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

In 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 or classification methods. 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 dataframe and a numeric vector of cluster identity input. The unordered output table can be sorted automatically with synsort function in this package.

Usage

syntable(spec, cluster, abund = "perc", type = "percfreq")

Arguments

spec	Species matrix or dataframe with species in columns and samples in rows. Values must be numeric, with point "." as decimal character, or integer. Missing values, NA or NaN are not allowed. Species and sample names must be defined as column- and rownames, respectively.
cluster	Integer vector with classification cluster identity. Ensure matching order of cluster identity and samples in dataframe for correct allocation of cluster numbers to samples.
abund	Data input type. Define whether input species matrix or dataframe is presence/absence data (abund = "freq") or percentage cover (abund = "perc", default).
type	Type of synoptic table output type = c("percfreq", "totalfreq", "mean", "median", "diffspec", "phi"). See Details.

Value

The function returns a list of result components.

$syntable	unordered synoptic table for given species and clusters
$samplesize	total samples in clusters

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" Default, creates a percentage frequency table

• type = "totalfreq" Creates an absolute frequency table

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

• type = "median" Calculates median of species values given in spec 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.

• 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])

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
unordered <- syntable(schedenveg, pam1$clustering, abund = "perc",
                      type = "percfreq")
unordered                   # view results

## 2) differential species analysis
differential <- syntable(schedenveg, pam1$clustering, abund = "perc",
                         type = "diffspec")
# show complete table with differential character of species
differential$syntable
# list differential species for second cluster
differential$differentials$group2

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

goeveg documentation built on July 9, 2023, 7:35 p.m.