specaccum {vegan} | R Documentation |

Function `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 = "Sites", ylab = x$method, ylim, ...) ## S3 method for class 'specaccum': boxplot(x, add = FALSE, ...)

`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. |

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.

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 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` . |

`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. |

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.

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.

`rarefy`

and `rrarefy`

are related
individual based models. Other accumulation models are
`poolaccum`

for extrapoltated 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="+")

[Package *vegan* version 1.16-32 Index]