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,
              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, ...)

### 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"`.
`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 `specpool`
`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.
`ylim` | the y limits of the plot.
`...` | 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 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.

### 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.

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.

### See Also

`rarefy` and `renyiaccum`. 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="+")

[Package vegan version 1.8-8 Index]