Vegetation Analysis Final Report
Ordination, Cluster Analysis, and Indicator Species Analysis
David Golowo, Jr.
December 5, 2005
TA: Lee Ann Jacobson
Fundamentals of Ecology – BIOL 112
Introduction
Often in plant ecology, species of interest are grouped for proper analysis. One method used to
achieve proper analysis is ordination. According to Gauch, ordination primarily endeavors to
represent sample and species relationships as faithfully as possible in a low-dimensional space
(Gauch 1982). UNC Ecology professor R.K. Peet (and professor of this course) goes on to state
that ordination itself can assist with subjective classifications (Peet 1980). Ordination is thus
described as a technique used in plant ecology that enables one to group multiple objects of
interest. These objects may include species, samples, plots, quadrats, etc. The objects are ordered
along axes according to their relative resemblances. So, a main goal in using ordination is to
compare many objects that are related and then place the most similar of those objects closer
together in a 2 or 3-dimensional space.
Area of Study
The tree species studied in this work are species found in North Carolinian forests. Most of these
species like the hickory, maple, white oak, etc are species native to North Carolina and are
commonly found in the Duke Forest, the Blue Ridge Mountains, and the Croatan National Forest
regions that were visited during the course of the Fall 2005 semester. These regions provide an
abundance of species and an environment suitable for vegetational analysis. The ordination
method used throughout this work is the PCOrd method the method was NMS (nonmetric
multidimensional scaling) and PCord was the software used.
Methods
The ordination method used throughout this work is the PCOrd method. Designed by McCune,
this program is a program that performs multivariate analysis of ecological data. It offers many
ordination and classification techniques that may not be available in many statistical packages. It
has newer features contains software for better analysis (eg. it has autopilot mode for NMS), it
has better graphic (eg it allows the plotting of scree plots in NMS), has newer features like the
edit matrix spreadsheet and enhanced graphics.
The laboratory experiment was performed in three parts. In the first part, an ordination analysis
was run and the results were interpreted. In the second part, groups within the data were
identified and the ordination analysis was interpreted using the groups. In the last part, indicator
species analysis was used to identify the species the define the groups within the data.
Analyses
In the first part of the lab, an ordination was run and the data results were interpreted. The PCOrd
file was opened and the tree data file was then opened. In the tree data file, the first Main Matrix
file opened was the TreeLong.wk1 file don’t need this much detail (can leave out file names,
etc). Next, the step-down ordination was run. This was achieved by selecting NMS from the
Ordination pull-down menu. In NMS, Autopilot was left unchecked; the Distance Measure
selected was Sorensen (Bray-Curtis); the Output options selected were Write final configuration,
Varimax rotation, Run log, and Plot stress vs. iteration; and the Parameter Setup features were as
follows: Number of axes k, 6; Number of runs with real data, 20; Stability criterion, 0.0005;
Iterations to evaluate stability, 20; Maximum number of iterations, 400; the Step down in
dimensionality box was checked; and the Random numbers button was selected. Thereafter, the
OK button was clicked. In the window, NMS Random Numbers, the following were selected:
User Suppliers Seed > a random number was typed in > and OK clicked. A descriptive title was
chosen for the results. The title chosen was TreeLong_NMSstep > OK clicked. After this, the
ordination was run and three smaller windows namely the Main- TreeLong.wk1, GraphGraphrow.gph, and Result-Result.txt windows appeared in the main PCOrd window. The files
were then examined. Stress, instability, and the scree plot were three key components that were
examined. Stress measures how far the data in the ordination space diverged from the original
data. Since the data are rearranged in ordination space over many iterations, the stress reaches a
point of stabilization or until the maximum number of iterations has been reached. If the stress
value is less than 5, then there is a very good representation of the data. This, however, is not
frequently achieved; 5 – 10 is a good representation; 10 – 20 is a good representation but may
give misleading results; and stress above 20 most likely gives misleading results. Instability is
the amount of stress that continues to change with the number of increasing iterations. When the
analysis reaches a solution, the stress should reach a state of stability. At this point, the stress is
low. An acceptable instability is 10E-3. The scree plot shows the decrease in stress as each axis
is added to the analysis. The focal NMS was then run. All but the Number of axes k remained the
same. The number of axes was changed from 6 to 3. The new run was made and the result was
saved as Week1Results.txt Scree plots were then generated. TreeLong was chosen as a second
matrix and overlaid in the first plot.. All results will be shown and their interpretations will be
given in later in the report.
In the second part, groups were identified within the data and the ordination analysis was
interpreted using groups. The sets include the following: TreeLong.wk1 was opened as the main
matrix and as the second matrix. In the menu bar, Groups pull-down menu was opened and
Cluster Analysis was selected. The following settings were then chosen: Set beta, -0.25; Set
Group membership level, 6; Set Group variable name, Group; the caption To Write all higher
level groupings was checked; the results was given the descriptive name TreeLongWeek2.wk2
and OK was clicked. At the completion of this analysis, Temp2.wk1 was chosen a the new
second matrix and it was saved as a results.txt file to be used later. The descriptive name given it
was TreeLong_6Groups.wk1. These results were saved and the second matrix was closed as
well. Back in PCOrd, EnvLong.wk1 was opened as a second matrix and the above steps were
repeated to add grouping levels to the environmental data file. These new groupings allow each
of them to be used as a second matrix to display the ordination with the groups displayed in the
biplot. From this point, a dendogram was generated from the Graph menu. The grouping level
was changed or adjusted by choosing Select Grouping Variable from the Groups menu. This
dendogram was also saved. Plots with extreme values were identified by the Outlier analysis.
This was achieved by selecting Outlier Analysis from the Summary menu. For plots with
extreme environmental variables, such plots were identified in the EnvLong file. Before
removing the plots that were indicated as outliers, care was first taken to examine the ordination
graph again to see if the plots consistently fell on the boundary of the ordination. The results
were compared, the modifications were made be specific – what modifications did you make?
Did you remove plots? Which ones?, and the results were saved. In order to determine the most
biologically appropriate grouping level, the groups were identified qualitatively by examining
them in biplots with the ordination results from the first part of the study (we didn’t do an
experiment). TreeLong_6Groups from Part I was opened as the second matrix and the graph file
that contained the first graphs were also opened. By opening the Graph Ordination and
overlaying the EnvLong_6Groups (thus forming a new graph) information was then added to
TreeLong_6Groups for better interpretation.
In the third part, indicator species analysis was used to identify the species that define the groups
within the data. Key components for analysis in this part are indicator species analysis,
abundance, and frequency. Indicator species analysis combines species abundance and frequency
to calculate the indicator value for each species. Abundance (or fidelity) is the percentage of the
abundance for a species that occurs within a particular group. Frequency ( or constancy )
measures the percentage of plots within a group that contains a particular species. This section is
divided into two parts: Step 1: TreeLong_6Groups was opened as a second matrix. In the Groups
submenu, Indicator Species Analysis was selected. These classes were defined: Select Group 3,
Monte Carlo Test was checked, a random number was typed as the supplier seed, 1000 runs was
entered, and the result was save as TreeLong_indicators_3Groups.txt. The same process was
repeated for Groups 4, 5, and 6. In order to determine the groups to use for the dataset, average
p-values and indicators were analyzed from each group. Each group was first imported into
Excel. The group was located using the import tab. The row where the p-values begun was
selected, the Delimited button and tab were selected, and OK was clicked. This lead to the
display of the p-values, standard deviation, and all essential values in the Spreadsheet. The pvalue data was then sorted according to size and the average was taken. The number of indicators
below the threshold value of 0.05 was selected. This process was repeated for each of the
grouping level. The grouping level with both the highest number of significant indicators and the
lowest average p-value was selected and used for the rest of the analysis. The selected group was
6Groups why? Explain how you made this decision; a new Spreadsheet was then opened and the
entire 6Group data was imported into the new Spreadsheet without changing any settings.
Worksheets for Abundance, Frequency, and Indicator values were then inserted and the results
were analyzed.
Good detail, but too much! Would be more useful to use the space to explain what each method
does and why it is used, rather than to list the specific steps
Results
More useful to start results section with text to explain results before
introducing graphs and tables
Part I
NewTreeLong1_NMSstep
Axis 2
QUAL
CACR
ULAL
OXAR
ACRU
QUPR
Axis 1
Figure 1 Axis 1 x Axis 2
LIST
NewTreeLong1_NMSstep
FAGR
LITU
CACR
ULAL
OXAR
Axis 3
QUAL
JUVI
QUST
Axis 1
Figure 2: Axis 1 x Axis 3
LIST
NewTreeLong1_NMSstep
FAGR
LITU
Axis 3
ACRU
QUPR
JUVI
QUST
Axis 2
Figure 3: Axis 2 x Axis 3
Figures 1 – 3 Biplots of species in NMS ordination space. Fig. 1 and 3 show the
separation along Axis 2 between hardwood species such as the Red Maple, Acer
rubrum; Sweet-gum, Liquidambar styraciflua; the post oak, Quercus rubra, etc.
Fig. 2 shows similar separation among hardwood trees along Axes 1 and 3.
Should explain and interpret patterns you see
Part II
ClusterSetUp2
Distance (Objective Function)
1.6E-02
5.1E+00
100
75
1E+01
1.5E+01
2E+01
Information Remaining (%)
00001
PSP37
00018
00021
00574
00004
00008
00014
00002
00005
00007
00509
00520
00016
00024
00023
00033
00010
00031
00020
00042
00019
00069
00517
PSP36
00555
00581
00598
00589
00571
00579
00009
00012
00011
00015
00017
00022
00067
00618
00513
00514
00620
00537
00619
00501
00504
00524
00502
PSP88
PSP86
00508
PSP87
00617
PSP35
PSP34
00081
00606
00590
00510
00596
00512
00511
00607
00621
00608
00609
00003
00612
00616
00611
00614
00615
00622
00624
00575
00593
00582
00013
00029
PSP44
PSP61
00503
00507
00025
00026
00583
00584
00585
00032
00505
00506
00625
PSP10
00027
00587
00518
00588
00602
00515
PSP43
00028
00516
00030
00610
00613
00623
50
25
0
Figure 4. Clustering
Dendogram
Part III
Column
6
27
28
41
42
48
51
57
58
59
60
29
21
12
56
31
54
55
10
34
8
26
35
44
24
5
38
52
20
18
16
45
47
3
2
25
15
19
13
30
49
50
17
1
33
11
23
Maxgrp
CACR
JUVI
LIST
QUAL
QUCO
QUPR
QUST
Group6
Group5
Group4
Group2
LITU
FAGR
CAPA
ULRU
MORU
ULAL
ULAM
CAOL
OXAR
CACO
JUNI
PITA
QUMA
ILDE
BENI
PLOC
QUVE
DIVI
CRUN
COFL
QUMI
QUPH
ACSA
ACRU
ILOP
CEOC
CRAT
CATO
MATR
QURU
QUSH
CRMA
ACNE
OSVI
CAOV
ILAM
Value
(IV)
3
1
3
1
36
36
1
36
36
36
3
3
3
36
3
3
3
3
1
36
3
3
1
36
3
3
3
1
36
36
1
3
3
3
36
3
3
1
1
3
3
3
3
3
3
3
36
61.8
69.8
70.4
68.6
38.9
98.9
57.7
63.8
72.1
76.3
60
54.2
54.8
18.2
37.5
47.6
39.4
20
39.9
42.6
17.5
17.1
28.9
17
20.9
12.5
10
37.9
26.6
8
42.5
11.9
16.7
21
45.1
19.2
13.4
9.1
39.9
7.5
35.6
7.5
8
7.5
22.7
35.2
8
Mean
21.5
34.5
27.4
34.1
12.6
10.9
21.8
41.6
44
42.9
36.7
28
20.5
4.5
12.8
25.7
19.5
7.1
22.2
29.4
6.9
7.5
17.4
8.7
11.8
5.8
5.1
27.5
15.6
4.5
36.7
6.2
10.2
13.4
39.2
12.8
9.4
5.8
33.6
4.6
30.4
4.4
5.6
4.5
18.9
30.5
5.8
S.Dev
6.07
6.65
5.89
4.53
4.71
4.35
5.39
5.09
6.34
5.43
2.25
6
6.27
2.76
5.57
6.59
6.16
3.6
6.3
5.1
3.88
3.99
5.31
4.23
4.97
3.35
3.07
5.34
5.87
2.49
4.03
3.41
4.65
5.23
4.67
5.51
4.51
3.28
6.24
2.8
6.08
2.92
3.2
2.99
6.11
6.52
3.21
p
*
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.002
0.003
0.004
0.007
0.008
0.008
0.017
0.02
0.021
0.022
0.03
0.035
0.037
0.041
0.057
0.058
0.058
0.059
0.071
0.082
0.082
0.083
0.09
0.109
0.126
0.132
0.137
0.139
0.152
0.16
0.165
0.167
0.172
0.185
0.2
0.2
22
14
9
36
37
43
40
53
46
39
4
7
32
equal
=
p
proportion
--------
FRAX
CECA
CAGL
PIEC
PIVI
QUFA
PRSE
SAAL
QUNI
PRAM
AMAR
CACA
NYSY
to
Group
=
of
--------
1
3
1
36
36
1
1
36
3
1
1
3
1
38.3
23.4
34.3
17.9
15
15.3
26.4
10.3
4.1
3.6
5.8
2.5
33.2
or
identifier
(1
randomized
-----
exceeding
for
+
trials
------
Indicator
values
finished
*************************
33.5
19
30.9
16.7
14.4
14.6
29
11.9
4.7
3.9
6.8
3.7
36.7
the
group
number
with
-------
7.15
6.05
6.08
5.57
5.35
5.12
8.27
4.53
2.89
2.68
3.68
2.54
5.35
observed
with
of
indicator
0.203
0.207
0.229
0.313
0.333
0.335
0.551
0.565
0.567
0.606
0.607
0.726
0.731
indicator
maximum
runs
value
0.14872
value.
observed
>=
Figure 5. Indicator values for the species data which group is which? Not clear
what you’re presenting in the table
Discussion
The principal species in this community analysis are the Acer rubrum species names in italics,
Carpinus caroliniana, Liquidambar styraciflua, Quercus alba, Quercus stellata, Fagus grandifolia,
and Ulmus rubra. In the first part, the biplots show how the species separate along the axes. In
Figure 1,Carpinus caroliniana is consistent with Liquidambar styraciflua. Acer rubrum is
consistent with the oaks. This same trend is seen in Fig 2 and 3 as the Red maple is seen in
higher concentration along with the oaks axes 2 and 3.. The indication is that the maple and the
oaks likely survive better in the same habitat, pH, at the same elevation, or in the same soil type.
Species that are grouped together succeed better in the same environmental conditions. As a
result, where they do better, their concentrations tend to be higher along that axis ( eg Carpinus
caroliniana and Liquidambar styraciflua in Fig 2). In the second part of the experiment, groups
are classified based on species composition. Species that seem to be extreme are weeded out by
the outlier analysis?. The dendogram indicates that the tree species in the plot are biologically
related as is seen by the lines that links from one plot to the next. Consequently, those species
that are closely we did not look at relatedness among species tend to agglomerate in higher
concentrations. They tend to do well in similar conditions such as soil type, pH, elevation, etc. In
the third part, indicator species analysis is used to identify the groups of plots within the data.
Four groups were made and the quality of a group was based on the group with both the lowest
average p- values and the highest number of significant indicators. Group 6 not group 6, but a
grouping level of 6, meaning we divided the plots into 6 groups, so your analysis should identify
and describe those 6 groups fit that description and was the model group selected. It had a p-
IV
observed)/(1
value average of 0.14872 and 30 indicators. This group had greater abundance of species, more
plots that contained particular species (fidelity), and larger number of indicators species. These
indicator species are important in determining the ecosystem type and the types of species that
will likely succeed there. Their decline may be due to disturbances such as fires, floods, etc.
References
Gauch, Jr. H. G. Noise Reduction by Eigenvalue Ordinations. 1982 Ecology 63: 1643 – 9.
Peet, R. K. Ordination as a tool for analyzing complex data sets. 1980 Vegatio 42: 171 – 4
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