Description Usage Arguments Details Value Note Author(s) References See Also Examples
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.
1 2 3 4 5 6 7 8 9  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 
pool 
A vector giving a classification for pooling the sites in the species data. If missing, all sites are pooled together. 
X, object 
A 
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 
... 
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 incidencebased 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*(N1)/N 
Second order jackknife  S_P = S_0 + a1*(2*n3)/n  a2*(n2)^2/n/(n1) 
Bootstrap  S_P = S_0 + Sum (1p_i)^N 
The abundancebased 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*(a11)/(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*(i1)*a_i) / N_rare/(N_rare1) 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 firstorder Jackknife and the bootstrap (secondorder 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 capturerecapture 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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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,])

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