rarefy
Rarefaction Species Richness
Rarefied species richness for community ecologists.
 Keywords
 univar
Usage
rarefy(x, sample, se = FALSE, MARGIN = 1)
rrarefy(x, sample)
drarefy(x, sample)
rarecurve(x, step = 1, sample, xlab = "Sample Size", ylab = "Species", label = TRUE, col, lty, ...)
rareslope(x, sample)
Arguments
 x
 Community data, a matrixlike object or a vector.
 MARGIN
 Margin for which the index is computed.
 sample
 Subsample size for rarefying community, either a single value or a vector.
 se
 Estimate standard errors.
 step
 Step size for sample sizes in rarefaction curves.
 xlab, ylab
 Axis labels in plots of rarefaction curves.
 label
 Label rarefaction curves by rownames of
x
(logical).  col, lty
 plotting colour and line type, see
par
. Can be a vector of lengthnrow(x)
, one per sample, and will be extended to such a length internally.  ...
 Parameters passed to
nlm
, or toplot
,lines
andordilabel
inrarecurve
.
Details
Function rarefy
gives the expected species richness in random
subsamples of size sample
from the community. The size of
sample
should be smaller than total community size, but the
function will work for larger sample
as well (with a warning)
and return nonrarefied species richness (and standard error =
0). If sample
is a vector, rarefaction of all observations is
performed for each sample size separately. Rarefaction can be
performed only with genuine counts of individuals. The function
rarefy
is based on Hurlbert's (1971) formulation, and the
standard errors on Heck et al. (1975).
Function rrarefy
generates one randomly rarefied community
data frame or vector of given sample
size. The sample
can be a vector giving the sample sizes for each row. If the
sample
size is equal to or smaller than the observed number
of individuals, the nonrarefied community will be returned. The
random rarefaction is made without replacement so that the variance
of rarefied communities is rather related to rarefaction proportion
than to the size of the sample
.
Function drarefy
returns probabilities that species occur in
a rarefied community of size sample
. The sample
can be
a vector giving the sample sizes for each row. If the sample
is equal to or smaller than the observed number of individuals, all
observed species will have sampling probability 1.
Function rarecurve
draws a rarefaction curve for each row of
the input data. The rarefaction curves are evaluated using the
interval of step
sample sizes, always including 1 and total
sample size. If sample
is specified, a vertical line is
drawn at sample
with horizontal lines for the rarefied
species richnesses.
Function rareslope
calculates the slope of rarecurve
(derivative of rarefy
) at given sample
size; the
sample
need not be an integer.
Value

A vector of rarefied species richness values. With a single
sample
and se = TRUE
, function rarefy
returns a
2row matrix with rarefied richness (S
) and its standard error
(se
). If sample
is a vector in rarefy
, the
function returns a matrix with a column for each sample
size,
and if se = TRUE
, rarefied richness and its standard error are
on consecutive lines.Function rarecurve
returns invisible
list of
rarefy
results corresponding each drawn curve.
References
Heck, K.L., van Belle, G. & Simberloff, D. (1975). Explicit calculation of the rarefaction diversity measurement and the determination of sufficient sample size. Ecology 56, 14591461. Hurlbert, S.H. (1971). The nonconcept of species diversity: a critique and alternative parameters. Ecology 52, 577586.
See Also
Use specaccum
for species accumulation curves
where sites are sampled instead of individuals. specpool
extrapolates richness to an unknown sample size.
Examples
data(BCI)
S < specnumber(BCI) # observed number of species
(raremax < min(rowSums(BCI)))
Srare < rarefy(BCI, raremax)
plot(S, Srare, xlab = "Observed No. of Species", ylab = "Rarefied No. of Species")
abline(0, 1)
rarecurve(BCI, step = 20, sample = raremax, col = "blue", cex = 0.6)