specpool {vegan} | R Documentation |

The functions estimate the extrapolated species richness in a species
pool, or the number of unobserved species. Function `specpool`

is based on incidences in sample sites, and gives a single estimate
for a collection of sample sites (matrix). Function `estimateR`

is based on abundances (counts) on single sample site.

specpool(x, pool) estimateR(x, ...) specpool2vect(X, index = c("jack1","jack2", "chao", "boot","Species")) poolaccum(x, permutations = 100, minsize = 3) estaccumR(x, permutations = 100) ## S3 method for class 'poolaccum': summary(object, display, alpha = 0.05, ...) ## S3 method for class 'poolaccum': plot(x, alpha = 0.05, type = c("l","g"), ...)

`x` |
Data frame or matrix with species data or the analysis result
for `plot` function. |

`pool` |
A vector giving a classification for pooling the sites in the species data. If missing, all sites are pooled together. |

`X, object` |
A `specpool` result object. |

`index` |
The selected index of extrapolated richness. |

`permutations` |
Number of permutations of sampling order of sites. |

`minsize` |
Smallest number of sampling units reported. |

`display` |
Indices to be displayed. |

`alpha` |
Level of quantiles shown. This proportion will be left outside symmetric limits. |

`type` |
Type of graph produced in `xyplot` . |

`...` |
Other parameters (not used). |

Many species will always remain unseen or undetected in a collection of sample plots. The function uses some popular ways of estimating the number of these unseen species and adding them to the observed species richness (Palmer 1990, Colwell & Coddington 1994).

The incidence-based estimates in `specpool`

use the frequencies
of species in a collection of sites.
In the following, *S_P* is the extrapolated richness in a pool,
*S_0* is the observed number of species in the
collection, *a1* and *a2* are the number of species
occurring only in one or only in two sites in the collection, *p_i*
is the frequency of species *i*, and *N* is the number of
sites in the collection. The variants of extrapolated richness in
`specpool`

are:

Chao | S_P = S_0 + a1^2/(2*a2) |

First order jackknife | S_P = S_0 + a1*(N-1)/N |

Second order jackknife | S_P = S_0 + a1*(2*n-3)/n - a2*(n-2)^2/n/(n-1) |

Bootstrap | S_P = S_0 + Sum (1-p_i)^N |

The abundance-based estimates in `estimateR`

use counts (frequencies) of
species in a single site. If called for a matrix or data frame, the
function will give separate estimates for each site. The two
variants of extrapolated richness in `estimateR`

are Chao
(unbiased variant) and ACE. In the Chao estimate
*a_i* refers to number of species with abundance *i* instead
of incidence:

Chao | S_P = S_0 + a1*(a1-1)/(2*(a2+1)) |

ACE | S_P = S_abund + S_rare/C_ace + a1/C_ace * gamma^2 |

where | C_{ace} = 1- a1/N_{rare} |

gamma^2 = max( S_rare/C_ace (sum[i=1..10] i*(i-1)*a_i) / N_rare/(N_rare-1) -1 , 0) |

Here *a_i* refers to number of species with abundance *i*
and *S_rare* is the number of rare
species,
*S_abund* is the number of abundant species, with an
arbitrary
threshold of abundance 10 for rare species, and *N_rare* is
the number
of individuals in rare species.

Functions estimate the standard errors of the estimates. These
only concern the number of added species, and assume that there is
no variance in the observed richness.
The equations of standard errors are too complicated to be reproduced in
this help page, but they can be studied in the **R** source code of the
function.
The standard error are based on the following sources: Chao (1987)
for the Chao estimate and Smith and van Belle (1984) for the
first-order Jackknife and the bootstrap (second-order jackknife is
still missing).
The variance estimator of *S_ace* was
developed by Bob O'Hara (unpublished).

Functions `poolaccum`

and `estaccumR`

are similar to
`specaccum`

, but estimate extrapolated richness indices
of `specpool`

or `estimateR`

in addition to number of
species for random ordering of sampling units. Function
`specpool`

uses presence data and `estaccumR`

count
data. The functions share `summary`

and `plot`

methods. The `summary`

returns quantile envelopes of
permutations corresponding the given level of `alpha`

and
standard deviation of permutations for each sample size. The
`plot`

function shows the mean and envelope of permutations
with given `alpha`

for models. The selection of models can be
restricted and order changes using the `display`

argument in
`summary`

or `plot`

. For configuration of `plot`

command, see `xyplot`

Function `specpool`

returns a data frame with entries for
observed richness and each of the indices for each class in
`pool`

vector. The utility function `specpool2vect`

maps
the pooled values into a vector giving the value of selected
`index`

for each original site. Function `estimateR`

returns the estimates and their standard errors for each
site. Functions `poolaccum`

and `estimateR`

return
matrices of permutation results for each richness estimator, the
vector of sample sizes and a table of `means`

of permutations
for each estimator.

The functions are based on assumption that there is a species pool:
The community is closed so that there is a fixed pool size *S_P*.
Such cases may exist, although I have not seen them yet. All indices
are biased for open communities.

See http://viceroy.eeb.uconn.edu/EstimateS for a more complete (and positive) discussion and alternative software for some platforms.

Bob O'Hara (`estimateR`

) and Jari Oksanen.

Chao, A. (1987). Estimating the population size for capture-recapture
data with unequal catchability. *Biometrics* 43, 783–791.

Colwell, R.K. & Coddington, J.A. (1994). Estimating terrestrial
biodiversity through
extrapolation. *Phil. Trans. Roy. Soc. London* B 345, 101–118.

Palmer, M.W. (1990). The estimation of species richness by
extrapolation. *Ecology* 71, 1195–1198.

Smith, E.P & van Belle, G. (1984). Nonparametric estimation of
species richness. *Biometrics* 40, 119–129.

`veiledspec`

, `diversity`

, `beals`

,
`specaccum`

.

data(dune) data(dune.env) attach(dune.env) pool <- specpool(dune, Management) pool op <- par(mfrow=c(1,2)) boxplot(specnumber(dune) ~ Management, col="hotpink", border="cyan3", notch=TRUE) boxplot(specnumber(dune)/specpool2vect(pool) ~ Management, col="hotpink", border="cyan3", notch=TRUE) par(op) data(BCI) ## Accumulation model pool <- poolaccum(BCI) summary(pool, display = "chao") plot(pool) ## Quantitative model estimateR(BCI[1:5,])

[Package *vegan* version 1.16-32 Index]