BiodiversityR (version 2.16-1)

accumresult: 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, ...)

Value

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

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.

Author

Roeland Kindt (World Agroforestry Centre)

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.

References

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

https://rpubs.com/Roeland-KINDT

See Also

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).

if (FALSE) {
# 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
} # dontrun