syntable: Synoptic tables and calculation of cluster-wise frequencies,...

In fgoral/goeveg: Functions for Community Data and Ordinations

Synoptic tables and calculation of cluster-wise frequencies, fidelity and differential species character

Description

Synoptic tables are a tool for the visualization and interpretation of previously defined plant species groups (clusters), e.g. from cluster analysis, classification methods or pre-defined categories, e.g. spatial distribution units. They help to determine characteristic patterning of species occurrences in plant communities by calculating cluster-wise percentage or absolute frequencies, mean/median cover values, fidelity (phi) or differential species character.

syntable function calculates an unordered synoptic table for plant community analysis, using an input species-sample data frame and a vector of cluster identity input. The unordered output table can be sorted automatically with synsort function in this package.

Usage

syntable(matrix, cluster, abund = "percentage", type = "percfreq")

Arguments

matrix	Species matrix or data frame with species in columns and samples in rows. Missing values (NA) will be transformed to 0. If non-numeric abundance values are present, the matrix will be transformed to presence/absence with all non-zero values defined as 1. Species and sample names must be defined as column- and row names, respectively.
cluster	Integer or character vector/factor with classification cluster identity. Ensure matching order of cluster identity and samples in matrix for correct allocation of cluster numbers to samples.
abund	Type of abundances. Define whether input species matrix or data frame is percentage cover (abund = "percentage", default) or presence/absence data (abund = "pa", with values 0/1). You may use function cov2per to transform cover-abundance values from different scales into percentage cover.
type	Type of synoptic table output type = c("percfreq", "totalfreq", "mean", "median", "diffspec", "phi"). See Details.

Value

The function returns an (invisible) list of result components.

$syntable	unordered synoptic table for given species and clusters
$samplesize	total number of samples per cluster

Additionally for differential species character calculation:

$onlydiff	Synoptic table only with differential species
$others	List of non-differential species
$differentials	Lists differential species for each cluster

Details

For synoptic table calculation, six types are available.

• type = "percfreq" Creates a percentage frequency table (default)

• type = "totalfreq" Creates an absolute frequency table

• type = "mean" Calculates mean of species values given in matrix per cluster

• type = "median" Calculates median of species values given in matrix per cluster

• type = "diffspec" Calculates differential character of species according to Tsiripidis et al. 2009, with resulting character p = positive, n = negative, pn = positive- negative or no differential character (-). Consider that differential character is always restricted to some and not necessarily all of the other units, thus considering percentage frequency is essential for correct interpretation of the diagnostic species character. This calculation needs at least three groups.

• type = "phi" Calculates fidelity measure phi (algorithm basing on Sokal & Rohlf 1995, Bruelheide 2000). Values are ranging between -1 and 1 with high values near 1 indicating high fidelity.

For sorting the output synoptic table, use synsort function, providing several options.

Author(s)

Jenny Schellenberg ([e-mail]) and Friedemann von Lampe

References

Bruelheide, H. (2000): A new measure of fidelity and its application to defining species groups. Journal of Vegetation Science 11: 167-178. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.2307/3236796")}

Chytry, M., Tichy, L., Holt, J., Botta-Dukat, Z. (2002): Determination of diagnostic species with statistical fidelity measures. Journal of Vegetation Science 13: 79-90. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1111/j.1654-1103.2002.tb02025.x")}

Sokal, R.R. & Rohlf, F.J. (1995): Biometry. 3rd edition Freemann, New York.

Tsiripidis, I., Bergmeier, E., Fotiadis, G. & Dimopoulos, P. (2009): A new algorithm for the determination of differential taxa. Journal of Vegetation Science 20: 233-240. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1111/j.1654-1103.2009.05273.x")}

See Also

synsort

Examples

## Synoptic table of Scheden vegetation data
library(cluster)
pam1 <- pam(schedenveg, 4)  # PAM clustering with 4 clusters output

## 1) Unordered synoptic percentage frequency table
percfreq <- syntable(schedenveg, pam1$clustering, abund = "percentage",
                         type = "percfreq")
                         percfreq                   # view results

## 2) Differential species analysis
differential <- syntable(schedenveg, pam1$clustering, abund = "percentage",
                             type = "diffspec")
# show complete table with differential character of species
differential$syntable
# list differential species for second cluster
differential$differentials[2]

## 3) Synoptic table with phi fidelity
phitable <- syntable(schedenveg, pam1$clustering, abund = "percentage",
                         type = "phi")
phitable

## 4) Synoptic percentage frequency table based on historical classification from 1997
percfreq <- syntable(schedenveg, schedenenv$comm, abund = "percentage",
                         type = "percfreq")
percfreq

fgoral/goeveg documentation built on Feb. 27, 2024, 6:32 a.m.