PROYECTO INNOVA DOCENTIA: Internacionalización de los recursos
educativos de la asignatura de Ecología y adaptación al Espacio Superior de
Educación Europeo (EEES). (Ref 240; http//web.bioucm.es/innovadoc)
SESSION 9: ASSOCIATION AND CORRELATION BETWEEN SPECIES
In this practice you will learn how to use different numerical techniques to identify plant
communities, in the site of study (Morata de Tajuña). You will use statistical inference such
as Chi-square and correlation tests, taking species by pairs, in each slope. Remember,
that the null hypothesis, H0, is the independence (no association between species).
IMPORTANT: In this practice you will only consider the following species: Carex
halleriana, Cistus clusii, Genista scorpius, Jasminum fruticans, Stipa tenacissima, Quercus
coccifera, Rosmarinus officinalis, Teucrium pseudochamaepitys and Thymus spp.
Activity 1.- Quantifying the association between species
By means of the analysis of contingency tables through Chi-square, you will test whether
there is a significant association (positive or negative) between pairs of species.
Contingency tables are built using presence/absence data of plant species (categorical data),
so first of all you must convert your quantitative data (lineal cover) into categorical data.
Now, let’s start!!
Double click in the file called “Session 9”, inside the folder “INNOVA”
Save this file with your own name: filesave asOKselect folderwrite the name
First, you must load your data matrix typing the following command:
setwd("C:/Users/Prácticas/Desktop/INNOVA") ### Write the route of the
folder where you will keep and read your data.
data<-read.table("data.txt",header=TRUE) ### read your main matrix
View(data) ### visualize the matrix (you can repeat this command whenever
you want to visualize matrices)
north<-subset(data,transect=="north") ### create a sub-matrix of the north
slope
View(north) ### visualize the matrix
south<-subset(data,transect=="south") ### create a sub-matrix of the south
slope
View(south) ### visualize the matrix
Now, let’s start the analysis of the north slope (just the north!!!)
north_P<-as.data.frame(ifelse(north<0.001,0,1)) ### create a new matrix
transforming quantitative data in data of presence (1), absence (0).
Let’s do an example with a pair of species (Carex halleriana and Cistus clusii)
chi<-chisq.test(north_P$car_hal,north_P$cis_clu) ### Analyze presence,
absence data by means of chi square test of two species
chi ### summary of the analysis
chi$observed ### see table of observed values
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INNOVA Ref 240
