[,phy$tip.label]
# PD-dissimilarity indices that use Nipperess et al. (2010)
# parameters can be obtained thanks to function evodiss_family.
# For example, with incidence data,
# indices evoDJaccard, evoDSorensen, and evoDOchiai
# (supplementary Appendix 1 in Pavoine 2016)
# can be obtained as follows:
evodiss_family(phy, ab, method=1, abundance=FALSE) # Jaccard
evodiss_family(phy, ab, method=5, abundance=FALSE) # Sorensen
evodiss_family(phy, ab, method=7, abundance=FALSE) # Ochiai
# With abundance data, indices evoDTJ, evoDTS, evoDTO
# (supplementary Appendix 1 in Pavoine 2016)
# can be obtained as follows:
evodiss_family(phy, ab, method=1) # evoDTJ
evodiss_family(phy, ab, method=5) # evoDTS
evodiss_family(phy, ab, method=7) # evoDTO
# Ternary plots can be obtained for Nipperess et al. (2010)
# parameters a, b, c (incidence data)
# (see Supplementary material Appendix 4 in Pavoine 2016):
evodiss_ternaryplot(phy, ab, abundance = FALSE)
# and for Nipperess et al. (2010) parameters A, B, C
# (abundance data):
evodiss_ternaryplot(phy, ab, abundance = TRUE)
# The ternary plots can be adjusted thanks
# to the arguments of function triangle.plot (package ade4).
# For example, full triangles can be obtained as follows
# (previous graphs were zoomed on the smallest principal
# equilateral triangle that contained the points,
# as indicated by the embedded close grey triangle
# at the left-hand corner of ternary plot given above):
evodiss_ternaryplot(phy, ab, abundance = FALSE, adjust=FALSE, showposition=FALSE)
# Incidence data
evodiss_ternaryplot(phy, ab, abundance = TRUE, adjust=FALSE, showposition=FALSE)
# abundance data
}
## End(Not run)
```

*adiv*version 2.2.1 Index]