specaccum {vegan}R Documentation

Species Accumulation Curves

Description

Function specaccum finds species accumulation curves or the number of species for a certain number of sampled sites or individuals.

Usage

specaccum(comm, method = "exact", permutations = 100, ...)
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, ...)
boxplot(x, add = FALSE, ...)

Arguments

comm Community data set.
method Species accumulation method (partial match). Method "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 richness, "coleman" finds the expected richness following Coleman et al. 1982, and "rarefaction" finds the mean when accumulating individuals instead of sites.
permutations Number of permutations with method = "random".
x A specaccum result object
add Add to an existing graph.
ci Multiplier used to get confidence intervals from standard deviation (standard error of the estimate). Value ci = 0 suppresses drawing confidence intervals.
ci.type Type of confidence intervals in the graph: "bar" draws vertical bars, "line" draws lines, and "polygon" draws a shaded area.
col Colour for drawing lines.
ci.col Colour for drawing lines or filling the "polygon".
ci.lty Line type for confidence intervals or border of the "polygon".
xlab,ylab Labels for x and y axis.
... Other parameters to functions.

Details

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 of Kindt (2003), and its standard deviation. 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.

Value

The function returns an object of class "specaccum" with items:

call Function call.
method Accumulator method.
sites Number of sites. For method = "rarefaction" this is the average number of sites corresponding to a certain number of individuals.
richness The number of species corresponding to number of sites. With method = "collector" this is the observed richness, for other methods the average or expected richness.
sd The standard deviation of SAC (or its standard error). This is NULL in method = "collector", and it is estimated from permutations in method = "random", and from analytic equations in other methods.
perm Permutation results with method = "random" and NULL in other cases. Each column in perm holds one permutation.

Note

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.

Author(s)

Roeland Kindt r.kindt@cgiar.org and Jari Oksanen.

References

Coleman, B.D, Mares, M.A., Willis, M.R. & Hsieh, Y. (1982). Randomness, area and species richness. Ecology 63: 1121–1133.

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.

See Also

rarefy. Underlying graphical functions are boxplot, matlines, segments and polygon.

Examples

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="+")

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