rankabundance {BiodiversityR}

Rank Abundance Curves

Description

Provides methods of calculating rank-abundance curves.

Usage

rankabundance(x, y="", factor="", level, digits=1, t=qt(0.975, df=n-1))

rankabunplot(xr, addit=F, labels="", scale="abundance", scaledx=F, type="o",
    xlim=c(min(xpos), max(xpos)),
    ylim=c(0, max(x[,scale])),
    specnames=c(1:5), srt=0, ...)

rankabuncomp(x, y="", factor, return.data=T, specnames=c(1:3),
    scale="abundance", scaledx=F, type="o", rainbow=T,
    legend=T, xlim=c(1, max1), ylim=c(0, max2), ...)

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 rank abundance curves for.

level

Level of the variable to create the subset to calculate rank abundance curves.

digits

Number of digits in the results.

t

t-value to calculate confidence interval limits for the species proportion for cluster sampling (following Hayek and Buzas 1997).

xr

Result from rankabundance.

addit

Add rank abundance curve to an existing graph.

labels

Labels to plot at left of the rank abundance curves.

scale

Method of scaling the vertical axis. Method "abundance" uses abundance, "proportion" uses proportional abundance (species abundance / total abundance), "logabun" calculates the logarithm of abundance using base 10 and "accumfreq" accumulates the proportional abundance.

scaledx

Scale the horizontal axis to 100 percent of total number of species.

type

Type of plot (as in function plot)

xlim

Limits for the horizontal axis.

ylim

Limits for the vertical axis.

specnames

Vector positions of species names to add to the rank-abundance curve.

srt

The string rotation in degrees of the species names (as in par).

return.data

Return the data used for plotting.

rainbow

Use rainbow colouring for the different curves.

legend

Add the legend (you need to click in the graph where the legend needs to be plotted).

...

Other arguments to be passed to functions plot or points.

Details

These functions provide methods of calculating and plotting rank-abundance curves.

The vertical axis can be scaled by various methods. Method "abundance" uses abundance, "proportion" uses proportional abundance (species abundance / total abundance), "logabun" calculates the logarithm of abundance using base 10 and "accumfreq" accumulates the proportional abundance.

The horizontal axis can be scaled by the total number of species, or by 100 percent of all species by option "scaledx".

The method of calculating the confidence interval for species proportion is described in Hayek and Buzas (1997).

Functions rankabundance and rankabuncomp allow to calculate rank abundance curves for subsets of the community and environmental data sets. Function rankabundance calculates the rank abundance curve for the specified level of a selected environmental variable. Method rankabuncomp calculates the rank abundance curve for all levels of a selected environmental variable separatedly.

Value

The functions provide information on rankabundance curves. Function rankabundance provides information on abundance, proportional abundance, logarithmic abundance and accumulated proportional abundance. The function also provides confidence interval limits for the proportion of each species (plower, pupper) and the proportion of species ranks (in percentage).

Author(s)

Roeland Kindt (World Agroforestry Centre)

References

Hayek, L.-A. C. & Buzas, M.A. (1997). Surveying Natural Populations. Columbia University Press.

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

https://www.worldagroforestry.org/output/tree-diversity-analysis

Examples

library(vegan)
data(dune.env)
data(dune)
RankAbun.1 <- rankabundance(dune)
RankAbun.1
rankabunplot(RankAbun.1, scale='abundance', addit=FALSE, specnames=c(1,2,3))
rankabunplot(RankAbun.1, scale='logabun', addit=FALSE, specnames=c(1:30), 
    srt=45, ylim=c(1,100))
rankabuncomp(dune, y=dune.env, factor='Management', 
    scale='proportion', legend=FALSE)
## CLICK IN THE GRAPH TO INDICATE WHERE THE LEGEND NEEDS TO BE PLACED
## IF YOU OPT FOR LEGEND=TRUE.

## Not run: 
# ggplot2 plotting method

# Only label the two most abundant species
RA.data <- rankabuncomp(dune, y=dune.env, factor='Management', 
    return.data=TRUE, specnames=c(1:2), legend=FALSE)

library(ggplot2)
library(ggrepel)

# 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())

plotgg1 <- ggplot(data=RA.data, aes(x = rank, y = abundance)) + 
    scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1) +
    geom_point(aes(colour=Grouping, shape=Grouping), size=5, alpha=0.7) +
    geom_text_repel(data=subset(RA.data, labelit == TRUE), 
        aes(colour=Grouping, label=species), 
        angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
    BioR.theme +
    scale_color_brewer(palette = "Set1") +
    labs(x = "rank", y = "abundance", colour = "Management", shape = "Management")

plotgg1

# use different facets
# now label first 10 species
RA.data <- rankabuncomp(dune, y=dune.env, factor='Management', 
    return.data=TRUE, specnames=c(1:10), legend=FALSE)

plotgg2 <- ggplot(data=RA.data, aes(x = rank, y = abundance)) + 
    scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1) +
    geom_point(aes(colour=Grouping), size=5, alpha=0.7) +
    geom_text_repel(data=subset(RA.data, labelit == TRUE), 
        aes(label=species), 
        angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
    BioR.theme +
    scale_color_brewer(palette = "Set1") +
    facet_wrap(~ Grouping) +
    labs(x = "rank", y = "abundance", colour = "Management")

plotgg2



## End(Not run) # dontrun