Description Usage Arguments Details Value Note Author(s) References See Also Examples
Function specaccum
finds species accumulation curves or the
number of species for a certain number of sampled sites or
individuals.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  specaccum(comm, method = "exact", permutations = 100,
conditioned =TRUE, gamma = "jack1", w = NULL, subset, ...)
## S3 method for class 'specaccum'
plot(x, add = FALSE, random = FALSE, ci = 2,
ci.type = c("bar", "line", "polygon"), col = par("fg"), ci.col = col,
ci.lty = 1, xlab, ylab = x$method, ylim,
xvar = c("sites", "individuals", "effort"), ...)
## S3 method for class 'specaccum'
boxplot(x, add = FALSE, ...)
fitspecaccum(object, model, method = "random", ...)
## S3 method for class 'fitspecaccum'
plot(x, col = par("fg"), lty = 1, xlab = "Sites",
ylab = x$method, ...)
## S3 method for class 'specaccum'
predict(object, newdata, interpolation = c("linear", "spline"), ...)
## S3 method for class 'fitspecaccum'
predict(object, newdata, ...)

comm 
Community data set. 
method 
Species accumulation method (partial match). Method

permutations 
Number of permutations with 
conditioned 
Estimation of standard deviation is conditional on the empirical dataset for the exact SAC 
gamma 
Method for estimating the total extrapolated number of species in the
survey area by function 
w 
Weights giving the sampling effort (an experimental feature that may be removed). 
subset 
logical expression indicating sites (rows) to keep: missing
values are taken as 
x 
A 
add 
Add to an existing graph. 
random 
... 
ci 
Multiplier used to get confidence intervals from standard
deviation (standard error of the estimate). Value 
ci.type 
Type of confidence intervals in the graph: 
col 
Colour for drawing lines. 
ci.col 
Colour for drawing lines or filling the

ci.lty 
Line type for confidence intervals or border of the

xlab,ylab 
Labels for 
ylim 
the y limits of the plot. 
xvar 
Variable used for the horizontal axis:

object 
Either a community data set or fitted 
model 
Nonlinear regression model ( 
lty 
line type code (see 
newdata 
Optional data used in prediction interpreted as number of sampling units (sites). If missing, fitted values are returned. 
interpolation 
Interpolation method used with 
... 
Other parameters to functions. 
Species accumulation curves (SAC) are used to compare diversity properties
of community data sets using different accumulator functions. The
classic method is "random"
which finds the mean SAC and its
standard deviation from random permutations of the data, or
subsampling without replacement (Gotelli & Colwell 2001).
The "exact"
method finds the
expected SAC using the method that was independently developed by
Ugland et al. (2003), Colwell et al. (2004) and Kindt et al. (2006).
The unconditional standard deviation for the exact SAC represents a
momentbased estimation that is not conditioned on the empirical data
set (sd for all samples > 0), unlike the conditional standard deviation
that was developed by Jari Oksanen (not published, sd=0 for all
samples). The unconditional standard deviation is based on an estimation
of the total extrapolated number of species in the survey area
(a.k.a. gamma diversity), as estimated by
function specpool
.
Method "coleman"
finds the expected SAC and its standard
deviation following Coleman et al. (1982). All these methods are
based on sampling sites without replacement. In contrast, the
method = "rarefaction"
finds the expected species richness and
its standard deviation by sampling individuals instead of sites. It
achieves this by applying function rarefy
with number of individuals
corresponding to average number of individuals per site.
The function has a plot
method. In addition, method = "random"
has summary
and boxplot
methods.
Function predict
can return the values corresponding to
newdata
using linear (approx
) or spline
(spline
) interpolation. The function cannot
extrapolate with linear interpolation, and with spline the type and
sensibility of the extrapolation depends on argument method
which is passed to spline
. If newdata
is not
given, the function returns the values corresponding to the data.
Function fitspecaccum
fits a nonlinear (nls
)
selfstarting species accumulation model. The input object
can be a result of specaccum
or a community in data frame. In
the latter case the function first fits a specaccum
model and
then proceeds with fitting the a nonlinear model. The function can
apply a limited set of nonlinear regression models suggested for
speciesarea relationship (Dengler 2009). All these are
selfStart
models. The permissible alternatives are
"arrhenius"
(SSarrhenius
), "gleason"
(SSgleason
), "gitay"
(SSgitay
),
"lomolino"
(SSlomolino
) of vegan
package. In addition the following standard R models are available:
"asymp"
(SSasymp
), "gompertz"
(SSgompertz
), "michaelismenten"
)
(SSmicmen
), "logis"
(SSlogis
),
"weibull"
(SSweibull
). See these functions for
model specification and details.
Function predict
uses predict.nls
, and you can
pass all arguments to that function. In addition, fitted
,
residuals
and coef
work on the result object.
Nonlinear regression may fail for any reason, and some of the
fitspecaccum
models are fragile and may not succeed.
Function specaccum
returns an object of class
"specaccum"
, and fitspecaccum
a model of class
"fitspecaccum"
that adds a few items to the
"specaccum"
(see the end of the list below):
call 
Function call. 
method 
Accumulator method. 
sites 
Number of sites. For 
richness 
The number of species corresponding to number of
sites. With 
sd 
The standard deviation of SAC (or its standard error). This
is 
perm 
Permutation results with 
fitted, residuals, coefficients 
Only in 
models 
Only in 
The SAC with method = "exact"
was
developed by Roeland Kindt, and its standard deviation by Jari
Oksanen (both are unpublished). The method = "coleman"
underestimates the SAC because it does not handle properly sampling
without replacement. Further, its standard deviation does not take
into account species correlations, and is generally too low.
Roeland Kindt r.kindt@cgiar.org and Jari Oksanen.
Coleman, B.D, Mares, M.A., Willis, M.R. & Hsieh, Y. (1982). Randomness, area and species richness. Ecology 63: 1121–1133.
Colwell, R.K., Mao, C.X. & Chang, J. (2004). Interpolating, extrapolating, and comparing incidencebased species accumulation curves. Ecology 85: 2717–2727.
Dengler, J. (2009). Which function describes the speciesarea relationship best? A review and empirical evaluation. Journal of Biogeography 36, 728–744.
Gotellli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in measurement and comparison of species richness. Ecol. Lett. 4, 379–391.
Kindt, R. (2003). Exact species richness for samplebased accumulation curves. Manuscript.
Kindt R., Van Damme, P. & Simons, A.J. (2006) Patterns of species richness at varying scales in western Kenya: planning for agroecosystem diversification. Biodiversity and Conservation, 10: 3235–3249.
Ugland, K.I., Gray, J.S. & Ellingsen, K.E. (2003). The speciesaccumulation curve and estimation of species richness. Journal of Animal Ecology 72: 888–897.
rarefy
and rrarefy
are related
individual based models. Other accumulation models are
poolaccum
for extrapolated richness, and
renyiaccum
and tsallisaccum
for
diversity indices. Underlying graphical functions are
boxplot
, matlines
,
segments
and polygon
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  data(BCI)
sp1 < specaccum(BCI)
sp2 < specaccum(BCI, "random")
sp2
summary(sp2)
plot(sp1, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
boxplot(sp2, col="yellow", add=TRUE, pch="+")
## Fit Lomolino model to the exact accumulation
mod1 < fitspecaccum(sp1, "lomolino")
coef(mod1)
fitted(mod1)
plot(sp1)
## Add Lomolino model using argument 'add'
plot(mod1, add = TRUE, col=2, lwd=2)
## Fit Arrhenius models to all random accumulations
mods < fitspecaccum(sp2, "arrh")
plot(mods, col="hotpink")
boxplot(sp2, col = "yellow", border = "blue", lty=1, cex=0.3, add= TRUE)
## Use nls() methods to the list of models
sapply(mods$models, AIC)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.