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

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`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.

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