Package ‘spaa’

August 29, 2016

Type Package
Title SPecies Association Analysis
Version 0.2.2
Date 2016-06-09
Author Jinlong Zhang [aut, cre]
Maintainer Jinlong Zhang <jinlongzhang01@gmail.com>
Description Miscellaneous functions for analysing species association
and niche overlap.
License GPL-2
LazyLoad yes
Suggests vegan

URL https://github.com/helixcn/spaa
NeedsCompilation no
Repository CRAN
Date/Publication 2016-06-09 19:59:58

R topics documented:
spaa-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
add.col . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
data2mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
datasample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
deg2dec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
dist2list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
freq.calc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
geodist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
lab.mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
lgeodist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
list2dist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
niche.overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
niche.overlap.boot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1
2 spaa-package

niche.overlap.boot.pair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
niche.overlap.pair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
niche.width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
plotlowertri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
plotnetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
sp.assoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
sp.pair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
splist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
sub.sp.matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
testdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
turnover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
XYname . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Index 31

spaa-package SPecies Association Analysis

Description
Miscellaneous functions for analysis of species association and niche overlap.

Details

Package: spaa
Type: Package
Version: 0.2.1
Date: 2013-8-23
License: GPL-2
LazyLoad: yes

Author(s)
Author: Jinlong Zhang <jinlongzhang01@gmail.com>
Maintainer: Jinlong Zhang <jinlongzhang01@gmail.com>

Examples
data(testdata)
testdata
data(splist)
splist

## adding information
spaa-package 3

## add genera from dataframe B to dataframe A.

add.col(inputA = testdata, inputB = splist, add = "genera",
according = "species")
## add family from dataframe B to dataframe A.
add.col(inputA = testdata, inputB = splist, add = "family",
according = "species")

### data tranformation

(spmatrix <- data2mat(testdata))
#Species association
sp.assoc(spmatrix)

# Species association between each pair of species

(result <- sp.pair(spmatrix))

# simple network with positive lines in red and negative lines

# in blue
plotnetwork(result$Pearson)
title("Pearson Correlation Network")

# The lower matrix plot illustrating Pearson's correlation

# between each pair of species Note the triangle didn't appeared
# in the plots, but have been added to the legend. This is due
# to the distribution of data. Be carefull in seletion of intervals.

plotlowertri(result$Pearson, int = 0.5, cex=3, interval = 4,

pchlist = c(19, 17, 15, 1, 5), size = TRUE)
title("Pearson Correlation Lower Matrix Plot")

## plot lower matrix

## Using BCI data for lower matrix plot
library(vegan)
data(BCI)
## select the top 30 species according to relative frequeny.
sub <- sub.sp.matrix(BCI, common = 30)
## Set the digits to 1
plotlowertri(cor(sub), size = TRUE, cex = 3, digits = 1)

#### Niche width and niche overlap

data(datasample)
niche.overlap.boot(datasample[,1:3], method = "levins")
niche.overlap(datasample, method = "levins")
niche.width(datasample[,1:3], method = "shannon")

##example turnover()
plotlab1 <- XYname(4,6)
xxx <- 1:240
dim(xxx) <- c(24, 10)
rownames(xxx) <- plotlab1
### Distance between each pair of plots
ddd <- dist(xxx)
### label matrix
labmat1 <- lab.mat(plotlab1)
4 add.col

yyy <- turnover(labmat1, ddd)

## geodist() example
## Paris
L1 = deg2dec(-2,20,14)
phi1 = deg2dec(48, 50, 11)
## Washington DC
L2 = deg2dec(77,03,56)
phi2 = deg2dec(38,55,17)
##High precision Great Circle distance
geodist(L1, phi1, L2, phi2)

add.col Add one column from A to B, according to one column of common

name.

Description
This function can be used to add one column from dataframe B to dataframe A, according to the
column names speciefied.

Usage
add.col(inputA, inputB, add, according)

Arguments
inputA inputA A dataframe which a column to be added according to one row infor-
mation. Must contain a column name equals to one column name in dataframe
B at the least.
inputB inputB A dataframe which a column to be abstracted from, must contain a col-
umn equals to at least one column name in dataframe A.
add add the column name in dataframe B to be add to dataframe A. musth be char-
acter.
according according the common column name specified to match the data entries from
dataframe A and dataframe B. must be character.

Details
This function can be used to add one column from dataframe B to dataframe A, according to the
column names specified. Users have to make sure the to dataframes at least share the common
names specified. This function may be an alternative for merge

Author(s)
Jinlong Zhang
data2mat 5

See Also
See Alsomerge

Examples

data(splist)
data(testdata)
## add genera from dataframe B to dataframe A.
add.col(inputA = testdata, inputB = splist, add = "genera",
according = "species")
## add family from dataframe B to dataframe A.
add.col(inputA = testdata, inputB = splist, add = "family",
according = "species")

data2mat Convert species list data to species matrix

Description
This function can be used to convert the species list to species matrix. The rows of the output matrix
are plots, or sites. The columns are the species.

Usage
data2mat(data = data)

Arguments
data The input data

Details
The input data will have to include :species,plots or sites, abundance, specifically, a column
named "abundance" must be specified.

Value
Return a species matrix with each row for each plot, and each column for species.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
None
6 deg2dec

Examples
data(testdata)
spmatrix <- data2mat(testdata)

datasample community matrix example

Description
Community matrix

Usage
data(datasample)

Format
A data frame with 8 plots on the following 14 species.

Details
including 14 species, 8 plots of Gutianshan Natural Reserve, Zhejiang, China, values are the value
of importance in the plot for each species.

Source
Hu Zheng-hua, Qian Hai-Yuan, Yu Ming-jian. 2009. The niche of dominant species populations
in Castanopsis eyrei forest in Gutian Mountain National Natural Reserve. Acta Ecologica Sinica.
Vol.29, 3670-3677

Examples
data(datasample)

deg2dec Degree to decimal

Description
Convert latitude or longitude from degree to decimal format

Usage
deg2dec(h, m, s)
dist2list 7

Arguments
h Degree
m Minute
s Second

Details
Convert latitude or longitude from degree to decimal format.

Value
Degree of decimal format

Note
Places with eastern hemisphere should have longitude and southern hemisphere less than zero.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

Examples

## deg2dec() example
##Paris
L1 = deg2dec(-2,20,14)
phi1 = deg2dec(48, 50, 11)
##Washington DC
L2 = deg2dec(77,03,56)
phi2 = deg2dec(38,55,17)

dist2list Convert distance matrix to pairwised list

Description
Convert distance matrix to pairwised list

Usage
dist2list(dist)

Arguments
dist distance matrix
8 freq.calc

Details
Pairwise list with first column indicates the rows of the original distance matrix, second column
indicates the columns indicates the rows of the original distance matrix, and the third indicats the
values.

Value
Dataframe with three columns.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Tuomisto, H. (2003). "Dispersal, Environment, and Floristic Variation of Western Amazonian
Forests." Science 299(5604): 241-244.

See Also
list2dist

Examples

##dist2list() example
x <- matrix(rnorm(100), nrow=5)
sampledata <- dist(x)
ddd <- dist2list(sampledata)

freq.calc Species relative frequency

Description
This function calculates the species relative frequency which equals to the numbers of occupied
plots partitioned by the total number of plots for each species.

Usage
freq.calc(matr)

Arguments
matr The standard species matrix
geodist 9

Details
The input data is a standard species matrix with rows for plots and column for species.

Value
Returns a vector that contains relative frequency for each species included in the input matrix.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
None

Examples
data(testdata)
spmatrix <- data2mat(testdata)
freq.calc(spmatrix)

geodist Hight precision Great circle distance between two places

Description
Hight precision Great circle distance between two places assuming the earth is elliptic sphere.

Usage
geodist(L1, phi1, L2, phi2)

Arguments
L1 Longitude of first place in decimal format.
phi1 Latitude of first place in decimal format.
L2 Longitude of second place in decimal format.
phi2 Latitude of second place in decimal format.

Details
Hight precision great circle distance between two places assuming the earth is elliptic sphere.

Value
Hight precision great circle distance.
10 lab.mat

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Jean Meeus 1991 Astronomical Algorithms Willmann-Bell 80-83

See Also
lgeodist

Examples

## geodist() example
## Paris
L1 = deg2dec(-2,20,14)
phi1 = deg2dec(48, 50, 11)
## Washington DC
L2 = deg2dec(77,03,56)
phi2 = deg2dec(38,55,17)
##High precision Great Circle distance
geodist(L1, phi1, L2, phi2)

lab.mat Convert vector of XY labels to label matrix

Description
Convert vector of XY labels to label matrix

Usage
lab.mat(plotlab)

Arguments
plotlab Vector of XY labels

Value
XY label matrix

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>
lgeodist 11

See Also
turnover

Examples

### lab.mat() example

plotlab1 <- XYname(4,6)
labmat <- lab.mat(plotlab1)

lgeodist Low precision Great circle distance between two places

Description
Calculating Great circle distance between two places assuming that the earth is sphere.

Usage
lgeodist(L1, phi1, L2, phi2)

Arguments
L1 Longitude of first place in decimal format.
phi1 Latitude of first place in decimal format.
L2 Longitude of second place in decimal format.
phi2 Latitude of second place in decimal format.

Value
Low precision great circle distance between two places.

Note
This function assuming that the earth is sphere.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Jean Meeus 1991 Astronomical Algorithms Willmann-Bell 80-81

See Also
geodist
12 list2dist

Examples

#lgeodist() example
##Paris
L1 = deg2dec(-2,20,14)
phi1 = deg2dec(48, 50, 11)
##Washington DC
L2 = deg2dec(77,03,56)
phi2 = deg2dec(38,55,17)
#Great circle distance
lgeodist(L1, phi1, L2, phi2)

list2dist Convert pairwise list to distance matrix

Description
Convert pairwise list to distance matrix

Usage
list2dist(dat)

Arguments
dat dataframe with three columns

Details
Dataframe with first column as the column names in the distance matrix, second column as the
rownames in the distance matrix, third column the values.

Value
distance matrix

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Tuomisto, H. (2003). "Dispersal, Environment, and Floristic Variation of Western Amazonian
Forests." Science 299(5604): 241-244.

See Also
dist2list
niche.overlap 13

Examples

##list2dist() example
x <- matrix(rnorm(100), nrow=5)
sampledata <- dist(x)
ddd <- dist2list(sampledata)
list2dist(ddd)

niche.overlap Niche overlap between each pair of species

Description
Compute niche overlap between each pair of species.

Usage
niche.overlap(mat, method = c("levins", "schoener",
"petraitis", "pianka", "czech", "morisita"))

Arguments
mat A community data matrix with each column for each species, and each row for
each plot.
method Index of niche overlap to be specified.

Details
To be added.

Value
A distance matrix contains niche overlap index between each pair of species.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Jin-tun,(2004 ) Quantitative Ecology, Science Press, Beijing
Nicholas J. Gotelli. 2000. Null model analysis of species co-occurrence patterns. Ecology 81:2606-
2621. http://esapubs.org/archive/ecol/E081/022/EcoSim

See Also
niche.overlap.pair
14 niche.overlap.boot

Examples

### niche.overlap.boot() example

data(datasample)
niche.overlap(datasample, method = "levins")

niche.overlap.boot Boostrap of niche overlap

Description
Bootstrap of niche overlap between species.

Usage
niche.overlap.boot(mat, method = c("pianka", "schoener", "petraitis",
"czech", "morisita", "levins"), times = 999, quant = c(0.025, 0.975))

Arguments
mat A community data matrix with columns representing species, and rows repre-
senting sites.
method A character string indicating the index of niche overlap to be applied.
times Interger, Number of bootstrap to be implemented.
quant Quantile of bootstrap results, the confidence intervals.

Details
This function bootstraps the niche overlap within each pair of species. \ pianka: Pianka’s niche
overlap index\ schoener: Schoener’s niche overlap index\ petraitis: Petraitis’ niche overlap index\
czech: Czechanowski index \ morisita: Morisita’s overlap index\ levins: Levin’s overlap index\ see
more information from Gotelli, N(2009).\

Value
a data frame with each row for each pair of species the columns are "Observed", \ "Boot mean", \
"Boot std", \ "Boot CI1", \ "Boot CI2", \ "times" \

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Jin-tun,(2004 ) Quantitative Ecology, Science Press, Beijing\
Nicholas J. Gotelli. 2000. Null model analysis of species co-occurrence patterns. Ecology 81:2606-
2621. http://esapubs.org/archive/ecol/E081/022/EcoSim
niche.overlap.boot.pair 15

See Also
niche.overlap.boot.pair

Examples

data(datasample)
niche.overlap.boot(datasample[,1:4], method = "pianka")
niche.overlap.boot(datasample[,1:4], method = "schoener")
niche.overlap.boot(datasample[,1:4], method = "czech")
niche.overlap.boot(datasample[,1:4], method = "levins")

niche.overlap.boot.pair
Niche overlap boostrap utility function

Description
Compute the bootstrap value between two vectors. This is a internal function called by niche.overlap.boot,
users are encouraged to use the latter function.

Usage
niche.overlap.boot.pair(vectorA, vectorB, method = c("levins",
"schoener", "petraitis", "pianka", "czech", "morisita"),
times = 999, quant = c(0.025, 0.975))

Arguments
vectorA A numerical vector including species A’s abundance or value of importance.
vectorB A numerical vector including species B’s abundance or value of importance.
method Niche overlap indeces to be applied.
times Number of bootstraps
quant Confidence interval of the bootstrap results.

Details
To do.

Value
This function will return a vector including the following elements:\ "Observed", \ "Boot mean", \
"Boot std", \ "Boot CI1", \ "Boot CI2", \ "times" \

Note
Users are ecouraged to call niche.overlap.boot rather than this function.
16 niche.overlap.pair

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Jin-tun,(2004 ) Quantitative Ecology, Science Press, Beijing

See Also
niche.overlap.boot

Examples

### niche.overlap.boot.pair() example

data(datasample)
niche.overlap.boot.pair(datasample[,1],datasample[,2], method = "levins")

niche.overlap.pair Niche overlap between one pair of species.

Description
Compute niche overlap index between one pair of species. Users are encouraged to used niche.overlap
istead of this function.

Usage
niche.overlap.pair(vectA, vectB, method = c("pianka",
"schoener","petraitis","czech","morisita", "levins"))

Arguments
vectA A numerical vector including species A’s abundance or value of importance.
vectB A numerical vector including species B’s abundance or value of importance.
method Niche overlap index to be applied.

Details
None

Value
Niche overlap index.
niche.width 17

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Jin-tun,(2004 ) Quantitative Ecology, Science Press, Beijing
Nicholas J. Gotelli. 2000. Null model analysis of species co-occurrence patterns. Ecology 81:2606-
2621. http://esapubs.org/archive/ecol/E081/022/EcoSim

See Also
niche.overlap

Examples

### niche.overlap.pair() example

data(datasample)
niche.overlap.pair(datasample[,1],datasample[,2], method = "levins")

niche.width Niche width

Description
Compute niche width of the species in a community.

Usage
niche.width(mat, method = c("shannon", "levins"))

Arguments
mat A community data matrix with each column for each species, and each row for
each plot.
method Index of niche width.

Details
To be added

Value
A vetor contains niche width index of species in community.
18 plotlowertri

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Jin-tun,(2004 ) Quantitative Ecology, Science Press, Beijing

See Also
niche.overlap for niche overlap

Examples

# ### niche.width() example

data(datasample)
niche.width(datasample, method = "levins")
niche.width(datasample, method = "shannon")

plotlowertri Plotting lower semi matrix

Description
Function for plotting lower semi matrix. These plots are often used to illustrate the relationship in
Pearson’s correlation, similarity or dissimilarity index between sites or species.

Usage
plotlowertri(input, valuename = "r",
pchlist = c(19, 17, 15, 1, 5, 2, 7), interval = 6,
cex = 1, ncex = 1, int =1.2, add.number = TRUE,
size = FALSE, add.text = FALSE, show.legend = TRUE,
digits = 2)

Arguments
input The input data, often the results of correlation matrix, can also be class dist
object.
valuename Value name that will be used in the legend.
pchlist The types of point shape to plot see pch par().
interval Number of intervals to illustrate the shape of points
cex Point size
ncex Text size for the add.number, which appeared at the top of each column.
plotlowertri 19

int Row space between lines in legend

add.number Species number for each column.
size Whether the size of points would change according to value.
add.text To add value to the grid.
show.legend Whether the legend should be drawn.
digits Number of digits of for each interval

Details
If the matrix contains less than 15 rows/columns, you may have to adjust the row space between
the text lines in the legend, using argument int. Data in class dist can be include, and will be
converted to matrix at first internally.
The lower matrix plot illustrating Pearson’s Correlation between each pair of species. Note some
value didn’t appeared in the plots, may have appeared the legend. This is due to the distribution of
data. Be careful in selection of intervals. In this situation you may set show.legend = FALSE, and
add the legend manually. This may be fixed in the future.

Value
lower matrix plot

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Qiaoying, Peng Shaolin, Zhang Sumei, Zhang Yunchun, Hou Yuping.(2008). Association of
dormintant species in Guia hill Municipal Park of Macao. Ecology and Environment. 17:1541-1547

See Also
See Also plotnetwork

Examples
data(testdata)
spmatrix <- data2mat(testdata)
result <- sp.pair(spmatrix)

## Check the legend for 0.00 to 0.33 (Unwanted label)

plotlowertri(result$Pearson, int = 0.5, cex=1.5)
title("Pearson Correlation Lower Matrix Plot")

## Change the size of points and reset the intervals.

## Warning: The lower matrix plot illustrating Pearson
## Correlation between each pair of species. Note the
## triangle didn't appeared in the plots, but have been
## added to the legend. This is due to the distribution
20 plotnetwork

## of data. Be careful in selection of intervals.

plotlowertri(result$Pearson, int = 0.5, cex=1.5,

interval = 4, pchlist = c(19, 17, 15, 1, 5), size = TRUE)

title("Pearson Correlation Lower Matrix Plot")

## "Pure" dots, may have to add legend manually...

plotlowertri(result$Pearson, int = 0.5, cex=2.5,
interval = 4, pchlist = rep(19, 5), size = TRUE,
show.legend = FALSE)

title("Pearson Correlation Lower Matrix Plot")

## Using BCI data

library(vegan)
data(BCI)
## select the top 30 species according to relative frequency.
sub <- sub.sp.matrix(BCI, common = 30)
## Original
plotlowertri(cor(sub))

## Change size
plotlowertri(cor(sub), size = TRUE, cex = 3)

## Set the digits to 1

plotlowertri(cor(sub), size = TRUE, cex = 3, digits = 1,
ncex = 0.7)

plotnetwork Correlation network plot

Description
This function could be used to plot correlation network, with less than 15 sites (recommended). The
points lie in a circle with lines connected. Blue lines indicate negative values and the red ones the
positive ones.

Usage
plotnetwork(datainput, interval = 8, xlim = c(-2.5,5),
ylim=c(-3.2,3.2), lty = c(1,2,3,4,4,3,2,1,5), value = "r",
legendx = 3, legendy = 0, right = 1.2, intcept = 0.22,
left = 0.35, linelength = 0.3, cex = 3, lwd = 1.5,
show.legend = TRUE, digits = 2, dit = 1.2,
number.label = TRUE, text.label = TRUE,
linecol = c("red", "black"), ...)
plotnetwork 21

Arguments
datainput The correlation matrix, ex. Pearson’s correlation matrix
interval Number of intervals of the values, indicating how to partition the range of input
data
xlim The x range of the plot. The users need not to change it.
ylim The y range of the plot. The users need not to change it.
lty Line styles used in connection lines for each interval. Must have number of
elements + 1.
value Value of the matrix, should be a character, will used in legend.
legendx Legend position x.
legendy Legend position y.
right Legend position adjustment parameter.
intcept Legend position adjustment. can be used to specify the row space between each
line for the legend.
left Legend position adjustment parameter.
linelength Line length in the legend.
cex Point size for each circle.
lwd Line width for each circle.
... Other arguments to be passed from.
show.legend Whether the legend should be drawn.
digits Number of digits displayed in legend.
dit Distance of text labels from each corner.
number.label Whether the number label should be drawn.
text.label Whether the text label should be drawn.
linecol Colours of the lines specified. The positive correlation will be drawn in the first
colour specified.

Details
This function could be used to plot the pairwise connections between less than 20 sites ( above 20
is not recommended since there would be too many connections).
The lines will be in red or blue, according the sign of the value of association. Users can adjust the
line style and legends based on their requirements.

Value
Correlation network plots.

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>
22 sp.assoc

References
None

Examples
data(testdata)
spmatrix <- data2mat(testdata)
result <- sp.pair(spmatrix)
plotnetwork(result$Pearson)

plotnetwork(result$Pearson, linecol = c("orange", "blue"),

number.label = FALSE)

title("Pearson Correlation Network")

sp.assoc Total species association

Description
Calculate species association

Usage
sp.assoc(matr)

Arguments
matr standard species matrix , with rows for plots and columns for species.

Details
Calculate species association using the following formula.
Number of plots.
N
Number of species.
S
Number of plots occupied by certain species.
n
total number of species for each plot.
Tj
mean species number for all the plots.
t
Variance of species relative frequency:
sp.assoc 23

sigma^{2}{T}= sum{i}=1^{s}P{i}(1-P{i}).
Variance of species number:
S^{2}{T}=({1}{N})sum{j=1}^{N}(T{j}-t)^{2} .
Species relative frequency
P{i}={n{i}}{N}.
Variance ratio:
If VR > 1 Positively associated,
If VR < 1 Negative associated
VR = {S{T}^{2}}/{sigma{T}^{2}}
W: used in comparison with chi square with n degrees of freedom.
W = VR * N

Value
Return Variance ratio, W, Chisq, etc, see details

pi Species frequency
N Number of plots
S Number of species
Tj Total number of species for each plot
Numspmean Mean number of species
sigmaTsq Variance of species relative frequency
STsq Variance of species number
var.ratio Variance ratio
W W statiscit value: used in comparison with chi square.(n)

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Qiaoying, Peng Shaolin, Zhang Sumei, Zhang Yunchun, Hou Yuping. (2008) Association of
dormintant species in Guia hill Municipal Park of Macao. Ecology and Environment. 17:1541-1547
GUO zhongling, MA yuandan, ZHENG Jiping, LIU Wande , JIN Zefeng.(2004) Biodiversity of tree
species,their populations’spatial distribution pattern and interspecific association in mixed decidu-
ous broadleaved forest in Changbai Mountains. Chinese Journal of Applied Ecology. 15:2013-2018
Shi Zuomin, Liu Shirong, Cheng Ruimei, Jiang Youxu.(2001) Interspecific association of plant
populations in deciduous broad leaved forest in Baotianman. Scientia Silvae Sinicae. 37:30-35

See Also
See also sp.pair for association between each pair of species.
24 sp.pair

Examples
data(testdata)
spmatrix <- data2mat(testdata)
sp.assoc(spmatrix)

sp.pair Species association between each pair of species

Description
Calculate species association between each pair of species.

Usage
sp.pair(matr)

Arguments
matr Standard species matrix, with rows for plots and columns for species.

Details
Assume we have speciesA and speciesB, a, b, c, d that corresponding to the co-occurrence could
be used to conduct the species association analysis between the two species.
a = number of plots both occupied by speciesA and speciesB.
b = number of plots only found speciesA.
c = number of plots only found speciesB.
d = number of plots without speciesA or speciesB.
N = a+b+c+d
This function are using the following formula:
Chi square (Yate’s correction):
chi^{2}=((((a*d-b*c)-0.5*N)^2)*N)/(a+b)*(a+c)*(b+d)*(c+d)
V ratio:
V = ((a+d)-(b+c))/(a + b + c + d)
Jaccard index:
Jaccard =a/(a + b + c)
Ochiai index:
Ochiai = a/sqrt((a+b)*(a+c))
Dice index:
Dice = 2*a/(2*a + b + c)
The Association Coefficient(AC):
sp.pair 25

if a*d>= b*c:
AC = (a*d - b*c)/((a+b)*(b+d))
if b*c>= a*d and d>=a:
AC=(a*d - b*c)/((a+b)*(a+c))
if b*c>a*d and a<a:
AC = (a*d - b*c/((b+d)(d+c))
Point correlation coefficient
(PCC):
PCC = {a*d-b*c}/{(a+b)*(a+c)*(c+d)*(b+d)}

Value
chisq chi square matrix
chisqass chi square matrix information
V V Value indicating species association is positive or negative
Ochiai Ochiai’s index
Dice Dice’s index
Jaccard Jaccard’s index
Pearson Pearson’s correlation
Spearman Spearman’s rank correlation
PCC Point correlation coefficient
AC Association coefficient

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
Zhang Qiaoying, Peng Shaolin, Zhang Sumei, Zhang Yunchun, Hou Yuping.(2008). Association of
dormintant species in Guia hill Municipal Park of Macao. Ecology and Environment. 17:1541-1547
Zhou XY, Wang BS, Li MG, Zan QJ.(2000). An analysis of interspecific associations in secondary
succession forest communities in Heishiding Nature Reserve, Guangdong Province. Acta Phytoe-
cologica Sinica. 24:332-339.
JIAN Minfei, LIU qijing, ZHU du, YOU hai.(2009). Inter-specific correlations among dorminant
populations of tree layer species in evergreen broad-leaved forest in Jiulianshan Mountain of sub-
tropical China. Chinese Journal of Plant Ecology. 33: 672-680

See Also
See Also as sp.assoc for species association for total species.
26 splist

Examples
data(testdata)
spmatrix <- data2mat(testdata)
result <- sp.pair(spmatrix)

splist species list used in function add.col()

Description

A species check list example to be used in add.col()

Usage

data(splist)

Format

A data frame with 9 observations on the following 3 variables.

species a factor with levels sp1 to sp8

genera a factor with levels gen1 to gen6
family a factor with levels fam1 to fam5

References

None

Examples

data(splist)
data(testdata)
## add genera from dataframe B to dataframe A.
add.col(inputA = testdata, inputB = splist, add = "genera",
according = "species")
## add family from dataframe B to dataframe A.
add.col(inputA = testdata, inputB = splist, add = "family",
according = "species")
sub.sp.matrix 27

sub.sp.matrix Subset species matrix according to relative frequency

Description

Subset species matrix according to relative frequency.

Usage

sub.sp.matrix(spmatrix, freq = 0.5, common = NULL)

Arguments

spmatrix The spmatrix is a standard species matrix with rows for plots and column for
species.
freq The value of relative frequency to be specified, species with higher relative fre-
quency will be reserved in the output matrix.
common The number of most common (according to relative frequency) species to be
specified.

Details

sub.sp.matrix will select the species whose relative frequency above 0.5 (default), or select certain
number of species according to relative frequency.

Value

A subset matrix with certain number of top relative frequency.

Author(s)

Jinlong Zhang <jinlongzhang01@gmail.com>

References

None

See Also

See Also subset

28 turnover

Examples

library(vegan)
data(BCI)
## Select the species whose relative frequency
## more than 0.5, from BCI data
sub <- sub.sp.matrix(BCI, freq = 0.5)
## Select the top 30 species according to relative frequency
sub <- sub.sp.matrix(BCI, common = 30)

testdata Example Data

Description
Data used in example in list format.

Usage
data(testdata)

Format
A data frame with 11 observations on the following 3 variables.

plotname a factor with levels plot1, plot2, plot3.

species a factor with levels sp1 to sp7.
abundance a numeric vector indicating number of individuals appeared in each plot.

Examples
data(testdata)
testdata

turnover Calculating species turnover

Description
Calculating species turnover based on the mean value between focus quadrat and their neighbours.

Usage
turnover(lab.mat, dist.mat, type = c("quart", "octal"))
turnover 29

Arguments

lab.mat matrix of quadrat labels.

dist.mat distance matrix between each pair of quadrats
type "quart" indicates four neighbouring quadrats, "octal" indicate eight neighbour-
ing quadrats.

Details

species turnover based on the mean value between centred quadrat and its neighbours.

Value

matrix with species turnover.

Author(s)

Jinlong Zhang <jinlongzhang01@gmail.com>

References

Lennon J. 2001 The geographical structure of British bird distributions - diversity, spatial turnover
and scale Journal of Animal Ecology 70,966-979

See Also

XYname and lab.mat

Examples

##example turnover()
plotlab1 <- XYname(4,6)
xxx <- 1:240
dim(xxx) <- c(24, 10)
rownames(xxx) <- plotlab1
### Distance between each pair of plots
ddd <- dist(xxx)
### label matrix
labmat1 <- lab.mat(plotlab1)
yyy <- turnover(labmat1, ddd)
30 XYname

XYname Generating vector of XY labels

Description
Generating vector of XY labels by providing number of rows and columns

Usage
XYname(x, y)

Arguments
x Number of X labels
y Number of Y labels

Value
Vector of XY labels

Author(s)
Jinlong Zhang <jinlongzhang01@gmail.com>

References
None

See Also
lab.mat for converting the vector to matrix of XY labels.

Examples

## XYname() example
XYname(4,6)
Index

∗Topic \textasciitildekwd1 niche.overlap.boot.pair, 15

niche.overlap, 13 niche.overlap.pair, 16
∗Topic \textasciitildekwd2 niche.width, 17
niche.overlap, 13 ∗Topic overlap
∗Topic add niche.overlap.boot, 14
add.col, 4 niche.overlap.boot.pair, 15
∗Topic association niche.overlap.pair, 16
sp.assoc, 22 ∗Topic plot
sp.pair, 24 plotnetwork, 20
spaa-package, 2 ∗Topic semimatrix
∗Topic bootstrap plotlowertri, 18
niche.overlap.boot, 14 ∗Topic species
niche.overlap.boot.pair, 15 data2mat, 5
∗Topic datasets freq.calc, 8
datasample, 6 sp.assoc, 22
splist, 26 sp.pair, 24
testdata, 28 spaa-package, 2
∗Topic decimal ∗Topic sub
deg2dec, 6 sub.sp.matrix, 27
∗Topic distance ∗Topic turnover
dist2list, 7 lab.mat, 10
geodist, 9 turnover, 28
lgeodist, 11 XYname, 30
list2dist, 12
add.col, 4
∗Topic frequency
freq.calc, 8
data2mat, 5
sub.sp.matrix, 27
datasample, 6
∗Topic list
deg2dec, 6
dist2list, 7 dist2list, 7, 12
∗Topic lower
plotlowertri, 18 freq.calc, 8
∗Topic matrix
data2mat, 5 geodist, 9, 11
plotlowertri, 18
sub.sp.matrix, 27 lab.mat, 10, 29, 30
∗Topic network lgeodist, 10, 11
plotnetwork, 20 list2dist, 8, 12
∗Topic niche
niche.overlap.boot, 14 merge, 4, 5

31
32 INDEX

niche.overlap, 13, 16–18

niche.overlap.boot, 14, 15, 16
niche.overlap.boot.pair, 15, 15
niche.overlap.pair, 13, 16
niche.width, 17

plotlowertri, 18
plotnetwork, 19, 20

sp.assoc, 22, 25
sp.pair, 23, 24
spaa (spaa-package), 2
spaa-package, 2
splist, 26
sub.sp.matrix, 27
subset, 27

testdata, 28
turnover, 11, 28

XYname, 29, 30