rarefaction: Rarefaction

In tesselle/tabula: Analysis and Visualization of Archaeological Count Data

Rarefaction

Description

Computes Hurlbert's unbiased estimate of Sander's rarefaction.

Usage

rarefaction(object, ...)

index_hurlbert(x, ...)

## S4 method for signature 'matrix'
rarefaction(object, sample = NULL, method = c("hurlbert"), step = 1)

## S4 method for signature 'data.frame'
rarefaction(object, sample = NULL, method = c("hurlbert"), step = 1)

## S4 method for signature 'numeric'
index_hurlbert(x, sample, ...)

Arguments

object	A m x p numeric matrix or data.frame of count data (absolute frequencies).
...	Currently not used.
x	A numeric vector of count data (absolute frequencies).
sample	A length-one numeric vector giving the sub-sample size. The size of sample should be smaller than total community size.
method	A character string or vector of strings specifying the index to be computed (see details). Any unambiguous substring can be given.
step	An integer giving the increment of the sample size.

Value

• rarefaction() returns a RarefactionIndex object.

• index_*() return a numeric vector.

Details

The number of different 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.

Author(s)

N. Frerebeau

References

Hurlbert, S. H. (1971). The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology, 52(4), 577-586. doi: 10.2307/1934145.

Sander, H. L. (1968). Marine Benthic Diversity: A Comparative Study. The American Naturalist, 102(925), 243-282.

See Also

Other diversity measures: heterogeneity(), occurrence(), plot_diversity, richness(), similarity(), simulate(), turnover()

Examples

## Richness
## Margalef and Menhinick index
## Data from Magurran 1988, p. 128-129
trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1,
                        1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0),
               nrow = 2, byrow = TRUE, dimnames = list(c("A", "B"), NULL))
richness(trap, method = "margalef") # 2.55 1.88
richness(trap, method = "menhinick") # 1.95 1.66

## Asymptotic species richness
## Chao1-type estimators
## Data from Chao & Chiu 2016
brazil <- matrix(
  data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41,
                   45, 46, 49, 52, 89, 110, 123, 140),
             times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4,
                       3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1,
                       0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)),
  nrow = 1, byrow = TRUE
)

composition(brazil, method = c("chao1"), unbiased = FALSE) # 461.625
composition(brazil, method = c("ace"), k = 10) # 445.822

## Rarefaction
rarefaction(trap, sample = 13) # 6.56 6.00

tesselle/tabula documentation built on June 27, 2022, 7:21 p.m.