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 = "Jack.1", ...)
## 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 = "Sites",
ylab = x$method, ylim, ...)
## S3 method for class 'specaccum':
boxplot(x, add = FALSE, ...)"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 and y axis."specaccum" with items:method = "rarefaction" this
is the average number of sites corresponding to a certain number of
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."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.
Colwell, R.K., Mao, C.X. & Chang, J. (2004). Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85: 2717--2727.
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 renyiaccum.
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="+")Run the code above in your browser using DataLab