Vegetation Analysis Final Lab Report
Katy Flinn
Biology 112
Due: December 5, 2005
Introduction:
Ordination is a process that utilizes complex mathematical and graphical analyses
to analyze the effects of several variables. By linking how multiple variables are related
to each other, ordination enables one to see how many different factors come into the
composition of a community. This process helps in defining and categorizing
communities. In our experiment, we use woody species stem count data collected from
Duke Forest. We were also able to correlate this information with environmental data
collected from the same plots.
We used the program called PCOrd for our analyses. With this program we were
able to correlate both environmental and species-specific data. Ultimately, we correlated
these two data sets in an attempt to determine the prominent features of the examined
plots. We used several layers of analysis: we analyzed the number of axes that would be
most useful, overlaid environmental data, separated the plots into groups, and examined
how indicator species played into the group composition.
Methods:
Our first step in analyzing the Duke Forest data was to determine the appropriate
number of axes to use in our ordination analysis. Using a step-down ordination enabled
us to examine the data on several different numbers of axes – we examined up to 6 axes
in attempting to discern the usefulness of each. Stress values indicate how divergent the
data is from the created axes – lower stress values indicate better fits. After examining
the stress levels of each, the stress levels stopped declining at three axes. Therefore after
three axes, the analysis stops becoming more accurate.
The next step in our ordination analysis is to run a focal ordination which will
enable us to graphically view the results of the ordination on three axes. We choose to
use a focal nonmetric multidimensional scaling, NMS, run. Next we overlay a second
matrix. An overlay of the same matrix results in a graph displaying the divergence of
species from the axes. An overlay of the environmental data matrix enables us to
examine how environmental features vary with the axes. Overlaying the environmental
and species data enabled us to examine how various environmental features vary with
each axis. R-values indicate how closely linked the vector diagrammed is to the actual
data. An r-value of 1 indicates that the data and vector are the same. I decided that rvalues of less than 0.2 were not strong enough to yield significant results, and so
excluded any indicators lower than that from my analysis.
In the next step of analysis, the data is divided into groups based on similarities.
We use cluster analysis in PCOrd. This step provides us with a dendrogram (see
appendix 1). The percent chaining of the dendrogram is key to determining how
biologically useful it is: if the percent chaining is more than 25%, then the groups created
by the cluster analysis may not accurately tell us about the features of the data and forest.
The cluster analysis also divides the data into groups and, like with the step-down
analysis, shows which grouping levels are most useful. The grouping analysis strives to
make the smallest number of groups with the highest amount of biological meaning. This
means that while two groups would be small and convenient, it would have so much
variation among group members that it would have little significance scientifically. We
strive for a grouping level that is both easy to analyze and meaningful biologically.
The final step in our ordination is to examine indicator species. Indicator species
are species which help to characterize a group of plots. We examine these species by
looking at three values: fidelity, constancy and indicator values. Fidelity is a measure of
how much of the total abundance of a species occurs within a group. Constancy
measures how many times the species occurs in plots within a group. Indicator values are
a combination of constancy and fidelity.
This analysis examines p-values to determine how many groupings will be most
useful in examining the data. Lower p-values mean that a species is more useful, so we
take the p-values from groupings of 3, 4, 5 and 6 groups and examine which has the
highest number of p-values below a threshold of 0.05. Our indicator analysis then
examines the setup with 6 groups to see which species play most heavily into defining the
community structure. Finally we take all these analyses together to examine which
environmental features and major species groups determine the composition of the plots
within each group.
Results:
The step-down analysis in the first section of our experiment shows us, by
examining stress levels at increasing axis numbers, that after three axes the stress planes
(see figure 1). After three axes, adding more axes only complicates the graph and does
little to actually assist in
the analysis.
Figure 1: Scree plot
indicating changing stress
values with number of axes.
The environmental and species overlay data enable more precise examination of
each group. In looking at only r-values above 0.2, Figure 2 shows how environmental
data vary by axis and grouping. Figure 3 is a display of the same 6 groups with the
species data as the second matrix. These displays help to visualize how the species and
environmental features correlate to each group.
Figure 2:
Figure 3:
Our dendrogram yields a percent chaining of 1.66%, which means that chaining
does not play a role in impeding our analysis (see appendix 1). I found the cluster
analysis to show that a grouping of 6 groups would be most useful. According to a plot
of stress values by grouping, the stress continues to decline after even 6 groups, but I felt
that the six group analysis was most useful because the decline in stress after this point is
less significant.
Next in our analysis, we determine results for indicator species. Using 6 groups
proved to have the highest number of p-values below 0.05 and also the highest number of
significant indicator values, which I set as those above 50 (see Figure 4). This analysis
indicates that the most useful set of groupings is 6 groupings.
IV's and p-values by Group
Figure 4
Number of Items
30
25
Number of p-values
below 0.05
20
15
Number of IV's above
50
10
5
0
3
4
5
6
Group Num ber
The results of the indicator species tests showed that many groups were strongly
grouped by species. The primary species present in each group can be seen in Table 1.
Table 1: Five most Significant Species/Group in Varying Values
Grp
1:
Grp
2:
Grp
3:
Grp
4:
Top 5
Species Abundanc
e
AMAR
CRAT
CAOL
QUVE
OXAR
Top 5
Species Abundanc
e
ACNE
CACA
QUSH
ILDE
QUMI
Top 5
Species Abundanc
e
MATR
FAGR
LITU
ILOP
ULAM
Top 5
Species -
Abundanc
e
82
65
51
45
44
Top 5 Species in
Frequency
ACRU
QUAL
COFL
NYSY
CATO
Frequenc
y
100
100
98
98
95
Top 5 Species
in IV
QUAL
OXAR
QUVE
CATO
COFL
Abundanc
e
100
100
100
97
97
Top 5 Species in
Frequency
LIST
COFL
FRAX
ULAL
ACRU
Frequenc
y
100
82
82
82
73
Top 5 Species
in IV
ULAL
ILDE
LIST
CACR
ULRU
Abundanc
e
100
88
68
53
47
Abundanc
e
Top 5 Species in
Frequency
ACRU
COFL
LITU
NYSY
FAGR
Top 5 Species in
Frequency
Frequenc
y
100
93
86
86
83
Frequenc
y
Top 5 Species
in IV
FAGR
LITU
COFL
QURU
ACRU
Top 5 Species
in IV
IV
44
37
35
34
33
IV
66
62
60
49
49
IV
73
58
34
30
29
IV
Grp
5:
Grp
6:
Abundanc
e
CAPA
QUPR
CRUN
QUCO
QUMA
Top 5
Species Abundanc
e
PRAM
FRAX
CECA
OSVI
QURU
Top 5
Species Abundanc
e
PITA
PIVI
JUVI
QUST
PIEC
100
99
85
82
76
ACRU
NYSY
QUPR
OXAR
COFL
100
100
100
91
82
QUPR
OXAR
QUCO
ACRU
CAPA
Abundanc
e
100
74
59
52
43
Top 5 Species in
Frequency
CECA
FRAX
JUVI
MORU
QUAL
Frequenc
y
100
100
100
100
100
Top 5 Species
in IV
FRAX
CECA
OSVI
QURU
QUAL
Abundanc
e
84
69
63
59
39
Top 5 Species in
Frequency
ACRU
CAOV
CATO
DIVI
FRAX
Frequenc
y
100
100
100
100
100
Top 5 Species
in IV
PITA
PIVI
JUVI
QUST
PIEC
99
37
37
33
18
IV
74
59
45
38
35
IV
84
69
63
59
39
Discussion:
Our evaluation on different levels using ordination enables us to decipher, so
some extent at least, the composition and primary features of our 6 groups. Group 1
seems to be primarily affected by Quercus alba and Oxydendrum arboretum, according
to the species overlay. These two species also appear as the most significant when
looking at overall indicator values. Looking at the environmental overlay, fewer minerals
seem to be present in these plots. They also seem to have higher elevations and be father
from water.
Group 2 is characterized by higher mineral content, closer proximity to water and
has a slight correlation with lower elevation. Species that help define this plot are
Liquidambar styriciflua, which is present in every plot, ULMUS ALATA and ILEX
OPACA. ACER NEGUNDO, CARYA CAOLINAE and QUERCUS SHUMARDII appear
only in group 2, and so also help distinguish it from other groups.
Group 3 appears to be correlated with high concentrations of mineral, calcium and
magnesium specifically, lower elevations, higher levels of pH and lower elevation.
According to the IV calculations, the most important species in this group are FAGUS
GRANDIFOLIA, LIRIODENDRON TULIPIFERA and CORNUS FLORIDA. ACER
NEGUNDA appears in every plot in the group, and MAGNOLIA TRIPETALA appears
only in group 3. In looking at the vector plot, CARPINUS CAROLINA, ULMUS ALATA
and Liquidambar styriciflua seem to be most strongly correlated with this group, but the
numerical data from the IV does not support this.
Group 4 correlates with higher elevation, lower pH, higher concentrations of
aluminum and lower concentrations of calcium and magnesium. NYSSA SYLVATICA,
ACER NEGUNDA and QUERCUS PRINUS appear in every plot, and CARYA PALLIDA
appears only in this group. QUERCUS PRINUS seems to be the best indicator species for
this group.
The fifth group is marked by a high volume of Fraxinus sp, which appears in
every plot in the group along with JUNIPERUS VIRGINIANA, CERCIS CANADENSIS,
MORUS RUBRA and Quercus alba. I think that the smaller number of plots in this group
make for the heightened number of species with 100% frequency. This group also shows
to have higher pH compared to the other groups. PRUNUS AMERICANA appears only in
group 5.
Group six was extremely small and difficult to categorize. It had no strong
environmental correlations, but the higher IV value went with PINUS TAEDA, whose r-
value was too small for it to appear on the vector plot. I think that the fact that ACER
NEGUNDA appears in almost every plot makes it a feature of the forest in general, and
relatively useless in distinguishing the groups from each other. Overall, however, we
were able to define the groups with relative certainty because of the numerical and visual
data available to us through to ordination process.
Appendix:
Apendix 1: Dendrogram: