Turnover

Returns the degree of turnover in taxa composition along a gradient or transect.

Usage

turnover(object, ...)

# S4 method for class 'matrix'
turnover(
  object,
  ...,
  method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson")
)

# S4 method for class 'data.frame'
turnover(
  object,
  ...,
  method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson")
)

Arguments

object

A \(m \times p\) numeric matrix or data.frame of count data or incidence data. A data.frame will be coerced to a numeric matrix via data.matrix().

...

Further arguments to be passed to internal methods.

method

A character string specifying the method to be used (see details). Any unambiguous substring can be given.

Value

A numeric vector.

Details

The following methods can be used to ascertain the degree of turnover in taxa composition along a gradient (\(\beta\)-diversity) on qualitative (presence/absence) data:

cody

Cody measure.

routledge1

Routledge first measure.

routledge2

Routledge second measure.

routledge3

Routledge third measure (exponential form of the second measure).

whittaker

Whittaker measure.

wilson

Wilson measure.

This assumes that the order of the matrix rows (from \(1\) to \(n\)) follows the progression along the gradient/transect.

See also

index_cody(), index_routledge1(), index_routledge2(), index_routledge3(), index_whittaker(), index_wilson()

Other diversity measures: heterogeneity(), occurrence(), plot_diversity, plot_rarefaction, profiles(), rarefaction(), richness(), she(), similarity(), simulate()

Author

N. Frerebeau

Examples

## Data from Magurran 1988, p. 162
data("woodland")

## Whittaker's measure
turnover(woodland, "whittaker") # 1
#> [1] 1

## Cody's measure
turnover(woodland, "cody") # 3
#> [1] 1

## Routledge's measures
turnover(woodland, "routledge1") # 0.29
#> [1] 1
turnover(woodland, "routledge2") # 0.56
#> [1] 1
turnover(woodland, "routledge3") # 1.75
#> [1] 1

## Wilson and Shmida's measure
turnover(woodland, "wilson") # 1
#> [1] 1