accumresult: Alternative Species Accumulation Curve Results

In BiodiversityR: Package for Community Ecology and Suitability Analysis

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.

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).

## 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

BiodiversityR documentation built on June 22, 2024, 11:57 a.m.