specaccum finds species accumulation curves or the
number of species for a certain number of sampled sites or
individuals.specaccum(comm, method = "exact", permutations = 100,
conditioned =TRUE, gamma = "jack1", ...)
## S3 method for class 'specaccum':
plot(x, add = FALSE, ci = 2, ci.type = c("bar", "line", "polygon"),
col = par("fg"), ci.col = col, ci.lty = 1, xlab,
ylab = x$method, ylim, xvar = c("sites", "individuals"), ...)
## S3 method for class 'specaccum':
boxplot(x, add = FALSE, ...)
fitspecaccum(object, model, method = "random", ...)
## S3 method for class 'fitspecaccum':
plot(x, col = par("fg"), lty = 1, xlab = "Sites",
ylab = x$method, ...)
## S3 method for class 'specaccum':
predict(object, newdata, interpolation = c("linear", "spline"), ...)
## S3 method for class 'fitspecaccum':
predict(object, newdata, ...)"collector"
adds sites in the order they happen to be in the data,
"random" adds sites in random order, "exact" finds the
expected (mean) species rimethod =
"random".specpoolspecaccum result objectci = 0
suppresses drawing confidence intervals."bar"
draws vertical bars, "line" draws lines, and
"polygon" draws a shaded area."polygon"."polygon".x (defaults xvar) and
y axis."individuals" can be used only with
method = "rarefaction".specaccum model.nls). See Details.par.newdata.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):method = "rarefaction" this
is the number of sites corresponding to a certain number of
individuals and generally not an integer, and the average
number of individuals is also returned in item individuals.method = "collector" this is the observed
richness, for other methods the average or expected richness.NULL in method = "collector", and it
is estimated from permutations in method = "random", and from
analytic equations in other methods.method = "random" and
NULL in other cases. Each column in perm holds one
permutation.fitspecacum:
fitted values, residuals and nonlinear model coefficients. For
method = "random" these are matrices with a column for
each random accumulation.fitspecaccum: list of fitted
nls models (see Examples on accessing these models)."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
moment-based 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)
self-starting 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
species-area relationship (Dengler 2009). All these are
selfStart models. The permissible alternatives are
"arrhenius" (SSarrhenius), "gleason"
(SSgleason), "gitay" (SSgitay),
"lomolino" (SSlomolino) of "asymp" (SSasymp), "gompertz"
(SSgompertz), "michaelis-menten")
(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.
Colwell, R.K., Mao, C.X. & Chang, J. (2004). Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85: 2717--2727.
Dengler, J. (2009). Which function describes the species-area 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 sample-based 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, online first: DOI 10.1007/s10531-005-0311-9
Ugland, K.I., Gray, J.S. & Ellingsen, K.E. (2003). The species-accumulation 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.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)Run the code above in your browser using DataLab