heterogeneity {tabula} | R Documentation |
Heterogeneity and Evenness
Description
-
heterogeneity()
computes an heterogeneity or dominance index. -
evenness()
computes an evenness measure.
Usage
heterogeneity(object, ...)
evenness(object, ...)
## S4 method for signature 'matrix'
heterogeneity(
object,
...,
method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson")
)
## S4 method for signature 'data.frame'
heterogeneity(
object,
...,
method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson")
)
## S4 method for signature 'matrix'
evenness(
object,
...,
method = c("shannon", "brillouin", "mcintosh", "simpson")
)
## S4 method for signature 'data.frame'
evenness(
object,
...,
method = c("shannon", "brillouin", "mcintosh", "simpson")
)
Arguments
object |
A |
... |
Further arguments to be passed to internal methods (see below). |
method |
A |
evenness |
A |
Details
Diversity measurement assumes that all individuals in a specific taxa are equivalent and that all types are equally different from each other (Peet 1974). A measure of diversity can be achieved by using indices built on the relative abundance of taxa. These indices (sometimes referred to as non-parametric indices) benefit from not making assumptions about the underlying distribution of taxa abundance: they only take relative abundances of the species that are present and species richness into account. Peet (1974) refers to them as indices of heterogeneity.
Diversity indices focus on one aspect of the taxa abundance and emphasize either richness (weighting towards uncommon taxa) or dominance (weighting towards abundant taxa; Magurran 1988).
Evenness is a measure of how evenly individuals are distributed across the sample.
Value
-
heterogeneity()
returns an HeterogeneityIndex object. -
evenness()
returns an EvennessIndex object.
Heterogeneity and Evenness Measures
The following heterogeneity index and corresponding evenness measures are available (see Magurran 1988 for details):
berger
boone
brillouin
mcintosh
shannon
simpson
The berger
, mcintosh
and simpson
methods return a dominance index,
not the reciprocal or inverse form usually adopted, so that an increase in
the value of the index accompanies a decrease in diversity.
Author(s)
N. Frerebeau
References
Magurran, A. E. (1988). Ecological Diversity and its Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0.
Peet, R. K. (1974). The Measurement of Species Diversity. Annual Review of Ecology and Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441.
See Also
index_berger()
, index_boone()
, index_brillouin()
,
index_mcintosh()
, index_shannon()
, index_simpson()
Other diversity measures:
occurrence()
,
profiles()
,
rarefaction()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
Examples
## Data from Conkey 1980, Kintigh 1989
data("cantabria")
## Shannon diversity index
(h <- heterogeneity(cantabria, method = "shannon"))
(e <- evenness(cantabria, method = "shannon"))
plot(h)