ace: Abundance- and Incidence-based Coverage Estimators
In fossil: Palaeoecological and Palaeogeographical Analysis Tools

Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the extrapolated species richness of a population using the Abundance- and Incidence-based Coerage Estimators

1 2	ACE(x, taxa.row = TRUE) ICE(x, taxa.row = TRUE)

`x`	a vector, matrix or data frame of positive integers or zero of any size
`taxa.row`	whether each row of the matrix is a different taxon; if so, value is T

These functions compute the ACE and ICE richness estimators, respectively. Both functions will accept a vector, matrix or data frame of any size made up of positive integers and zeros. Matrices are by default treated such that each row is a different taxon and each column is a sample or locality, however if they are arranged with the taxa as columns, change the argument taxa.row to FALSE Take note that ACE is intended only for use with abundance data, and not presence absence data. While ICE will accept abundance matrices, it will internally convert the matrix to presence absence data. Note that if ACE returns NaN or Inf as a value, that Chao1 will be used in it's place as per the recommendation made by Colwell in EstimateS.

A value representing a minimum number of species present in the assemblage if the entire population were to be censused.

Matthew Vavrek, with recommendations from the EstimateS reference manual by R.K. Colwell

Chao, A., M.-C. Ma, & M. C. K. Yang. 1993. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193-201.

Chao, A., W.-H. Hwang, Y.-C. Chen, and C.-Y. Kuo. 2000. Estimating the number of shared species in two communities. Statistica Sinica 10:227-246.

Chazdon, R. L., R. K. Colwell, J. S. Denslow, & M. R. Guariguata. 1998. Statistical methods for estimating species richness of woody regeneration in primary and secondary rain forests of NE Costa Rica. Pp. 285-309 in F. Dallmeier and J. A. Comiskey, eds. Forest biodiversity research, monitoring and modeling: Conceptual background and Old World case studies. Parthenon Publishing, Paris.

For related species estimators, see chao1, bootstrap and jack1, and spp.est to calculate multiple indices at once.

## sample vector
a<-c(0,5,1,1,2,0,0,1,0,0,8,45)
ACE(a)


## matrix format
a<-matrix(c(0,5,1,1,2,0,0,1,0,0,8,45),4,3)
ACE(a)
ICE(a)

## presence absence matrix
a<-matrix(c(0,1,1,1,1,0,0,1,0,0,1,1),4,3)
ACE(a)
ICE(a)