dissimilarity {bioregion} | R Documentation |

## Compute dissimilarity metrics (beta-diversity) between sites based on species composition

### Description

This function creates a `data.frame`

where each row provides one or
several dissimilarity metric(s) between each pair of sites from a
co-occurrence `matrix`

with sites as rows and species as columns.

### Usage

```
dissimilarity(comat, metric = "Simpson", formula = NULL, method = "prodmat")
```

### Arguments

`comat` |
a co-occurrence |

`metric` |
a |

`formula` |
a |

`method` |
a |

### Details

With `a`

the number of species shared by a pair of sites, `b`

species only
present in the first site and `c`

species only present in the second site.

\(Jaccard = (b + c) / (a + b + c)\)

\(Jaccardturn = 2min(b, c) / (a + 2min(b, c))\)(Baselga 2012)

\(Sorensen = (b + c) / (2a + b + c)\)

\(Simpson = min(b, c) / (a + min(b, c))\)

If abundances data are available, Bray-Curtis and its turnover component can also be computed with the following equation:

\(Bray = (B + C) / (2A + B + C)\)

\(Brayturn = min(B, C)/(A + min(B, C))\) (Baselga 2013)

with A the sum of the lesser values for common species shared by a pair of sites. B and C are the total number of specimens counted at both sites minus A.

`formula`

can be used to compute customized metrics with the terms
`a`

, `b`

, `c`

, `A`

, `B`

, and `C`

. For example
`formula = c("pmin(b,c) / (a + pmin(b,c))", "(B + C) / (2*A + B + C)")`

will compute the Simpson and Bray-Curtis dissimilarity metrics, respectively.
**Note that pmin is used in the Simpson formula because a, b, c, A, B and C
are numeric vectors.**

Euclidean computes the Euclidean distance between each pair of sites.

### Value

A `data.frame`

with additional class `bioregion.pairwise.metric`

,
providing one or several dissimilarity
metric(s) between each pair of sites. The two first columns represent each
pair of sites.
One column per dissimilarity metric provided in `metric`

and
`formula`

except for the metric *abc* and *ABC* that
are stored in three columns (one for each letter).

### Author(s)

Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and Boris Leroy (leroy.boris@gmail.com)

### References

Baselga A (2012).
“The Relationship between Species Replacement, Dissimilarity Derived from Nestedness, and Nestedness.”
*Global Ecology and Biogeography*, **21**(12), 1223–1232.

Baselga A (2013).
“Separating the two components of abundance-based dissimilarity: balanced changes in abundance vs. abundance gradients.”
*Methods in Ecology and Evolution*, **4**(6), 552–557.

### See Also

`similarity()`

dissimilarity_to_similarity
similarity_to_dissimilarity

### Examples

```
comat <- matrix(sample(0:1000, size = 50, replace = TRUE,
prob = 1 / 1:1001), 5, 10)
rownames(comat) <- paste0("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)
dissim <- dissimilarity(comat,
metric = c("abc", "ABC", "Simpson", "Brayturn"))
dissim <- dissimilarity(comat, metric = "all",
formula = "1 - (b + c) / (a + b + c)")
```

*bioregion*version 1.1.1 Index]