accumresult {BiodiversityR} R Documentation

## Alternative Species Accumulation Curve Results

### Description

Provides alternative methods of obtaining species accumulation results than provided by functions specaccum and plot.specaccum (vegan).

### Usage

accumresult(x, y="", factor="", level, scale="", method="exact", permutations=100,
conditioned=T, gamma="boot", ...)

accumplot(xr, addit=F, labels="", col=1, ci=2, pch=1, type="p", cex=1,
xlim=c(1, xmax), ylim=c(1, rich),
xlab="sites", ylab="species richness", cex.lab=1, cex.axis=1, ...)

accumcomp(x, y="", factor, scale="", method="exact", permutations=100,
conditioned=T, gamma="boot", plotit=T, labelit=T, legend=T, rainbow=T,
xlim=c(1, max), ylim=c(0, rich),type="p", xlab="sites",
ylab="species richness", cex.lab=1, cex.axis=1, ...)


### Arguments

 x Community data frame with sites as rows, species as columns and species abundance as cell values. y Environmental data frame. factor Variable of the environmental data frame that defines subsets to calculate species accumulation curves for. level Level of the variable to create the subset to calculate species accumulation curves. scale Continuous variable of the environmental data frame that defines the variable that scales the horizontal axis of the species accumulation curves. method Method of calculating the species accumulation curve (as in function specaccum). 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 to calculate the species accumulation curve (as in function specaccum). conditioned Estimation of standard deviation is conditional on the empirical dataset for the exact SAC (as in function specaccum). gamma Method for estimating the total extrapolated number of species in the survey area (as in specaccum). addit Add species accumulation curve to an existing graph. xr Result from specaccum or accumresult. col Colour for drawing lines of the species accumulation curve (as in function plot.specaccum). labels Labels to plot at left and right of the species accumulation curves. ci Multiplier used to get confidence intervals from standard deviatione (as in function plot.specaccum). pch Symbol used for drawing the species accumulation curve (as in function points). type Type of plot (as in function plot). cex Character expansion factor (as in function plot). xlim Limits for the X = horizontal axis. ylim Limits for the Y = vertical axis. xlab Label for the X = horizontal axis (as in function title). ylab Label for the Y = vertical axis (as in function title). cex.lab The magnification to be used for X and Y labels relative to the current setting of cex. (as in function par). cex.axis The magnification to be used for axis annotation relative to the current setting of cex (as in function par). plotit Plot the results. labelit Label the species accumulation curves with the levels of the categorical variable. legend Add the legend (you need to click in the graph where the legend needs to be plotted). rainbow Use rainbow colouring for the different curves. ... Other items passed to function specaccum or plot.specaccum.

### Details

These functions provide some alternative methods of obtaining species accumulation results, although function specaccum is called by these functions to calculate the actual species accumulation curve.

Functions accumresult and accumcomp allow to calculate species accumulation curves for subsets of the community and environmental data sets. Function accumresult calculates the species accumulation curve for the specified level of a selected environmental variable. Method accumcomp calculates the species accumulation curve for all levels of a selected environmental variable separatedly. Both methods allow to scale the horizontal axis by multiples of the average of a selected continuous variable from the environmental dataset (hint: add the abundance of each site to the environmental data frame to scale accumulation results by mean abundance).

Functions accumcomp and accumplot provide alternative methods of plotting species accumulation curve results, although function plot.specaccum is called by these functions. When you choose to add a legend, make sure that you click in the graph on the spot where you want to put the legend.

### Value

The functions provide alternative methods of obtaining species accumulation curve results, although results are similar as obtained by functions specaccum and plot.specaccum.

### Author(s)

Roeland Kindt (World Agroforestry Centre)

### References

Kindt, R. & Coe, R. (2005) Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies.

accumcomp.long

### Examples

library(vegan)
data(dune.env)
data(dune)
dune.env\$site.totals <- apply(dune,1,sum)
Accum.1 <- accumresult(dune, y=dune.env, scale='site.totals', method='exact', conditioned=TRUE)
Accum.1
accumplot(Accum.1)

Accum.2 <- accumcomp(dune, y=dune.env, factor='Management', method='exact',
legend=FALSE, conditioned=TRUE, scale='site.totals')
## CLICK IN THE GRAPH TO INDICATE WHERE THE LEGEND NEEDS TO BE PLACED FOR
## OPTION WHERE LEGEND=TRUE (DEFAULT).

## Not run:
# ggplot2 plotting method

data(warcom)
data(warenv)

Accum.3 <- accumcomp(warcom, y=warenv, factor='population',
method='exact', conditioned=F, plotit=F)

library(ggplot2)

# possibly need for extrafont::loadfonts(device="win") to have Arial
# as alternative, use library(ggThemeAssist)
BioR.theme <- theme(
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.line = element_line("gray25"),
text = element_text(size = 12, family="Arial"),
axis.text = element_text(size = 10, colour = "gray25"),
axis.title = element_text(size = 14, colour = "gray25"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.key = element_blank())

accum.long3 <- accumcomp.long(Accum.3, ci=NA, label.freq=5)

plotgg1 <- ggplot(data=accum.long3, aes(x = Sites, y = Richness, ymax =  UPR, ymin= LWR)) +
scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), size=2) +
geom_point(data=subset(accum.long3, labelit==TRUE),
aes(colour=Grouping, shape=Grouping), size=5) +
geom_ribbon(aes(colour=Grouping), alpha=0.2, show.legend=FALSE) +
BioR.theme +
scale_color_brewer(palette = "Set1") +
labs(x = "Trees", y = "Loci", colour = "Population", shape = "Population")

plotgg1

## End(Not run) # dontrun


[Package BiodiversityR version 2.16-1 Index]