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"), ...)plot function.specpool result object.xyplot.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 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, $a_1$ and $a_2$ 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:
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:
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 Rsource 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
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,])Run the code above in your browser using DataLab