rarefaction | R Documentation |
Rarefaction
rarefaction(object, ...)
## S4 method for signature 'matrix'
rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1)
## S4 method for signature 'data.frame'
rarefaction(object, sample = NULL, method = c("hurlbert", "baxter"), step = 1)
object |
A |
... |
Currently not used. |
sample |
A length-one |
method |
A |
step |
An |
A RarefactionIndex object.
The following rarefaction measures are available for count data:
baxter
Baxter's rarefaction.
hurlbert
Hurlbert's unbiased estimate of Sander's rarefaction.
The number of observed taxa, provides an instantly comprehensible
expression of diversity. While the number of taxa within a sample
is easy to ascertain, as a term, it makes little sense: some taxa
may not have been seen, or there may not be a fixed number of taxa
(e.g. in an open system; Peet 1974). As an alternative, richness
(S
) can be used for the concept of taxa number (McIntosh 1967).
It is not always possible to ensure that all sample sizes are equal
and the number of different taxa increases with sample size and
sampling effort (Magurran 1988). Then, rarefaction
(E(S)
) is the number of taxa expected if all samples were of a
standard size (i.e. taxa per fixed number of individuals).
Rarefaction assumes that imbalances between taxa are due to sampling and
not to differences in actual abundances.
N. Frerebeau
index_baxter()
, index_hurlbert()
, plot()
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
plot_rarefaction
,
profiles()
,
richness()
,
she()
,
similarity()
,
simulate()
,
turnover()
## Data from Conkey 1980, Kintigh 1989
data("cantabria")
## Replicate fig. 3 from Baxter 2011
rare <- rarefaction(cantabria, sample = 23, method = "baxter")
plot(rare, panel.first = graphics::grid())
## Change graphical parameters
plot(rare, color = color("bright")(5), symbol = 1:5)
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