Cambridge International A Level Biology Revision Guide

Chapter 18: Biodiversity, classification and conservation
QUESTION
Correlation
While doing random sampling or carrying out a belt
transect you may observe that two plant species always
seem to occur together. Is there in fact an association
between them? Or you might want to know if there is any
relationship between the distribution and abundance of
a species and an abiotic factor, such as exposure to light,
temperature, soil water content or salinity (saltiness).
To decide if there is an association, you can plot scatter
graphs and make a judgment by eye. Alternatively, you can
calculate a correlation coefficient (r) to assess the strength
of any correlation that you suspect to exist. Figure 18.13
shows the sorts of relationships that you may find.
Variable A
18.7 In a survey of trees in a dry tropical forest, some
students identified five tree species (A to E). They
counted the numbers of trees in an area 100 m × 100 m
with these results:
Tree species Number
A 56
B 48
C 12
D 6
E 3
a Calculate the Simpson’s Index of Diversity for the
trees within the area sampled.
b Explain the advantage of using data on species
diversity and abundance when calculating an index of
diversity.
c The Simpson’s Index of Diversity for the vegetation in
an area of open grassland was 0.8; for a similar sized
area of vegetation beneath some conifer trees it was
0.2. What do you conclude from these results?
Variable B
Figure 18.13 Three types of association: a a positive
linear correlation, b no correlation, and c a negative
linear correlation.
The strongest correlation you can have is when all the
points lie on a straight line: there is a linear correlation.
This is a correlation coefficient of 1. If as variable A
increases so does variable B, the relationship is a positive
correlation. If as variable A increases, variable B decreases
then the relationship is a negative correlation. A
correlation coefficient of 0 means there is no correlation at
all (Figure 18.13).
You can calculate a correlation coefficient to determine
whether there is indeed a linear relationship and also to
find out the strength of that relationship. The strength
means how close the points are to the straight line.
Pearson’s correlation coefficient can only be used
where you can see that there might be a linear correlation
(a and c in Figure 18.13) and when you have collected
quantitative data as measurements (for example, length,
height, depth, light intensity, mass) or counts (for example,
number of plant species in quadrats). The data must be
distributed normally, or you must be fairly sure that this is
the case.
Sometimes you may not have collected quantitative
data, but used an abundance scale (Table 18.1) or you
may not be sure if your quantitative data is normally
distributed. It might also be possible that a graph of your
results shows that the data is correlated, but not in a linear
fashion. If so, then you can calculate Spearman’s rank
correlation coefficient, which involves ranking the data
recorded for each variable and assessing the difference
between the ranks.
You should always remember that correlation does
not mean that changes in one variable cause changes in
the other variable. These correlation coefficients are ways
for you to test a relationship that you have observed and
recorded to see if the variables are correlated and, if so, to
find the strength of that correlation.
Before going any further, you should read pages
501–504 in Chapter P2 which show you how to calculate
these correlation coefficients.
Spearman’s rank correlation
An ecologist was studying the composition of vegetation
on moorland following a reclamation scheme. Two
species – common heather, Calluna vulgaris, and bilberry,
Vaccinium myrtillus appeared to be growing together. He
assessed the abundance of these two species by recording
the percentage cover in 11 quadrats as shown in Table 18.3.
To find out if there is a relationship between the
percentage cover of these two species, the first task is
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