Returns the degree of turnover in taxa composition along a gradient or transect.
Usage
turnover(object, ...)
index_whittaker(x, ...)
index_cody(x, ...)
index_routledge1(x, ...)
index_routledge2(x, ...)
index_routledge3(x, ...)
index_wilson(x, ...)
# S4 method for matrix
turnover(
object,
method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson"),
...
)
# S4 method for data.frame
turnover(
object,
method = c("whittaker", "cody", "routledge1", "routledge2", "routledge3", "wilson"),
...
)
# S4 method for matrix
index_whittaker(x)
# S4 method for matrix
index_cody(x)
# S4 method for matrix
index_routledge1(x)
# S4 method for matrix
index_routledge2(x)
# S4 method for matrix
index_routledge3(x)
# S4 method for matrix
index_wilson(x)
Arguments
- object, x
A \(m \times p\) matrix of count data or incidence data.
- ...
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. This assumes that the order of the matrix rows (from \(1\) to \(n\)) follows the progression along the gradient/transect.
whittaker
Whittaker measure.
cody
Cody measure.
routledge1
Routledge first measure.
routledge2
Routledge second measure.
routledge3
Routledge third measure. This is the exponential form of the second measure.
wilson
Wilson measure.
References
Cody, M. L. (1975). Towards a theory of continental species diversity: Bird distributions over Mediterranean habitat gradients. In M. L. Cody & J. M. Diamond (Eds.), Ecology and Evolution of Communities. Cambridge, MA: Harvard University Press, p. 214-257.
Routledge, R. D. (1977). On Whittaker's Components of Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932 .
Whittaker, R. H. (1960). Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563 .
Wilson, M. V., & Shmida, A. (1984). Measuring Beta Diversity with Presence-Absence Data. The Journal of Ecology, 72(3), 1055-1064. doi:10.2307/2259551 .
See also
Other diversity measures:
heterogeneity()
,
occurrence()
,
plot_diversity
,
rarefaction()
,
richness()
,
similarity()
,
simulate()
Examples
## Data from Magurran 1988, p. 162
trees <- matrix(
data = c(1, 1, 1, 0, 0, 0,
1, 1, 1, 1, 1, 1,
0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 0, 1),
nrow = 6, byrow = FALSE
)
colnames(trees) <- c("Birch", "Oak", "Rowan", "Beech", "Hazel", "Holly")
## Whittaker's measure
turnover(trees, "whittaker") # 1
#> [1] 1
## Cody's measure
turnover(trees, "cody") # 3
#> [1] 3
## Routledge's measures
turnover(trees, "routledge1") # 0.29
#> [1] 0.2857143
turnover(trees, "routledge2") # 0.56
#> [1] 0.5594978
turnover(trees, "routledge3") # 1.75
#> [1] 1.749794
## Wilson and Shmida's measure
turnover(trees, "wilson") # 1
#> [1] 1