A Vegetation Analysis using Ordination of Species Composition, Environmental
Variables, and Indicator Species in Duke Forest
Temperate Forest of the Piedmont
North Carolina
Jennifer Gruhn
December 12, 2006
Vegetation Analysis – Biol 112
I. INTRODUCTION
Vegetation, such as the woody stems used in this analysis, is better understood when the
community composition is correlated with environmental variables using ordination.
Ordination relates multiple variables to each other by combining multiple dimensions
into a small number of dimensions, based on similarity of them. In this lab,
environmental variables were ordinated with species abundance data (Part I) no! the env
data were used only for interpretation, not in the actual analysis. A Cluster Analysis was
used to put the species data into groups of the tightest fit, and ordination was again used
to interpret these groups by key environmental variables (Part II).
Lastly, the groups were identified further using an indicator species analysis (Part III).
This analysis, based on abundance and frequency of species, determines the extent to
which a species represents a group. The Indicator Species Analysis was used also to
determine the appropriate number of groups in which the species should be divided,
based on the number of groups that will provide the most useful indicator species. This is
based on the number of indicator species found in each group, as well as a “p-value” or
the chance that a species will occur by chance, rather than by predicted indication of an
environment.
II. METHODS
Part I
The woody stems of Duke Forest, located North of Chapel Hill, NC, were counted and
totaled based on their relative abundance. Environmental variable data, such as mineral
content and soil conditions, was also gathered quantitatively.
An ordination analysis what type? NMS based on combinations of the species and the
environmental variables, using Pcord Software, was performed on the species abundance
and data. A 3-dimensional solution of 74 iterations was used to produce a stress value
that was reasonable explain for the data set. The 3-dimensional solution was best-suited
for this analysis, based on the stress and instability values providing the recommended
number of three axes from the scree plot (refer to Figure 1).
Part II
A Cluster Analysis was used to create a distance matrix based on species composition
and abundance in the Duke Forest plots. The plots were clustered (refer to Figure 4 why
jump to figure 4? Number in order that you mention them) into six groups using a
Sorensen (Bray-Curtis) distance matrix. A multivariable analysis was used to cluster the
plots.
The percent chaining in the dendrogram, representing the frequency at which individual
plots are added to already-existing groups, could not exceed 25%. As in any
dendrogram, the plots were divided up based on the tightest fit possible (similar
characteristics among the groups) but with as few groups as possible. The correlation of
environmental variables to the species clusters was analyzed using ordination.
Part III
The number of groups that would best represent indicator species was chosen. While
significant indicators determine how many indicator species of significant value are in a
plot, the p-value determines the ability for a species to occur by chance the likelihood that
the indicator score occurred by chance. Therefore, a high number of significant
indicators and a low p-value is the desired outcome for the appropriate group number.
The data of abundance (fidelity) and frequency (constancy) of the data were then
compared among all groups. Whereas a species may be very abundant (have high
fidelity) in the plots of a group, it may not be very frequent (have high constancy) among
all the group’s plots. good
III. RESULTS
Part I
The ordination analysis results for the woody stems after a 3-dimensional solution of 74
iterations yielded a final stress value of 15.78 and a final instability value of 0.00048,
which were both acceptable. The stress level of 15.78 represents a value for data that is
useful but may produce misleading information. As expected, this value of stress leveled
off (refer to figure 1) in the scree plot as more parameters were considered in the
analysis, because the ordination fit the real data more consistently with each additional
parameter. The amount the stress changes with increasing parameters is defined by the
calculation of this instability value, and the scree plot shows the increase in parameters
and decrease of stress.
Using a compilation of tree abundance data and environmental data, one could betterunderstand the species’ similarity levels based on their response to environmental
gradients. These environmental gradients can be compared to species abundance, though
not spatially. For example, one can see the gradient of calcium in Figure 2, with a large
amount of calcium represented by larger triangles. The left side is therefore represented
by higher calcium levels. Manganese and Potassium produced a very similar gradient.
Figure 3 shows the tree species response to all of the environmental variables, and the
tree species similarity. The length of the vectors refers to the strength of the tree species
correlation with each axis according to the similarity of plots based on species
composition, represented by the triangles. There do not seem to be many species needing
or being able to subsist with high calcium/manganese/potassium levels, represented by
the lack of vectors going to the left of the graph.
Part II
The cluster analysis produced a dendrogram (refer to Figure 4) of six groups and the very
reasonable amount of 1.86% chaining. The ordination of the species clusters and
environmental variables once again produced biplots for species and environmental data.
The required r^2 value on the graph of the ordination may be increased to show only the
best-fitting species or best-fitting environmental variables. The r^2 value was increased
to 0.2 in order to point out two species in particular; one can see that Quercus alba
species names in italics and Liquidambar styraciflua rarely co-occur (refer to Figure 5)
because they require different environmental factors, represented by their long vectors
pointing in different directions. It is evident that H2O distribution may affect the
appearance of these two species (refer to figure 6); Quercus alba dominates in the wetter
area. This is shown by the long water distribution vector pointing down, and resembling
the vector for Quercus alba. The vectors for Acer rubrum and Ulnus alata are not very
long (refer to Figure 4) suggesting that they are ubiquitous in many of the plots.
Part III
While performing the indicator species analysis, it was found that 6 groups of the data
produced the highest number of significant indicators, as well as the lowest p-value,
among all the possible numbers of groupings (refer to figure 7). Having 6 groupings
produced a value of 27 significant indicator species and a p-value of 0.129. The groups
were afterward sorted by fidelity (abundance).
The results for fidelity and constancy can fool the eye at first glance. For example, the
results for 6 groupings do you mean 6 groupings or group number 6? actually resembled
those for 3 groups to some extent, because Acer negundo and Carya carolinae were
highly abundant, both with a fidelity of 100. However, the two species had higher
constancy in Group 6, allowing them to be better indicator species for this group.
Furthermore, Acer negundo’s relative fidelity of 27 rated with Carya carolinae’s relative
constancyof 9 made Acer negundo a better indicator species than Carya carolinae when
the data was in 6 groupings.
Would help to present results in a table. Need to identify and describe all 6 groups.
IV. DISCUSSION
Overall, this woody species data taken from Duke Forest seemed to follow patterns
expected to be seen in a temperate North Carolina piedmont forest. Quercus alba and
Liquidambar styraciflua were dominants in particular environmental conditions, while
Acer rubrum and Ulnus alata were ubiquitously found in multiple environments. There
was a gradient of water distribution that would be expected from sloping terrain which
also expresses a gradient of mineral deposits. Cluster analysis with results of six
groupings shows that the species are indeed heterogeneous in their environment, yet
create patterns from which groups can be defined.
It is interesting to notice that the most ubiquitous and abundant trees, such as Acer
rubrum or a Quercus did not make indicator species. Instead, it takes a species with both
fidelity and constancy, such as Acer negundo. True, but Acer negundo also had very low
abundance. The results of this analysis provided valuable data on the environmental
variables in which you may expect species. It also gives light on the species you may see
existing together, because of their shared environmental preferences. Yes! Lastly, these
multiples species one sees may then be most-clearly defined by one particular species, the
indicator species.
Figure 1: Scree Plot representing decrease in Stress
PLOT OF STRESS V. ITERATION NUMBER
(to prevent wrapping of wide plots when printing, use small font)
38.4943008............................................................................
.
.
.
.
.*
.
.
.
. ******
.
.
******
.
.
***
.
.
*
.
.
**
.
.
*
.
.
**
.
.
**
.
.
**
.
.
**
.
STRESS
.
****
.
.
*******
.
.
****************
.
.
*******************.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.0000000............................................................................
10
20
30
40
50
60
ITERATION NUMBER
Figure 2: Calcium Environmental Gradient
Axis 2
EnvLong_NMSstep
0
4
8
12
Ca-A
Axis 1
r = -.457 tau = -.369
Axis 2
r = .532 tau = .348
Axis 1
12
8
4
0
70
Figure 3: plot Similarity Based on species composition
TreeLong_NMSstep
Axis 2
LIST
CACR
ULAL
ACRU
QUPR
OXAR
QUAL
Axis 1
Figure 4: Dendrogram tightest Species Groupings based on Species Composition
TreeLongGROUPING.1
Distance (Objective Function)
1.6E-02
5.1E+00
100
75
1E+01
1.5E+01
2E+01
Information Remaining (%)
a
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 5: Tree correlation with each axis based on grouping by species composition
TreeLong_NMSstep
Group6
1
3
12
36
37
87
Axis 2
LIST
CACR
ULAL
ACRU
QUPR
OXAR
QUAL
Axis 1
Figure 6: Water distribution vector correlating with Quercus alba vector in Figure 4
EnvLong_NMSstep
Group6
1
3
12
36
37
87
Axis 2
Mg-A
Ca-A
pH Mn
Al
Elev
Dist-H2O
Axis 1
Figure 7: Group 6 Expressing the Highest Number of Species Indicators
and Lowest p-value
29
0.180
27
0.170
0.160
0.150
0.140
25
23
21
0.130
0.120
0.110
0.100
19
17
15
3
4
5
6
# Significant
Indicators
Avg P-values
Bibliography?
20/26