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 18  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, ...)
specslope(object, at)

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. 
subset 
logical expression indicating sites (rows) to keep: missing
values are taken as 
x 
A 
add 
Add to an existing graph. 
random 
Draw each random simulation separately instead of drawing their average and confidence intervals. 
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 
at 
Number of plots where the slope is evaluated. Can be a real number. 
... 
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 samplebased
rarefaction method that has been independently developed numerous
times (Chiarucci et al. 2008) and it is often known as Mao Tau
estimate (Colwell et al. 2012). 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). The
unconditional standard deviation is based on an estimation of the
extrapolated number of species in the survey area (a.k.a. gamma
diversity), as estimated by function specpool
. The
conditional standard deviation that was developed by Jari Oksanen (not
published, sd=0 for all samples). 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.
Methods "random"
and "collector"
can take weights
(w
) that give the sampling effort for each site. The weights
w
do not influence the order the sites are accumulated, but
only the value of the sampling effort so that not all sites are
equal. The summary results are expressed against sites even when the
accumulation uses weights (methods "random"
,
"collector"
), or is based on individuals
("rarefaction"
). The actual sampling effort is given as item
Effort
or Individuals
in the printed result. For
weighted "random"
method the effort refers to the average
effort per site, or sum of weights per number of sites. With
weighted method = "random"
, the averaged species richness is
found from linear interpolation of single random permutations.
Therefore at least the first value (and often several first) have
NA
richness, because these values cannot be interpolated in
all cases but should be extrapolated. The plot
function
defaults to display the results as scaled to sites, but this can be
changed selecting xvar = "effort"
(weighted methods) or
xvar = "individuals"
(with method = "rarefaction"
).
The summary
and boxplot
methods are available for
method = "random"
.
Function predict
for specaccum
can return the values
corresponding to newdata
. With method
"exact"
,
"rarefaction"
and "coleman"
the function uses analytic
equations for interpolated noninteger values, and for other methods
linear (approx
) or spline (spline
)
interpolation. If newdata
is not given, the function returns
the values corresponding to the data. NB., the fitted values with
method="rarefaction"
are based on rounded integer counts, but
predict
can use fractional noninteger counts with
newdata
and give slightly different results.
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.
When weights w
were used the fit is based on accumulated
effort and in model = "rarefaction"
on accumulated number of
individuals. The plot
is still based on sites, unless other
alternative is selected with xvar
.
Function predict
for fitspecaccum
uses
predict.nls
, and you can pass all arguments to that
function. In addition, fitted
, residuals
and
coef
, AIC
and deviance
work on the result
object.
Function specslope
evaluates the derivative of the species
accumulation curve at given number of sample plots, and gives the
rate of increase in the number of species. The function works with
specaccum
result object when this is based on analytic models
"exact"
, "rarefaction"
or "coleman"
, and with
nonlinear regression results of fitspecaccum
.
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 
effort 
Average sum of weights corresponding to the number of
sites when model was fitted with argument 
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 
weights 
Matrix of accumulated weights corresponding to the
columns of the 
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.
Chiarucci, A., Bacaro, G., Rocchini, D. & Fattorini, L. (2008). Discovering and rediscovering the samplebased rarefaction formula in the ecological literature. Commun. Ecol. 9: 121–123.
Coleman, B.D, Mares, M.A., Willis, M.R. & Hsieh, Y. (1982). Randomness, area and species richness. Ecology 63: 1121–1133.
Colwell, R.K., Chao, A., Gotelli, N.J., Lin, S.Y., Mao, C.X., Chazdon, R.L. & Longino, J.T. (2012). Models and estimators linking individualbased and samplebased rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5: 3–21.
Dengler, J. (2009). Which function describes the speciesarea relationship best? A review and empirical evaluation. Journal of Biogeography 36, 728–744.
Gotelli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in measurement and comparison of species richness. Ecol. Lett. 4, 379–391.
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)

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