Objecti ity in s

Objecti ity in s
Objectivity
in stratification,
and classification
sampling
of vegetation
by
ROBERT
Submitted
in partial
HOWARD
WESTFALL
fulfilment
of the requirements
the degree
PHILOSOPHlAE
DOCTOR
in the Faculty
(Department
(BOTANY)
of Science
of Botany)
University
of Pretoria
Pretoria
October
Promoter:
Prof.
Co-promoter:
Dr
1992
G.K. Theron
Dr N. van Rooyen
© University of Pretoria
for
ABSTRACT
in stratification,
Objectivity
and classification
sampling
of vegetation
by
ROBERT
Promoter:
Prof.
co-promoter:
Dr
HOWARD
WESTFALL
G.K. Theron
Dr N. van Rooyen
in the
Department
of Botany
for the degree
PHILOSOPHIAE
The
aims
tion,
ing
of this
sampling
study
DOCTOR
are to increase
and classification
repeatability,
(BOTANY)
objectivity
of vegetation,
predictability
and
in stratificathereby,
relevancy
of
improv-
vegetation
classifications.
The
aims
are
achieved
classification
using,
computer
improved
imagery;
program
study
national
relating
and
package
reduce
that
priority
conservation
the
by
was
time
plant
minimum
developed
spent
vegetation
because
stratification,
small-scale
improved
classification
recommended
ensure
scale;
satellite
vegetation
this
to
by:
correct
on
vegetation
cover
A
to facilitate
vegetation
be
vegetation
and
mapping
estimations;
entropy.
resource
sampling
comprehensive
the
aims
of
It
is
analyses.
given
and
the
management
highest
can
also
of soil and soil water.
L
CONTENTS
1.
2.
3.
Page No.
INTRODUCTION
1
1.1
AIMS
1
1.2
JUSTIFICATION
2
1.3
HYPOTHESES
6
1.4
THESIS
6
1.5
REFERENCES
STUDY
ARRANGEMENT
7
AREAS
11
2.1
PHYSIOGRAPHY
11
2.2
GEOLOGY
13
2.3
SOILS
13
2.4
CLIMATE
14
2.5
BIOTIC
2.6
PREVIOUS
2.7
REFERENCES
FACTORS
15
RESEARCH
17
19
METHODS
3.1
24
PREPARATORY
3.1.1
3.1.2
3.1.
WORK
25
Scale
25
3.1.1.1
Background
25
3.1.1.2
Methods
27
Stand
appLied
area
28
3.1.2.1
Background
28
3.1.2.2
Methods
29
applied
3 Reconnaissance
30
3.1.3.1
Background
30
3.1.3.2
Methods
31
applied
3.1.4
Stratification
32
3.1.4.1
Background
33
3.1.4.2
A method for vegetation
stratifica-
tion using scale-related,
enhanced
3.1.5
Stand
satellite
imagery
35
location
41
3.1.5.1
Background
41
3.1.5.2
Methods
41
3.1.5.3
PHYTOLOC
applied
-
A
random
and sample-set
stratified
3.1.6
vegetation-
Sampling
number
location
random
generator
program
vegetation
for
sampling
unit area
3.1.6.1
Background
3.1.6.2
Predictive
45
45
species-area
determination
vegetation
relations
of subsample
sampling
SAMPLING
3.2.1
Sampling
3.2.2
in the Transvaal
47
52
unit location
52
3.2.1.1
Background
52
3.2.1.2
Methods
54
Plant
applied
identification
and verification
Background
3.2.2.2
A new identification
analytical
3.2.2.2.1
Species
cover
55
55
3.2.2.1
the features
3.2.3
and
size for
Waterberg
3.2 FIELD
44
aid combining
of a polyclave
key
Improvements
and an
56
66
67
iii
I
3.2.3.1
Background
3.2.3.2
The plant
method
67
number
of cover estimation
variable-sized
3.2.3.2.1
3.2.4 Floristic
scale - an improved
belt transects
73
data recording
74
3.2.4.1
Background
74
3.2.4.2
Methods
74
3.2.4.3
PHYTOCAP.
applied
field-data
A
3.2.4.3.1
capture
program
Improvements
propackage
77
78
Habitat
data
78
3.2.5.1
Background
78
3.2.5.2
Methods
applied
80
3.2.5.2
A) Field
data
80
3.2.5.2
a) stand data
80
b) Sampling
86
B) Derived
unit data
data
89
a) Input
90
b) Output
92
3.3 CLASSIFICATION
3.3.1
69
Improvements
gram for the PHYTOTAB
3.2.5
using
94
Background
3.3.2 Methods
94
applied
98
3.3.2.1
Releve
sequencing
3.3.2.1
A) Commonality
3.3.2.1
B) Similarity
sequence
103
3.3.2.1
C) separation
unit sequence
104
3.3.2.2
Releve
sequence
grouping
99
101
105
iv
.:
------------
3.3.2.3
Species
sequencing
106
3.4 VERIFICATION
109
3.4.1
Background
109
3.4.2
Methods
applied
110
3.4.2.1
Classification
3.4.2.2
Spatial
3.4.2.3
Floristic
3.4.2.4
Classification
efficiency
110
relationships
and habitat
111
relationships
113
and field relation-
ships
115
3.5 DERIVATIVES
116
3.5.1
Background
117
3.5.2
Methods
applied
117
3.5.2.1
Primary
3.5.2.1
A)
Plant
3.5.2.1
B)
Community
3.5.2.1
C)
Gradients
3.5.2.2
Secondary
3.5.2.2
A)
Structure
3.5.2.2
B)
Community
3.5.2.2
C)
Stand phase
3.5.2.2
D)
Community
derivatives
118
communities
118
definition
121
123
derivatives
125
125
composition
analysis
analysis
cover
assessment
3.6 PHYTOTAB-PC
125
127
128
129
3.6.1
Classification
3.6.2
Data bank
3.6.3
External
and data processing
130
139
utility
programs
141
3.7 REFERENCES
142
4. RESULTS
149
AND DISCUSSION
4.1 PREPARATORY
WORK
149
v
-----
4.1.1
Scale
4.1.2
Stand
4.1.3
Reconnaissance
154
4.1.4
Stratification
155
4.1.5
Stand
158
150
area
location
4.1. 6 Sampling
4.2 FIELD
152
unit area
160
SAl1PLING
4.2.1
Sampling
4.2.2
Plant
4.2.3
Species
4.2.4
Floristic
162
unit
location
identification
4.2.5 Habitat
and verification
cover
162
164
166
data recording
data
169
171
4.2.5.1
Field
4.2.5.2
Derived
data
data
173
181
4.3 CLASSIFICATION
185
4.4 VERIFICATION
222
4.5 DERIVATIVES
225
4.6 PHYTOTAB-PC
228
4. 7 REFERENCES
230
5. CRITICAL
EVALUATION
AND CONCLUSIONS
233
OPSOMMING
245
SUMMARY
247
ACKNOWLEDGEMENTS
249
CURRICULUM
250
VITAE
REFERENCES
APPENDIX
256
I
269
vi
CHAPTER
criticisms
of the Braun-B1anquet
tion
studies
have mainly
i.
minimum
sampling
Werger
1974);
ii. observer
southern
tation
classifications
cations
in the sampling
1953, 1961; Poore
African
context,
can
method
of
vegetation
bias
processes
can
prediction,
approach
in
the
(Goodall
1961,
also
be
1974).
on the relevancy
expressed:
inventorizing
resource?
and classification
1956; Werger
doubts
and
Is there
stratification,
affect
of the
criticisms
hence
size
namely,
Is
this
describing
a demand
of vegethe
the
most
varia-
for classifi-
and how are they put to use?
Observer
the
to vegeta-
and
In the
in the
approach
with two facets,
unit area or quadrat
(Goodall
cost-effective
(1928, 1951)
been concerned
bias, both
phases
tion
1. INTRODUCTION
repeatability
results.
and
justify
in vegetation
observer
application
of
stratification,
This
bias,
through
sampling
and
study
expanded
science,
computers.
sampling
method
the
classification
potential
is an attempt
use
of
by reducing
Objectivity
and
hence,
or
the
classification
to overcome
Braun-Blanquet
decision-making,
refinement
in the
for
and
basic
should
and
increased
processes
of
thereby
be
increased.
1.1 AIMS
The aims of this study
i.
reduce
subjective
are threefold,
decision-making
namely,
to:
in the stratification,
samp1
ling and classification
processes
for improved
repeatability
and predictability;
ii.
reduce
time
fication
spent on the stratification,
processes
le, for greater
iii. increase
through
efficiency;
relevancy
phasizing
cqmputer
automation,
and classi-
where
possib-
and
and significance
possible
sampling
classification
of classifications
by em-
derivatives.
1.2 JUSTIFICATION
Data
collection
tion.
be
That
this
the
to
Reduction
processes,
cal.
greater
and
scope
with
of Europe
17 000 specific
times
of the
with
the
South
plant
the methods
South
Africa,
areas
ecology
subjective
could
to
ensure
become
verify
should
can
decision-
and significance
This
opera-
that
technithe
ade-
of class-
also
provide
of technicians.
with
(Gibbs
about
24 000
Russell
depauperized
of South
methods
on
freed
covered.
Africa,
taxa
plant
Africa
et
with
taxa
natural
and
1985),
the
about
15
000
in an area more
(Polunin
1969).
can be relatively
vegetation
specific
al.
in the depauperized
originated,
where
be
the relevancy
and infra-specific
size
of vegetation
classification,
thus
is relatively
Braun-Blanquet
in which
could
increase
is a technical
decision-making
for the employment
infra-specific
eight
field
placed
of subjective
as improve
comparison
flora
reliance
personnel
as well
ifications,
the
fields
up to and including
Research
quacy,
scientific
is not so in the
attributed
making.
In
in many
still
to
than
Application
European
flora,
simple
compared
covers
over
80%
2
SOIL
Airborne
particles
Oxygen
VEGETATION
Carbon
dioxide
Food
Fibre
SOIL WATER
FIGURE
3
1.1. - A model of vegetation,
soil and soil water interactions, illustrating
the necessity for vegetation cover
for
soil and soil water conservation, as well as reducing airborne particles.
of
the
need
surface
for
areas
the
of
the
floristic
work
Savanna,
plateau
environmental
scales.
(Rutherford
small-scale
interior
weak
area
and
This
environmental
decision-making,
carbon
dioxide
primary
food
without
which
increase
and
and reduced
tial
to plant
streams.
These
furthermore,
and primary
Figure
tion
effort
is
fibre
source.
would
and maintenance
that
with
costs
be made
the highest
the
not
subjec-
shading.
to
This
and
It
is
grow.
Vegetation
run-
in many
1.1.
It is,
of vegetation,
as can be inferred
and the total
by according
necessary
can
is essen-
supply
in Figure
a
soil
decreased
Soil water
re-
removal
conserves
of the natural
vegetation
life.
cover
conservation
effective
a luxury
atmosphere.
of the water
can be saved
more
Is this
infiltration,
is assured,
priority
of
small
insufficient
production
are illustrated
conservation
classification
from
through
relationships
could
is essential
increased
evaporation
water
at
of
of the vegeta-
with
at all?
Vegetation
plants
through
1.1. Therefore,
conservation
But
pollutants
suggested
understanding
can complicate
workers
on
generally
especially
for oxygen
and
growth
largest
occur
with
complexity
vegetation
Vegetation
terrestrial
off
1986),
to Europe,
responsible
soil water
the
Biomes
can complicate
for
consequent
methodology.
to study
is primarily
Grassland
and floristic
in Braun-Blanquet
and
Furthermore,
relationships,
relative
afford?
1986)
& Westfall
especially
is it necessary
source
of
Africa,
we cannot
and
which
environmental
tive
that
Nama-Karoo
gradients
in south
Why
required.
(Rutherford
tion
experience
is
Westfall
&
soil
from
conservavegetation
resources.
in
the
study
and
4
conservation
of vegetation?
servation
efforts,
ation
the
to
areas.
For
generally
This
scale
to
example,
larger
difference
the
The
assumption
its
physical
significant
areas
those
area
can
which
Plant
form larger
which
factors,
scales,
plant
This
can
related
therefore,
in
to scale.
be suitable
has
related
district.
of
classes
is
not
to
for
basic
been
scale
to
&
dis-
because
can be grouped
to
that the environmental
communities
Areas,
a
Mueller-Dombois
communities
plant
relate
effects
natural
assumption
national
example,
the
area
will
farms.
also
magisterial
a given
This implies
level
For
integrates
be
appropriate
should
1956;
also
smaller
communities.
a
con-
determin-
where
intended.
on
(Poore
1974).
scale
indicating
continua
differentiate
are also
should,
based
and
for individual
areas
is
to
national
to
Such
community
method
related
at
related
thereby
where
be
studies
from policy
interest
area
be
communities
form hierarchies
of
be
a plant
Werger
must
scale.
not
environment,
1974;
vegetation
interest
the
environmental
Ellenberg
proved.
farm
should
that
of
than
Braun-Blanquet
the
ties
for
area
thereof,
than
purpose
catchment
they
in
is smaller
only
but any form of land use,
execution
be
Not
based
at
different
on plant
for many purposes
communi-
and at vari-
ous scales.
Classification
areas
tices,
of the vegetation
or communities
but
of South
can be advantageous
is essential
tion management.
Reducing
stratification,
sampling
for vegetation
the number
and
Africa
into appropriate
for many
conservation
of decisions
classification
land use pracand
required
processes
utilizafor the
can
not
5
only
improve
the
also
facilitate
scientific
validity
of these
processes,
but
can
these processes.
1.3 HYPOTHESES
i. That
adjacent
above-ground
These
of the plant
differences
sampling
at the given
iii. That more
structure,
"noise"
bered
within
except
a multi-spectral
in
cha-
charactscanner.
delimit
plant
and vegetation
refers
to plant
is possible
vegetation
structure.
size and spacing.
in the classification
data set.
proportional
and the absence
data
attribute
of a classified
to pattern.
of species
"Noise"
data
refers
in a species-group
in
set.
ARRANGEMENT
Page numbering
taining
of scale,
is a quantifiable
a classified
1.5 THESIS
with
The spectral
unit area for objective
here,
is inversely
to outliers
in the spectral
communities.
than one solution
of a vegetation
variation
scale differ
scales.
sampling
is a function
Vegetation
at a certain
can be used to objectively
ii. That the minimum
set and
causing
are those detectable
communities
iv. That
communities
phytomass
racteristics,
eristics
plant
text
in this work
or figures.
accordingly.
each
Published
Tables
chapter,
is consecutive
and
and
according
articles
figures
are
to pages
are, therefore,
numbered
are
preceded
by
in the case of published
articles
which
the
renum-
consecutively
chapter
retain
con-
number,
the original
6
numbering.
A wide
referred
to,
Literature
each
unless
cited
ed articles
in the
with
package,
ings are available
including
literature
by negotiation
thesis.
at the
form the
end
at the
to the aims
indicated.
is not
of publish-
is referenced
jointly
otherwise
the
references
relevant
but
but
to
is referenced
list
program,
where
consulted,
materially
chapter,
separately,
except
was
contributed
Each computer
is dealt
program
literature
in each chapter,
and a full
of this work.
of
it
included
chapter
study
range
of
end
of this
PHYTOTAB-PC
Program
list-
with the author.
1.6 REFERENCES
BRAUN-BLANQUET,
J. 1928. pflanzensoziologie.
1 Aufl.
Springer,
J. 1951. pflanzensoziologie.
2 Aufl.
Springer,
Wien.
BRAUN-BLANQUET,
Wien.
GIBBS
RUSSELL,
G.E., REID, C., VAN ROOY,
List of species
Recent
literature
Gymnosperms,
Survey
GOODALL,
D.W.
Australian
D.W.
vegetation.
of Botany
African
and synonyms.
Monocotyledons.
of South Africa
vegetation.
GOODALL,
of southern
Edition
2.
Part 1. Cryptogams,
Memoirs
methods
of the Botanical
for the classification
i. The use of positive
of Botany
1961. Objective
iv. Pattern
plants.
L. 1985.
51: 1-152.
1953. Objective
Journal
J. & SMOOK,
interspecific
of
correlation.
1: 39-63.
methods
for the classification
and minimal
area. Australian
of
Journal
9: 162-193.
7
MUELLER-DOMBOISr
vegetation
POLUNINr
ecology.
H.E.D.
ecological
Pressr
RUTHERFORDr
Africa
of Europe:
A field
iv. General
problems.
& WESTFALLr
Survey
of
New York.
investigations.
- An objective
M.J.A.
and methods
guide.
1956. The use of phytosociological
M.C.
Botanical
H. 1974. Aims
Oxford
London.
phytosociological
WERGERr
WileYr
O. 1969. Flowers
University
POOREr
D. & ELLENBERGr
Journal
of South Africa
1974. On concepts
Ztlrich-Montpellier
method
discussion
of Ecology
R.H. 1986. Biomes
categorization.
methods
in
of
44: 28-50.
of southern
Memoirs
of the
54: 1-98.
and techniques
of vegetation
applied
survey.
in the
Bothalia
11: 309-323.
8
Punda Maria
•
•
Louis Trichardt
•
Tzaneen
•Potgietersrus
•
Phalaborwa
•
Thabazimbi
•
Warmbaths
•
•
Skukuza
Marble Hall
•
Rustenburg
•
• Pretoria
Nelspruit
•
Witbank
Johannesburg
Potchefstroom.
•
Ermelo
1
N
FIGURE
9
2.1 - The Transvaal Province, Republic of South Africa,
showing the location of the main study area, in the north
western sector of the province.
27°30'
23~5'i---~----------~--------~--------~~-'--~----------~--------------~~------~
Marken
•
.1228
.1290
1257(l
~
•L.-1200~
• 1315
N
~~~
-L__~
~~~~~L-~==~~~
~
~-L __~24°30
28°45'
o
10
20
I
I
I
km
Legend
.•.Mountain peak
Contour interval: 300 m
FIGURE
10
2.2 - The physiography of the main study area in the north
western Transvaal, showing the main drainage lines.
CHAPTER
Two
separate
situated
1984)
study
in
the
between
longitudes
Bushveld
berg,
north
contrast
shape
The
to
which
area
were
selected.
north-western
southern
270
Sour
areas
2. STUDY AREAS
(Acocks
1953,
Kransberg
many
vegetation
is intended
230
to
1975,
and
1988)
13
is the largest
west of the study
area
(Figure 2.1).
The
area
is situated
second
study
and Transport
comprises
two
homogeneous
graphy,
Technology,
10
x
10
grassland
geology,
m
plots
with
soils,
the
no
climate
the
area
later
town,
CSIR
observable
eastern
portion
2.2).
&
In
in
of data.
dimensions
of
in the north-
Division
of Roads
site. The study
apart
of
Water-
rectangular
situated
metres
or biotic
is
with
Pretoria
ten
and
Transvaal
2.1
is
Westfall
integration
km2
at the
Silverton,
30'
(Figure
000
100 x 130 km. Ellisras
240
area
&
the major
of
Hanglip
facilitate
study
(Rutherford
include
studies
approximately
main
35' and
45' which
from
covers
Transvaal
latitudes
30' and 280
The
in
variation
a
area
visually
in
physio-
factors.
2.1 PHYSIOGRAPHY
The
physiography
being
mainly
ranges
sea
south
and
Extensive
plains
sea
occur
level
the
main
mountainous
in the
level
of
with
having
decreasing
the
area
in
is extremely
Waterberg·
altitudes
at altitudes
in the
study
far northwest
Sandriviersberg
of up to 2 088 m above
altitude
between
and
irregular,
northwards
800 and
of the
(Figure
1 200 m above
study
area.
mean
2.2).
mean
The main
11
o
....•
tv
27 30
•
I
•
23 o 35:::::;::=
_I r:;>
"
_..
UX;END
I
,
c
8
G
G
G
h
h
r
.Alma"\. 0
~
10
20
-
p
~
lei
L
U
~
Sarxlstone, grit
san:irivieub::!x:g
Sarrlstcn:!, grit
Mal<gabes:xJ
San::lstone
~
Siltstone,
SetJaoJe
Sarrlstone, grit
Slc:iJ.p3dla::p
San::lstone,
COlXJ
Al.aB.
sandstone,
=n;J'larerate
I
I
n
st:ex:kriviex:
San::lstone, =n;Jlarerate
o
~
San::lstone, ccoqlorerate
I
km
FIGURE 2.3.
-
The geological
formations
dominant
surface
lithology
in the main study
area
from Jansen
(1982).
showing
rrudstone,
shale
larerate
~24030'
28045'
o
DiaOOse
San::lstone, grit
9
~,/
p::>st-Waterl:erg
c:J..ex::an:nt.
m
.
urrlifferentiated.
f
o~
~l---'\)
Sarxlstcn:!, siltst.ore
Sarxlstone, s i.Lt.store , shale
\10
';)\0
CJ..arena
Vaalwater
.I
I'/-S
Basal.t;
e I
N
»'fY
Etxrrat j en an::l d:::minant li UolCX]'f
G
Urrlif ferentiate::l
pre-Waterl:erg
perennial
which
rivers
drain
are
northwards
by these drainage
within
2.2
the
Hogol,
into the Limpopo
the mountain
geology
the
study
stone,
with
of
Southward
lower-lying
Rivers
incisions
floodplains
area.
the
area
Waterberg
mainly
berg
the
with
The
main
central
four
sandstone,
of the
deposits
sediments
mined
of
at
these
grit
arkose,
south,
shale.
both
Subgroup.
and Hokolian
value
Hine
near
the Hogal-
study
coal
in close proximity
Formation
with
area
and
with
lastmentioned
Both the
is mined
Ellisras
and
Formation
The
Erathem,
The
but
Hatlabas
Sandriviers-
C1eremont
and
Grootgeluk
at Thabazimbi,
the
siltstone
economic
silt-
Formation,
and west,
Vaalwater
1 300 Ha.
mainly
the
the
1 700 Ha to
the
is
and
Group
Jansen
grit and conglomerate;
area,
are of the Kransberg
of
with
are of the
in the north
study
by
the Hakgabeng
formations
sandstone,
the
described
(Figure 2.3) in the east of
and
in the
grit;
and
Waterberg
approximately
Both
been
Formation,
laharite
with mainly
sandstone
formations
known
and
formation
part
has
formations
with mainly
Formation
mainly
mainly
shale
area
Aasvoe Lkop
the
sandstone.
Formation
akwena
are
mudstone,
Subgroup.
2.3
Hogalakwena
River.
lines have formed extensive
(1982). The main geological
are
and
GEOLOGY
The
in
Palala
has
subgroups
ages
very
in the
iron
to the study
from
few
Karoo
ore
is
area.
SOILS
One of the most recent
soil maps of the Republic
of South Africa
13
is the map of MacVicar
sified
as
neutral
being
a
(1973) where the entire
Red-Yellow-Grey
sands/loarns, yellow-grey
lack of differentiation
attributed
fication
sification
for more
have
ever,
a comparison
a mapping
small
part
(Westfall
limits
of
the
Furthermore,
were
tion,
1: 30
study
the
the study
sarne as
far less
Consequently,
greater
properties
than
units
with
in
of
often
the
soil
can
and
attributed
to
not
the
a
the
being
vegetation.
soil chemical
proper-
natural
properties,
vegeta-
in the
on physical
of the soil in the main
series
correlation
influencing
is placed
How-
poor
classification
soil physical
the
within
in differentiating
emphasis
within
proj ect
be
that the recorded
1991)
systems.
forms
showed
soil
limits
these
soil clas1977,
the soils
a pilot
land. The
this soil classi-
et al.
using
correlation
significant
scale,
of soils,
000,
the
found
(MacVicar
with
area can be
and taxonomic
mapped
area,
poor
units
it was
at this
chemical
of
198.1). This
necessarily
mapping
of floristic
the
for
Africa
not as yet been
scale
set
ties
for South
detailed
area
the binomial
is clas-
catena
and much rocky
of soil units within
Although
systems
study
at
dominant
area
plinthic
to the scale of 1: 2 500 000 at which
is mapped.
provides
latosol
study
rather
area.
than
study area.
2.4 CLIMATE
According
area
is classified
with
22°
to K~ppen's
summer
C. The
annual
classification
as Cwa which
rainfall
climate
rainfall
and
a
describes
January
is continental
falling
(Schulze
in summer,
with
1947) the main
a warm temperate
mean
more
temperature
than
from October
study
climate
exceeding
85% of the mean
to March
(Schulze
14
1965).
from
Precipitation
650
to
the plains
obtained
0001,
900 mm
from
the
by request.
fine
with
vegetation
grid
study
are
be
of
of climate
the
area
(Westfall
from
interpolation
would
000. The
influence
is, however,
farm
a
of climate
importance
in
weather
data
these
data
sources,
where
occur.
correlation
insufficient
records,
indirect
of prime
published
summer
necessitate
of 1:250
and
Pretoria,
should
climatic
from
and other
growth
on
data was
of predominantly
area
Because
mm
are November
to allow
~98l).
varying
500
X447,
stations
official
determined
Bag
plant
study
and below
of the year
because
optimum
differentiation
available
could
when
rainfall
Precipitation
Private
months
which,
at a scale
on vegetation
the
period
areas
area.
Bureau,
1965)
topography
very
study
The four hottest
is the
irregular
of the
Weather
(Schulze
rainfall,
with mean annual
in the mountainous
in the north
to February
The
is variable
available
and
by
means.
2.5 BIOTIC FACTORS
Observations
during
of area,
veld
the
study
main
natural
areas
Sterk,
grazing
area
vegetation.
where
but
the
is the main
game
Cultivation
on many
is mainly
is possible,
and Mogol Rivers.
indicate
that
in terms
land use practice
farms
also
the
to the
flatter
such as the floodplains
of the
Crops
confined
utilize
in
include maize,
grain
sorghum,
and melons.
Observations
increase
of fieldwork
by cattle
irrigation
Palala
tobacco
the course
is
also
taking
indicate
place
that
at
a
Ellisras
dramatic
with
its
human
newly
population
developed
15
18
(
I
N
-8
-,
20
2.0
o
• 24.30
,
Ll)
0'"
CO
N
FIGURE
16
2.4. - The Veld Types (Acocks, 1953, 1975, 1988) of the main study
area in the north-western
Transvaal. 8, North-Eastern
Mountain
Sourveld; 14, Arid Sweet Bushveld; 18, Mixed Bushveld; 20, SourBushveld.
coalfields
and the
relatively
unspoilt,
Kransberg
massif
facilities
in
Transvaal
private
the
the- Waterberg
development
study
lure
could
be
following
represented
Veld
Types
indicated
Bushveld
(43%);
Mountain
Sourveld
the mountainous
in
areas
and
with
on
the
to the
floodplains
in
north.
Outliers
2.6
PREVIOUS
above
the
environment.
underutilized
could
and the
be
increase
Although
in
terms
of
drastically
re-
increase.
1988)
Sour
Bushveld
Bushveld
and
on the
floodplains.
(53%);
Sweet
Pal ala
and
Mountain
area
Mixed
North-Eastern
is found
plains
Arid
North-Eastern
are
of the study
Bushveld
(2%);
of the Mogol,
of
the
complex
and
(Acocks
Mixed
both
Park
(2%) (Figure 2.4). The Sour Bushveld
limited
found only
National
of visitors
potential
brackets:
Sweet
of
the many game and
area and the percentage
Arid
mountains
the
of
the
rock-climbing
members
of a large human population
in the study
is
best
The
with
Pretoria-Witwatersrand
considered
its
station.
Waterberg
Clubs;
numbers
area
The
the
to the
increasing
production,
to
Kransberg
exploitation
by the effects
to
new
human
duced
covered,
the
power
the
Mountain
and
agricultural
the
according
Transvaal
of
of
to be amongst
Transvaal,
reserves;
to
of the Matimba
grandeur
considered
nature
combine
the
scenic
and Northern
proximity
can
construction
in
adjacent
Bushveld
sterk
is
Rivers
Sourveld
are
1 500 m altitude.
RESEARCH
Early
scientific
interest
sized
showy plants
in the vegetation
and plants
of nutritional
of South Africa
and medicinal
emphavalue
17
(Werger
1978).
in south
century
stein
Africa
because
(1811,
criptive
mainly
was
Interest
and
world-wide,
1812)
and
on
Burchell
placed
in
the:
Region
Palaeotropic
and
(Grisebach
Dry
Region
1886);
and the Highveld
During
the
first
two
such as Sch6nland,
checklists
decades
in south Africa
Natal
also
studied
1916)
and
describes
the
where
the
Transvaal
formed
Guidelines
for future
and
physiognomy,
plant
succession
(1916)
During
part
on plant
succession
of the:
Evergreen
Evans
surveys,
emphasized.
this
time
the
Kalahari
and
Waterberg
1823);
Region
(Rehmann
1880);
Kalahari
Region
(Bolus
1908).
century,
botanists
and Bews publishnotes
for
1922). Bews, who worked
of plants
of
suggested
The works
greatly
Africa
Africa
(Bews
Eastern
including
codes
and
mainly
in south
south
of the
various
Grassland
of Bews
influenced
(Bews
1918),
Region.
for habitat
checklists
in
factors
and
plant
(1916) and Clements
vegetation
surveys
for the next four decades.
and Deciduous
1936)
part
based
(Schouw
ecological
grasslands
des-
Africa,
Transvaal
Burtt-Davy
and
succession
were
in south Africa
Phillips,
were
In subsequent
of the twentieth
(Sch6nland
the
the
nineteenth
such as Lichten-
southern
1882);
(Marloth
species
areas
of
Highveld
(Engler
Region
plant
1824).
of plants,
the
Mesembryanthemorum
1872);
Marloth,
of
(1822,
experience,
Regnum
during
of travellers
divisions
observations
distribution
developed
of the descriptions
phytogeographic
Kalahari
ed
in the geographic
Small
Transvaal
Park
and
Waterberg
Bush
was
Province
described
(Pole
as
Evans
Tree
and Bush variation
of Parkland
Tree
Savanna
of
variation
the
being
1922);
Bush
(Pole
Veld
18
Savanna
the
(Adamson
vegetation
Meester
1938).
of
(1965),
southern
Werger
descriptions
of
formity,
the
major
closely
with
most
Westfall
1986)
More
these
recent,
Africa
(1978),
the
where
includes
and
vegetation
vegetation
small-scale
Biomes
White
types
units
of
that
indicate
of Adamson
describing
of: Eyre
(1981).
southern
the Waterberg
work,
(1963),
Although
a
lack
(1938)
Africa
of
the
con-
correspond
(Rutherford
ared
forms part of the
with
the
&
Savanna
Biome.
More
detailed
agriculture
classified
vegetation
subsequently
the
types,
based
relied
largely
in this
without
on
grazing
on these
The
the
in
data
area
the
south
Acocks
(1953, 1975,
sampling
approach
pling
2.7
of the Kransberg
Westfall
Although
west
of
the
formal
and classification
1988) inter
only
with
were
introduced
1975,
(1941)
veld
1988)
of veld types,
block
in the
et
al.
studied
study
the
area,
south
(1981)
farm
at
a
approach.
alia, adopted
Werger
Braun-Blanquet
into
(1953,
Coetzee
(1981)
the phytosociological
it was
Transvaal
Acocks
by
improving
Irvine
for his classification
and
using
the
and
of
role.
northern
described
scale,
that
the
purpose
greater
was
detailed
workers
a
potential
sampling
also
of
vegetation
study
formal
Groothoek,
played
vegetation
area.
of
west
studies,
(1973)
methods
of
a floristic
and
subsequent
floristic
sam-
to South Africa.
REFERENCES
ACOCKS,
J.P.H.
Botanical
1953. Veld types
Survey
of South
of South Africa.
Africa
Memoirs
of the
28: 1-192.
19
-----------------------
ACOCKS,
J.P.H.
Memoirs
ACOCKS,
J.P.H.
ADAMSON,
R.S.
J.W.
1988. Veld
1938.
South Africa
Ecology
BOLUS,
Survey
40: 1-128.
3rd edition.
of South Africa
57: 1-146.
of South Africa.
White-
Account
with
of the chief types of vegetation
notes
on plant
succession.
in
Journal
of
4: 129-159.
1918.
Davis,
of South Africa
types of south Africa.
The vegetation
2nd edition.
London.
1916.
J.W.
of South Africa.
Survey
of the Botanical
friars,
BEWS,
types
of the Botanical
Memoirs
BEWS,
1975. Veld
The grasses
and grasslands
of South Africa.
Pietermaritzburg.
H. 1886.
handbook
Sketch
of the flora of South Africa.
of the Cape of Good Hope 286-317.
Official
Richards,
Cape Town.
BURCHELL,
W.J.
Africa.
BURCHELL,
Vol.
W.J.
Africa.
CLEMENTS,
1822. Travels
B.J.,
JOUBERT,
VAN WYK,
S.C.J.
die Waterberggebied
Koedoe
ENGLER,
Carnegie
P., GERTENBACH,
1981.
of southern
London.
succession:
of vegetation.
of southern
London.
in the interior
2. Paternoster,
1916. Plant
development
COETZEE,
1. Paternoster,
1824. Travels
Vol.
F.C.
in the interior
An analysis
Institute,
W.P.D.,
'n Plantekologiese
of the
Washington.
HALL-MARTIN,
verkenning
in die Noord-Transvaalse
A. &
van
bosveld.
24: 1-23.
A. 1882.
pflazenwelt
Versuch
einer Entwicklungsgeschichte
inbesondere
Terti~rperiode.
der Florengebiete
E~gelmann,
der
seit dar
Leipzig.
20
EYRE,
S.R. 1963.
Vegetation
and soils. A world picture.
Arnold,
London.
GRISEBACH,
A. 1872. Die vegetation
klimatischen
IRVINE,
L.O.F.
Transvaal
thesis,
JANSEN,
Anordnung.
Transvaal,
Republic
Geological
Survey
LICHTENSTEIN,
1804,
LICHTENSTEIN,
of South Africa
1803, 1804,
C.N.
in the
of the
71: 1-98.
Africa
Vol.
1.
im sadlichen
1973. Soil map, Republic
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Salfeld,
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1805, 1806. Vol. 2. Salfeld,
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H. 1812. Reisen
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1803,
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The geology
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veld types of the northern
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H. 1982.
MACVICAR,
Engelmann,
1941. The major
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der Erde nach ihrer
Berlin.
in der Jahren
Berlin.
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Research
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MACVICAR,
C.N.,
DE VILLIERS,
LAMBRECHTS,
ROOYEN,
J.J.N.,
T.H.
Agricultural
MACVICAR,
C.N.,
DE VILLIERS,
M.V.,
M., IDEMA,
H.J.VON
LAMBRECHTS,
J.J.N.,
DOHSE,
HARMSE,
LAKER,
SCHOEMAN,
J.L.,
BENNIE,
H.J.VON
J.H., MICHAEL,
E.,
J., VAN
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of
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T.E., ELLIS,
M.C.,
VERSTER,
F.R., LE ROUX,
for South Africa.
Services,
S.W.J.,
R.F.,
M. 1977. Soil classifica-
Technical
D.C.,
F.R., MEYER,
B.H.A.,
system
J.M.,
GREY,
MERRYWEATHER,
& HARMSE,
tion: A binomial
J.M., LOXTON,
A.T.P.,
F., ELOFF,
M., HARTMANN,
LOXTON,
R.F.,
D., PATERSON,
SCHONAU,
BRUCE,
J.F.,
M.O.,
FEY,
HENSLEY,
MERRYWEATHER,
D.G.,
A.P.G.,
R.W.,
SCHLOMS,
SCOTNEY,
D.M.,
21
SNYMAN,
K., TURNER,
E. & YAGER,
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taxonomic
Natural
MARLOTH,
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T.U.
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R. 1908. Das Kapland,
das Waldgebiet
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Fischer,
Zoologica
POLE EVANS,
Memoirs
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A
on the Agricultural
15: 1-257.
insonderheit
der Reich
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I.B. 1922. The main botanical
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T.H.,
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B.J., VAN ROOYEN,
1991. Soil classification:
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Resources
Kapflora,
MEESTER,
D.P., VAN NIEKERK,
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Survey
of South
of South Africa
4: 49-53.
POLE EVANS,
Memoirs
REHMANN,
I.B. 1936. A vegetation
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of the Botanical
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A. 1880. Geo-botanische
Botanische
RUTHERFORD,
RUTHERFORD,
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& WESTFALL,
Survey
Verh~ltnisse
R.H.
R.H.
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J.F.
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15: 294-295.
of southern
Memoirs
of the
54: 1-98.
botanical
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1823. Grundztige einer
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1986. Biomes
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of the Botanical
15: 1-23.
von Stid-Afrika.
1984. Sectors
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- An objective
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1: 1119-1128.
& WESTFALL,
Province
H.C.
Botanical
SCH6NLAND,
Centralblad
H.C.
Transvaal
SCHOUW,
Survey
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Memoirs
4: 69-85.
allgemeinenPflanzen-
und pflanzengeographischer
Atlas.
Reimer,
Berlin.
22
-~
SCHULZE,
B.R.
1947. The climates
the classifications
African
SCHULZE,
B.R.
Survey.
WERGER,
Geographic
M.J.A.
Valley,
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1965. Climate
Government
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to
South
29: 32-42.
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1973. Phytosociology
South
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Pretoria.
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University
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Nijmegen.
WERGER,
M.J.A.
Africa.
In Werger,
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WESTFALL,
1978. Biogeographical
R.H.
Thabazimbi
M.J.A.
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division
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(ed.). Biogeography
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Junk,
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The Hague.
1981. The plant
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District.
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University
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Pretoria.
WHITE,
F. 1981. Vegetation
UNESCO,
map of Africa.
Scale
1:5 000 000.
Paris.
23
CHAPTER
In order
viewed
to achieve
from a basic
processes
than
to
of
basic
ity
were
in
only
the
have,
of the
therefore,
been
and
components.
sequence,
method
The
methods
method,
(Coetzee
over a ten year period
added
to
published
build
within
1975).
rather
that
that
the
The
was
of the
classification,
techniques
Braun-Blanquet
and refined
method
in terms of the components
sampling
selected
Braun-Blanquet
developed
aims, the Braun-Blanquet
perspective,
application·
elements
of
the stated
stratification,
view
describe,
3. METHODS
follow
on
the
flexibiltechniques
and improvements
versions
where
applic-
able.
The
basic
elements
familiarization
species
form with
of
species
cover-abundance
into 25 contiguous
The
Braun-Blanquet
method
are
to a formal or an informal
sampled
the two 10 x 10 m plots
only were
the
leading
presence,
classification
of
in
quadrats,
(rows)
values
and
samples
forming
in the second
2 x 2 m sampling
cover
vegetation
stratification,
estimation
(columns)
the matrix.
in matrix
Accordingly,
study area were
units, wherein
and
each divided
species
presence
recorded.
components
for convenience,
of the
stratification
be grouped
according
and
sampling
to preparatory
processes
can,
work and field
sampling.
24
3.1
PREPARATORY
Preparatory
commence.
WORK
work
This
probably
includes
the most
appropriate
pretation
achieve
methods
linear
Bureau,
sampling
will
Scale
Scale
determines
particular
study
can
is
aspect of a study because
selection
of
on the aim. Results
6e dependent
on the methods
of rainfall
annual
and the inter-
work also included
data,
of altitude
rainfall
selected,
obtained
from
and latitude,
could be estimated
to
dethe
so that
for each
the study area.
the
maximum
detail
and is dependent
that
can
be obtained
with
a
on the aims of the study.
Background
The plant
and
community
is a "group of plants
is distinguished
(Mueller-Dombois
plant.
It
agreed
generally
1956;
that
Mueller-Dombois
communities
can
be
sharing
by a particular
& Ellenberg
of the individual
plant
sampling
aim which
regressions
site within
field
well-defined
is dependent
of mean
3.1.1
(Poore
before
a clearly stated,
and a combination
interpolations
is
required
the aim. In this study, preparatory
Weather
ment
work
important
thereof,
termining
3.1.1.1
is that
1974)
plant
&
included
floristic
at any scale
in
form
1974)
larger
in
plant
environ-
composition"
smaller
communities
Ellenberg
a common
than
that
hierarchies
that
smaller
communities.
25
However,
Reitz
this
(in Poore
ecological
plant
to
inclusion
groups
community
include
depends
pose
of the
in a study
often
states
that
or vegetation
border
in a plant
nize
explicit
1956)
is
Scale,
In other
determines
units".
community.
the
This
and the
Which
than
words,
role in the stratification,
level
whole.
of other
complicate
community
to
recog-
by the
amount
of detail
should,
therefore,
sampling
Du
of heterogeneity
is determined
the
Scale
can often
plant
in turn,
scale.
rather
"a stand may have parts
recognition
on scale.
study.
partial
pur-
required
have
and classification
an
of
vegetation.
According
study determines
turn
is
determines
also
the working
the
related
accuracy
than
& Westfall
to Rutherford
and
user
a millimetre
the
a given
lines
requirements
which
Westfall
include
Each
& Westfall
not be closer
ii. not be closer
It follows
scale,
that
SMUA
area
&
Westfall
the
area
units
1986).
under
potential
study
(SMUA).
at working
of
scale
The
in
SMUA
cartographic
seldom
To these
of printed
less
may
a
be
maps.
is subdivided
containing
sampling
in a
scale which
precision
and shrinkage
each
required
number
sites
(Rutherford
should,
according
by
of
&
to
(1986):
than 1 rom to a stratified
than 2 rom to another
the width
is scale-dependent,
unit
considerations
stretch
stratified
can
1986).
Rutherford
i.
working
sampling
of a linear
(Rutherford
separating
SMUA's
mappable
practical
added the humidity-related,
At
or associated
smallest
to
(1986) the detail
being
unit's
sampling
of an unmappable
border;
and
site.
ecotone
or transition
less than 2 rom on a map at a given
work26
A unit
ing scale.
have
community
working
scale
status
to or broader
than
2 mm could,
at the
relevant
scale.
to the smallest
mappable
unit area
from the distance
and is given
equal
from sampling
The
therefore,
relationship
of
(SMUA) is derived
unit area to stratified
unit border
by:
x)
(-12~67-ln
y=e
= SMUA in
where
y
tion.
In their
account,
of MSDB
(minimum
and seD
(shortest
king scale
and
m>,
= scale,
Rutherford
sampling
cross
corresponds
x
& Westfall
distance
distance
scale will not improve
3.1.1.2
Methods
frac-
(1986) give examples
from a stratified
of a mappable
with detail,
working
as a representative
unit
unit).
border)
Because
any map enlargement
wor-
beyond
the
the detail.
applied
If the smallest
mappable
the relationship
between
unit area
(SMUA) is taken as a circle
SMUA and working
then
scale can be simplified,
as follows:
r = x/1 000
where
r
scale
as
metres
is also
=
radius
SMUA
a representative
of the
SMUA
the
minimum
unit border
SMUA
of
is, therefore,
fraction.
for a working
distance
and the SMUA,
and minimum
in metres,
distance
always
and
For
=
denominator
example,
the
scale of 1:50 ODD,
on the
ground
at this working
between
x
SMUA
between
scale.
radius,
1 rom on a map at any working
the
in
is 50 m. This
a stratified
The radius
and stratified
of
unit
of the
border
scale.
27
The
scale
the
detail
used
required
Agricultural
3.1.2
The
in this
regional
planning
(see section
Concise
Dictionary
a stand as "a growth
ially trees
by
is that
vegetation
corresponds
the
with
Department
of
4.1.1).
of the English
of plants
in sampling
it becomes
stands because
Language
in a particular
in a forest or a crop in a field".
into account
pling)
is 1: 250 000 which
area
New Collins
scale
for
Development
Stand
defines
study
area, espec-
The effect
at 1:250 000 scale
(1985)
of taking
(small scale sam-
increasingly
more difficult
to recognize
of increasing
heterogeneity
with decreas-
ing scale.
3.1.2.1
Background
Gabriel
& Talbot
abstract)
in
(1984) define a stand as follows:
aggregation
species
physiognomy,
condition
stands
to
we
sample
Werger
be
in such
selected
vegetation
should
in
(1974)
of which
yield
terms
it
or
and communities
vegetation".
a more
of
further
both
of more or less similar
composition,
distinguish
which
communities
of plants
from
are
(1974) states
spatial
adjacent
measure
a manner
are
further
that
that
communities.
aggregated
abstracted
"Stands
each
or less typical
states
that
composition
"Stands
which
and
are
and
Concrete
into
abstract
into a general
for sampling
is representative
description
(vs.
uniformity
arrangement
it is part and that each plant
floristic
"A concrete
sampled
should
of the
therein
of that vegetation
structure".
obviously
Werger
heteroge28
neous
therefore,
units
logically
information
community
floristic
should
which
be avoided
smaller
smaller
Furthermore,
than
than
the
a stand
parts
because
the
should
be
of two or more
do not con-
the two
or more
From these definitions
a relatively
community
sampling
and might,
they
can be used to describe
that a stand is generally
of vegetation,
composition,
to represent
types that they represent".
be inferred
not
or
be expected
or associations,
tribute
but
structure
in habitat,
plot
homogeneous
of which
with
unit
it is a part,
which
representative
it can
it is sampled.
of a community
and
not of an ecotone.
3.1.2.2
stand
Methods
area
previous
is not explicit
section
representative
hand,
a stand
of the
(SMUA) because
stand
should
community
definitions
be
given
sufficiently
it is to represent.
of scale
limitations,
where
in the
large
to be
On the
other
mappable
unit
it is intended
to
communities.
Werger
(1972)
itself,
in terms
of
but
in the
a stand should not be smaller than the smallest
area
map
applied
about
plot
area.
one
says
a
of species
half
However,
then the following
that
to
one
vegetation
stand
composition
and structure,
hectare
but
can
recommends
fully
would
in an area
a much
if stand area were to be equated
manifest
smaller
with the SMUA
apply:
1: 5 000 scale represents
a stand area of 78 m2;
1: 8 000 scale represents
a stand area of 201 m2;
1: 10 000 scale represents
a stand area of 314 m2;
29
1: 20 000 scale represents
a stand area of 1257 m2;
1: 50 000 scale represents
a stand area of 7854 m2; and
1: 250 000 scale represents
Equating
stand
account
when
area with .the SMUA permits
sampling
vegetation,
is then representative
fined
in section
The stand
tified
3.1.2.1
with
radius
a representative
is
a
function
increase
providing
to be taken
into
that the sampling
plot
defined
as a circular
area within
equal to the denominator
Thus,
scale
with decreasing
the criteria
de-
can still be applicable.
fraction.
of
scale
of the stand. Furthermore,
is, therefore,
unit
a stand area of 20 ha.
and
the minimum
stand
area
scale,
as
for a community
heterogeneity
scale. The stand radius
area is 250 m, representing
of the
a stra-
is
likely
to
for the main study
a stand area of about
20 ha.
3.1.3 Reconnaissance
A reconnaissance
prior
to sampling
3.1.3.1
and can have various
to Werger
known
in all
point
is
of the
study
area
objectives.
before
the study
by Mueller-Dombois
&
cover
established".
has
In
been
Ellenberg
clarified,
other
must
is started".
in stating that "Once the entitation
vegetation
essentially
(1974) "The area of investigation
its variety
supported
even further
the
inspection
Background
According
of
is the preliminary
words,
the
be well
This
(1974)
viewwho
go
or subdivisioning
communities
tentative
are
communities
30
should
be mapped
mapping
and
confirms
ever,
prior to field sampling.
classification
the mapping
what
details
Although
plant
systematically
tific
value
3.1.3.2
The
of
later
work
observations
of
during
give
can only
reconnoitring
This
entails
floristic
and camping
are
more
reconnaissance.
added
perspective,
increase
variation
study
area
over
sites,
the
if required.
roads,
area
is
throughout
scien-
terrain
the
variation,
permission
study
familiari-
from landowners
Routes
followed
for
during
to the main environmental
topography,
these
study
and environmental
should correspond
available
istic
the
travelling
the flora and to obtain
such as rainfall,
geology
and soil, where
in order to detect
gradients.
Data
the range
recorded
graposs-
of flor-
for the
main
units
as an
include:
estimation
of the number
indication
of
area.
can
researcher
How-
applied
purpose
ible, using
L
1977}
classification
of a reconnaissance.
the reconnaissance
dients
necessary
recorded
area to estimate
with
actions
the
of separate
to in this study.
inexperienced
{Tinley
familiarization.
zation
for the
the
in which
has been adhered
empathy
Methods
main
units,
is required
explicit
processes,
The principle
This
precise,
estimate
the
is
but
for
different
variation
required
an
colour
structural/floristic
of structural/floristic
in
for
plant
stratification
overestimate
allocation
units,
can
structural/floristic
communities
is
be
and
preferable
{section
units
which
by
the
need
to
3.1.4}.
estimated
in
an
The
study
not
be
under-
number
of
counting
the
int.ercept
the
31
reconnaissance
route.
to
Edwards
or
co-dominant
these
(19B3)
units
and
the
species
of
can
intercept
the
necessary.
Scale
nizing
the
those
stand
be
structural
floristic
each
can
for
be
stand,
with
a
either
side
of
the
buffer
the
unit
(see
a
route.
zone
This
to
section
be
to
3.1.4).
by
that
only
exceed
a
the
included
where
of
they
validation,
permits
equal
dominant
Borders
map,
account
units
according
the
stratum.
route
into
is
is
stratification
taken
stand,
component
on
structural/floristic
on
component
structural
indicated
route
radius
floristic
The
in
Aerial
recog4
minimum
stand
the
if
times
of
one
radius,
on
structural/
photographs
could
also be used for these estimations;
ii.
estimation
in
each
of species
by
the
of
sampling
counting
commensurate
in
in terms of species
structural/floristic
determination
done
richness,
with
unit.
unit
area
This
(section
the
number
of
plant
plant
height
and
spacing
structural/floristic
unit
specimen
of
is
would
per m2,
required
3.1.6).
species
such
result
This
in
that
in
for
is
an
area
any
area
a
similar
count;
iii.
voucher
plant
species.
tered
during
plant
species
required
iv.
collection
These
field
are
effectively
records
plants
sampling.
for field sampling
photographic
the
dominant
in
The
the
most
and
other
likely
ability
field
to
to
common
be
encoun-
identify
reduces
these
the
(see section 3.2.2); and,
and notes on any features commensurate
with the aims of the study can also be taken.
32
time
3.1.4
stratification
stratification
prior
to
is
the
process
vegetation
sampling
sampling
intensity
in
approach,
is far too
according
to the
a
low
of
preliminary
and
classification.
community,
using
for the community
sampling
units,
vegetation
without
mapping
Generally,
the
the
Braun-Blanquet
borders
to be defined
some form of stratifica-
tion.
3.1.4.1
Background
It can
be
Blanquet
can
argued
that
approach
be
is
recognized
field
ignores
the
spatial
ties,
is difficult
case,
some
for sampling
In stratified
vegetation
total
for those
sort
sample.
since
that
up
make
dancy
not
each
the
familiar
unit
with
map
the
plant
community
relationships
the vegetation
to which
the
Braun-
community
diagnosis.
between
communi-
the techniques
is required,
total
is divided
allocated
to
in any
that
vegetation
the
are
into preliminary
specific
can actually
is ensured
portions
improve
various
the
accuracy
vegetation
represented
of the
in the
of
units
sample
1958).
prior
at vegetation
are
sampling
it
(Freund & Williams
Such divisions
using
using
the vegetation.
Such
estimates
because
of vegetation
sampling
units
communities
necessary
the
however,
and
not
plant
in
This,
apply,
to map
to sampling
unit borders
also reduce the problem
of redun-
(Werger 1974), and ensure .an even
33
distribution
of sampling units in each vegetation
of vegetation
unit area.
stratification
aerial
as
photo
well
for random
mappable
geomorphology
the
to
and
experience,
factors
can
process,
although
the
usefulness
purposes.
usually
stratification,
The
include
suitable
following
aerial
where
integration
be
photo
factors
their
may
after.
interpretation
limits
only
different
is
subj ect
be
correspond
to
corre-
(environmental)
the
be implicit,
can
geology,
Furthermore,
non-vegetation
shown
the case,
as
of
on
classification
which
for
can
limit
stratification
reliably
with
used
those
for
of
the
units.
stratification
ery.
only
such
in vegetation,
and
correspondence
of
factors,
Vieual
can be based
as is often
for repeatability.
vegetation
Non-vegetation
vegetation
types.
discontinuities
between
sampling
of a study area,
non-vegetation
soil
detect
observer's
spondence
or systematic
interpretation
as
factors,
unit, irrespective
of vegetation
advantages
improved
of satellite
geographic
for processing,
article
can also be based
imagery
fidelity,
and suitability
describes
the methods
over
data
in
on satellite
aerial
applied
photography
a digitized
for small
imag-
form,
scale work.
in this
The
study.
34
Bothalia 16,2: 263-268 (1986)
A method for vegetation stratification using scale-related,
vegetation-enhanced satellite imagery
R. H. WESTFALL" and O. G. MALA
**
Keywords: Landsat, Munsell parameters, South Africa, stratification, Transvaal, vegetation
ABSTRACT
A method for visual vegetation stratification and pattern refinement, using scale-related, vegetation-enhanced
satellite imagery, is described. The method simplifies colour assignment, facilitates accurate vegetation mapping
and could lead 10 balanced floristic classifications.
UITTREKSEL
'n Metode vir visuele plantegroei-srratifikasie en patroon verbetering wat van skaalverwante, plantegroei-versterkte satellietbeelde gebruik maak, word beskryf. Die metode vereenvoudig kleurtoekenning en vergernaklik
akkurate plantegroei-kartering en kan lOt meer gebalanseerde floristiese klassifikasies lei.
INTRODUCTIOl
In vegetation sampling (Werger 1974) the subjective selection of sample sites, based on vegetation
homogeneity, can only be done effectively by an operator with considerable experience. In addition,
there is often a lack of repeatability in the methods
and a tendency to ignore vegetation dynamics by
only sampling those areas representative of 'good'
vegetation.
Stratified random sampling overcomes these problems and increases sampling efficiency by ensuring
adequate representation of subdivisions (Elliott
1983). Furthermore, in contrast to random or systematic sampling, the heterogeneity of vegetation, in
terms of possible number of communities, can be related to the number of stratified units. Stratification
also facilitates the avoidance of transitions which
generally do not contribute more information than
the adjacent communities (Werger 1974).
Stratification of vegetation prior to floristic sampling entails primarily the categorization of vegetation according to structural characteristics. The categories can be further refined according to the factors
apparently responsible for differentiating the strata.
The most important factor is usually taken to be topography but others such as geology, pedology, climate or combinations of the four may also be decisive.
Problems encountered with vegetation stratification when using aerial photographs include radial
distortions, altitude-related scale differences and often inconvenient scales, which do not facilitate precise vegetation mapping. The excessive detail present in aerial photographs can be potentially confusing and time consuming for stratification, especially
• Botanical Research Institute, Department of Agriculture and
Water Supply, Private Bag X101, Pretoria 000l.
•• National Physical Research Laboratories, CSIR, P.O. Box
395, Pretoria 0001.
for small-scale work. The use of small-scale, almost
orthographic satellite imagery for stratification overcomes these problems but introduces the problems
of pattern interference by factors such ·as soil, and
colour assignment where patterns are formed by unfamiliar colours and textures. This paper describes a
method for vegetation stratification, using satellite
imagery that can overcome the problems of pattern
interference and colour assignment and facilitates
later pattern refinement.
CONVENTIONAL USE OF LANDSAT DATA IN
VEGETATIO
STRATIFICATION
False colour images
The Landsat multi-spectral scanner (MSS) records
radiance from the earth's surface in four spectral
bands: 500--600,600-700,700-800 and 800-1100 nm,
usually referred to respectively as bands 4, 5, 6 and
7. These data are obtainable in image or digital
form. The ground resolution of the system is nominally 79 x 56 m which corresponds to a picture element of about 0,30 x 0,22 mm at a scale of
1:250000.
The MSS collects six lines of data simultaneously
using a set of six detectors for each spectral interval.
The mismatch in these sets is one of the major causes
of noise in MSS data, which is apparent as striping
with a periodic cycle of six lines at extreme radiometric enhancements.
Traditionally Landsat MSS data have been used in
mapping at scales between 1: 1 000 000 and
1:250 000 (under special circumstances up to
1:100 000 or even 1:50000 scale) in the form of false
colour images, i.e. bands 4, 5 and 7 displayed respectively as the colour primaries, blue, green and
red.
This type of display suffers from two disadvantages: firstly only three of the four bands can be displayed, with a possible loss of crucial information in
band 6. Secondly, because of the high degree of cor"
relation between bands (Table 1), a very limited re35
264
Borhalia
16,2 (1986)
gion of the available three-dimensional
colour space
is utilized: live vegetation
appears exclusively in
various shades of red, depending on its structure and
vigour.
tral differences in surface cover. Although the third
component normally contains considerably less variance than the second, this may be crucial information for stratification.
The fourth component contains predominantly
noise.
TABLE
For optimum results peA must be based on the
statistics of a (composite) subscene, approximately
equally representative
of the relevant floristic subdivisions, of the area to be stratified. The three principal components may be displayed in any combination of the colour primaries. However, particularly
in regions of large variations in overall brightness,
such as in areas of rough topography, this results in
multicoloured imagery which is difficult to interpret.
1. - Correlation
matrix of the radiance values of the
Landsat MSS data, in the four Landsat MSS bands, for the
study area
Band
4
5
6
7
4
5
6
7
1,0
0,88
0,71
0,59
1,00
0,62
0,47
1,00
0,94
1,00
Digital multispectral
classification
Digital multispectral
classification methods have
been used successfully in crop mapping. The methods have proved to be a problem in the stratification
of natural vegetation,
particularly under southern
African conditions. The reasons are mainly: rugged
topography
(crops are normally grown on level
fields), heterogeneity
of stands with the consequent
problems of selecting 'typical' training sites of sufficient size (i.e. several hectares in size) for the extraction of spectral signatures and the interference
of
soil reflectance caused by incomplete canopy cover.
Furthermore,
this method relies exclusively on
spectral characteristics
and cannot make use of the
contextural or contextual information consciously or
subconsciously available to the human interpreter.
'DIGITAL
E 'HANCEMENT
OF LANDSAT
DATA
In the alternative approach of enhancement of the
Landsat MSS data by digital image processing followed by visual interpretation,
the versatility of the
human analyst is assisted by means of quantitative
enhancement
of vegetation differences in stratification but particularly in pattern refinement.
Principal Component
Analysis
Principal component
analysis (peA) provides a
convenient
method of data compression
and removal of redundant correlation between bands (Lasserre et al. 1983). Experience has shown that typically more than 99% of the total variance in the data
is retained in the first three principal components
(Table 2), the maximum which can be accommodated in any colour display.
TABLE
2. - Variance in terms of proportional
eigenvalues for
the first four principal components
of the Landsat MSS
data for the study area
Principal
Component
First
Second
Third
Fourth
0,872
0,106
0,016
0,005
The first component represents shadow-enhanced
topography
and overall terrain brightness differences, whereas the second and third reflect the spec-
36
A much more informative
product can be obtained by displaying the first three components
respectively as the Munsell colour parameters, brightness, hue and saturation.
Display in the Munsell colour space
In order to generate a practicable colour display,
the components
representing
the Munsell colour
parameters
must be converted into the colour primaries, blue, green and red by computation,
special
care being taken that visual hue differences in the
display truly reflect numerical differences in the data
(Malan & Lamb 1985)". The first and third components are first contrast-stretched
to about 1-2% of
the data in maximum and minimum values. The distribution of the first component
is approximately
Gaussian (Figure 1) while a histogram equalization
stretch is applied to the second component (Figure
2). These stretch lookup tables must be based on the
statistics of the representative
subscene used for
peA.
In the final product the overall impression of terrain brightness of the original image is retained, thus
facilitating registration with overlays and later pattern refinement. The effect of the histogram equalization stretch of the hue component is to spread the
spectral differences of the cover over the complete
hue gamut (as modified in saturation by the third
component) as opposed to the limited range of hues
in the conventional
false colour representation
(compare Figures 3 & 4).
Filtering
When vegetation mapping is to be done at a scale
where the shortest cross distance of a mappable unit
is greater than a ground resolution of 79 x 56 m, the
original resolution of the Landsat MSS data could
lead to a product with too much small detail. Prior
smoothing of the data produces a scale-related product which facilitates stratification
and pattern refinement.
The minimum colour-stratified
area is given by
12,56 n mm2 with a shortest cross distance of 4 mm
(Rutherford
& Westfall 1986). The value of n is determined by the minimum number of samples required for possible floristic subdivision of a colourstratified area which is dependent on scale as related
to sample area and spacing as well as the area of the
colour-stratified
unit. For example, with n=4, full
resolution Landsat MSS data could therefore theo-
Bothalia
265
16,2 (1986)
2
Cumulative
histogram
(1)
0
e
(1)
~
~
:::J
0
0
0
0
>c
0
(1)
:::J
C'
(1)
~
(1)
0)
~
e
(1)
0
~
(1)
a.
0
255
0
Grey
FIGURE
1. Munsell
Histogram
brightness
showing the approximately
parameter.
2
level
Gau
ian di tribution
of the first prirtcipal component,
0
e
as the
Cumulative
histogram
I,
(1)
displayed
d
, I
(1)
~
~
:::J
0
0
0
I
-
,
0
,
I I
,
I I
i!
I
I,
II II
Ill,1.1
I
I I
1'1
I I I
1,1
v'_-1
1,1
II I I
II
, ill I
, I )II I
I
!
II
I
'll'lHldl'l'lllll
k11,Ihl
,I'lijil'
j 1111,.,11
,
II ,Iii
T'l
ill
Il~ ll'I'!'I!
I I \1I II I I ! I i I I j I i j !'~-1 I I I I i n
I
II
II III 1llll'IIlllii 111"1' I III '11'111
,
'11'
Illl
P
nu
I I jI I' jl I li i I I l'i 1;-1 !jl II I Ii I III I I'
1
>0
e
I
II
Iii
I II
i I j!
I I
i H I
I I
i"",1
I I III j I
I"
I i III Ii Iii
1
.
lit
'11
"I
1
,
1
I
'i i,
'lllll
I
1
,I
I,
1111
I jjj I\
1'1'II
m l! u I III Il .(1I I 'P II n 1 mu l! IP II q PI!
11l1/1.11!'j' u Ililli / d;-flt H! lilll hi n ldllhillhidII
II III u II II HI I I t '-II I III i I I u Ill! !I u nu n u HIiI hi. ,
~
H
C'
G)
~
1.111 I
II I I I ')
I I II
I
"
,I II I' I I, ),,1
!
II P W,II IlllI! II !-l-i '.j 1 ii',' !! ! 11111111 j I' 1111PI PI I!III II I 11!11! 1li
G)
Ill! ! I! I ! 1111 H I!! llllllllll
n HII Illl!! Id II
.,-11
h I 1I1l!! III II !II! II lil' 1111l11IL!I\l1!! Ijll!! II 1,1
h 111111\"1 In Illl
u ! II! q P n lj I P q n !ill
H Iill h I j!! 111111111 ! III i ! I!I II !!! II Ill!1I!lh II ill !!I1I!l
1'nlli Illtl IiII H I III'II
Ii Ii i L dIP m Ii ,uuu PIIW ,Ihl
pIli IIil
1'1II
H IJIll!
u II jll !I 1i H l n i! jl HltilldII IlJdI nI! Iiduhqdm IH!lnh Hn Pm
H
I 11111P II',' I it!14jlll
-
0)
~
'illll!
e
j
G)
0
~
(1)
a.
0
, i
IIII
t ..
\jlll
jl
I
II
,
III
j I I I
II
I Ii
Ii
j
t I
I ' , I lil
jl
il
I!
II
1lI
II I '
III
t,
llll Ii i III
I 'Ii jl
I
255
0
Grey level
FIGURE
2_ -
Equalized
histogram
of the second
principal
component,
displayed
as the Munsell
hue parameter.
37
Bothalia 16,2 (1986)
266
retically be used for vegetation stratification up to a
scale of 1: 10 000. However, PCA dramatically enhances noise in the data in the higher principal components, particularly the striping in Landsat MSS
data. The application of a median filter with a kernel
at least six lines wide smoothes the data and removes
striping effectively.
Consequently,
in practice, a
median filter with a minimum kernel size of 6 x 9
picture elements corresponding
to a square ground
resolution of about 24 ha, is used. This corresponds
to a maximum useful working scale of almost
1:50 000.
The (first) brightness component is excluded from
the filtering process, to retain fine detail in the representation
of topography, which assists in pattern
refinement as well as in the identification of ground
control points for registration with map overlays.
PATIERN
REFINEMENT
Pattern refinement refers to the process of modifying the stratified units, usually after sampling and
classification of the vegetation and prior to mapping
floristic units. This process includes the grouping together of similar, smaller, discreet areas and subdivision -of larger, uniform colour-stratified
areas by
contextual
comparison
if required,
with suitable,
simplified topographical,
geological, pedological or
meteorological overlays and sample-set classification
at the given working scale. The choice of any or all
of these overlays is determined by the range of variation exhibited by the overlays that can relate to the
structural variation. For example, smaller units of
differing colour can be grouped together on the basis
of floristics and soils or topography while larger, uniform units could be subdivided on the basis of floristics and geology or climate in order to refine the
patterns.
Minor inaccuracies can occur in the registration of
Landsat hard copy images and other maps at the
same scale used for overlays which are attributed to
differential stretch and shrinkage caused by fluctuating humidity as well as some obvious inaccuracies in
the maps. These errors can be compensated
for by
shifting local fit to achieve the maximum number of
registration
points rather than be compounded
by
maintaining fixed registration points. However, the
effect of stretch and shrinkage can be further reduced by the use of dimensionally stable transparencies of both satellite images and other maps, where
available.
RESULTS
The results of colour-stratification
using scale-related vegetation-enhanced
satellite
imagery
are
given for a portion of the Transvaal Waterberg in the
north-western
Transvaal
(Rutherford
& Westfall
1984) at 1 250 000 scale. The area has a highly diverse topography with a consequently high variation
in vegetation structure which is inferred from the variation in colour pattern. The vegetation is mainly
representative
of Sour and Sourish Mixed Bushveld
veld types (Acocks 1975). Structural heterogeneities
related to topographic diversity were indicative of
potentially small stratification units and the value of
n=4 was accordingly assigned. This corresponds to a
38
map area of 50 mm? which is equivalent to 320 ha.
The appropriate filter kernel size was therefore 32 x
22 picture elements.
A contrast-stretched
falsecolour MSS image (acquisition date March 1981) is
shown in Figure 3. A vegetation-enhanced
image, at
the original resolution of the same scene, is shown in
Figure 4, which illustrates the complexities of colour
assignment
exacerbated
by noise, particularly
six
line striping. A scale-related,
vegetation-enhanced
image at the hard copy scale of 1:250 000 of the same
scene is shown in Figure 5 with resultant simplification of colour pattern to form stratified units.
INTERPRET ATIO
A critical prerequisite for successful stratification
is the choice of the optimum data acquisition date
for maximum differentiation
between the structural
subdivisions. Because the four wettest months in the
study area are from November to February, the data
acquisition date of March ensured high vegetation
cover with minimal cloud interference.
The inputs required from the user are location of
study area, working scale and data acquisition dates
of scenes required. Primary colour-stratification
is
automated
after these inputs and is, therefore, objective and time and labour saving. Many researchers use topographic, geologic, pedologic or meteorologic maps for comparison with floristic units or simply to show the variation in these factors. Unless
working scale is standardized at the outset of a project, comparisons
are difficult. Overlays at a standardized scale are used for pattern refinement and
can also be used for later comparisons with floristic
units in addition to the enhanced
Landsat MSS
image. They can, therefore, serve dual purposes and
their effective use is increased.
Training sites would often be required for unsimplified images (Westfall & Malan in press), whereas
the use of scale-simplified images largely overcomes
the need for this training. Furthermore,
scale-related stratified units should improve floristic classifications by providing a balanced distribution of sample sites commensurate
with vegetation heterogeneity and the amount of detail required for a given
working scale. It should be pointed out that, like
PCA, the colour stratification
by vegetation
enhancement
is highly scene-dependent.
In practice,
however, this is not a great disadvantage
because
one Landsat scene covers 34 000 krn-'. Also multiples
of this size can be treated identically if they are contiguous images on one north-south Landsat swath.
The use of satellite images, which have better geometric fidelity than aerial photographs,
also simplifies the process of accurate vegetation
mapping,
especially where first-order stereo-restitution
instruments are not available. This ensures greater mapping precision when compared to base maps and facilitates the effective use of overlays for comparison
or pattern refinement.
The proposed methods also ensure objectivity because the primary colour-stratification
process is
computerized,
which produces repeatable stratification units. It is doubtful that subjectively stratified
units could be repeated by different workers. This
Bothaiia 16,2 (1986)
267
FIGURE 3. - A contrast-stretched, false-colour. multispectral-scanner (MSS) image
of a portion of the Transvaal Waterberg with limited range of hues. Scale
1:250000.
FIGURE 4. - A vegetation-enhanced image at the original resolution of the same
scene as shown in Figure 1
using the complete hue
gamut.
FIGURE 5. - A scale-related vegetation-enhanced image of
the same scene as shown in
Figure 2 showing simplification of colour pattern to
form primary stratified
units.
could affect the balance of the resultant floristic classifications and could be especially significant in comparisons over time.
CONCLUSIONS
The proposed methods of vegetation stratification
prior to' floristic sampling are objective, and timeand labour-saving. The process of colour assignment
to stratified units is simplified. The stratified units
are related to working scale and the structural heterogeneity present in the vegetation. Overlay comparison and accurate vegetation mapping are facilitated and more balanced floristic classifications can
be expected. Landsat data are generally more detailed than required for vegetation stratification at
scales smaller than 1:5000 and hence filtering is
necessary.
39
Bothalia 16,2 (1986)
268
ACKNOWLEDGEMENTS
The authors thank Dr J. C. Scheepers for comments and suggestions.
REFERENCES
ACOCKS, J. P. H. 1975. Veld types of South Africa. Memoirs of
the Botanical Survey of SOUlIlAfrica '0. 40.
ELLlOTI, J. M. 1983. Some methods for the statistical analysis of
samples of benthic invertebrates. Kendall, Wilson.
LASSERRE, M., MALAN, O. G. & TURNER, B. 1983. The
application of principal component analysis to Landsat
MSS data. Proceedings of seminar on Principal component analysis in the atmospheric and earth sciences, Pretoria 7-8 February 1983.
MALAN, O. G. & LAMB, A. D. 1985. Display of digital image
data; quantitative and oprimal. Proceedings of the third
40
South African symposium on Digital image processing,
Durban 22-23 July 1985.
RUTHERFORD, M. C. & WESTFALL, R. H. 1984. Sectors of
the Transvaal Province of South Africa. Bothalia 15:
294-295.
RUTHERFORD, M. C. & WESTFALL. R. H. 1986. Biomes of
southern Africa - an objective categorization. Memoirs of
the Botanical Survey of SOUlhAfrica No. 54.
\VERGER, M. J. A. 1974. On concepts and techniques applied in
the Ztirich-Mompellier method of vegetation survey. Bothalia 11: 309-323.
WESTFALL, R. H. & MALAJ"l",O. G. in press. A comparison of
vegetation units derived from vegetation-enhanced satellite
imagery wirh the vegetation units derived from the florisric
classification of the farm Groothoek, Thabazimbi District.
Proceedings of the symposium on Pattern recognition in
remote sensing and geophysics.
location
3.1.5 Stand
The
main
disadvantage
possibility
data,
of hidden
which
could
stratified
sampling
unit
random
the
localities
does
sampling
process
sampling
within
tical
tests
3.1.5.1
periodicities
sampling
is
(Freund·
preferred.
of ·stands within
reduce
observer
not necessarily
because
the
a vegetation
infer
number
unit
In
each
bias.
&
is
phenomena
in the
Williams
stratified
is generally
so
random
vegetation
Randomization
of
validity
repetitions
the
1958)
stratified
statistical
of
on normal
sampling
stand
on the
in vegetation
too few for statis-
distributions.
Background
co-ordinate
of stand
system
or a random
selection
is used,
number
the
and hence grid
states
that
unnecessary
sampling,
be
Whatever
minimum
not
done.
Apart
of stands.
3.1.5.2
Methods
applied
considerations
given
by a grid
using
random
system of co-ordinate
spacing
between
stand
because
Werger
(1974)
to be
does not appear
spacing
geometric
are
achieved
are selected
should be known,
stands
minimum
the
co-ordinates
appropriate
would
literature
is generally
generator.
size,
contiguous
work
the
localities
in which
sites,
From
systematic
or recurring
results
that are based
Randomization
tables
to
stratified
influence
location
is random,
of
recommended
from
to address
in section
as much
non-contiguous
the problem
3.1.1,.a
of
grid
41
l
a
a
1
3
2
4
...•
<,
/
1
5
I
I
,
\
2
I
/
~"
,- ...
I
3
f
\
\
"-
....
/
--
"-
I
4
\
f
I
\
\
5
,,
"-
.•. ..-
;'
/
/
--
-,
\
\
,
\
f
-,
f
-
-
I
./
4mm
t
-4mm--
FIGURE 3.1. - A 4 x 4 mm numbered grid for map overlay to determine possible stand sites at the grid intersections.
solid
circles represent
stands; and dotted circles represent buffer
zones around stands. Buffer zone radius = 2x stand radius =
maximum ecotone width = minimum distance between stands (from
Rutherford
& Westfall 1986).
42
size of 4 x 4 mm should
map
overlay
ensures
Stand
The
to
locate
a minimum
area
use
of
computer
minutes
of
one
grid,
of
their
and seconds,
to facilitate
it is ignored
also be ignored
selected
the
co-ordinates
stand
location
stand includes
not commensurate
and the following
stand
more
quired.
a stratified
stand
Observer
regarding
stand
regarding
stand
localities
bias
is,
location,
with
4
minutes
grid
and seconds
and
The
is selected.
grid
It
is for
than
following
heterogenscale,
Stands
can
Furthermore,
are
by
these
reasons
actually
re-
decision-making
may be necessary
article
stand co-ordinates
for field location
In
is less than one stand
reduced
co-ordinate
co-
degrees,
in the field.
obvious
some decisions
of random
grid
with the working
generated
therefore,
suitability.
for the generation
mm
are
border.
although
program
a
unit
3.1.
simplify
of
in the field if they are inaccessible.
from
stands.
can
selecting
as a
This
in Figure
scale,
for
may not be used if the circumference
diameter
between
is illustrated
to
scale,
co-ordinates.
diameter
numbers
conversion
then
random
for any working
random
if a randomly
from
stand
a standard
such as disturbance
that
sites
to the grid
eity
stands
at any given working
in relation
and
the field,
stand
spacing
generation
ordinates
be used,
conversion
describes
a
to be used
to
degrees,
of stands.
43
Bothalia 16,2: 269-271 (1986)
PHYTOLOC -
A RA~DOM-NUMBER GE1\ERATOR AND SAMPLE-SET LOCATION PROG RAM FOR
STRATIFIED RAl'mO\1 VEGETATION SAYIPLING
The stratified random method of egetation sarnpiing is objective and efficient, in terms of sampleset distributions, for floristic classifications (Westfall
& Malan 1986). However, the commonly used randam number tables and calculator-generated
randam numbers often require number abbreviation
. and manual recording of the numbers, which can be
time-consuming.
But these inconveniences are insignificant when compared
with the time taken to
measure the location of random sample sets and express their location in terms of the latitude and longitude co-ordinates of degrees, minutes and seconds.
The PHYTOLOC
program was developed to generate random numbers for sample set location in terms
of random co-ordinates and, in addition, to express
these co-ordinates as latitudes and longitudes in degrees, minutes and seconds, thereby sa ing considerable time and effort.
The program is written in Basic and runs on a
Sharp PC 1500 computer. Use is made of a consec-
can also be used on larger-scale maps for more precise field location of sample sites.
For valid categorization
and analysis of floristic
units, based on multivariate data, a minimum of four
sample sets are required, although a single sample
set is mappable at the given working scale. Co.n.sequently for a single floristic division of a stratified
unit a minimum of eight sample sets would be required. However, for statistical comparisons of univariate data such as biomass or number of taxa, sarnpie-set number should be proportional
to area (EIliott 1983). It is, therefore, suggested that a sampling
intensity of 2,5% of the potential sampling sites (i.e.
total number of co-ordinate interceptions)
within a
stratified unit should be maintained to ensure proportionality.
This is approximately
commensurate
with the relationship of study area to sample number
(Rutherford
& Westfall 1986) but modified by vegetation heterogeneity
in terms of number of stratified
units.
utively numbered 4 mm transparent grid map overlay which is related to any working scale, in terms of
sample set spacing and size (Rutherford & Westfall
1986). Grid overlay registration with a base map is
according to the zero co-ordinates
of the overlay
with the intersection of minimum latitude and longitude of the study area on the base map, as well as
with the zero x-axis of the overlay with the minimum
latitude of the base map. Inputs required for the program are:
(i) maximum latitude of the study area in decimal
A non-random
set of sample sites could, therefore, be required to fulfil the categorization
and
analysis requirements
for stratified units with le~s
than a total of 320 interception
points. The additional non-random sample sites can be selected objectively by:
f
(i) using best fit of additional samrle sets . or
areas equal to those of eight or less interception
Points (i.e. 100% sampling intensity), and
degrees;
(ii) minimum latitude of the study area in decimal
degrees;
(iii) minimum longitude of the study area in decimal degrees;
(iv) difference in millimetres, between minimum
and maximum latitudes, at the given working scale;
(v) mean distance in millimetres, between 1 minute longitudes at the minimum latitude;
(vi) mean distance in millimetres, between 1 minute longitudes at the maximum latitude;
. (vii) number of sample sets required, estimated
by 10 SU + (0,25 x 10 SU) where SU is the number
of stratified units. This should generally allow for
omissions due to transitions and proportionality;
(viii) upper limit (integer), within the study area,
of the x-axis of the grid overlay, and
(ix) upper limit (integer), within the study area,
of the y-axis of the grid overlay.
The program generates and prints random numbers for the x- and y-axes of the grid overlay and
computes and prints the equivalent values in degrees, minutes, seconds and decimal fractions of seconds for longitude and latitude respectively. Convergence of longitude is also taken into account. Each
set of co-ordinates, representing
a potential sample
site, is numbered
consecutively.
In addition the
means and standard deviations of the x- and y-arrays
are computed to show the statistical distribution of
potential sample sites. Co-ordinates are transferred
to the base map using the grid overlay and the printout can be used for field allocation of latitude and
longitude to the field data sheets. These co-ordinates
(ii) using additional random sample sets, to ensure representation
of vegetation variati~n, for ar~as
8
d 320
equal to those with between
an
In~erc~ptlon
points. (i.e. > 2,5% but < 100% sampling intensity).
Additional
random sample sets can be obtained
together with the relevant co-ordinates, if require~,
by the same procedure, but with each relevant ~tratlfied unit registered separately on the 4 mm gnd.
ACKNOWLEDGEMENTS
The author thanks Drs J. C. Scheepers
Ark for comments and suggestions.
REFERE
and H. van
ICES
ELLIOT, J. M. 1983. Some methods for the statistical analysis of
samples of benthic ill vertebrates. Kendall, Wilson.
RUTHERFORD, M. C. & WESTFALL, R. H. 1986. Biomes of
southern Africa - an objective categorization. Memoirs of
the Botanical Survey of SOUlh Africa No. 54.
WESTFALL, R. H. & MALAN, O. G. 1986. A method for vegetation stratification using scale-related, vegetation-enhanced satellite imagery. Bothalia 16: 263-268.
R. H. WESTFALL
44
3.1.6
The
Sampling
sampling
vegetation
a releve
unit area
unit
stand
is the vegetation
sample
and in the classification
& Ellenberg
(Mueller-Dombois
plot is used to sample
a vegetation
plot
phase
1974).
used
to
sample
is referred
Where
more
to as
than
stand then the sampling
a
one
unit is
a sub-plot.
3.1.6.1
Background
Werger
(1972),
concludes
that
has been
in
formulated
a suitable
further
suggests
which
sampling
a
unit
area
10 x
10 m;
vary
Coetzee
size
unit
on species
may be an extreme
statement
al.
for
sampling
unsuitable
much
richness
but
relationships,
for sampling
size
considerably.
et
species-area
the concept
unit
sampling
This
of
minimal-area
and regards
depends
can
review
no convincing
taining
hectare
his
(1976) used
for ascer-
than
and structure
example
size
vegetation.
smaller
in southern
For
unit
half
10 x 20 m
a
of stands.
Africa,
Coetzee
He
sampling
(1975)
for
used
herbaceous
"
plants
and
Theron
(1978)
Van
der
used
et
Meulen
used
sized
(1988)
(1985)
(1979)
used
used
units
for
woody
16 m2 for grasslands
used
10 x 20 m and variable
al.
trees
variable
plants;
Van
Rooyen
size for the woody
all
10 x 20 m for herbaceous
&
and 100 m2 for woodlands;
10 x 20 m;
10 x 20 m for
Bredenkamp
plants
plants
et
al.
component;
and
and
Le
(1981)
Westfall
Raux
et
al.
100 x 100 m for
and shrubs.
Such variation
in sampling
unit area can be expected
to complicate
45
comparisons
within
Furthermore,
20
m
recording
a
often
plot
was,
emphasized
for
difficulty
sampling
approach
species
stand
the
to
represent
than
that
a
adopted
sub-samples
presence
is less
communities
was experienced
therefore,
that
more
and between
in selecting
20
to
are
from different
ha
stand.
sample
based
only and although
than' 200 m2 the
which
could
be
a single
A
It
minimum
must
area
the total
sample
number
recorded
species
expected
from
10 x
sub-sampling
stands.
on
studies.
a
10 x
be
for
area
was
20
m
plot.
The
following
to determine
article
minimum
describes
sub-sample
the methods
applied
in this
study
area.
46
S.-Arr.
44
Tydskr.
Planlk.,
1987,53(1)
Predictive species - area relations and determination of subsample size for vegetation
sampling in the Transvaal Waterberg
R.H. Westfall*,
J.M. van Staden and M.D, Panagos
Botanical Research Institute, Department of Agriculture and Water Supply, Private Bag X101, Pretoria, 0001 Republic
of South Africa
Accepted 20 August 1986
An expression for predicting the number of species in a given area is described. Derivatives of this expression,
to increase sampling efficiency in a egetation stand, include a minimum of four separate subsamples; a maximum
number of subsamples when less than 10% increment in new species is achieved; and subsample size. It is also
suggested that species diversity in terms of species per unit area can be more consistent when derived from
this expression.
'n Uitdrukking om die aantal spesies vir 'n bepaalde gebied te voorspel word beskryf. Afleidings van hierdie
uitdrukking, om doeltreffendheid by die monsterneming van 'n plantegroeistand te verbeter, sluit die volgende
in: 'n minimum van vier afsonderlike submonsterpersele; 'n maksimum aantal submonsterpersele wanneer die
nuwe spesiesaanwas minder as 10% is; en submonsterperseelgrootte.
Dit word ook voorgestel dat spesies
diversiteit, ooreenkomstig spesies per eenheidoppervlakte, meer konsekwent kan wees wanneer dit van hierdie
uitdrukking afgelei word.
Keywords: sample size, savanna, species number, Transvaal, vegetation sampling
'To whom correspondence
should be addressed
Introduction
The vegetation ecology of the Trans aal Waterberg is currently
being investigated at a scale of 1:250 ()()()(Westfall, in prep.).
The smallest mappable unit area or vegetation stand, of which
a sample should be representative, is determined by scale
(Rutherford & Westfall 1986) and is 20 ha for this study. A
single sample of this dimension would be prohibitive both in
terms of cost and time. Random subsamples within the stand
offer an objective method of obtaining a representative
sample. The efficiency of the subsamples, in terms of plant
species recorded, can be determined if the total number of
plant species in the stand can be estimated.
The object of this study is to determine the optimum
subsample size, in terms of reduced effort and improved
efficiency, for sampling the vegetation of the Transvaal
Waterberg.
Methods
Vegetation analysis
Two observers, A and 8, counted species in eight samples
representing four very different vegetation types, namely
closed grassland (sample I); open woodland (sample 2); closed
woodland (samples 3 and 4) and unidorninant forest (samples
5 and 6). In samples I and 2 each observer repeated the
countings of the Other observer for the same area, each
without knowledge of the other's results. In each sample
cumulative plant species totals were recorded in 14 nested
rectangular subsamples, each with a width-to-length ratio of
1:2 (Figure 1). Subsamples size increased from 62,5 mm X
125 mrn (0,0078125 rrr') to 10 m x 20 m (200 nr') and the
length of each subsample was perpendicular to the contour.
In addition, both observers jointly counted the observable
species present in a 20-ha stand of which sample 2 was
representative. In this stand nested circular subsamples were
also used with radii of 6,3 m (128 11l2), 12,7 m (512 m '),
25,5 III (2 048 1112), 51 m (8 192 11l2), 102 m (32 768 rrr'),
204 111 (131 070 1112) and 252 111 (200 000 111).
Synthesis
The data in the form of species number for a given area were
tested for best fit with various curves (Parton & Innis 1972)
as well as linear relationships. The curve which best fitted the
data is described by the function:
f (x, a, b, c, d)
= (1
a
+ b/x) +
(I
c
+ d/x)
where a and c = the parameters which control the maximum
value of the function and band d = the parameters which
control the rate at which the function approaches its maximum
value (Parton & Innis 1972).
o parameters could be found with actual or derived values
similar to the computed values for parameters a, b, c & d.
This reduced the application potential of the function and it
was accordingly not further applied.
The linear regression which best fitted the data is described
by:
y =
e(lI/lm
+
Ine)
where y = number of species for a given area (x), III = slope
(rate of species increase for increasing area), x = area (nr'),
c = number of species in 1m2.
This linear regression was then used to derive the number
of species in 1 ~2 from a given area and to synthesize data
for illustrating species ~area relationships.
The validity of the relationships was tested by using data
pertaining to the first three reieves recorded in the Transvaal
Waterberg (Westfall, in prep.) which utilized these relationships.
Results
The number of species recorded by observers A and B for
the nested samples 1 to 6, with predicted values, are given
in Table I and Figure 2. The linear regressions of these
samples are illustrated in Figure 3. With the exception of
sample A6, the slopes are generally similar. Sample A6
represents a small stand of unidorninant Podocarpus latifolius
47
S. Afr.
1. \301., 1987,53(1)
- - - - - ---
----
- --
45
-----
---
----
----
----
- ---
- - - - - --I
,
type concerned. The values of c, derived from different areas
and corresponding derived species number are given in Figure
I
I
I
I
I
9m
4.
,
,,
The derived relationship between number of subsamples
and percentage new species increment is given in Figure 5 and
the relationship between subsample number and percentage
accumulated new species total is given in Figure 6.
. The results of the 20-ha sample together with predicted
values is given in Table 2.
I
I
I
I
I
7m
I
I
I
,,
Sm
I
I
I
,
I
I
,
I
I
I
3m
,
I
I
,
,
I
I
I
I
trn:
E
o
N
E
E
E
co
N
(0
,....
or-
E
,....E~
.r
,
:
E
N
,
E
(0
E
0
,....
E
.r
,....
E
co
,....
I
I
I
I
I
2m
I
I
,
I
I
,
I
I
4m
I
I
I
I
I
I
I
I
I
6m
I
,
I
I
I
8m
I
I
,,
I
,
10m
Figure 1 Layout of rectangular nested subsamples.
62,5 rnrn x 125 mm; 125 nun x 250 rom; YO rom
x 1000 mm; 1 m x 2 m; 2 m x 4 m; 3 m x
5111 X 10111;6 m x 12 m; 7 m x 14111;8 m x
and 10 111X 20 m.
Subsarnple sizes are:
x 500 rnm; 500 mm
6 01; 4 01 X 8 01;
16 m; 9111 x 18m;
(Thunb.) R. Br. Ex Mirb. in which forest-margin species
were recorded before the maximum sample size of 200 nl
was reached and can therefore be considered atypical. The
mean slope of the regressions, excluding sample A6, is 0,34
but is taken as 0,3 which was used for calculating the predicted
values in Tables 1 and 2. The mean correlation co-efficient
for the recorded and predicted values for the seven samples
(excluding sample A6) is R = 0,9860, whereas the mean
correlation co-efficient for the corresponding recorded and
predicted values using the curve function is R = 0,9819. The
difference between these correlation co-efficients is not significant. The correlation co-efficient for recorded and predicted values in the 20-ha sample is 0,9725 (Table 2).
The value of c is derived from' y = e(minr+ lOC) and is given
by
c
=
e' -
mlltr
+
Iny)
which is dependent on the species diversity of the vegetation
Discussion
The prediction of number of species for a given area has been
ested for a wide range of vegetation types with satisfactory
results. The larger difference between recorded and predicted
number of species for areas greater than 2 048 m2 (Table 2)
can be attributed to the difficulty in observing all species in
these areas. Destructive sampling would be necessary to ensure
the recording of all species. Both observers encountered
increasing difficulty in observing all species with increasing
sample size where sample size was greater than 20 m2. With
nested subsamples, however, species omitted in a particular
subsample were often recorded in a larger subsample. This
is illustrated by the similarity in totals obtained by observers
A and B for 200 m2 and the differences in totals for 18 m2
to 128 m2 in samples 1 and 2 (Table I).
The effect of sampling two highly distinct vegetation types
in a small sample is shown in Table 1 (A6) where a greater
proportion of new species was recorded from 8 m2 than in
the other samples. This is also illustrated in Figure 3 where
the slopes (m) of all samples except A6 are similar regardless
. of egetation type. Local heterogeneity in the 20-ha sample
did not influence the results as much as with the smaller
sample. This can be attributed to the logarithmic increase in
area used in the prediction expression where slope is not
significantly affected by a greater proportion of new species
in a large area. It is, therefore, essential for predictive purposes
that the vegetation should be relatively homogeneous for a
sampling area of at least 200 rrr'.
For a given vegetation type, the value of c in the expression
should be more or less constant when derived from different
areas and a horizontal line could, therefore, be expected when
these values are plotted against the area from which they are
derived. However, in Figure 4 the values of c increase to an
area equivalent to 2 m2 before remaining more or less constant. This increase can be attributed to the edge effect of
small· subsample size where the proportion of species intercepted by the subsample border to species within the subsample is greater than for larger subsamples. It is difficult
to record fractions of species present so that in practice
intercepted species are often ignored. Subsample size should,
therefore, be greater than or equal to 2 m2 in the vegetation
concerned to reduce this edge effect. Similarly the value of
c should also be derived from an area greater than or equal
to 2 m2 in the vegetation concerned which will also ensure
greater precision in comparability of species diversity in terms
of species per unit area.
The predicted increment of new species as a percentage of
the total number of species for increasing number of subsamples (Figure 5) has maximum inflexion for four subsamples or less or an increment of 8,3070,or greater for given
c values and subsample sizes. This indicates an optimum
efficiency of four subsamples for vegetation sampling or an
increment of 8,3070.With additional subsamples only relatively
small increments decreasing from 6,50/0 could be expected
(Figure 5). In practice this could be taken as an increment
48
S.-Afr.
46
Table 1 The number of species recorded by observers A and B for nested quadrats
values' in brackets and correlations with observed and predicted values
Area
(rrr')
.AI
BI
A2
B2
Tydskr.
°
B5
°
0,00781
2 (4)
2 (3)
1 (3)
1 (3)
0,03125
4 (5)
3 (4)
I (4)
3 (4)
0,12500
5 (8)
4 (6)
4 (6)
5 (6)
3 (5)
2 (8)
0,5‫סס‬00
9 (12)
8 (10)
8 (10)
9 (9)
7 (7)
8 (12)
(2)
0(3)
1987,53(1)
1 to 6 with the predicted
A4
B3
Plantk.,
°
°
°
(3)
I (5)
(0)
(I)
(I)
1 (I)
A6
°
°
°
(0)
(I)
(I)
1 (I)
2,0
17 (18)
15 (15)
13 (15)
14 (13)
10 (II)
14 (18)
1 (2)
8,0
28 (28)
22 (22)
22 (22)
20 (20)
17 (17)
27 (27)
3 (3)
3 (3)
18~0
37 (36)
22 (28)
26 (28)
24 (26)
19 (22)
34 (34)
3 (4)
8 (4)
32,0
38 (42)
27 (33)
34 (33)
26 (30)
25 (26)
36 (41)
3 (5)
8 (5)
50,0
72,0
44 (49)
34 (38)
38 (38)
35 (35)
28 (29)
40 (47)
4 (5)
12 (5)
46 (54)
35 (43)
46 (43)
39 (39)
35 (33)
49 (52)
4 (6)
16 (6)
98,0
50 (59)
40 (47)
49 (47)
41 (42)
39 (36)
52 (57)
6 (6)
24 (6)
128,0
53 (64)
48 (51)
52 (51)
43 (46)
47 (39)
57 (62)
8 (7)
26 (7)
162,0
55 (69)
55 (54)
54 (54)
49 (49)
51 (42)
60 (67)
9 (7)
30 (7)
200,0
57 (74)
57 (58)
55 (5 )
520)
55 (45)
61 (71)
9 (8)
33 (8)
Slope
Correlation
co-efficient
(R)
Value' of InC**
*Prediction
··C
according
= species number
1,199
1,022
0,945
1,002
0,791
1,044
0,773
0,207
0,9891
0,9894
0,9970
0,9971
0,9910
0,9948
0,9434
2,7085
2,4673
2,4673
2,3720
2,2095
2,6721
0,4749
0,9365
0,4749
e(mlnr
to: y =
2 (2)
~
Jne)
for 1 rrr' derived
where y = predicted
from
number
of species,
III
= 0,3 (slope),
x
= area (nr')
8 m2
y:bcr::
y
-4
Quadrat
o
-2
x
2
4
- <I ---':2~--':-O~'
6
2
4
~
x
A1
Quadrat
81
y'ke 'kL
y:kc :b::L
...
y
.
2
,
.'
,,
,
I
-4
Quadrat
-2
. .
2
o
I
x
2
4
:
,
,
~!
-4
6
Quadrat
A2
-2
o
x
2
4
6
2
4
6
82
v
-4
-2
o
x
2
4
6
Quadrat
Quadrat 83
<[,,'~,
-4
Quadrat
-2
-4
o
x
2
-2
0
x
A4
6
85
Figure 2 The relationship
between In area (nr') (x-axis) and In number of species (j-axis) showing observed numbers (dots) and predicted
values (regression lines). Generally, fewer species are observed in quadrats smaller than 2 m2 (broken lines) than predicted, which is attributed
to edge effect. Observer difficulty in recording all species is indicated by the trend of fewer observed species than predicted for the larger quadrat
sizes (greater
than 20 rrr').
49
S. Afr. J. noi., 1987.53(1)
47
6
100
Ii
.D 5
·E
~
90
c
.,
.s
4
~
3
a.
80
~
ct)
c
os
2
E
1
-
II>
1:
70
60
c
C>
·4
-3
-2
o
In area
-1
2
3
5
4
E
so
e
40
eo
6
(rn 2)
en
C>
·u
30
II>
Figure 3 The relationship between In species number and In area for
eight samples, showing the similarity in slope (m). Sample A6 is atypical.
a.
en
~
c
Z
Table 2 The number of species
recorded in nested subsamples with
predicted values in brackets (C
2,5147 derived from 128 m2)
53
82
120
165
199
213
274
2
(53)
(80)
(122)
(184)
(280)
(424)
(481)
9
<ii
8
1:
7
N
E
II>
.~
o
II>
a.
6
5
139
100 y
4
II>
'0
~
3
Y =
II>
.D
E
5
6
7
8
9
10
frequency). Too small a subsample size would not include
sufficient high frequency species for the relationship in Figure
5 to be valid as each subsample would include only a fraction
of the high frequency species resulting in a greater number
of new species for successive subsamples. In contrast, too large
a subsample size would include too many of the less frequent
species with the same result. The percentage difference in the
increment between subsamples 3 and 4 is 11,5 - 8,3 = 3,2%.
This difference should be reflected in a limited number of
new species. If the number- is only a fraction, then the
subsample size is too small and conversely, if too large then
the subsample is too large. For convenience and to allow for
some local vegetation variation 3,2070 difference can be taken
to represent 3 new species. This difference can also be
expressed as percentage accumulated totals between subsamples 4 and 3 where the first subsample is taken as 100%:
151,6-139 = 12,6% (Figure 6). The number of species (y)
in the first subsample with difference of 12,6% = 3 new
species between subsamples 3 and 4 is, therefore:
10
>
4
Figure 5 The relationship between number of subsamples and new
species increment expressed as a percentage of the accumulated total.
Subsample increments: I = 100OJo;2 = 18,8OJo;3 = II ,5OJo;4 = 8,3OJo;
5 = 6,5OJo; 6 = 5,3OJo;7 = 4,5OJo;8 = 3,9070;9 = 3,5OJo& 10 = 3,IOJo.
11
o
3
Number of subsamples
Correlation co-efficient (R) between recorded and
predicted values: R = 0,9725
~
10
0
'umber of species
128
512
2048
8 192
32768
131072
200 000
20
2
+ 3
3
0,126
:l
= 23,81 species
C
C
ca
II>
E
-5
-4
-3
-2
-1
0
2
3
4
5
6
In area (m2)
Figure 4 The relationship between In c values, derived from different
areas and corresponding In area showing the increase in c values to
2 m2 (In 2 = 0,693) representing sample edge effect.
of less than 10070 to allow for local heterogeneity. A minimum
of four subsamples is recommended because if an increment
of less than 10% is achieved with fewer subsamples local
disturbance could be indicated.
Species distribution in a stand varies from closely spaced
species (high frequency) to I species in the stand (low
If c = 12 species m - 2 then the area (x) corresponding to 23,81
species is:
x
=
=
(Jny-Ine)
e -'-~---'.111
9,82 m2
Subsample size is, therefore, 9,82 rrr'. This relationship also
takes the diversity of the vegetation into account because
subsample size increases as diversity (c value) decreases. The
subsample size is also within the range of: greater or equal
to 2 m2, for reduced edge effect; and less than 20 m2 for
observer efficiency, in the vegetation concerned in this study.
For less diverse vegetation, maximum subsample size could
be increased if required. To· ensure a width to length ratio
50
S.-Afr.
48
200
180
160
~
140
CO
"0
"C
s
120
100
CO
"3
E
80
:>
o
o
co
60
'"
.s
40
o
8.
'"
~
20
q)
Z
234
Number
5
of
6
7
8
9
10
subsamples
Figure 6 The relationship
between number of subsarnples and accumulated total of new species expressed as a percentage.
Subsarnple
accumulated
totals: I = 100070; 2 = 123,20:0; 3 = 139,0070; 4 =
151,6070; 5 = 162,10;'0; 6 = 171,10;0; 7
= 193,30;0 & 10 = 199,50;'0.
=
179,20;'0; 8
=
186,5Ojo; 9
of 1:2 for rectangularity of subsamples (Mueller-Dornbois &
Ellenberg 1974) the following convenient sizes can be used:
I m x 2 m (2 rrr')
I,S m x 3 m (4,S rrr')
2 m x 4 m (8 rrr')
2,S m x 5 m (12,5 m')
3 m x 6 m (I8 m2)
3,S m x 7 m (24,S rrr')
The nearest convenient size which is not less than 9,82 m2
is, therefore, 2,S m x 5 m (12,5 rrr'). In the first three releves
recorded in the investigation of the vegetation ecology of the
Transvaal Waterberg (Westfall, in prep.) using this subsample,
size increments of less than 10070 were reached with four
subsamples in two cases and with five subsamples in one case.
These results substantiate the validity of the subsample number
and area relationships. Species recorded in each reIeve aried
from 38 to 60 and sample areas covered varied from 50 [Q
62,S rn". This is a considerably higher number of species than
that recorded by Westfall (1981) in vegetation with similar
diversity using single 200 m2 sample areas. Separate random
subsamples, therefore, appear to increase efficiency. The
smaller area also facilitates cover estimation of the herbaceous
stratum. It is noteworthy that in all three releves (Westfall,
in prep.) only approximately 10070 of the total number of
Tydsk r , Plantk.,
1987,53(1)
species estimated to be present in each 20-ha stand was
recorded. Recording of all species within the stand would have
been prohibitive in terms of time and effort. It can, therefore,
be assumed that the remaining 90070 of the species have a very
low frequency and that the so-called 'infrequent species' in
phytosociological classifications are often not as infrequent
as assumed. The method proposed here is also objective in
. distinguishing species, which on a frequency basis, should be
more relevant in classifications. In randomly selected subsamples each species chance for inclusion in the subsamples
is proportionate to its frequency.
The alue of 0,3 for slope (171) can be refined with more
data for improved predictions especially for areas greater than
20 ha.
The expressions described in this paper, as well as those
describing area, sample number and scale relations (Rutherford & Westfall 1986) have, for convenience, been programmed for the SHARP PC ISOO computer. This program,
titled VEGFORM, is available from the first author on
request.
Conclusions
The advantages of expressing species diversity in terms of the
c value include:
(I) overcoming the edge effects of small quadrat size in certain
vegetation types; thereby
(2) improving comparability of species diversity when expressed as species per m2;
(3) associating subsample size with species diversity;
(4) determining number of subsamples required for sampling
a vegetation stand; and
(5) predicting the approximate number of plant species which
could occur within a vegetation stand.
Acknowledgements
The authors thank Dr J.e. Scheepers, Dr M.e. Rutherford,
Dr H_ van Ark and Mrs J. Schaap for assistance.
References
MUELLER-DO\IBOIS,
D. & ELLENBERG,
H. 1974. Aims and
methods of vegetation ecology. Wiley, New York.
PARTON,
w.r. & INNIS, G.S. 1972. Some graphs and their
functional
forms. U.S. International.Biological
Program,
Grassland
Biorne Project Technical Report No. 153: 1 -41.
RUTHERFORD,
M.C. & WESTFALL,
R.H. 1986. The Biomes
of southern Africa - An objective categorization.
Mem. bot.
Surv. S. Afr.
o. 54.
WESTFALL,
R.H. 1981. The plant ecology of the farm
Groothoek,
Thabazimbi
District. M. Sc. thesis, Univ. of
Pretoria.
WESTFALL,
R.H. (in prep.) The vegetation ecology of Sour
Bushveld in the Transvaal
Waterberg.
51
3.2 FIELD
SAMPLING
The purpose
of field
essing
logical
and
study. Because
subsistence
sampling
interpretation
field sampling
costs,
savings
in the field,
through
3.2.1 Sampling
unit
This
refers
vegetation
is to acquire
to
that
the
often incurs additional
improvements
location
and
achieve
can be effected
sampling
stand
will
data,
with
aims
procof
the
transport
and
by reducing
time
spent
in methodology.
and number
unit
not stand
location
(sample
plot)
location,
as described
within
a
in section
3.1. 5.
3.2.1.1
Background
Where
working
stand
can
quadrat
most
be
scale
efficient
the
carried
the
a single
they
plots
then
representativeness,
is
quadrat
location.
vegetation
stand
used for sampling
locations
(sampling
represent
scale-related
quadrat
units)
unless
out when the question
vegetation
selection
probably
However,
the stand,
by Werger
should
random
where
is far larger
will reduce the number
to be made. This is confirmed
"sampling
stands
of
quadrat
ization of sub-quadrat
that
on
scale-related
that
by
based
method
area of a single
decisions
such
represented
location,
area of the
is
then
than
the
the
the
random-
of observer
(1974) who states
be representative
or systematic
of representativeness
of
sampling
of
is
is by-passed."
52
north
/\
Contour
...................
(downslope)
2
r-------------------,
3
.
Sampling
unit
Contour
...................
(upslope)
FIGURE
53
'--------------------'
1 Reference corner
.
4
3.2. - Reference corner (1) of sampling unit in terms of
direction, for level terrain; and slope, for sloping
terrain.
In this
3.1.6)
study,
to
where
sample
difficult
to
2,5
a 20
x 5 m sampling
ha
determine
stand,
units
are
used
representativeness
and
random
sampling
point
in
field
(section
is
unit
extremely
placement
is
required.
3.2.1.2
The
Methods
stand
centre
intercepts
units
on
The
method
stand
can
duced
in
metres,
be
printed
by standardizing
re-location.
presented
corner
sides
then
with
by the sampling
vice
prior
to
stand
can
where
be
is the
versa.
field
corner
centre
is a con-
used
3.2).
south
to
A
distance
generate
is a
from
list
of
these
sampling,
to
save
can be
the
co-
time.
further
re-
corner.
This
in terms of sampling
unit
is that point
unit co-ordinates
is the
as
system based
unit reference
monitoring
(Figure
centre
the x co-ordinate
unit orientation
corner
grid
of sampling
stand
on the ground
re-
and is the left upslope
unit, when facing downslope,
downslope
reference
the
3.1.5.3)
or
the
A co-ordinate
co-ordinate
vegetation
of the sampling
the
y
Placement
by
unit.
on the sampling
The reference
orientated
a sampling
for sampling
can also facilitate
from
for this purpose,
the
represented
3.1.5).
simplest
(section
and
Decision-making
be
distance
of locating
bearing
ordinates
(section
can
and
PHYTOLOC
centre
the
the stand is circular.
co-ordinates
compass
a
stand
bearing
program
random
the
because
compass
venient
is
and co-ordinates
within
reference
applied
If the
west
with the longer
ground
corner,
is level,
when
facing
54
north,
with
the longer
3.2.2 Plant
Plant
sides orientated
identification
identification
field.
These
and verification
aids
are
can be portable
photostats
of
due north.
herbarium
often
used
by
field herbaria,
specimens
and
researchers
in
photographic
notes
on
the
records,
identifying
characteristics.
3.2.2.1
Background
Although
it
specific
and
reference
these
purposes,
is
are
The
identification
aids,
purposes,
cation
knowledge,
of standardization
states
used
studies
is
for
known.
if the
and inhibit
which
by
the
evident
for identifying
plants
for
identification
of
with
a
voucher
reliance
of
for
if the criteria
most
whereas,
can
example,
for
identifi-
to each field researcher.
in the character
are
This
of species
in published
of
area
material.
study,
the transfer
is also
study
for comparison
sterile
given
is unique
be repeatable
used
specimens
on fertile material,
on
a
a
Comparison
exacerbated
repeat
in
field
criteria
often
voucher
recorded
criteria
are
to
identifications
collect
such as floras,
identifications
monitoring
to
taxa
made
problem
efforts
plant
the
rarely
complicate
tion
practice
insufficient
given.
field
common
infra-specific
plants
specimen
not
is
A lack
and character
works.
Can vegeta-
used by researchers
for
in the field, are not made known?
55
Botanical Journal of the Linnran Society (1986), 92: 65-73
A new identification aid
combining features. of a polyclave
and an analytical key
ROBERT H. \\ ESTFALL,
}.tfICHAEL D. PANAGOS
H 'GH F. GLEN A.ND
Botanical Research Institute, Department of Agriculture and rVater Supply)
Private Bag XIOl) Pretoria 0001) South Africa
Received December 1984, accepted Jor publica/ion February 1985
WESTF.-\LL,
R. H., GLE?\', H. F. & P.~~AGOS,
xr. D., 1986. A new identification
aid
combining
features
of a polyclave
and an analytical
key .. -\ new identification aid combining
features of a polyclave and an analytical
'er is described. It is based on presence-or-absence
characters and is presented in the form of a matrix, with the characters in rows and the taxa in
columns. The PHYTOTAB
program package is used to order the matrix, in order to facilitate
identification. The method was used to construct an idcmification aid for vegetative material and
has wider taxonomic and teaching implications.
ADDITIO?\'.-\L
PHYTOTAB
KEY
\\'ORDS:-Computer-aided-identification
-
matrix
-
phytosociology
-
CO:-';TE:-';TS
Introduction
.
Existing identification
Analytical keys
Polyclavcs
Mcrhods
.
Results
Discussion.
Conclusions
Acknowledgements
References.
65
66
66
67
methods.
68
70
70
72
72
73
INTRODUCTIO?\,
During the course of work on a pilot study of the Transvaal \Vaterberg
(Westfall, 1981) the need for an objective method of site identification of the
numerous plant species arose. The use of field herbaria proved time-consuming
and cumbersome. Existing keys would have been of limited assistance because,
first, they often use evanescent characters at an early stage (e.g. requiring
flowers out of season) , and secondly, they are only available for a minority of the
0024-4074/86/010065
+ 10 S03.00/0
65
© 1986
The Linnean
Society of London
56
66
R. H. WESTFALL ET AL.
taxa present In this study area. \ hen a specimen is collected for later
identification by a reputable herbarium, the onus of identifying correctly the
same taxon at other sites rests entirely on the knowledge, memory and
experience of the field ecologist. During ph tosociological work, vegetative
characters are necessarily used for identification, whereas vegetative keys are
rarely available.
Bearing these problems in mind, we decided to investigate alternative
methods of identification, a subject which had been pursued by one of us in
some detail (Glen, 1974). Traditionally,
taxonomists have used one or another
form of dichotomous key for identification, and the alternative methods have
been gi\'en very little practical use. A selection of identification strategies is
discussed below.
EXISTI;\C lDE~TIFIC.\TIO;\
:-'!ETHODS
A wide variety of different manually operated identification systems has been
proposed for use in biology (Leenhouts, 1966), as well as a small but growing
number of computer-aided
systems (Morse, 1971; Pankhurst,
1978). These
include such apparently diverse systems as the 17th-century precursors of the
indented dichotomous key described by Voss (1952) and the computer-aided
system described by Morse (1971), in which the computer interrogates the
operator. All these techniques belong to one or other of two groups: analytical
keys, which have only one entry point, and polyclavesj this term, coined by
Duke (1969), is used here in the wider sense given (0 it by Morse, 1974), which
have many entry points. The method described later in this paper is unique in
being usable as both an analytical key and as a polyclave.
Analytical keys
Analytical keys are easily reproduced,
being printed documents,
and
relatively easily optimized, the theory of optimization having been first worked
ou t by Lamarck (1778). Possibly the most accessible review of these is by
Osborne (1963). A dichotomous key can be used with maximum efficiency if
each step splits the remaining group of taxa into two equal subgroups. In
general, a polychotornous step with N branches at any point in an analytical key
operates at maximum efficiency if it splits the group into 'IV equal subgroups.
This means that the mean path length from the start of the key to the level at
which an identification is achieved is minimized.
.
Attempts have been made (Morse, 1971, 1974) to weight different taxa
according to their relative commonness or rarity, so that common taxa are
keyed out with the shortest average path length, and to \V'eight characters so
that the easiest to observe are used preferentially. Taxon weighting would tend
to minimize the number of errors per unit time on the assumption that the key
will be used more often to identify common taxa than rarities. Character
weighting tends to minimize the chance of using a difficult character near the
start of the process, and so it minimizes the possibility of using the wrong major
section of the key, thus minimizing the number of errors per identification.
Analytical keys do have practical disadvantages,
despite their clear theory
and apparent simplicity. These are largely related to their single entry point and
57
A CO~IBI~ED POLYCL\VE
A~D .-\..."IALYTIC.-\LKEY
67
limited
number
of paths to the correct
identification.
The most frequently
encountered
problem is that it is very difficult to construct a key that will enable
one to identify a fragmentary
(sterile) specimen unambiguously
if it belongs to a
large group, for example the Mesembryanthernaceae.
Keys to the genera of this
family have been constructed
both by Bolus (1958) and by Herre & Volk in
Herre
(1971). Both of these require flowers and ripe fruit for certainty
in
identification,
yet both parts are seldom available at the same time.
Variable
taxa present a problem in the construction
of analytical
keys. If one
allows for the variation
by allowing the taxon in question to key out in more
than one place,
then one degrades
the overall
efficiency
of the key by
lengthening
it. If one allows for variation
by describing it where it occurs in the
key, then efficiency is degraded
by basing a dichotomy
on a property
which is
less than desirably clear, or by lowering the ease of observation.
The addition
of taxa to an existing
analytical
key is a relatively
severe
problem involving the rewriting of at least part of the key. If the additions are
more than minor, it is probably
most efficient to rev, rite the key from the
beginning,
if optimization
is to be retained.
Computer-constructed
keys can
achieve this end more easily, for example those of 110rse (1974) and Pankhurst
(1971).
Ease of observation
optimization
requires that a minimum
of characters
be
used at each step. In fact, the use of more than one character
in a step implies
that-either
the characters
used are not fully independent,
or that one or more of
those used are ·ariable. Rypka el at. (1967) ha ve shown that it is theoretically
possible to construct
a key to any number
(T) of taxa using not more than N
characters,
where N = log? T, rounded
up to the nearest integer.
In fact,
considerably
more characters
than this minimum
number are usually used, but
the number of characters
rarely exceeds the number of taxa. Therefore,
it may
happen
that a highly distinctive
and potentially
useful character,
for example
flower colour or an accurate locality label, is present on the specimen but is not
used in the key.
Polyclaues
A polyclave has as many entry points as there are characters,
and an effectively
infinite number
of pathways
from the start to identification
of the specimen.
Very little skill is necessary to identify a plant using a polyclave, but the speed
and accuracy
of identifications
improve significantly
with increasing
skill. In
general, it appears that the most efficient strategy for using a polyclave is at
each step to use the rarest character
that is available and still unused.
Sneath & Sokal (1973) divide polyclave
algorithms
into simultaneous
and
sequential.
The basic strategy of a simultaneous
polyclave is to calculate
the
value of one or another similarity function between the unknown specimen and
all taxa in the identification
matrix. The value of this function should be high
with only one taxon, to which the unknown
may be assigned if the value is
above a predetermined
threshold.
Alternatively,
taxa are considered
to be
regions in character
hyperspace,
and the locali ty of the unknown in this space
may be calculated.
The unknown is assigned to a particular
taxon if it falls into
that region of the space. Typical simultaneous
methods are those of Gyllenberg
(1965) and Lapage et al. (1970, 1973).
58
68
R. H. \\'ESTFALL ET AL.
In sequential
polyclaves,
characters
are considered
one at a time. In each
step, a subset of taxa having the character
in question in the same state as the
unknown
is extracted
from the complete
set, and compared
with the subset
resulting from the previous step. Those taxa common to both subsets form the
set of possible identifications
for the next step. \\ hen this step contains only one
taxon, the unknown has been identified.
A wide variety of such polyclaves has
been described
by Leenhouts
(1966). Essentially,
they fall into three groups:
printed,
card- and computer-operated,
although
the same polyclave
may be
converted
from one form to another.
Printed polyclaves may be even cheaper
to reproduce
than the equivalent
dichotomous
key, because they can be compressed
into less space. For example,
the key of :'leyer
(1969)
to the families
of flowering
plants
of SW
Africa/Narnibia,
a polyclave,
occupies about 220 lines (excluding
explanatory
figures), while the dichotomous
key by Merxrnueller
(1972) to the same taxa
plus 15 families of Pteridophyta
occupies about 1500 lines. However, a polycla ve
is frequently
more expensive to reproduce.
/\. form of polyclave on plastic cards has been described
(Leenhouts,
1966),
but polyclaves on paper cards are far commoner.
Most punch-card
polyclaves
fall into two groups: centre-punched
one card per character
state, and edgepunched
one card per taxon. The former kind is exemplified
by the keys of
Bianchi (1931) (the first punch-card
polyclave},
Hansen & Rahn (1969) and
Weber & Nelson (l972); the latter by Baker (1970), a key to the species of the
genus Erica.
Wi th the possible exception
of com pu ter-genera ted punch-card
polycla ves,
this kind of key is very expensive to reproduce,
as accurate punching dies must
be made for each card in the key. \\ ith computer-generated
keys, standard
80column cards are punched
with a standard
card punch, and the key can be
produced
for a realistic price. With the almost total extinction of the computer
card reader-punch
as a result of improved
magnetic
storage media, it is not
certain whether computer-generated
punch-card
keys will remain viable for any
length of lime.
Edge-punched
card keys can be operated
with a mechanical
card sorter or
with a skewer, but are more subject to damage than centre-punched
cards. On
the other hand, the latter do not usually have any space for annotation.
Computer-aided
identification
systems (automated
polyclaves) are many, for
example
those of Boughey,
Bridges & Ikeda (1968), Goodall (1968), Morse
(1974), Pankhurst
(1978) and the simultaneous
methods
mentioned
above.
Each has its own advantages
and disadvantages,
but at present none can be
used in the field. It is possible that in the future one or more may be made to
work on a portable personal computer.
Morse (1975) has given a particularly
lucid account of the computer-aided
systems available at that time. \Ve plan to
add to the system described here, routines enabling it to be used in the field on a
. micro-com pu tel".
:-'lETHODS
Eighty plant specImens
were collected
in the Transvaal
Waterberg
during
March
1983. The specimens
were numbered
and sent to the National
Herbarium,
Pretoria (PRE) for identification.
59
--------------~------.---~
Table I. A preliminary
vegetative key for some species of the Transvaal
Growth tips
C:haracters
+ pre-sent
• sometimes
Waterberg
Venation
present
"
~
o
~
>
>
'"
;;
E
o
o
.Q
.Q
1
'0
~
>
o
o
u
"
U
es
'iij
'0
"
IV
"
C
C
0
o >
C .c
~11
"
> ""
'" >
.....
u
"
0
Species
Trees/shrubs
Rhus teotoaictvs
Rhus sp. ct. R. dentets
Ozoroa oeoicutose
Rhus sp. ct. R. dentste
Rhus keettii
+
+
+
+ +
Cambretum erythrophyllum
Cambrerum zeyheri
Vangueria ;n{Busta
Vangueria inFausta
Cambretum zeyheri
Tapiphyllum parvifolium
Pseudolechnosty/is maprouneifolia
Vitex pooara
Scbrebere alata
Syzygium coradatum
$yzgyium coredeturn
Schrebera alata
Croton gratissim~5
Olea europaea subsp. africana
Syzygium cordatum
Syzygium corda rum
Cambrerum motte
Fadogia monticola
Mimusops zeyheri
Apodytes dimidiata subsp. atmtateis
Mundu/ea sericee
Rhoicissus digitata
Elephantorrhiza burke;
+
+
+
Ferns
Pel/aea calomelanos
Cheifanthes viridis
Cttettenttves
virdis
+
+
+
+
+
+
+
+
+
+
.+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Hereropyxis nacalensis
- ~S3:igna
rtexstotnrs monopetalus
Turraea ob:usifolia
Rothmannia cepensis
Vangueria cyanescens
Forbs
Nidorella reseditotie subsp. resedifolia
Helichrysum kreussii
Oicoma anomale subsp. enamels
Oldenlandia neroecee
Zinnia peruviana
Agathisanthemum bojeri subsp. sustrete var. australe
Berterie oretoriensis
Penranisia angustifolia
Phyllanthus parvulus
Solanum panduriforme
Zornia milneana
Cleome maculara
Cassia quarrei
Scabiosa columba ria
Pavon;a cransvaalensis
Stachys natalensis va-. galpinii
Uppia rehmannii
Triumtette sonaeri
+
+
+
Vitex rebmsrmit
Climbers/creepers
Crvptotepts oblongifolia
Lando/phia capensis
Rhoicissus cridenrara
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
rilutolhUII"hll
~'
01)1111 tlf jl.,lIldutlrHllHHJrI
o·
,
Rotlculodromous
Hyphodromous
~
~
n
rt
Brochidodromous
3-
Cra spcdodromous
rt
...,
Eucarnorodrornous
Asvrnmemcal
base
Asymmetrical
lamina
~
~
...,
Oblong
Narrow I/obiong
rt
Leaf form
<1>
U">
Obovate
U">
Ovalo
::;
...,
Elliptic
C
Cuneate
0"""
Lobate
U">
Decurrent
Q.
Leaf base
Cordate
:3
Obtuse
0"""
<1>
Acute
jA
Even pinnate
Multitolinte
Twice pinnate
o
...,
Leaf type
<1>
<1>
Odd pinnate
Digitately
'"0
trifoliate
<1>
...,
Simple
~U">
Opposite
Whorled
0'
...,
Leaf arrangement
Decussate
Alternate/scat
0"""
Y'
rered
Very dlffcrel"'ri
Colour
Similar
Texture
-I- -I-
+-+-+
:., +
+
+1 +1
-I-
-+- •••
+ .•. + +1 +
-I-
+
......
+ .•. +
+++++
++++++++++
+
-I. ""
+
·1
+
+
+
+
01'0)(
+
++
Acute
Eroso
Serrate
...
+
Dentate
...
.•....
novorurc
Leaf margins
Crunute
Lobed
+ + -+ -+ .•.
'1-
+1 + + .•. + +t
+ + +
-I-
+
-I-
+ +
-I- -+- •••
+
-I- -I-
+ + +
-I- .•..•.
-I
1-
+ +
-I-
1 .•.
-+-+++++++++++
-I- .•. -+-
+
-I- -I- -I-
+
.j
Entire
Amolexlcnut
+
-I-
~+ + +
Leaf
Obtuse
+
'
Mucronate
-I- +1-1-
-I-
+ + ++
Snssue
-I
",-1
+++++++++++++++++,+++++++++++++++++
Petiolate
(b>
...,
::l
U">
Truncate
Rounded
-I-
Leaf surfaces
Leaf attachment
.-\ CO:-"IBI;\ED POLYCLWE
·
.-\:\D .-\:\.-\LYTIC.-\L KEY
69
Characters
relevant to each specimen were observed in the field and recorded
on a field data sheet on which provision
is made for recording
the collector's
number, provisional name and locality or releve. If more than 60 characters
are
recorded additional
page(s) could be used.
The morphological
characters
used in the preliminary
key were taken from
various sources such as Henkel (1934), :\letcalfe
& Chalk (1979) and Tainton,
Bransby
& Booysen (1976) and condensed
into an illustrated
photocopied
booklet for field use. This proved
useful in maintaining
consistency
in the
allocation of characters.
The PHYTOTA.B
program
package
(\ \ estfall el al., 1982) was used to
construct
the matrix. The data from the field data sheets were transferred
to
encoding forms using the PHYTOTAB
input format where each character
was
regarded
as a releve. Taxa were grouped
into trees and shrubs, climbers and
creepers, forbs, ferns and graminoids
for convenience.
This would also allow for
each group to be 5ho\\"I1 on a separate
table when more data are collected.
Characters
can also form groups such as leaf-margin
patterns
and venation
patterns. The character
groups are sequenced
in descending
order of character
(group) frequency from right to left. Characters
not in groups are on the left of
the table in frequency
order (Tables I & 2). Spaces were inserted
between
characters
to facilitate vertical reading.
Taxa are sequenced within groups so that dichotomies
may be formed. That
is, taxa with similar characters
are grouped
together.
The dichotomies
are
continued
until they consist of a single line for each taxon, and are ended with a
vertical
blocking
line after the first unique
character
for each taxon when
compared
with the two adjacent
characters.
Blocking was done by hand. If a
taxon has two or more character
states that express possible variation
within a
character
group falling within the blocked area, the species may be repeated
to
facilitate use of the key. This is illustrated
in our example by Combretum reyheri
Sond., in which the leaf apice
may be either obtuse or mucronate,
and by
S)I<)gium cordatum Hcchst., in which the leaf variability
is so great as to require
that the same taxon appears four times (Table I).
To use the table, the specimen
to be identified
is compared
with the
characters
gi\'en for the group to which the specimen belongs, starting from the
right-hand
side of the table. The user determines
whether
the character
is
present or absent in the specimen.
If the character
is present in the state
described,
then the user follows the 'present'
pathway
indicated
by +. If the
character
is sometimes absent it is still shown as +. If it is always absent. then
the 'absent'
pathway,
indicated
by blanks, is followed. The next character
is
found to the left of the newly completed
one. This procedure
is repeated
until
the end of a block, indicated
by a vertical blocking line, is reached. The user
should now be able to look across to only one name on the left-hand side of the
table. This will gi\'e him the desired identification.
The blocked pathways
indicate
the minimum
characters
needed
for identification.
Additional
characters
outside the blocks may be used for corroboration.
Two other possible methods of using the table suggest themselves. However,
these methods should only be necessary
as a teaching aid, or if the specimen
lacks important
characters
or in a preliminary
version of the key. It must be
stressed that these methods are by no means obligatory.
First, it is possible to use the table as a form of tabular key (Newell, 1970).
61
70
R. H. "'ESTFALL ET AL.
A profile of the unknown specimen is obtained by noting the characters present,
in the order of the table, and this profile is compared with those of the taxa in
the table, one by one, until a matching.profile is found. This would be the name
of the unknown specimen. This method is probably too cumbersome to be of
practical value.
Another method of using the table would be as a classical pol yclave. The
unknown specimen is exarni ned for any conspicuous (or unusual, if one is
familiar with the table) character, and the taxa which display this character in
this state are determined. Another character is searched for, to see which of the
taxa found in the previous step display this character in the required state. The
specimen has been identified when only one taxon remains.
RESCLTS
The problems discussed in the introduction were overcome by devising a
method for constructing vegetative keys during the reconnaisance
phase of
phytosociological work. The method had to allow for increasing the number of
taxa as the work progressed. It had also to be simple to apply in the field. The
key presented here will operate ati factorily only in the geographical area
concerned; it is used in this paper solely as an example.
In Table 2, Schiracliyriuni sanguineum (Retz.) Alst. in the Graminoid group has
an open pathway indicating that this species does not key out with the available
characters. However, its identity can be inferred from the absence of flattened
stem bases which are presen t in Hyparrhenia filipendula (Hochst.) S ta pf var. pilosa
(Hochst.) Stapf.
DISCCSSIO:'-i
The matrix method of key construction described in this paper allows for
biological variation where more than one character state, such as different leaf
forms, may be present in the same taxon, without detracting
from the
effectiveness of the key. Characters which are periodically available for which
the population is variable may be used, as well as corroborative characters. The
right-to-Ieft method of use described above in the Methods may seem at first
sight to' be. idiosyncratic. This method is used because the phytosociological
example makes use of the species name file for both vegetation and taxonomic
classification. The PHYTOTAB
program package allows the user to print
species names to the right of the table as well as printing the matrix in mirror
image, thereby reading from left to right. Thus, in ordinary taxonomic usage
the table would be rearranged to read 'correctly'. A further advantage of this
package is that in the printout form, both horizontal and vertical lines may be
employed to improve legibility.
This form of key is unique in that it can be used as both a polyclave and an
analytical key. It combines the flexible strategy of the former with the ease of
reproduction of the latter to produce a very efficient identification system. One
advantage of polyciaves is that either taxa or characters can be added at will,
with minimal effort required, although the addition of taxa may result in
ambiguities in identifications, which may only become apparent after long use.
By using PHYTOTAB, both taxa and characters can be added to existing keys
62
Table 2. A preliminary
vegetative key [or some species of the Transvaal
Waterberg:
Graminoid
I
o
.~
s:
s
I
-3
~ I:.:;
VI
V)
0)
I i~
zI
~
ro
~
VI
~
(1J
"'0
~
Q)"O
ceo..
... w
E
~roEE
~~
~
E_nJ,.-
V)
VI
~
C
(I)
~
g
'6..
~
1J
(I)
Q)
••.•
cu
B
Q>-:J
>-.§ 9
.= 1J
~
~
-5 ~ ~ -g --g ~ ~
';::.S
-5
E ~ e e- "Cl
6 ~
E g ~
:J
:J
Q)
QJ
.9 ~ .J:. ro :J C 0 ~ 0 J::. ro ~ c s:
~u~I~~~u~C~~Z~I~~~CZ~rororo~Oococ~~roC~Z
~
~
e.
A
.c ~
E ro .~ .2
~
~
.D
.s
a :;
~ ~
.D
0
.D
E
~
Z
~
c
trt
rororo~
(0'0
s
0
0
r-
Em
cuQ,)Q)CU:J
(I)
0
~.t::..r::.-
~ ~ ~ ~ :6 ~
g
V
A
II
~ ~ ~
ro
1'0
ro
~
eru
:;
o
~
'm
~
s:
:"Q
0
.Ql
"'0
e E
e
5s: ~= roe
~"O
C
~
ro '0
~
C
~
:J
VI
~
0
s~~u
Aristide canescens subsp. canescells
Brachiaria nigropedara
Loudotie simp/ex
Tnemede rriandra
Digiraria erienthe subsp. erienttu:
Sera ria perennis
Andropogon schirensis var. angusrifo/ius
Eragrosris racemosa
Digiraria eriontbe subsp. srofonifcra
Cymbopogon excavarus
Hyperrhe/ia dissolute
Dihereropogon
ampfecrens
+
+
+
+
+...
+ .•.
+......
+
+
+ + +
+ .•.
+
+
.•. .•. .•.
.•. .•.
+
+
+
+
+
+
+
...",
+
+
+
+
+
.•.
+
+
.•.
+
.•.
+
+
+
+
+...
+
.•..•.
+
+ +
+
.•.
+
+
prolixus
Seraria sphace/ara var. sphscetets
Pogonarrhria squarrosa
Setaria ustifata
I"''''
1+
Panicum maximum
0>
c.;l
""\j
~
r
()
~
Asrrida aequig/umis
Hyparrhenia fi/ipendu/a var. pifosa
Schizechvrium sanguineum
Perosis parens
Arisrida spectsbilis
Trecbvondrs spicara
Rhynche/yrrum serifofium
Trisrachya biseriara
Cymbopogon
::::::
to
...
0
Q)
~
III
Vi:g.fj
Q)
Oo!l.l
ell .0
o
o
E
~ ~ 'x
~
~
:.:;
I I
>-
3
VI
.E§
VI
(I)
~
+
+
+
+
+
+ +
+
+ .•.
+ .•.
+
+
+
+'"
+
+
+
+
+
+
+
+
+
+
+ +
+
+ +
+ +
+ +
+ +
+ +
+
+
+
+
+
+...
+ +
.•. +
+ +
+ +
+ +
+ +
+ .•.
+ +
+
+ +
+
+ +
+
+ +
.•..•.
+
-I- +
.•.
~
~
d
.>%
>-
s;:
....;
0
>-
l'
'"
:<
72
R. H. WESTL\LL
ET AL.
at will, and ambiguities
will be revealed when the tables are blocked. Correcting
a key is also a straightforward
task.
It will be noted that the horizontal
length of the block for each taxon is
roughly proportional
to the path length that would be required in an analytical
key to identify that taxon. Two uses for this proportionality
(which, it should be
stressed, is no more than a rough approximation)
suggest themselves.
First, it
offers a check on the discriminatory
efficiency of the characters
chosen. If all the
blocks are long, then redundant
characters
which do not contribute
to any
identifications
are present. These can be removed, or placed where they are only
used as corroborative
characters.
Secondly,
the proportionality
can be used to
demonstrate
Lamarck's
principles
of efficient key construction
to students
elegantly
and graphically
(Lamarck
1778; Osborne,
1963). This form of key
seems to be the only one available which demonstrates
its effectiveness with field
data.
A further teaching application
of the 'PHYTOTAB-key'
may be mentioned.
\\ ith the falling cost of computer
usage (at least in terms of constant units), and
with the relative ease and accessibility
of use of the PHYTOTAB
package,
a
method
is presented
of allowing students
to learn the mysteries of good key
construction
in the most effective
possible manner,
by making
their own
mistakes in the course of constructing
their 0\ n keys to taxa they think they
know.
The PHYTOT AB program
package (Westfall et al., 1982) allows the user to
construct a key to which taxa and characters
may be added as and when more
specimens
are collected.
The keys can be subdivided
easily to form tables of
convenient
size for field use .. An advantage
with computer
printout is that keys
may be duplicated
conveniently
and cheaply.
COl\CLUSIO)iS
Our identification
aid has peculiar and significant advantages
in three areas:
in teaching, in reconnaisance
and in the field. Its suitability
as a teaching tool
stems from the way in which the printed
table, while being usable as a
polyclave,
displays explicitly
the structure
of an analytical
key. Because the
PHYTOTAB
program
package is relatively simple, quick and inexpensive
to
use, students can be given practical
experience
of key construction
using this
package. For the same reasons, 'PHYTOTAB-keys'
can be produced
repeatedly
during the course of a taxonomic
investigation.
Ambiguities
in the key at each
stage may be taken to indicate
areas requiring
further research, and the keys
produced
will help in field-work while the group is under revision. In the field,
use of keys made by this method will mean that characters
present only in fresh
material
can be used to improve
the accuracy
of determinations.
It may be
expected that the facility with which both taxa and characters
may be added to
the table will be found useful in both taxonomic
and ecological work.
:\CKl\io\\·LEDGE:--IENTS
We thank A. V. Hall, O. A. Leistner, R. \. Lubke, E. J Pankhurst
and J. C.
Scheepers for helpful suggestions.
Miss A. P. Backer and Mr J. F. van Blerk are
thanked for assistance in the early stages of this work. The staff of the National
Herbarium,
Pretoria identified
the specimens collected.
64
A CO~IBINED
POLYCLAVE
.·\ND ANALYTICAL
KEY
73
REFERENCES
BAKER, H. A., 1970. A key for the genus Erica using edge-punched
cards. Journal of South African Botany, 36:
151-156.
BIA~CHI,
.-\. T. J., 1931. Een nieuwe dererminatie-rnethode.
Tectono 2-1: 884-893.
BOLuS, H. ~1. L., 1958. Key to the genera of Mesembryaruhernaceae.
Solts on Mesembryanthemum and allied
Gmera, 2: 392-403.
BOuGHEY,
A. S., BRIDGES, K. w. & IKEDA, .-\. G., 1968. An automated biological identification
key .
.Ilustum of S)'Slemalic Biology, Unicersity of California, Irnne, Research Series, 2: i-xix, 1-35.
DuKE, j. A., 1969. On tropical tree seedlings. L Seeds, seedling systems and systematics. Annals of the
.llissouri Botanical Gardens, 56: 125-161.
GLEN,
H. F., 197+. 'A taxonomic
re\'islon of the subtribe
Gibbaeinae
(~Iesembryanthemaceae)'.
unpublished
:-'I.Sc. Thesis, University of Cape Town, Cape Town.
GOODALL,
D. W., 1968. Identification by computer. BioSciena, 18: 485-488.
GYLLE?\BERG,
H. G., 1965. A model for the computer identification of micro-organisms.
Journal of Cmeral
Xl irrobiology, 39: 401-405.
HA!\'SEN, B. & RAH:-.i, K., 1969. Determination
of anciosperm families by means ofa punched-card
system.
Donsk botamsk .·Irchiv, 26: 1-46 & 172 punched cards.
HE~ KEL, J. S., 193+. A jitld book of the uoody planls of Xalal and ,(ululand. Durban: Robinson.
HERRE, A. G.J., 1971. Genera of Mescmbryanthemaceae: Cape Town: Tafelberg.
LA~I.-\RCK, J. B. A. P. ~L de, 1778. FloTt Francoise, 3 eels. Paris: Imprimerie Royale.
L.-\P.-\GE, S. P., B.-\SCO~IB, S., \\'ILCOX,
\\'. R. & CCRTIS,
~1. .-\., 1970. Computer idenuficarion
of
bacteria. In A. Baillie & R. J. Gilbert (Eds), Aulomalion, Mechanisotion and Data Handling in .llicrobiologr
1-22. London: Academic Press.
L.-\PAGF" S. P., BASCO~IB, S., \\"ILCOX,
W. R. & CCRTIS,
~1. A., 1973. Idcnrificarion of bacteria by
computer: general aspects and perspectives. Journal of Genera! Xl urobiology, 77: 273-290.
LEE~HOCTS,
P. W., 1966. Keys in biology: a survey and a proposal of a new kind. Proceedings of tlre
Koninklijke .\'fdalandsche Akademie mn fIItlroschappe Amsterdam, Series C, 6'1: 571-596.
~IERX\luELLER,
H. 1972. Prodromus finer Flora <'OnSueduest Afrika. Lehre: Cramer.
~IETC:\LFE,
C. R. & CH.-\LK, L., 1979 .. 4nalom)' of the Dicotyledons, \'01. I, 2nd edition. Oxford: Oxford
University Press.
~IEYER, P. G., 1969. Eintuehrung in die Pfianreniccl! Suedicestafrikas.
\\'indhock:
S.\\·.A. \\'issenschafdiches
Ccscllschafr.
~[ORSE, L. E., 1971. Specimen ideruificarion and key construction with time-sharing computers. Taxol/,20:
269-282.
~!ORSE, L. E., 19i4. Computer programs for specimen identification,
key construction
and description
priming using taxonomic data matrices. Publications of the .Ifuseum, .I/ichigan Stale University, Biological Series,
5: 1-128.
~IORSE, L. E., 1975. Recent advances in the theory and practice of specimen identification.
In R. j.
Pankhurst
(Ed.), Biological identification irith Computers: II-52.
London: Academic
Press (Systematics
Association special volume, 7).
NE\\'ELL, L ~L, 1970. Construction and use of tabular keys. Panfic Insects, /2: 25-37.
OSBOR?\E, D. V., 1963. Some aspects of the theory of dichotomous keys .• \·ew Phytologist, 62: 144-160.
PA!\'KHuRST,
R. j., 1971. Botanical keys generated by computer. 1I"0Isonio,8: 357-368.
P.-\;-.iKHURST, R. J., 1978. Biologicat Identification. London: Edward Arnold.
RYPKA, B. \Y., CL-\PPER,
\Y. E., BOWE!',
I. G. & BABB, R., 1967 .. -\ model for the identification
of
bacteria. Journal of General .lficrobiology, 46: +07-+2+.
S!\'L\TH,
P. H .. A. & SOKAL, R. R., 1973. Numerical T'axonomy. San Francisco: Freeman.
T.-\I~TO!\', x. ~I., BR.-\i\SBY, D. L & BOOYSE:-.i, P. de \1.,1976. Common Veld and Pasture Grasses of Xotal.
Picterrnar irzburg: Shurer & Shooter.
.
VOSS, L. G., 1952. The history of keys and phylogeneuc
trees in biology. Journal of the Science Laboratory
Denison Unicersity 13: 1-25.
WEBER, \Y ... \. &-NELSON,
P. P., 1972. Random access kty 10 the gfllera ofColorado mosses. Boulder: Univcrsiry
of Colorado ~ Iuscurn.
WESTFALL,
R. H. 1981. 'The plant ecology of the farm Croorhoek, Thabazirnbi
disrricr'. unpublished
~I.Sc. Thesis, University of Pretoria, Pretoria.
WESTFALL,
R. H., DEDNA\I,
G., VAN ROOYEN,
N. & THEROi\',
G. K., 1982. PHYTOTAB-a
program package for Braun-Blanquet
tables. Vegllolio,49:
35-37.
65
3.2.2.2.1
Improvements
Subsequent
improvements
to the techniques
of plant
identification
are:
i.
ii.
an illustrated
booklet
cation
study
in this
a program
of plant characters
(Appendix
was written,
gram package
identification
ing to the required
are standardized
to small
trated
attributes.
booklet
groups
is easier
sequence,
a family
The slide
incorporates
of the slide
the specimen
It was
to apply
namely,
if char-
from large
in the illus-
key; and,
by means
A photographic
of the plant when the voucher
accord-
the same characters
identification
comparisons.
pro-
for plant
species
is also used
forms. Using
of plant
slide/plant
is taken
in their
to produce
verification
graphic
sequence
and encoding
species
for key production.
This sequence
it is also possible
iii. visual
This program
dichotomies
found that the character
acters
for the PHYTOTAB-PC
3.6), for sequencing
purposes.
for identifi-
I);
as an option
(section
used
colour
specimen
number.
slide
is collected.
After
film, the slides are not mounted
of photo-
development
but returned
to
the film cassette.
After
ette
a plant
with
viewer
is identified
the
relevant
for comparison.
drive
Konica
light
transmission
places
the
FS
specimen
of the matrix
number
The field viewer
1 camera
with
lens.
facilitates
The
is
inserted
mirror
key, the
into
cass-
a field
used here is an old motor-
a} a translucent
and b) a magnifier
standard
The motor-drive
by means
lens
perspex
for viewing
system
is
also
back
for
which
re-
removed.
slide location.
66
3.2.3
Species
In this
study
for a plant
cision
ative
species
species
suitable
species
Generally
ever,
cover
as in this
cover is visually
greater
precision
3.2.3.1
the sample
sampling
with
change
and
a pre-
determining
rel-
a visual
estimation
estimate
is achieved
unit area for cover
attributes
and not sampled.
is required,
through
should relate
of presence
How-
and cover
samp-
to cover.
can require
unit areas.
&
estimating
species
scale
eleven
with
classes
and
advocates
the
Ellenberg
(1974)
cover-abundance
classes,
the
Daubenrnire scale
a nine class
(1976),
following
however,
list
such
several
as
the
Braun-Blanquet
with
six
scales
for
Domin-Krajina
scale
classes.
with
Werger
seven
(1974)
scale.
states
that
a cover
scale
should
meet
the
separated.
It
requirements:
i.
the scale
ii.
its points
values
iii.
are required
cover
Background
Mueller-Dornbois
Londo
canopy
estimated
than
and cover
ling, then the sampling
different
cover
projected
composition.
study,
Therefore,
to total
and cover estimations
for monitoring
species
where
cover refers
cover
should be sufficiently
should be related
so that these
and
abundance
detailed;
to the actual
can be treated
should
be
cover
arithmetically;
strictly
67
is not
a single
iv.
be
the
If the aim
an eleven
to these
class
scale,
other
quired,
such
relative
species
inversely
proportional
should,
hand
be
monitoring
requirements
for monitoring
estimations
cover
pattern
should
crude.
to
to effort
required
for
cover
or species
then
even
Precision
proportional
be
cover
cover
achieved
then,
scale,
or
an eleven
cover
class
between
to
possibly,
could
data
to
is re-
determine
class
scale
and
size
meet
con-
scale.
determination
size
class
change.
five class
of cover
change
could
be considered.
is
is
directly
for such determinations.
determinations,
composition
in
cover
first
in a matrix
processing
monitoring
composition,
too
in
with repeatability,
such as the Domin-Krajina
for
therefore,
required,
commensurate
if arithmetic
as
features
as possible.
and the ideal might be a simplified
the
to
be as simple
aim of cover
is to detect
fuse pattern
likely
should
such dissimilar
scale; and,
of precision,
added
However,
On
quantitative
the symbols
The criterion
also
to combine
logical
A balance
and
for
effort
example,
requirements.
68
Bothalia 18,2: 289-291 (1988)
.Miscellaneous notes
VARIOUS AUTHORS
THE PLANT NUMBER SCALE AN IMPROvtD METHOD OF COYER ESTIMATION USING
VARIABLE-SIZED BELT TRANSECTS
INTRODUCTION
.The vegetation ecology of the Transvaal Water berg is
currently being investigated at a scale of 1:250 000
(Westfall in prep.). Vegetation structure is being analysed
according to Edwards (1983) using the cover meter
(Westfall & Panagos 1984) for cover determinations in
each height class. In the floristic analysis, individual
species cover is estimated by using the Dornin-Krajina
cover-abundance scale (Mueller-Dombois & Ellenberg
1974).
A comparison of recorded species cover, being the
.sum of the class midpoints according to the DominKrajina scale (Mueller-Dombois & Ellenberg 1974), with
the structural cover should result in the summed species
cover for a stand being: 1, greater than the cover of the
height class with the greatest cover, and 2, less than the
summed cover of all the height classes, provided that 3,
quadrat size is such as to include those species contributing significantly to the total cover of the stand.
In the vegetation being investigated, quadrat size is
generally commensurate with species richness (Westfall
et al. 1987). However, the summed estimated species
cover was often considerably less than the mean cover
for the height classes with the greatest cover for the
same stands. Overcompensation for the underestimation
of species cover often led to the summed estimated
species cover being considerably greater than the summed
cover of all the height classes for the same stands (Westfall in prep.). Clearly, improved species cover estimations
are required for species cover to have any relevance other
than an approximate indication of relative abundance.
In estimating species cover, according to Edwards
(1983), the observer is often inclined to ignore grasses
without inflorescences and to estimate from a static
position without taking plant size and distribution into
account. For example, a larger plant should require a
larger area to be observed than a smaller plant. Furthermore, although mean canopy diameter can be readily
estimated it is often far more difficult to estimate mean
distance apart in terms of mean canopy diameter because
of often highly irregular plant distribution.
To overcome these problems, the approach suggested
here is based on a simple estimate of area and a count of
the individuals of a species within the area.
METHODS
The cover of a species is given by Edwards (1983) for
hexagonal packing by
90,7
c=-(n+1)2
where c = percentage crown cover and n = the mean
number of crowndiameters by which the plant crowns
are separated.
Assuming hexagonal packing, the transect area, of
which the percentage crown cover is a proportion, is
given by:
sin 60° (n+1) 30D
where D = mean crown diameter and 30 = the value for
obtaining a minimum of 0,1 % cover. Cover of less than
0,1 % is not considered significant. Transect length is,
therefore, 30D and transect width is sin 60° (n+ 1). In
practice, transect width was taken as slightly less than
the average gap between plants within or nearest to the
sample quadrat plus the mean crown diameter. The
number of individuals of a species was then counted
within the transect. Only species occurring within the
sample quadrat were recorded and for each a count of
individuals within a transect commensurate with each
species spacing and size was made. Counts of individuals
did not include the first individual as the transect was
started adjacent to the first individual. This permitted a
cover of less than 0,1 % where no individuals were
counted. Transect width was never greater than the
length as this could have resulted in actual cover values
of less than 0,1 % being given higher cover values.
The mean number: of crown diameters (n) by which
the plant crowns are separated within each transect is
given by n = ~
where I = number of individuals
counted. Percentage crown cover can then be calculated
according to Edwards (1983). Table 1 shows number of
individuals counted, representation by a single character
symbol and percentage cover for recording purposes in
the field.
Vegetation structure was analysed using the cover
meter and the summed species cover was estimated with
both the Dornin-Krajina scale and the plant number scale
as outlined above for five vegetation stands represented
by 21 quadrats. The mean of the shortest and longest
cross distances of each crown was taken as the crown
diameter for each species and these distances were noted
in four categories (Edwards 1983) namely, forbs (herbs),
grasses, shrubs and trees. Class intervals were selected on
a basis of trial and error to give an approximately normal
distribution of occurrences within crown diameter class
intervals. All estimations were done by an independent
observer.
RESULTS
The results of the crown cover determinations
given in Table 2.
are
69
290
Bothalia 18,2 (1988)
of plant individuals counted with single
TABLE I.-Number
character symbol and percentage crown cover
No.
Symbol
% Crown cover
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
>31
+
1
2
3
4
5
6
7
8
9
A
B
0,00
0,10
0,40
0,91
1,61
2,52
3,63
4,94
6,45
8,18
10,08
12,20
14,51
17,03
19,75
22,68
25,80
29,12
32,65
36,38
40,31
44,44
48,78
53,31
58,05
62,99
68,13
73,47
79,10
84,76
90,70
96,85
100,00
C
D
E
F
G
H
I
J
K
L
M
N
0
P
Q
R
S
T
U
V
W
The Fibonacci sequence, where each number is the
sum of the preceding two numbers, provided the closest
resemblance to a normal distribution of occurrences
within crown diameter class intervals. This is illustrated
in Figure 1 with a frequency polygon with the class
intervals on a natural logarithmic scale to reduce the
effect of increasingly larger class intervals.
The class intervals used for mean crown diameters
according to the Fibonacci sequence, are shown in Table
3. Transect lengths were determined by the midpoints of
each class interval.
INTERPRETATION
In Table 2 estimations according to the Dornin-Krajina
cover-abundance scale are considerably lower than those
for the single height classes with the most cover. The
plant number scale, in contrast, yielded higher values
than that of the single height class with the most cover
and lower values than the cover of the combined height
classes for each releve, except releve 37 _This indicates a
greater precision in estimating cover when using the plant
number scale as opposed to the Dornin-Krajina scale.
According to Westfall et al, (1987), quadrat size
should have been larger for the vegetation type represented by releve 37, but this was not apparent using the
Dornin-Krajina scale at the time of sampling. However,
simple summation of the values obtained by the plant
number scale in the field indicated inadequate quadrat
size. It is far too time-consuming to verify quadrat size
for each quadrat according to Westfall et al. (1987)_ The
plant number scale together with a structural analysis of
the vegetation provides a simple means of verifying
adequacy of quadrat size.
In the frequency polygons (Figure I) the peaks to the
. left of the central troughs for forbs, grasses and shrubs
are caused by a relatively higher proportion of 0,2 m
diameter crowns. This can be attributed to the observer
rounding off crown diameters to 0,2 m some of which
should have fallen into the 0,211 to 0,34 class. If class
intervals had been known at the time of recording, it can
be expected that greater care would have been exercised
in measurements where crown diameters were close to
class borders. The troughs mentioned are, therefore,
considered to be a result of measurement inaccuracies
which could be overcome by using class intervals.
The use of standard transect lengths as illustrated in
Table 3 should simplify transect length determination
and provide for variability in crown size. A further advantage could be the simultaneous counting of individuals
of different species with similar crown diameters and
spacing to save time. It is also suggested that a simple
counter be used for recording number of individuals for
each species as marking paper for this purpose requires
stopping at each individual recorded.
It must be emphasized that the parameter determined
here is projected crown cover and not projected foliage
cover which is more species and age-dependent.
The class 'r' on the Braun-Blanquet scale and '+' on
the Dornin-Krajina scale (Mueller-Dombois & Ellenberg
1974) both with 'solitary, insignificant cover' are difficult
to determine. Species with a single occurrence in a sample
quadrat often have significant cover outside the quadrat.
If a stand is defined, as in this study, as 20 ha (Westfall
et al. 1987), it is impracticable to determine whether a
species is 'solitary' within that area. The concept of
'solitary' is relative to the area defined. In the plant
number scale used here the lowest cover class is less than
0,1 %, which seems better defined than 'solitary'.
In contrast to the Dornin-Krajina scale, the plant number scale has proportionately finer subdivisions at the
lower cover values of the scale. This is of significance in
the South African context with often high species richness characterized by many dominant species with
generally lower cover in contrast to the few dominant
species with higher cover often found in the relatively
impoverished European vegetation.
TABLE 2.-Percentage
crown cover in five vegetation stands
represented by releves 33 to 37
Percentage crown cover
Releve
no.
Single
height
class
Combined
height
classes
Domin-Krajina
scale
Plant number
scale
33
34
35
36
37
55
31
44
41
63
100
104
103
86
146
27
25
19
21
17
60
74
79
53
35
70
291
Bothalia 18,2 (1988)
U)
25
:s
20
::l
-ij
'5
.S
~
10
••>.,
TABLE 3.-Class intervals of crown diameters according to the
Fibonacci sequence for determining standard transect
lengths
Crown diameter
class interval (m)
Transect
length em)
0,001-0,01
0,011-0,02
0,021-0,03
0,'031-0,05
0,051-0,08
0,081-0,13
0,131-0,21
0,211-0,34
0,341-0,55
0,551-0,89
0,891-1,44
1,441-2,33
2,331-3,77
3,771-6,10
6,101-9,87
0,15
0,45
0,75
1,20
1,95
3,15
5,10
8,25
13,35
21,60
34,95
56,55
91,50
148,05
239,55
c
e:;
5
u
o
o
-6
-5
-4
Mean
-3
crown
-2
-1
diameter
0
2
3
In (m)
FIGURE I.-Frequency
polygons of occurrence of plants in
mean crown diameter class intervals for [orbs (.:..), grasses
(---),
shrubs (-'-'-)
and trees (-).
Class intervals
are according to the Fibonacci sequence on a In scale.
CONCLUSIO S
The projected crown cover determinations based on
area estimations and counting of individuals shows
improved precision compared to the Domin-Krajina
cover-abundance scale. Although this method is more
time-consuming than a purely visual estimation of cover,
the use of standard class intervals and a counter should
decrease the time required for cover determinations. The
method appears more suitable for the species-rich South
African vegetation than the traditional European coverabundance estimation scales. The method also provides a
means of verifying quadrat size adequacy.
ACKNOWLEDGEME~~S
The authors thank Dr J.C. Scheepers for comments
and suggestions.
REFERENCES
EDWARDS, D. 1983. A broad-scale structural classification of
vegetation for practical purposes. Bothalia 14: 705-712.
MUELLER-DOMBOIS, D. & ELLENBERG, H. 1974. Aims and
methods of vegetation ecology. Wiley, London.
WESTFALL, R.H. & PANAGOS, M.D. 1984. A cover meter for
canopy and basal cover estimations. Bothalia 15: 241244.
WESTFALL, R.H., VAN STADEN, 1.M. & PANAGOS, M.D.
1987. Predictive species area relations and determination
of subsarnple size for vegetation sampling in the Transvaal
Waterberg, South African Joul7U11of Botany 53: 44-48.
WESTFALL, R.H. in prep. The vegetation ecology of Sour Bushveld in the Transvaal Waterberg .•
R.H. WESTFALL and M.D. PANAGOS
MS. received: 1987.07.31.
71
TABLE
3.1.
Symbols
Crown diameter
for
crown
Symbol
0,001 - 0,010
A
0,011 - 0,020
B
0,021 - 0,030
C
0,031 - 0,050
D
0,051 - 0,080
E
0,081 - 0,130
F
0.131 - 0,210
G
0,211 - 0,340
H
0,341 - 0,550
I
0,551 - 0,890
J
0,891 - 1,440
K
1,441 - 2,330
L
2,331 - 3,770
M
-
6,100
N
6,101 - 9,870
0
3,771
72
(m)
used
diameter
classes
3.2.3.2.1.
Improvements
Subsequent
3.2.3.2
improvements
include
diameter
derived
from
the
the allocation
classes
with the cover
to
and
of symbols
recording
symbols
the
methods
of
between
in
section
(Table 3.1) to the crown
these
for each species.
relationship
described
class
symbols
The following
mean
cover
together
data can be
and mean
canopy
diameter:
mean
cover
(%);
ii . mean
crown
diameter
i.
iii. individuals
(m);
per hectare;
iv.
square
v.
mean
spacing;
vi.
mean
canopy
radius
vii. mean
canopy
to canopy
It was
metres
found,
stituted
forbs,
if half
within
ii. shrubs
to centre
(m);
(m); and,
gap
(m).
of these methods,
that the pre-
cover estimations
could be improved
considerably,
strata,
if rooting
overhang
is, therefore,
grasses
within
a sampling
in the sampling
unit
and trees:
In this way vegetation
sub-
applied:
and dwarf
the sampling
was
unit.
shrubs:
species
presence
or more than half of the stem or tuft base
of the canopy
presence
centre
field testing
for canopy
The following
i.
plant
during
cision of species
for the higher
per individual;
unit;
species
overhangs
structure
recorded
occurs
and,
presence
recorded
the sampling
is taken
if any part
unit.
into account
for sampling
and cover.
73
3.2.4
Floristic
Floristic
trial
data
plant
cording
3.2.2.
Gibbs
data recording
is taken
life
of
and
plant
3.2.4.1
nomenclature
et al.
species
&
and
presence
is
to terres-
area.
described
in this
work
is
The
in
re-
section
according
to
(1985, 1987).
optimum
results
that
3.2.4.2
Methods
be
should
that
apart
other
aspects
are
quantity,
purpose
recorded,
be
suggest
dispersion,
To this is added vitality
the
of
the
the
study
minimum
considered,
can be derived
taking
from
often
morphoor vigour
will
often
requirements
into
for
account
the
from those that are recorded.
applied
It follows
from
data
should
which
species
Although
should
aspects
as
or layering.
sociability.
what
(1974)
cover-abundance
such
structure
dictate
Ellenberg
and
recording
logical
in a given
data
used
relating
Background
Mueller-Dombois
worth
its distribution,
identification
Taxonomic
Russell
here to mean the data
the
previous
be
recorded
sections
that
in the
the minimum
field,
for this
floristic
study,
are
the following:
i. species
ber.
name
The
abbreviation
number
strokes
than
ture
used;
is
a
is
name
the
or voucher
preferred
because
abbreviation
plant
specimen
if
identification
it
direct
key
reference
requires
computer
can
serve
numfewer
key-
data
cap-
as
num-
a
74
ber
control
control
to
different
erence
whereas
ensure
that
species;
the
to
the
entification
ii.
plant
reference
Although
use
of
overall
data
correction,
used
for different
iii. crown
high,
D: Dwarf
than
by
is taken
over
not
improve
inclusion
be
then
it
numbers,
the
of
quicker
known,
same
for
some
can
is
separate
used
least
which
using
example,
a
than
is
especially
and,
(section 3.2.3).
form symbol
(from Edwards
1983 and Ruther-
as follows:
single-stemmed
plants
over 2 m high or
plants
from 1 to 5 m
5 m high;
woody,
are rooted,
multi-stemmed
when
less than or equal to 2 m high;
woody
are rooted,
or graminoid
resemble
or partly woody,
plants
less
plants
to the
family
belonging
such as Cyperaceae
and Restionaceae
and
non-graminoid
species
ed. Those occurring
plants
grasses;
are rooted,
Not all plant
herbaceous
occurring
herbaceous
in a sampling
plants.
unit
in seed form and those occurring
id-
can be recordunderground
num-
suggested
abbreviation
3.2.3);
ref-
possible.
into account;
(section
a
voucher
1 m high;
Poaceae
F: Forbs
for
sYmbol
at
can
are
cover class sYmbol
or single-stemmed
G: Grasses
which
saved
is
checklist
species
be
woody,
are rooted,
shrubs
a
1986) is also recorded,
multi-stemmed
keys
abbreviations
can
class
are rooted,
S: Shrubs
abbreviation
necessitates
in
the
require
programmatic
numbers
species,
the growth
ford & Westfall
and
where
canopy
diameter
In addition,
T: Trees
time
would
identification
if
that
same
number
name
initially,
percentage
the
accuracy;
specimen
bers,
abbreviations
at
75
if
is
the time of sampling,
larly, sterile
plant material
cult to identify.
significance
taxonomic
high.
cover
Furthermore,
likely
and
recording
criterion
seldom
The
voucher
as
that
identifiable
species
availability
It
than
is
of
for
within
the often-quoted
that
this
"total
low
their
might
cover
species.
plants
The
identifiable
suggested
a
diagnostic
occurrence
diffi-
although
species,
with
obtained.
Simi-
have a low ecological
material
be
is also often
endemic
doubtful
value
to identify.
concerned
plants
of a plant
be
and
It
with
is
high
criterion
for
the
a sampling
unit
is,
material
is
a
floristic
at the
more
time
realistic
recording"
which
classification,
how-
can be true.
minimum
ever,
as, for example,
cannot
sampling.
community
unidentifiable
of
the
with a low cover
plant
occurrence
therefore,
of
the
significance
are
are difficult
Such plants would generally
within
occurrence,
seldom
for example,
are
floristic
initially,
specimen
following
article
data
requirements
releve
reference
describes
number,
number
for
species
name
abbreviation
and cover percentage
a field data capture
symbol.
program
or
The
for these
data.
76
Bothalia
I~. -' 8: -1: 7-19-750 (Ige.-)
I'HYTOCAP.
A FIELD·DATA
CAPTURE
PROGRA~1 FOR THE I'HYTOTAB
Manual field recording of floristic data, for phyrosociological
studies, entails re-encoding
data on
computer encoding forms, transfer to magnetic tape
and finally loading onto the mainframe computer for
multi-variate
analyses. The time taken from re-e ncoding to access on the mainframe computer can be
from one to six weeks. Classification of data while
fieldwork is in progress, therefore, becomes impracticable in the summer-rainfall
areas because fieldwork generally takes place in the relatively short
growth period and the delays in computer
access
would limit fieldwork considerably if data were classified during this period. A serious disadvantage
of
classifying vegetation only after completion of fieldwork is that vegetation units are often either undersampled,
resulting in invalid syntaxa,
or oversampled resulting in wasted labour and expense.
Furthermore,
the potential for errors is increased by
re-encoding the data.
These problems were overcome by using computerized field-data capture where data is recorded directly onto a hand-held computer and loaded onto
the mainframe computer on return from the field.
Multivariate analysis techniques used are the PHYTOTAB program (Westfall et al., 1982), which are
compatible
with both the DECORANA
(Hill,
1979a) and TWINSPA
T (Hill,
1979b) programs.
Preliminary classification using either PHYT020
or
TWINSPAN
can then be available within a day of
returning from fieldwork.
The system used for field-data capture is the Sharp
PC 1 500 computer with an additional 8K expansion
memory module, printer/cassette
interface, cassette
recorder and programmable
RS232C interface. The
program PHYTOCAP,
written in BASIC, is used
for recording floristic data in the PHYTOT AB
(Westfall et al., 1982) format. The program features
include automatic line number allocation,
sample
and/or subsarnple numbers,
alphanumeric
species
codes, cover-abundance
values, data pertinent to individual species, data display, data printing in two
formats, halting and continuing program execution,
line editing, saving data to tape and loading data
from tape. Furthermore,
data input is verified for
errors such as sample number length, species omission and cover-abundance
omission. The user is also
informed when five lines of memory are left. Thecapacity of the computer is 70 lines which is approximately 14 releves or samples with 40 species per
sample, which is generally more than adequate for
the floristic data recorded in one day.
. Field procedure includes quadrat location, quadrat demarcation,
floristic sampling, voucher specimen collection and environmental
parameter sampling. The Sharp PC 1 500 computer is used for floristic sampling. Species for which voucher specimens
exist are input as a four-letter genus code and a
three-letter species code. Species for which voucher
specimens are required are tagged with pre-numbered, specimen number, adhesive address labels
PROGRA~1 PACKAGE
and input as a left-justified specimen number. Specimen collection is effected after completion of floristic data recording. This process ensures a smooth
flow of data input and reduces the possibility of
species
being overlooked.
Environmental
parameters are recorded directly on a field data sheet. It
is envisaged that a second Sharp PC 1 500 be used
for recording environmental
parameters
and as a
standby machine. Memory capacity currently precludes the use of a single machine for both floristic
and environmental
data.
After a day's recording the computer is attached
to the printer/cassette
interface, which remains in
the vehicle. A printout of the floristic data is obtained of each sample for stapling to the field data
sheets which form the hardcopy for eventual permanent safe-keeping
at the Botanical Research Institute, Pretoria. The data are then transferred to C 15
cassettes, when the computer can be cleared for the
following data set.
Loading data to the Burroughs B 7 900 mainframe
is effected by means of a Burroughs ET 1 100 terminal, RS232C interface and a transfer program
called DATATRAN
written by S. J. Crafford. Data
are read from the cassette and simultaneously
transmitted at 300 Baud.
The advantages of this system of data recording
include the cost-saving production
of preliminary
classifications for optimum sampling as well as the
labour- and cost-saving of not having to re-encode
data. The potential for errors is also reduced by the
reduction
in data handling.
Documentation
and
taped copies of the program are available from the
author. Please forward a blank C 15 cassette for
copying.
ACKNOWLEDGEMENTS
The author thanks Dr J. C. Scheepers for comments and suggestions and Mr S. J. Crafford for
writing the program DATA TRAN.
REFERENCES
HILL
M. 0 .. 1979a. DECORANA.
A FORTRAN
program for
detrended correspondence
analysis and reciprocal averaging.
Unpublished report. Ecology and Systematics. Cornell University, Ithaca, New York. pp. 1-30.
HILL, M. 0., 1979b. TWINSPAN.
A FORTRAN
program for
arranging multi-variate data in all ordered ovo- way cable by
classification of the individuals
and attributes. Unpublished
. report. Ecology and Systematics, Cornell University, Ithaca,
New York. pp. 1-48 .
WESTFALL. R. H .. DEDNAM. G., VAN ROOYEN, N. &
THERO . G. K.. 1982. PHYTOTAB. A program package
for Braun-Blanquet
tables. Vegetatio 49: 35-37.
R. H. WESTFALL'
• Botanical Research
Supply. Private
Water
Institute,
Bag XI~)).
Department
of Agriculture
Pretoria 0001.
&
77
..
3.2.4.3.1
Improvements
A program
PHYTOFORM
the sampling
eve
stand.
unit
one with
3.2.5
Habitat
which
the
lists
a
computer
the environment
floristic
single
species
on the mean
reducing
list
for
th~
each
is the same as for the origi-
can have two
data
sampling
Werger
(1974)
is empirically
states
sets,
namely,
unit data.
c~rrespond
with patterns
Group
Vegetation
minimum
"In the
that
Ecology
and
and
community
plant
abiotic,
and which
communities.
Ztlrich-Montpellier
patterns
in
in the environment".
environmental
correlation
between
both biotic
can
These
in the field.
that
determined
data,
of a plant
differences
recorded
Background
ronment
to
are the environmental
3.2.5.1
gested
is based
for the stand, thereby
data and the other with
are usually
for
conversion
rel-
data
describe
explain
The
recorded
the host
releve
data
stand.
converts
for each stand to a single
to the host computer
Thus
Habitat
for the Sharp PC 1500 in BASIC
(sub-plots),
species
Transfer
data.
data
each
for each species
sampling
nal
unit data
representing
cover
written
in South
parameters
prediction
of
floristic
approach
composition
The National
Africa,
for
floristic
Working
example,
for vegetation
it
and
potential.
sugenviThese
are:
i.
sample
number;
78
ii.
sample
iii.
date;
iv.
altitude
v.
aspect
of sample
vi.
slope
inclination
vii.
lithostratigraphy;
viii.
percentage
ix.
geomorphology;
x.
vegetation
xi.
land use and management.
The
first
site
is
an
then
mental
does
size
factor
not meet
example,
habitat
soil
of terms
if plant
factors,
the
to
of
to
communities
statistical
and
if the
However,
need
certain
clarification.
vegetation
is
described
facilitate
all
in
could
comparisons
If
that
is
descriptive
be improved
with
other
are to be correlated
differentiating
with
causes,
then
is necessary.
vegetation
If the sample
as
change.
be
determine
(1983);
especially
of the environment
criteria
be adequate
such
the
could
for both
statistical
depth
important,
of a study
factors
same
sampling?
factors,
require
aims
account
to Edwards
monitor
environment
of environmental
Is sample
not
the
However,
sampling
to
description
standardization
environmental
extremely
to the
A descriptive
studies.
are
cover;
according
re-sampled
environmental
terms.
by
be
in metres;
in degrees;
structure
items
longitude);
in degrees;
rock and outcrop
relating
required
(latitude,
(above sea level)
three
to
questions
co-ordinates
sampling
size for vegetation
then
using
how can
if
these
and
sampling
a sample
the same criteria?
lithostratigraphy
sampling
and environ-
soil
have
of,
for
Mappable
type
already
should
been
79
mapped.
However,
factors
sampled
by
are
equated
with
also
what
the
be taken
hill could
factors
correlations
The
data
plant
affect
scale
and
possible
influence
field
area
of
and
of
namely,
suitable
a) stand
L,
in
extent
categorization
those
Cognizance
should
factors.
is increased.
what
be
can
be
A
What
derived
community
and
environment
the value of a classification.
from
two
sources,
namely,
at the time of sampling
and geological
direct
and indirectly,
maps.
data
community
purposes,
when
are
cannot
environmental
sampled
from topographic
A) Field
the
be
depth
which
for vegetation.
determined
in the
disparity
study,
samples,
considering
should
and soil
applied
were
measurements
3.2.5.2
point
as a mountain
and hence
data
as derived
when
could
Methods
Habitat
used
slope
sources?
questions
3.2.5.2
scale
be regarded
from other
These
essentially
samples
of
environmental
such as aspect,
between
a
stand
particular
sampling
habitat
those
data
suitable
for sampling
and
sampling
variable's
two
for
stand
for
influence
considerations
into
unit
on
necessitated
groups
for
this
the
the
recording
characterization
and
unit characterization.
data
Lithostratigraphy,
being
formation
and
surface
rock
type
80
co
TABLE 3.2. - Geomorphology
classes
in terms of facets and segments
from Scheeper~
(unpubl.)
f-'
LANDFORM
I
Major
facet
LANDFACETS
(regular uud irregular)
d:lSSCS .
I
FA~ETS
I
Fla"
Flat summits'
or plateaux and structural
terraces
rial to rolling pin ills :1I1d
intcrf'luvcs
I
(0-2')
Slopes
~1J
FI:lt pfuteau
Flat suuuult and 11:11intcrf'luve
Structurul tcrruccs
[I!J
Flat intcrlluvcs
I
12
01
rises
Acute rugged crests
or peaks, l'O/WCX
crests or waxing
slopes. Ircc races
em
(Planes)
(>2-45')
Rolling pia ills
Iluves
)2
33
:lIItl
or
(>45')
rolling inter-
I Convex crests & lntcrnuvcs
Seepage slopes
W~~illb slupcx
Creep slopes
wnxing slopes
[W
(Curves)
SEG~J':NTS
I
Cliff,
)7
Free face or scarp or cliff
1 smooth
Crilg, l'fnc&y tree Incc. ('f:I&tY
scarp or cruggy emf
Acute rUlU~cdpcuk
Acute rugged crest or ridge. knife
)8
udgo or :Il~IC
I\C\ltC SIIHlOI!!
34
or
35
)(1
shaq)
Q3
Rounded ro Sleep, smooth
(10 rugged) rocky prominences
[d
Coucavo-convcx slopes or uppcrslupcs
uf cots or suddles
Convcxo-cuncavc slupes or IO\\'l'!
slopes
cols or saddtcs
Upllmds
Intcrfluvc,
water shed or rid!;:!.!
Debris
I Scree
slopes
[W
<12
Talus
4)
Detrital
slope
~
Zones of inflexion
stope
slope or upper pediment
Plane stope ur nrkldlc Ih:lIiUll'nt
stope or uudslupc
m
Lower pcdintcn t slopc~
or w;lllin~ slopes'
Low fhus
pcm:pl:dns. SW:dl'S
&. Ilal!l lluodpJains tcr ruccs
L".t
~
I
72
Wel bottumland
flals ur vlcls
Dry
Huts
7)
Ffuod plain
River !l'H;ICCS
74
~
I
H'
~8'1
Levees &
ll\lIlutlll:IIIU
cuuvexitics
bottomland
or
Lower pediment
Swnlc
slope or wallinl: slope
uonombuds
J Ilumm'ocks
Levee
Stranded levee und ~I1HJbanks
Fossil strcuutbcd, boulder, rock
nnd ~r:lvcl runs anti sandbanks
[ll]
M5
H6
rYil
~
Chaotlel hanks
Gullies, dongns & hatllillu.Is
Microlnccrs
1 J.e. Sch:!epers, PrivateBag X05, Lynn East, 0039
~ J
I
hl:tnus& l slc ts
Sandbnnks, slltbunks,
mudbnnks,
pcbblcbanks.
stoncbunks, rock banks,
and bouldcrbunk s
ClwlIlld banks
92 _ Gullies, dougus ami budlunds
M icroscgmcn
IS
a
FIGURE
82
a
3.3. - A flexible plastic cross for selecting random
pairs of plants in ground layer vegetation. a) intersecting arms, b) wing nut pivot - the first component of
the plant pair is that which is nearest to the pivot,
and c) marked - the second component of the plant pair is
that which first intercepts the arm or continuation
thereof.
FIGURE
83
3.4. - A tripod-mounted,
manual spinner for selecting
consecutive plants in pairs, at random, in vegetation
above the ground layer. a) spinner, b) sight, and c)
telescopic tripod.
TABLE
3.3. - A dual
level
system
Primary
symbol
Land use:
(level 1)
Vegetation utilization
None observable
Component mostly utilized:
Grass
Herbs
Shrubs
Trees
All
Vegetation replaced by:
Pasture
dry land
Pasture - irrigated
dry land
Crops
- irrigated
Crops
Plantation
Exotics (other)
-
-
Construction:
Dam
Road - unsurfaced
Road - surfaced
Footpath/track
Railway
Single storey
Multi-storey
Cleared
84
of land use categorization
A
B
C
D
E
F
I
J
K
L
M
N
Q
R
S
T
U
V
W
X
Land use:
(level 2)
Production:
Cattle
Game
Goats
Horses
ostriches
Sheep
Other stock
Bark
Hay
Flowers
Fruits
Leaves
Root
Seeds
Wood
Cereals
Vegetables
Alien
Waste:
solid
Liquid
Gas
Sectmdary
symbol
A
B
C
D
E
F
G
H
I
J
K
L
M
N
a
P
Q
R
W
X
Y
according
to
Jansen
(1982)
with
field
verification
of
out-
crops.
ii.
Altitude,
taken
being the altitude
from
nearest
the
half-contour
iii. Geomorphology
Table
iv.
3.2
class,
classes
by a cover
les were
meter
v.
as visually
SA topo
series
categorized
taken
and
being the total
suggested
(Westfall
in each
up to one metre,
mounted
000
in
metres,
map,
to
the
i.e. 10 m or 25 feet.
structure,
height
thrown
1:50
centre
according
to
.
Vegetation
the
relevant
of the stand
for the
spinner
height
height
cover
by Edwards· (1983)
& Panagos
a plastic
canopy
1984).
class.
For
cross
(Figure
classes
above
as determined
Five
the
assessment
random
height
3.3) was
according
samp-
classes
arbitrarily
one metre,
(Figure 3.4) was used to select
Land use, being a visual
for each of
a tripod-
random
pairs.
to the categor-
ies in Table 3.3 .
vi.
Insolation
angles
tal,
The
exposure,
of the
at
the
eastern
stand
directions
study
area,
difference
the
Tropic
taken
being
and western
centre
of the
were
of
as being
and
as
vii. Temperature
of bare ground,
tation
height,
five
stand
arbitrarily
centre.
due
Temperatures
western
east
from
with
and
stand
Maximum
solar
an
the
inclinometer.
horizons,
due
centre
horizon-
for
the
less
the
latitude
and
west
insolation
is
thus
solstice.
ground
being
selected
the sum of the
horizons
the
at the summer
at chest
and
between
Capricorn.
minus
measured
eastern
taken
in degrees
180 degrees
layer vegetation
the mean
sites
of the measurements
for each
were measured
and vege-
category
with
around
at
the
an Instatherm
85
infra-red
non-contact,
dard
grey
parative
scale
x.
according
for com-
to asssess
the eff-
to the following
layer grazed
short
1, ground
layer grazed
short and patchy;
2, ground
layer grazed
evenly;
Browsing,
four-
assessed
0, browse
line evident;
1, browse
line not evident.
Erosion,
(less than
100 mrn);
and
evident.
as visually
as visually
according
to a two-class
scale:
and
assessed
according
to the following
four-
scale:
0, donga
erosion
evident,
by donga or gully
1, sheet
erosion
evident,
by general
2, donga
direction
Fire,
soil accumulation
in a
plants;
evident;
assessed
according
of fire present;
1, no evidence
and
to a two-class
scale:
and
of fire present.
unit data
Slope
inclination,
mined
with
Aspect
formation;
evident.
as visually
0, evidence
b) Sampling
against
and sheet erosion
3, no erosion
ii.
assessed
0, ground
similar
i.
stand
scale:
class
xi.
of a stan-
cover on soil temperature.
as visually
3, no grazing
ix.
at each
These data are intended
ect of vegetation
class
The temperature
card was also measured
purposes.
viii.Grazing,
thermometer.
from the horizontal
in degrees
as
deter-
an inclinometer.
of inclination,
in degrees
as determined
with
a magne86
TABLE
3.4. - The "sausage" method of determining the clay percentage or texture of soil at field capacity (from F.S.S.A.
1974)
No sausage
Sand
C::""'~
Loamy
sand
10
-
15% clay
~
Sandy
loam
>15
-
20% clay
~
Sandy
clay loan
>20
-
35% clay
Sandy
clay
>35
-
55% clay
~
"
«»
"
'.,.
TABLE
"--
Clay
>55% clay
'
.
3.5. - The "finger test" method of determining the clay
percentage or texture of soil at field capacity (from
F.S.S.A. 1974)
Fingers
stay clean
Fingers
slightly
dirty
Fingers
slightly
sticky
Slight
glossy
smear
Highly
glossy
smear
87
<10% clay
and concretions
0
-
6% clay
>6
-
12% clay
>12
-
20% clay
>20
-
35% clay
>35% clay
and allowing
tic compass
iii. Percentage
surface
for magnetic
rock and outcrop
deviation.
cover,
as a visual
esti-
mate.
iv.
Soil surface
at the
sampling
trometer,
v.
centre
These
if the
corners,
recordings
Soil texture,
percentage
clay
"sausage
(Table
greater
spike measurements
if the
soil
depth
a pocket
than
pene-
300 rom or as
at the sampling
was
less
than
(Table
3.4)
unit
300
rom.
10 mm.
the mean of the class midpoints
in the A horizon,
method"
as estimated
and
the
of the
using
"finger
both
test"
the
method
3.5).
vii. Soil colour
city
was
are to the nearest
being
using
with a soil auger at the sampling
soil depth
of five depth
and
and corners,
in kg.cm-2.
as determined
centre,
the mean
being the mean of five measurements
unit centre
calibrated
Soil depth,
unit
vi.
compaction,
of the A and B horizons,
with
a
soil
colour
chart
determined
(Munsell
at field
Soil
Color
capaCharts
1954) .
viii.Soil
form,
according
ix.
Litter
as determined
to MacVicar
cover,
by diagnostic
et al.
horizon
combinations
(1977).
as an estimated
percentage
of soil surface
covered.
x.
Litter
the
depth,
nearest
being
10 mm
the mean of five depths measured
at
the
sampling
unit
centre
and
in rom to
the
four
corners.
xi.
Relative
biomass
of the ground
measurements
at the
disc pasture
meter
sampling
(Trollope
layer, being
unit
centre
& Potgieter
the mean of five
and corners,
using
a
1983), calibrated
88
in
cm.
Conversion
clipped,
made
material.
between
A program
the
dry
stands
HABIMEAN
sampling
generally
unit
based
applicable.
rence
of the
However,
relative
although
written
in BASIC
habitat
data
forms
is calculated
as the
each
unit.
case
of soil
mean
soil
values
for brightness,
Munsell
class.
This
program
because
program
data.
form,
conversion
the
of
percentage
slope
aspect
aspect
as the means
part
is
occur-
Mean
and
and hue,
form
in
be
to SI units,
is calculated.
saturation
difficulty
can
PC 1500 converts
The
is taken
not
with
less precise.
vectors
colour
does
comparisons
are transformed
stands
of the
Munsell
the
stand
which
in the
Mean
of
to
correlations
for the Sharp
v~lues
In the
soil
sampling
requires
directly,
on mean
where
kg.ha-2
to
of the
to the
of the
standardizing
for
nearest
PHYTOTAB-PC
environmental
variables.
3.2.5.2
B) Derived
Derived
data
case,
were
included
are those
derived
in
the
in extracting
will
make
this
not usually
drainage
approach
here
in the
as
maps.
to
because
show
The
the
maps.
stand
position
which,
current
type
of
is not
developments
systems
description
information
of this
information
of difficulties
relative
in this
SIDA
information
program
Some
field because
stand
The program
from geographic
obsolete.
from topographic
such
for each
package
information
recorded
determinations,
data
from topographic
included
be derived
habitat
PHYTOTAB-PC
such
nevertheless,
can
data
to
is,
that
is
in field
watershed
and
line.
89
The
co-ordinates,
the program
in degrees,
PHYTOLOC
(section
plotted
on the relevant
ference
of each
scribed
around
stand
was
stand
the
noted
minutes
3.1.6)
and
(5 mm radius
on the
centres
sheets.
from the topo
sheets
for the Sharp
PC 1500 computer:
generated
for stand centre
1:50 000 SA topo
stand
seconds
series
at 1:50
and
The
the
scale)
releve
following
were
The circumwas
number
was
for i-nput to the program
location
sheets.
000
with
then
circumfor
each
determined
SIDA written
in BASIC
a) Input
i.
Stand
or releve
ii.
Contour
interval,
interval
iii. Sheet
iv.
number.
in feet or metres,
used on the relevant
grid number,
Latitude,
topo
for reference
in degrees,
minutes
depending
series
on the contour
sheet.
purposes.
and seconds
of the stand
centre.
v.
Altitude
dent)
vi.
vii.
of the stand centre,
to the nearest
Aspect,
in degrees
being
the
point
on
from
lowest
of the stand inclination
the
contour,
stand
centre
most
adjacent
This forms the aspect
Lowest
altitude,
tour most
viii. Lowest
stand
adjacent
contour
nearest
ix.
from north
circumference.
contour
point
to
the
to
nearest
the
stand
line.
in feet or metres
of the lowest
con-
to the stand circumference.
distance,
on
(map depen-
half-contour.
direction
the
in feet or metres
the
in mm from the stand centre
lowest
contour,
most
to the
adj acent
to
the
circumference.
Highest
contour
contour
most
altitude,
adjacent
in feet or metres
to the stand
of the highest
circumference.
90
x.
Highest
contour
nearest
point
stand
xi.
the
distance
stand
highest
contour,
most
stand
along
crest
line
drainage
adj acent
contour
the
distance,
nearest
stand
is
the
This is the watershed
a crest
then
the
line
to
line.
If
line
is
watershed
opposite
the
so that the stand position
first
can be re-
lines.
in feet or metres,
of the watershed
in mm, to the nearest
drainage
situated
nearest
watershed
related
from
distance
or
half-contour,
from the stand centre along the lowest contour
to
to
line.
xiii. Drainage
the
to the
half-contour
line approximately
(see xiii)
altitude,
drainage
highest
or watershed.
lated to two drainage
xii. Watershed
the
is situate-d on
to the nearest
drainage
in mm, to the nearest
centre
the nearest
the
on
in mm from the stand centre
circumference.
Watershed
the
distance,
line
line.
This
is the drainage
in a depression
watershed
(see
xi)
then
the
approximately
so
distance
that
the
line.
drainage
opposite
stand
line to
If the
line
the
position
is
first
can
be
to two watersheds.
xiv. Drainage
altitude,
in feet or metres
of the drainage
line or
watershed.
xv.
Angle
and
horizon
western
erence
east or west. The directions
horizons
between
in
section
are
determined
contour
centre,
line
the
are
stand
3.2.5.2
by
a
A)
due
east
centre
( a)
and
west
latitude
vi) .
subroutine
altitudes
and
of the eastern
of
The
the
distances
in mm, along the directions
and
plus
the
23~o
south
horizon
program
from
of the eastern
SIDA
the
diff(as
angles
when
stand
and
91
western
horizons,
extrapolated
number
contour
input.
so
that
lines
to
The
the
be
subroutine
user
input.
displays
can
determine
Contour
lines
the
lower
stand are ignored.
xvi. Cumulative
distance,
drainage
less
altitudes
of
than the
are
line
than
to
the
the watershed
in mm, being the distance
watershed
stand- centre
or
if
then
to
from
watershed
the
altitude
highest
point
is
along
line.
b) Output
The output
is based mainly
input variables.
The following
i.
stand
or releve
ii.
grid number,
iii. altitude,
iv.
aspect,
v.
mean
number,
slope
mean
annual
the
combined
as inputi
in mm, based on a linear regression
and
viii.relative
and
watershed
and
drainage
exceeded
on a linear
and
the
stations
regression
recorded
in the
of
rainfall
study
area
20 yearsi
in degreesi
moisture,
slopes,
latitude,
latitude
rainfall
generally
available
altitude,
in mm, based
recognized
exposure,
rainfall,
between
of the
soil depthi
altitude
recordings
insolation
position
rainfall,
for officially
annual
for each stand:
in degreesi
line and measured
vii.
of the
as inputi
soil depth,
where
are printed
functions
in metresi
combined
vi.
on simple trigonometric
in mm, as a function
aspect,
insolation
position
exposure
of mean
in the landscape,
and mean
soil
depthi
92
Watershed
a)
Drainage
Drainage
FIGURE
93
line (e)
Depression
(D)
Flat (B)
Rise Knoll
line (G)
3.5. - Geomorphology
classes determined with the SIDA program with class codes in brackets. a) Landscape slope
<=2°
b) Landscape slope >2°. Slope categorization
is
restricted if the slope is less than 12x stand radius (R)
to: crestjkoppie
if slope <4R; midslope if slope <8R;
lower slope and upper slope if slope <12R.
(A)
ix.
topographic
stand
from watershed
profile,
diameter
proportionally
x.
topographic
profile
slope
xi.
stand
slope,
xii.
scale
of topographic
indicated
line with the
according
to position;
(mean), in degrees;
in degrees;
profile;
xiii. geomorphology,
in classes
xiv.
class· according
stand drainage
stand
to drainage
as listed
in Figure
3.5; and,
to the topographic
profile
and
slopes where:
A
Outflow:
B
Throughflow:
C
No flow: Run-off
D
Inflow:
> Run-in
Run-off
=
Run-off
=
Run-in
0; Run-in
0
< Run-in.
Run-off
3.3 CLASSIFICATION
Classification
in
this
sense
presence/absence
data
releves
stands)
values
(sampled
at the
quantity
intercepts
a matrix
and
rows
of columns
to
form
Background
Gauch
(1982)
defines
English
Language
(1985)
animals
and plants
because
of similarities
The
New
defines
in a series
ordering
where
species.
species
represent
The
matrix
presence
and
symbol.
as "grouping
Collins
Concise
classification
of increasingly
in structure,
of
columns
and rows indicate
classification
in clusters".
the
represent
by a cover or cover-abundance
3.3.1
together
in
refers
origin,
similar
Dictionary
entities
of the
as "the placing
specialized
etc. that
of
groups
indicate
94
common
relationships".
description
appears
tion,
of
more
scatter
as
data
hierarchically
The
procedure
proposed
identification
releves".
occur,
The
concatenation
to achieve
and
diagnostic
refers
fluenced
scale
by
ties
species
group
be
were
show
the
achieved
within
according
releve
distributions
the
study
practice,
(Westfall
be
releves.
for
however,
Programs
such
and
initial
plant
species
study
area
these
two
species
process.
a totally
releves
are not
not
groups
If a set of
speciesclassi-
often
grouped
as a first
step in
(Hill
(Westfall
classification
distri-
are
different
are
as TWINSPAN
21
at the
on a set of diagnos-
gradients
PHYTO
in-
communi-
the
to that based
environmental
e t: e I , 1982)
used
of
area,
but definite
of
sense,
are
from the non-diagnostic
concatenation,
compared
to noticeable
sequencing
also
for example,
emerge,
In
recognition
en-
species
In this
Non-diagnostic
within
(1974)
to the problem
recognition.
have limited
limits
relation-
selected
rise
after the classification
chosen,
could
species.
their
of
as a key to grouping
in which
whose
gradients
reliable
for initial
fication
can
species
therefore,
classifica-
Ellenberg
&
gives
species
in the hierarchy.
and
Clearly,
can only
20
level
influenced
clear.
tic
They,
to
definition
diagnosis
in the study area and can be used to delimit
at any
thus
those
environmental
concerned.
butions
to
and
groups
reLeve s
sequence,
non-diagnostic
second
to a
such units.
species
of
a releve
diagnostic
and
Mueller-Dornbois
of "common
suited
for a vegetation
units
within,
by
the
identification
vegetation
and variation
seems more
whereas,
to the needs
reduction,
grouped
between
tails
diagrams,
appropriate
such
ships
The first definition
but
&
these
1979),
De
Wet
PHYTO
1988)
programs
do
95
1
1 2 1 3 1 415
a
FIGURE
c
b
3.6. - communities
(numbered) sequenced according to the
main environmental
gradient responsible for their differentiation
(a); subsidiary gradients (b and c) in which
communities
are sequenced similarly but inserted after
the main environmental
gradient.
li===1=1
=2==3=1 =4=11 10 1 II
a
FIGURE
96
b
a
c
3.7. - Communities
(numbered) sequenced according to the
main environmental
gradient responsible for their differentiation
(a); subsidiary gradients (b and c) in which
communities
are sequenced similarly but inserted in the
positions to which they correspond on the main environmental gradient.
a
not meet
the
needs
ing is generally
In a matrix
This
can
responsible
be shown
by
a
further
subsidiary
of
sequenc-
These
relationships
of insertion,
than
are
are
they
are
linear.
gradients
are
diff-
at the end of the comor inserted
respective
within
the
positions
on
is preferred,
as
likely that the two rele-
of a community
more
similar
to the
illustrated
is
communities
possibility
it is often
gradient,
floristics
Should
at their
last-mentioned
However,
sequence
environmental
be placed
gradient
to the point
terms
multiple
by the main gradient
The
later.
yes adjacent
and
number
differentiation.
of the main
gradient?
releve
gradients
differentiated
the main
ted
where
by subsidiary
communities
will
problems
the
for community
erentiated
classification
required.
classification
pose
munities
for a final
to
releves
differentiaeach
to
schematically
other
be
in
in
inserted.
Figures
3.6
and 3.7.
In
sequencing
problem
which
was
releves
found,
the other
tested
in the
first
and
factorial
number
namely,
releves
first
of
the
required
is a value
sequence
of releves.
similarity,
selection
and each
releves,
number
even
how can the
are compared
to
of the
are to be compared.
total
is prohibitive
the
position
subsequent
Furthermore,
releves
according
then
of
"best"
sequence
number
to the
of
with
sequences
be determined
effectiveness
are
with
tests
magnitude
at a time and not the entire
relating
releve
is compared
r-eLeve s . The
if mirror-image
first
additional
If all the releves
releve
the
an
the
is
of
a
this
are excluded.
if only
sequence?
What
two
is
of a particular
97
The
questions
Blanquet
i.
that
arise
classification
given
that
ii.
can
iii. what
is the basis
before
v.
objective
sampling
two
regarded
matrix
data
Should
species
similarity
in
be
distribu-
(1974)?
should
workers
be
possible
hence
to
achieve
where
the
discon-
and,
process
be automated
by
repeatable,
For
the
thereby
reducing
process?
automated
objective.
and classification
classification
a
will
classification
processes
of
should not have observer
to
not
be
stratification,
bias.
applied
adopted
for classification
to obtain
formed,
to
independent
It
followed
classification
The approach
Braun-
(1956)?
in the classification
sampling
3.3.2 Methods
i.
by Werger
are obvious;
and
be
in the classified
according
can the classification
the
a
the same
classification
of the classification?
for
decision-making
make
by Poore
classification?
Subjective
to
in a data set with little or no observable
discontinuities
tinuities
approach
have exactly
particular
releves
is it possible
same
species
relationships
tion as suggested
iv.
a
as suggested
sorted
objective
and, if not, what are the alternatives?
are the community
linear,
an
are:
no two plant
distribution,
as unique
from
in this
is in accordance
and has the following
a releve
based
study,
sequence
on floristic
where
with
the needs
aims:
(a) releve-groups
similarity;
and
can be
(b) the releve98
groups
formed
thus
similarity.
This
ii.
to delimit
iii. to obtain
according
a species
Releve
matrix.
The
an
total
gaps
often,
can
outset
form
taken
would
be
into
the
of
and their
because
formation.
Judgements
of
value
can
gaps
to
of
were
can
have
on
to
and
a species
the
last
of
after
in the
sequence
considered
of
then
sequence.
and,
species.
The
more
Visual
from
the
impossible.
not
be
in
and
in two or more
Pattern
be based
before
the
often
absence
minimization
as to be virtually
judgements
the
outliers
plant
gap
gaps
first
of releve
many
of species
1982) .
gaps
omissions,
according
is the occurrence
quantified
all
of
by
so that
irrespective
so difficult
(Gauch
If
sequence
the
caused
respectively,
spacing
rei eves
between
matrix,
sampling
irregular
reduction
consideration,
same
by
and
releve
re leve s . The
total.
be the
caused
sequenced,
in the
occurrence,
affect
pattern
of the possi-
the releve-groups
blanks,
last
is, however,
Redundancy
included
relevant
not
the
sequencing
are
in the
and
can
where
assessment
a species
species
r e Leve s
the
gaps
of
are
because
sequen-
sequencing
intuitive
matrix
the same as simply
are emphasized.
on
first
floristic
gradients;
sequence
depends
the
to
and,
of a preliminarily
that
according
to similarity
The refinement
occurrence
sequenced
of subsidiary
releve-groups;
relationships
3.3.2.1
also
is not necessarily
cing the releves
ble insertions
are
be
to
adequately
taken
a combination
releves
of
on
pattern
number
of
99
species
and
the releves
Gauch
re Leve s
forming
(1982)
and
the
the pattern.
defines
pattern
"noise",
the unco-ordinated
regards
"noise"
reduced
in
(1982)
does
noise
The
last
releve
matrix,
not
sequence.
"total
to
total
each
adequacy.
separation
releve
be
possibilities
program
units".
was
are:
possibilities
small
matrix
2
000
sequences
in
to
each
a
gaps
and
million
effect
of
of included
between
all
the
first
a
given
for
the
units
and hence
This
time
B7900
x 33 species.
Even
related
to
presupposes
that
The
of
it would
The
inversely
where
number
mainframe
as
is that
position.
total
species
species
are
are
classification
n is the
years.
is
Gauch
for a single
for
minimum.
the
the
He
by addition.
possible
determine
of 25 releves
this
are
"noise"
Although
matrix,
separation
optimum
on a Burroughs
million,
A heuristic
The
in, where
written
the
in the matrix
units
tested
these
of
in
units"
total
if
in terms
absences
the included
The
that
in the matrix
and
in the matrix.
quantifiable,
species
"separation
to redundancy
classification
the
each
occurrence
enhanced.
are species
sequence
as
separation
proportional
to gaps
of
is
so
in
are subjective.
of species
pattern
as
occurrence
co-ordinated
opposites,
"noise"
species
judgements
This can be quantified
releve
referred
as
blanks
occurrence
These
occurrence
then
regard
of
as _the
pattern
included
For a given
are
and
can be attributed
blanks.
and
a
constancy
resulting
releves.
take
to
computer,
answer
excluding
was
A
test
for a
in excess
mirror-image
is impracticable.
approach,
where
not
all,
but
only
the
best
possibili100
ties
are
also
playa
the
tested,
role
initial
It was
(Hill
was
in excluding
releve
found,
1979)
sequence
the initial
change
when
sequence
The
and hence
arbitrary.
The
to obtain
same,
irrespective
re1eve
should
releve
sequence.
done
for
each
proved
releve
either
of
The
have
other
extreme
releve
the
the
This
generally
This
this
species
sequence
The
that each
is to
of any
count
in a releve.
sequence,
can
have
species
the
the
This
then
to the
can exist
called
occurring
be the
independent
according
is
would
releves
highly
is, there-
implies
aim,
of the sequence
occurring
the least generally
releve
attribute
this
altered
is often
sequence
first
some similarity
the one extreme
most
of
was
as possible.
or descending,
extremes
TWINSPAN
could be to standardize
sequence.
matrix.
sequence.
using
sequence
could
However,
final
sequence
releve
achieving
the
the
numbering,
the
of all the
although
releves.
because
in
ascending
releve.
which
initial
some sort of unique
to be unique
sequence
the
of the sampling
A method
each
intermediate
final
in which
in the matrix,
sequenced,
for
a sequence
have
affect
a manner
index
permutations.
the
aim of standardizing
fore,
occurrences
still
in as unique
sequence,
similarity
to this problem
3.3.2.1 A) Commonality
sampling
a
many possible
that
4). A solution
sequence
Clearly,
could
for example,
could
(see Chapter
adopted.
is
be
totals
in tests,
between
some
commonality
represents
releves
in common
and the
species
in common.
101
TABLE
3.6.- Illustration
of the possible permutations of species
presence and absence (rows) in three releves (columns).
Presence is indicated by a "+" and absence by a "0".
Columns
3
1 2
Rows
102
1
+
+
+
2
+
+
0
3
0
+
+
4
+
0
+
5
+
0
0
6
0
+
0
7
0
0
+
8
0
0
0
3.3.2.1
B) Similarity
A problem
of
encountered
Jaccard,
Ellenberg
with
For
1974)
example
different
two
This
Ellenberg
taken
could
is because
found
releves
and
Gleason
are compared
re Lev es
have
in
a
in three's
position
in pairs
as those
&
in isolation,
they
matrix,
an extremely
in similarity
such
(Mueller-Dombois
of which
of the weight
are considered
central
co-efficients
of the matrix
adjacent
generally
If releves
similarity
is that releves
communities
efficient.
the
with
Sorensen,
no cognizance
ciation,
sequence
form
a part.
representing
low
attached
two
similarity
to negative
coas so-
co-efficients.
(Table 3.6), then
in the matrix,
the
in terms
following
of
can
be
stated:
i.
positive
associations
exist
in the case of the first
three
rows;
ii.
a possible
resulted
fourth
positive
association
from sampling
association
not be shown because
spacings
absence
Therefore,
then
in all three
if
only
positive
occurrences),
5 and
absence
omission
or irregular
spacing,
in the
exists,
but negative
association
of sampling
omissions
in the fifth to seventh
but not a negative
3.6)
if single
row;
iii. no positive
iv.
could exist
the
first
could
positive
or irregular
rows; and,
could be considered
association,
associations
possible
6 (absences
columns
two
in the eighth
releves
exist
for
associations
be because
can
are
row.
considered
rows
exist
of sampling
a similarity
1
and
for
(Table
2.
rows
omissions),
(joint
3, 4,
and
no
103
negative
association
indicate
similarity).
Similarity
on
and
given,
releves.
in rows
7 and
for
pair
z e Lev es
weighting
positive
values
exists
possible
as
The weightings
association
possible
positive
similarity
matrix
absence
therefore,
with
cannot
could
be
no
based
negative
shown
for
two
are a~ follows:
2
association
co-efficient
1.
for
(C)
comparing
=
1/2 S + J x100
S + J
two
releves
in
a
where
S
number
of single
J
number
of joint occurrences.
3.3.2.1
C) Separation
standardization
of
according
the
is calculated.
matrix
sively,
one
tion units
repeated
position
total
with
calculations
of releves,
the
The
in the
in both releves
sampling
new
separation
all the
releve
and
units,
releves
and the
have
and
This
been
units
moved,
and
for
succes-
total- separa-
The releve
where
releves
separation
is retained
sequence.
of
is then
sequence
each move.
units
releve
sequence
the total
first
releve
after
separation
of total
after
the
to similarity,
are calculated
lowest
occurrences
unit sequence
sequencing
the
is,
associations
associations
given
(joint
is, therefore:
C%
After
of
positive
negative
Positive
The
a
8
sequence
the
results
process
with
is
in
n2-n+1
n is the total
number
moved
in this
manner.
104
The
releve
sequence
reversed
and
decrease
in total
releve
more
the
sequence
separation
til
the total
lowest
continues.
tends
represent
a
occurring
is
in which
Releve
the
releves
process
represent
plant
achieved
represent
the
a
increment
groups
is reversed
a
are
when
separation
and
where
minimum
of
are
iterated
the
iteration
and the
sequence
then
achievrelevant
should
blanks.
affect
in the releve
un-
with
have been
included
do not
new similarity
the
units
releve
releve
of the
releve
the sequence
are terminated
This
they occur,
have been
communities.
it could
releve-group
then
species
the position
of
sequence.
decrement
differentiated
last occurrence
the
optimum
be expected
be
because
of
the next
of
by different
releve
towards
a sine
curve
Furthermore,
species
distribution
then
classi-
of which
could
sequence
that the releves
situated
species.
of each species
step in the
rei eve-groups , some
If
would
releve-group,
and
sequenced
is to delimit
then
relevant
a
no
grouping
fication
been
units
with
of
processes
increase
iterations
until
exits.
units
retained.
matrix
position
and these
total
is then
The reversal
is then used to determine
in all or a single
the releves
After
the
units
successively
is achieved.
for that releve
separation
separation
is repeated
units
sequences
manner,
total
average. the
sequence
unit
sequence
3.3.2.2
to
When the lowest
in this
releve
process
separation
total
lowest
separation
than one position
and
the
entire
The last retained
ed
with
the
that
middle
effect
if
the
the
of
has
best
of
the
releve-
first
and
in the matrix .could be
105
significant,
curve,
terms
of
or approximation
obtained
ing
in
releve-group
thereof,
by the difference
first
Species
and
last
occurring
in
could
between
occurrences
all
the
delimitation.
be expected
the number
in
the
in
for
a
a
sine
from the values
of species
matrix,
ze Leve s or
Thus
represent-
each
single
releve.
releve
are
ignored.
The
number
of releve-groups
of difference
to the
be
permitted
scale
closest
of the
between
study,
is taken
into
programs
allow
be recognized
and
Thus,
flexibility
sampling
at the appropriate
so that
This
are
Species
step
the
in vegetation
process.
and redundancy
should
stratified,
stratification
process,
However,
of releve-groups
that
the
can
even if the stratifi-
according
into the
releve
classification
and their
by
increasing
with
similar
combinations
species-groups.
are not
units
relate
to scale.
sequence
Releve-
automatic-
positions.
releve-groups
species
groups. or
in the number
inserted
this will
sequencing
is achieved
whereby
on the
on the degree
of releve-groups
classification
processes
ally,
final
Clearly,
so that data can be classified,
delimiters
The
depends
of vegetation
based
in the
group
3.3.2.3
number
scale,
account
releves.
and the number
to the original
for objectivity.
cation
to be recognized
A
of
balance
or pattern.
relationships
pattern
through
distributions
releve-groups
must
is to sequence
also
Various
be
are
are
placed
achieved
combinations
emphasized.
species
according
species
sequencing
to
releve-
together
between
of outliers
in
outliers
to re106
dundancy
were tested without
of total
separation
in the matrix
tern.
or
similarly
the
Outliers
are
the
pattern
Furthermore,
improvement
does
not
each
species
are
in terms
sequence
or the
dependent
between
on minimum
included
relationships
of outliers
on
an
that
the
outliers
and
It must
is present
species
does
not
releve-groups.
can be allowed
appreciation
or
corresponds
blanks
of
This is obviously
grouping
occurren-
outliers.
between
exceed
to pattern.
and also
and that sequencing
number
releve
on tests
of
a
releve-
not
because
could contribute
The relationship
to be based
for
the
pattern
subjective,
relationships
in
but
between
as has been stated.
also
sequenced
species-group
Species-group
any
formed.
..
in the matrix,
is based
"noise"
the
two,
does
that can be effected.
affect
as
of outliers
fraction.
is
taken
number
the maximum
releve-groups,
Species
is
is
the total
grouping
matrix
no pattern
in
of species
releve
given
occurrence
to pat-
significant
occurrences
for outliers
that
considered
because
species
in a releve-group
single
can be considered
be emphasized
affect
releves
be
of
to
for that
Fibonacci
regardless
cannot
In terms
contribute
of a species
than one in a releve-group
limit
occurrences
of releve-groups
limited
that
total
ces of more
with
of
species
also
provided
38%
occurrence
or combination
38% of the
The
or groups
single
as they cannot
releve-groups
minimum
releve-group
group,
of
group
Therefore,
a
a single
ratios being evident.
however,
are not significant
combination
for
units,
definite
order
in descending
in the diagnostic
is according
species
to presence
order
of
portion
occurrence
in
of the matrix.
in:
107
Releve-groups
3
1
2
Species-groups
1
[!]
I
3
4
5
6
8
I
6
7
1[2]
8
8
10
11
108
.
5
9
FIGURE
5
[!]
2
3
4
9
10
I
I
I
I
11
3.8. - Simplified schematic diagram showing the sequence
according to the releve
species-groups
construction,
group sequence.
of
i.
first
releve-group;
ii.
second
iii.
first
and second
iv.
third
releve-group;
v.
second
and third
vi.
first,
second
releve-group;
illustrated
Species-group
releve-groups;
releve-groups;
and third
in Figure
delimiters
and,
releve-groups
and so forth as
3.8.
are
then
inserted
into
the
species
sequence.
Species-groups
determine
combine
with
whether
such
species
a
single
or not this
species-groups.
sequencing
can
consisting
spond
limits
C). This
the
simplifies
can
occur.
is significant
The
be
species-groups,
with
species
flexibility
carried
even
of two or more
of environmental
the matrix,
in terms
The
user
and either
that
should
retain
is possible
further
or
with
by
combining
releve-groups,
to corre-
gradients
(section
of species-groups,
3.5.2.1
without
loss of information.
3.4 VERIFICATION
A classification
more
than
testing
3.4.1
one
should
solution
procedure
be tested
to ensure
to a classification
can be referred
its validity,
could
because
be possible.
to as a verification
This
process.
Background
109
Werger
(1974)
stresses
between
the
pattern
habitat
conditions".
on
relationships,
confirm
a
any
to
factors
with
between
community
fication
of
adequacy
of
necessarily
not
a
The
floristic
it
is
accepted
produce
environmental
total
but
variable.
separation
possible
each
is
rating
units
that
correspon-
Correspondence
but
position.
to jointly verify
that
not necessarily
units
the
also
distribution
or other
a classification,
separation
can
it is feasible
some
is, therefore,
of
every
it, then
and
a
can
support
rating
obtained
because
other
veri-
the
means
a
of
the
is
not
releves
should,
a classification.
sections
describe
some methods
in which
a classifi-
can be verified.
3.4.2.1
Classification
A measure
of
ship between
rix and the
which
into
specific
applied
following
cation
and
"co-incidence"
if
limit
could
total
be sought
3.4.2 Methods
may
classification
in
"coincidence
table has its own particular
validity
the minimum
of
table
insight
of
However,
other
The
tested
therefore,
degree
and habitat
the
classification.
are
the
which
or
confirmation
phytosociological
of releves
one
for
from gaining
classification.
combination
dence
the
in the classified
according
need
Apart
habitat
species
the
the
efficiency
efficiency
all the blanks
total
separation
classifications
of a classification
(not just included
units.
This
can be compared,
is the
blanks)
is a relative
unlike
the total
relation-
in the matvalue,
with
separation
110
units,
which
absolute
is an
(E) is calculated
value.
TSU = total
A matrix
have
with
a
ciency
values
indicate
step
is
groups.
to
releve-groups
formed
indicate
A
the
quantified
the other
the
spatial
exhibit
where
therefore,
appears
that,
classification
lower
than
process
A
the
in
effi-
this
could
or data.
low
an overlay
is
of
the
high
grouped
the releves
degree
that
number
according
grouped
of
to
spatial
can
be
could
or data.
relationships
classification
two
In general
integrity
process
The degree
releve-
mosaics
spatial
spatial
adequacy.
the
between
technique.
such
degree
in the
and
of a classifica-
relationships
scale
comparing
releves
representing
adequacy
in the classification
process
by
the
a relatively
the
of classification
representing
have
in the matrix.
would,
It
markedly
in verifying
correspondence
stratification
be
matrices
can be done by using
shortcomings
indicative
can
suggested
should
of
100%.
in the classification
releve-groups.
measure
efficiency
relationships
except
by
classified
of
60%. Values
determine
This
integrity,
efficiency
of above
Spatial
next
tion
and correctly
classified
shortcomings
3.4.2.2
The
no noise
adequately
x 100)
AG
units and AG = all gaps
separation
classification
tests,
classification
as follows:
E%=100-(TSU
where
The
process
between
can
be
of correspondence
sequences;
the
according
the
one
stratification;
to the classi-
111
a)
Habitat
1 Altitude
(m1O)
2 Soil depth
ranges
40-45
35-42
25-42
31-471
55-68
53-70
52-65
60-83
(cm) 10-14
11-13
15-20
16-181 10-13
12-16
17-18
17-19
Sand
Clay
Clay
sa:d I
Clay
3 Soil texture
4
variable
Y
Sand
Communities:
I C1:
1
3
Sand
4
5
6
8
b)
Habitat
1 Altitude
(m1O)
3 Soil texture
4
ranges
15-20
11-13
16-18
10-13
17-18
12-16
17-19
40-45
25-42
35-42
31-471
55-68
52-65
53-70
60-83
Sand
Sand
Clay
Clay
Sand
Sand
Clay
Clay
(cm) 10-14
2 Soil depth
variable
Communities:
1
3
2
4
5
7
6
c)
Soil depth
10-13
FIGURE
112
10-14
listed
11-13
according
12-16
15-20
to ascending
16-18
range
17-18
17-19
3.9. - Hypothetical
model illustrating the sequence of three.
environmental
factors, namely, altitude, soil depth and
soil texture to hierarchically
differentiate eight communities (a). If soil depth is first in the sequence (b)
then differentiation
of only four groups of two communities each, occurs. This is because in (b) the ranges
overlap so that no groups can be formed, as can be seen
in (c), whereas, the division of soil depth by altitude
in (a) creates non-intersecting
sub-groups of soil depth.
8
ication.
of
A
program
grouped
number
sequences
degree
to which
package.
The
intersect
the
indicates
stratification
with
the
the
two
3.4.2.3
an
Floristic
understanding
of
factors
which
derived.
process
factors
correspondence
ordination
CANOCO
between
techniques
(Ter Braak
selected
scattergram
to assess
cess
The
environment
available,
the
method
is limited
tested
floristics
tested
between
whereas,
the
relationships
can
also
lead
to
differentiation.
to those
habitat
that have been recorded
cause
problems
relationships
and habitat
in
between
can be assessed
such as DECORANA
However,
the former
variables
correspondence,
is based
is
the
the
then be incomplete.
programs
floristics
but
community
could
would
so that the classification
following
of
environmental
and
sequences
correspondence
process
process
1987).
which
and habitat
such as those
using
PHYTOTAB-PC
Furthermore,
hypothesis,
of floristic
causes
as
sets
relationships
and floristics
overlaying
bines
not
of
two
for mapping.
classification
Factors
the
in both
hypothesis
the
the basis
are available,
in
correspondence.
degree
inval idate
the
between
releve-groups
of
low
correspondence
correspondence
habitat
the
and habitat
the
included
a basic
A
can an examination
the
is
form
can
in verifying
correspondence
amount
can also provide
Obviously,
and
the
can
processes
Not only
aid
determine
classification.
converse
or
to
and the
in a single
is not based
on a simple
(Hill 1989b)
program
over
with
the
latter
requires
floristic
program
classification
on floristics
hierarchical
compro-
alone.
model
which
113
associated
step
correspondence
direct
tests
habitat
factors
is to construct
hypothetical
factors,
model
because
available
is merely
where
communities
has changed.
to test
sity
to
model
could
construct
the
particular.
mean
annual
are
because
in
programmatically,
factors
should
example,
Figure
the
The
Such
in
3. 9b
sequence
limitations
hence
the
neces-
a
hypothetical
of relevancy
and economy,
to record
usually
soil
3.9a.
is necessary
because
model.
first
of habitat
of permutation
in terms
The
the
in the
field.
The
be from the general
texture
should
not
to
precede
rainfall.
the
with
for
the sequence
illustrated
hypothetical
factors
basis.
hierarchical,
is
and
as in Figure
differentiated
habitat
For
one
class
sYmbol.
other
sets
model,
for
each
or
If
more
all
then
correspondence
first,
the
determined
ranges
the
which
from
determined
are
sequences
of habitat
starting
This
also be of benefit,
in determining
sequence
being
It is not possible
all possible
model,
to determine
the sequence,
correspondence.
releve-groups
on a releve
a hypothetical
determining
fewer
between
a
for
the
general,
ranges
the
range
sets
class
habitat
of
releve-group.
releve-groups
single
that
most
that
Each
is
habitat
unique
allocated
have
sYmbol
factor,
a
factor
set
habitat
factor
and
is then
tested
in the
allocated
the
of
different
interceptions
is
as
with
and
no
rei eve-group
is
implied.
The
next
habitat
separately
previous
within
habitat
factor
each
factor.
of
the
unique
The process
sets,
same
manner,
determined
is repeated
with
but
the
for all the
114
habitat
at
factors.
various
levels
differentiation
conditions
Individual
in
of
the
releve-groups
habitat
releve-groups
can
factor
is
be
differentiated
hierarchy.
achieved
then
If
a
poor
the
following
factor do not correspond
precisely
could apply:
i.
the classification
is inadequate;
ii.
the habitat
selection
iii.
the model
iv.
the limits
with
factor
is inadequate;
releves,
values
or,
for a habitat
the limits
of a releve-group.
representing
which
is inadequate;
two
In other words,
communities
do not correspond
with
have
adjacent
habitat
the border
factor
between
the
two communities.
In
the
using
case
of
either
releve-group
3.4.2.4
The
classified
step
condition
releves
or
in
and field
the
releve-groups
This
of the
plant
the
process
omitting
can
be
releves
repeated
adjacent
to
relationships
verification
with
the
step is not to confirm
species
in the
process
units
to
is
be
comparing
mapped,
the occurrence,
relevant
on
the
the
on the ground,
releve-groups,
which
should
be the case, but the following:
to assess
the degree
ant rei eve-group
ii.
last
delimiters.
ground.
i.
nodal
Classification
final
clearly
the
to assess
levant
to which
the plant
are representative
the variation
rei eve-group
represented
and the degree
species
of the relev-
of the unit to be mapped;
by the releves
to which
this
of the recorresponds
115
with
the variation
of the unit to be mapped,
at the relevant
scale;
iii. to assess
plant
the value of plant
community
community
diagnosis
iv.
habitat
the reliability
mapped;
the validity
evant
the
60%
or
sponds
which
can be
floristic
and
of the units
to be
of the releve-groups
are mappable
at the rel-
scale.
more,
invalidate
well
does
with
stratification,
not
the
processes
habitat
tion,
but
does
rather
the
a
classification
habitat
satisfactory.
these
derived
so that the community
of the borders
has
relevancy
size and regular-
of the hypothesized
classification
be deemed
spacing,
the plant
and,
to assess
If
of identifying
for
relationships;
to assess
vi.
is important
can be used
recognized;
to assess
v.
which
as a means
in the field. Here plant
ity of occurrence
easily
species
the
data
However,
not
concerned
of the classification
field
of
and
and classification
invalidate
could
will ultimately
be
about
corre-
assessment,
lack of correspondence
necessarily
process
stratification,
and the
correspondence
efficiency
the
then
can all
in any of
classifica-
questioned.
The
depend
on the uses
information
that can be
therefrom.
3.5 DERIVATIVES
The term derivative
derived
directly
is taken
(primary
here, to mean
derivatives)
or
indirectly
(secondary
116
from
derivatives)
as
the
locality
mappable
maps,
the
of
factors
vegetation
cess,
which
can
being
derivatives
3.5.1
Background
be
of such
interpret
tion.
and
Those
can
uses
be
and a map
as much
demand
information
able
to interpret
be the
from
matrix
be visually
from
units,
as
pro-
rather
than
relationships
will
to workers
who
are
able
the
classifica-
information
thereof.
in this
field
from classifications,
a
that
determine
for such work.
to
from
Information
largely
and hence the need
as possible
The classified
the
It is,
to derive
so that
the
of coded variables
for
can be increased.
can include
factor,
habitat
compilers
importance
applied
workers
and derive
a classification
for such work
of
with
of a study then the value
additional
information
fication
spatial
derive
of great
habitat
illustrating
to those
3.5.2 Methods
each
vegetation
classification,
be limited
for classifications
therefore,
with
of the stratification
are the end-products
should
derived
co-incidence
can be illustrated
will
best
classification
the
such
of the classification.
units
a study
by
relationships
their
maps. Mapped
are the product
modified
If a classification
of vegetation
or
or soils
or integrated
in this study,
Spatial
units
such as geology
map overlays
proposed
classification.
above
each releve,
variables,
correlated
a summary
(Deall
to form a passive
&
Westfall
1989)
classi-
which
can
with the releve-groups.
117
There
is usually
formation
much
contained
ucting
a synoptic
duced
to a single
redundancy
therein
matrix
A five class
Ellenberg
1974).
tance
of single
i.
This
according
scale
tends
a "+" sYmbol
classified
is generally
which
used
habitat
data
dent on the classification
and
based
are re-
on
species
&
(Mueller-Dombois
can be overcome
the
impor-
by using:
or,
in section
3.2.3.
matrix,
although
synoptic
in-
by constr-
over-emphasize
scale described
the
releve-groups
for single occurrences;
number
and
reduced
to constancy
to visually
occurrences
ii. the plant
The
can be considerably
(Werger 1974) where
column
presence.
in a classification
are not considered
derivatives,
depenin this
study.
3.5.2.1
Primary
The primary
derivatives
derivatives
that
follow,
can be derived
from the
syn-
optic matrix.
3.5.2.1
A) Plant
Braun-Blanquet
terized
theses
less
by
of
(1928)
"its
Terms
species
ional
species
1974).
Clearly,
cular
own
matrices
clear.
character
communities
study
were
species".
took
such
as
place
as
well
its
a plant
As
and
more
community
data
community
exclusive,
a plant
to
clarify
community
composition,
as being
were
and
situation
is restricted
diagnosis
and
synbecame
preferential
territorial
the
charac-
gathered,
distinctions
selective
as differential,
introduced
unless
area,
defined
and
reg-
(Werger
to a parti-
description
for
118
the study
area,
ity of which
could
differ
it is a part.
considerably
This
community
variation
present
not known
where
community
area.
The
following
areas
where
i.
a rei eve-group
the
plant
A
is
community
area,
because
the species
formal
that
of
is only
which
is usually
included
in a study
for individual
areas
included
only where
in
the
adequately
partially
study
is involved:
the
study
area
represent
included
of the variation
plant
the
of plant
that
a
in a study
represented
by
in the
study
is
the
area,
and
species;
should
but only with
insufficient
to the
community,
diagnostic
communities
study areas,
is restricted
plant
community,
to as community
is usually
of the
is not known;
that
ranking
area,
community
cannot
representing
for individual
there
completely
of the species-group
are referred
Hi.
study
a plant
the proportion
releve-group
diagnosis
study
suggested
releve-group
plant
to the proportion
is not entirely
with other
the releve-group,
ii.
in the
represents
community
concerned.
relates
is, therefore,
no synthesis
from that of the commun-
information
not be applied
syntheses,
because
to elucidate
rela-
tionships;
iv.
v.
informal
ranking
of plant
based
on combined
apply
for the combined
releve-groups
resent
munity,
tent
releve-groups,
without
ecotones,
communities
where
a study
conditions
area
is
i. and ii.
group;
community
extremes
or communities
within
diagnostic
in variation
for which
species
for a particular
the particular
in the study area is inappropriate;
can rep-
scale
comor ex-
or,
119
-
a)
Releves
community
diagnostic
species
+
+
+
+
+
+
+
+
+ + + +
+ + + +
+ + +
+
+ +
c)
Releves
Community
diagnostic
species
+
+
+
+
FIGURE
120
+
+
+
+
+
+
+
b)
Releves
Community
diagnostic
species
+ + + +
+ + +
+ +
+
+
+ + + + + + +
+ + + + +
+ + +
+
Releves
d)
community
diagnostic
species
+ + + +
+ + +
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
3.10. - Species presence in community diagnostic speciesgroups: a) few blanks indicating a well-defined community
corresponding
to an abrupt environmental change at the
community limits; b) species presence approximates a
normal curve, indicating a well-defined community but
with a less abrupt change at the community limits than in
(a); c) a variation of (b) where the releves have been re
-arranged so that the strongest community expression, in
terms of species presence, is at the left of the group,
decreasing towards the right. This pattern does not adequately show spatial relationships because the releves on
the extreme right can often be adjacent to different
communities;
and d) a poorly-defined
community.
vi.
plant
community
species,
composition
both
diagnostic
the
releve-group/s
is,
however,
is represented
and
non-diagnostic,
representing
only
a
sample
by all the
the
of
plant
the
present
community.
species
present
in
This
in
the
community.
This
information
as well
as plant
directly
from the synoptic
3.5.2.1.
B) Community
Community
degree
is according
unity
Figure
3.l0a,
differentiated
limits
pattern
co~munity
to minimum
diagnostic
can be obtained
matrix.
species
the
names
definition
diagnostic
to which
community
total
species
should
indicate
and can, therefore,
be
is defined,
separation
with
by an abrupt
can
no or
used
if the
units.
with
releve
as
a well-defined
be mapped
infer
comm-
illustrated
community
change
the
sequence
For example,
few gaps,
environmental
to
which
in
is
at the
community
a high degree
of preci-
sion.
A
community
where
approximates
abrupt
a normal
is
according
to
skewness,
the
in the
re-arranged
without
width
central
within
units
diagnostic
(Figure
at
3.10b)
the
well-defined.
r e Leve s
affecting
separation
change
still
the
community
curve
environmental
community
those
the
of
best
part
the
community
ecotone.
expressing
then
increase
Depending
a less
but
on
in Figure
the
the
original
be
sample
composition
However,
the
will
The releves
as illustrated
because
indicate
limits
community
relationships.
pattern
precision
of the releve-group.
community
will
should
Mapping
the releve-group
species
are
can be
3.10c
total
releve
121
Releve-groups
1
2
3
4
5
6
7
8
9
species-groups
FIGURE
3.11. - Species-groups
representing two or more communities, sequenced to correspond to an environm~ntal
gradient of which both the upper and lower limits for differentiated communities can be ascertained. This also
applies to the horizontal mirror-image of the illustrated pattern.
Releve-groups
1
2
3
4
5
6
7
8
9
Species-groups
FIGURE
122
3.12. - Species-groups
representing two or more communities, sequenced to correspond to an environmental
gradient of which only the upper or lower limits for differentiated communities can be ascertained. This also
applies to the horizontal mirror-image of the illustrated pattern.
sequence
A
is based
community
irregular
on minimum
diagnostic
spacing
to any of the
community;
caused
sample
Mapping
precision
3.5.2.1
C)
be
environmental
a)
a
(Figure
3.11),
of
should
be
(section
simplification
releves
matrix
the
limits;
over the
would be low.
total
rather
both
those
evident
after
patterns
and
lower
with either
3.12).
illustrated
in
overlapping
releve-groups,
upper
(Figure
grouping
Two main
or b) a gradient
only
by
two or more
with
correlation
of
can emerge,
environmental
upper
Both these
Figures
to corre-
or lower
types
3.11
can
3.12.
&
communities
be
The
with
3.4.2.3).
of
the
species-groups,
separation
or releve-groups,
but
can be attributed
disturbance
simplified
gradients.
gradient
mirror-images
affect
and
size.
representing
limits
The
blanks
over the community
by widespread
further
environmental
habitat
change
of such a community
can
species-groups,
limits
3.10d)
many
Gradients
matrix
limits
(Figure
with
or,
iv. inadequate
namely,
pattern
units.
community;
environmental
iii. heterogeneity
with
species
of occurrences
defined
ii. a gradual
spond
separation
following:
i. a poorly
The
total
units
so that
additional
or
in this
the
will
relationships
information
pattern
way,
becomes
not
between
is not lost from the
evident.
The
matrix
123
1
species-groups
2
3
4
5
6
7
1
2
3
a
4
5
6
7
8
9
10
11
b
12
13
14
15
16
FIGURE
124
_____
I}c
3.13. - Schematic illustration of the simplification of a
phytosociological
table into 3 sections: a) community
diagnostic species-groups;
b) species-groups corresponding to environmental
gradients. Horizontal
mirror-images
of the illustrated pattern also apply;
and c) non-diagnostic
species.
will
now
unity
consist
diagnostic
environmental
3.5.2.2
The
of
gradients
secondary
data
parts,
(Figure
species-groups;
Secondary
recorded
three
b)
3.13)
namely,
species-groups
and c) non-diagnostic·
a)
comm-
indicating
species.
derivatives
derivatives
but
are
those
are dependent
results
on the
computed
classification
from
for
the
group-
ing.
3.5.2.2
The
A)
Structure
general
height
correspondence
(section
ture,
according
within
to
each growth
to be made.
into
3.2.4),
This
grasses
recorded
as total
3.5.2.2
B)
A plant
species
dual plant
permit
total
which
canopy
growth
forms
of vegetation
cover
differentiation
which
is not
cover per height
Community
recorded
for
all
and
strucspecies
(after Ito 1979) for each community,
allows
forbs
the
layer diagrams
recorded
form class
also
and
between
composition
utilizes
possible
of the
where
ground
layer
structure
is
class.
analysis
a large
resource-space
per
indivi-
is likely to:
i. have a correspondingly
high crown cover;
ii. have a high resource-space
requirement,in
the mature
phase;
and,
iii. have a high cover
in relation
to its frequency.
125
The
converse
with
a
low
requires
plant
could
cover
a
in
be
valid,
relation
correspondingly
in the
mature
cover-to-frequency
with trees
also
namely,
to
its
small
phase.
as the disparity
a plant
frequency
of
resource-space
It is obvious
ratios, grasses
that
that
species
occurrence,
per
individual
in comparisons
of
for example, cannot be compared
in resource-space
requirements
is too
great in the mature phase. These statements
are generalizations
as
individual
in their
to
external
plants
factors.
dynamics
section
within
the
elucidation
and
in
each
between
for the
regressions
decreasing
to
the
plant
growth
Cover-to-frequency
calculated
relation
a defined
according
3.2.4.
frequencies
cover
However,
considerably
of
reactions
intra-community
not that
plants.
species
categorized
then
differ
in this study is dependent on species reactions,
of individual
Plant
can
a
linear
growth
cover
actual
and
ratios
class
frequency
classes
for
each
for
is
is
are,
therefore,
defined
species,
the
assumed.
The
A
in
are
cover
determined.
of each species,
is also calculated.
order
form
regression
form
frequency
community
and
linear
expected
according
to the
The
species
are then arranged
of the differences
between
actual
in
and predicted
cover.
Species
outside
frequency
with
a
standard
strong
the standard error of the mean,
regressions,
higher
error
form
cover,
and
of
mean.
competitor
the
species
two
those
The
distinct
with
first
because
a
groups,
lower
group
of
for the cover-tonamely,
cover
than
is referred
their
those
to
the
as
resource-space
126
requirements
and
weak
competitor
range
between
referred
a
their
these
can
to the
of
3.5.2.2
After
at
groups
a third
15
obtained
for
analysis.
For
used
only
and
differences
at
Thus,
to
as
within
a
which
can be
species
within
classes
(i.e.
form classes)
which
and the effect
of
requirements.
calculations
is the determina-
growth
classes,
of the
cover within
form
each growth
within
a
form class.
analysis
which
the
community
practical
in
group
in the community
composition
the
fall
different
analysis,
it is then
the cover of any stand of vegetation
scale
referred
species
range.
into
programmatic
C) Stand phase
the
are
in each of five growth
proportions,
community
most
competition
present
of these
group
forming
categorized
and the total
the
compare
be
second
Generally
resource-space
cover
community
the
two groups
species
differing
An extension
tion
species.
competitor
relate
in
to as the normal
community
three
those
community
with
purposes
cover
differences
cover
between
was
the
the
stand
with
community
into
and
that
composition
frequency
account.
the
to
the community,
sampled,
community
taken
the
within
possible
can
Thus
community
be
the
are
emphasized.
Five
distinct
community,
Phase
phases,
in
can be identified,
0: all the species
i.e.
there
the
comparison
between
stand
and
namely:
fall into the normal
are no strong
or weak
competition
competitor
range.
classes
and,
127
therefore,
Phase
1: weak
no class
competitors
indicating
Phase
2: strong
3: weak
The
competitors
competitors
class
could
of
classes
these
strong
Phase
4: Strong
These
the
main
stand
stages.
indicate
the
growth
form class,
form classes.
biome
or vegetation
a progression
to trees
juvenile
through
is possible.
stage
are
Some
potential
and
occur
in the woody
growth
as
in Phase
3,
A
tendency
towards
form
could
be
biome
or
unidominance
could exist.
the
stage
to the community
thus
Reversal
southern
3.5.2.2
phases
is
in
shrubs
classes,
moribundity
growth
on the
of savanna
dependent.
relative
Dynamics
case
competitors;
formation
in the grass
in the woody
from dwarf
The
form class,
utilization;
be dependent
competitors
growth
grass utilization;
occur
species
classes.
and/or
occur
In the
or disadvantaged;
in the grass
selective
excessive
formation.
the
occur
possible
indicating
Phase
is advantaged
inferred,
of vegetation
of which
assuming
of the sequence
development
the stand
progression
is probably
of
is a part.
through
the
a rare occurrence
in
Africa.
D) community
The
ideal
vegetation
the
type
of
composition
the
ideal
the
type
composition
utilization
ideally
for goats.
of
cover assessment
animal
suited
for utilization
applied.
to
For
example,
cattle
grazing
It would
appear
far more
utilization
and
degree
is dependent
the
is not
on
vegetation
necessarily
efficient
of utilization
to
adapt
to
the
128
specific
vegetation
resource
than to try to manage
the resource
to
suit the form of utilization.
condition
Vegetation
perspectives,
namely,
utilization
and
condition.
ecological
condition
vegetation
to
with
Tree
should
cover
analysis,
and
relationship
and error
the
mean
between
c
tree
This
is
merely
required
ground
adequacy
relate
particular
and
as
accordingly,
an
of
resilience
or
(section
1.3)
adequate
slope
two
type
to the ability
implies
penetration
from
of the
ground
increases,
describes
for
an elementary
adequacy.
in
community
slope
tree
and
cover
the
are
slope
community
used
was
as
taken
composition
input.
The
after
trial
minimum
tree
to be :
where
level
a
justification
This
determined
c
cover
of the
soil.
root
assessed
production
tree cover
values,
for
primarily
following,
for assessing
be
suitability
In terms
increasing
The
therefore,
sustained
protect
soil binding.
method
a)
b)
ecological
cover
can,
cover
a
for
= tan s x 100
(%) and s
provisional
root
soil
indication
binding
to 100% on a 45° slope.
of ground
of
which
the
varies
No assessment
from
is made
0%
on
of the
cover.
3.6 PHYTOTAB-PC
PHYTOTAB-PC
is a program
package
to
facilitate
the
methods
des129
cribed
in this
logical
data.
files
written
AT
286
or
5~"
in TURBO
drive,
C:\PT
b) sub-directory
Classification
tory,
Menu
a
by
items
single
menu
"PT".
according
items
are
can
be
executed
the
program
of BASIC,
disk
and
The
for an
a 360 KB,
package
is
namely,
and processing,
for a floristic
from the C:\PT
and
data bank.
main
menu
by entering
preceding
the
to the numeric
used
package
necessary.
are
file,
selection
the
symbol,
item.
then
which
invoked.
is bound
by
sequence
is
items. Alphabetic
functions
described,
from any direc-
is
and utility
The
or if ";C:\PT"
Application
main menu
for documentation
when
directory,
of the AUTOEXEC.BAT
The
are executed
bracket,
normally
and text
and processing
in the path
typing
version
configuration.
for classification
of phytosocio-
of 147 program
20 MB hard
both menu driven,
are executed
is included
a compiled
minimum
C:\PT\PD
handling
consists
640 KB RAM,
into two parts,
The programs
package
BASIC,
with
disk
a) directory
3.6.1
and also facilitate
The program
IBM-PC
floppy
divided
chapter
according
programs
main
which
and procedures
to
the
menus,
for
as
follows:
D) Documentation
This
details
initiation
on-line
as well
Data
initiates
format:
of
directories,
as program
program
usage
transfer
by displaying
and
program
or printing
the
manual.
1) Matrix
This
setup
releve
Input
keyboard
input
of
the
matrix
(species;
cover-abundance)
in either
or TABIN
format:
PHYTOTAB
species
130
releve;
cover-abundance).
continuation
or eight
of
input.
character
Provision
Species
is made
for a new data
are abbreviated
set or
to an eight
digit
code.
2) File Handling
This
option
view
and edit
files
can be used if PC-WRITE
sequential
to diskette
files,
Matrix
to QMAT
QMAT
random
access
process
done.
must
Outputs
codename
file
file
files
and
be
not
(if PHYTOTAB
be
transformed
species
sequence
split.
before
Eleven
option,
time,
000.
another
first
Matrix
is
the
This
are
It should
to
TABIN
and QMAT
file.
be
that
noted
computer
TABIN
need
not,
releve
2 cannot
Corformat
the
allocated
The
releve
numbers
therefore,
data
occur
should
in part
number.
is
in two
used
limitation
product
in the order
code numbers,
or
numbers
be
file,
of computation.
1 to n. However,
releve
can
sequence
PHYTOTAB
re Lev e number
This
Sequencing
option
option
the
Releve
from
available
size
for speed
contain
example,
and species
the
releves.
186
For
options
menu,
files
processing
input)
the
processing.
species
computer
either
again.
or range
and after
4) Releve
to
further
is the
file
and releves.
in sequence
be
format
further
file,
and releve
access
done
and
species
is resident,
as to back up and restore
for
any
sequence
for species
should
for the
before
releve
is a random
rections
file used
completed
are:
file
format
last
be
as well
3.00)
or, for· file transfer.
3) Transform
is the
(Version
of
can
of hours
for
On the
programmatically
for releve
species
also
submenus.
and
utilize
using
greater
considerable
to days, depending
sub-
sequencing
sequencing,
reLev es not
first
on matrix
this
than
processing
size,
131
noise
The
(see section
second
releve
the
previous
third
and
option,
sequence
to group
for
where
accordingly
full
inserting
characters
ed using
or
this
option
user
sequencing
_option
is
(see sections
and
the
downloading.
numbers
in
and releve-group
of subsequent
a
checking
or
to
option
is
Separation
can be insert-
Deleting
by one.
a number
It is important
that the numbers
on the releve
file are computer
which
be
the
numbers,
then
converted
On the
of
converted
a
second
submenu
or
from
species
1 to
sequence),
delimiters;
and a random
fifth
and
conversions
the previous
mutation
sixth
and
reversal
which
four options
and
an
is not
sequence
versa.
of
are
These
an
existing
generation
of
a number
affected
options
sequence
by the
number
must
releve
(for
presence
a
of
replicates.
to-sequence
be used
The final option
the optimum
entry
to
existing
sequence
and
submenu.
keyboard
from 1 to n, without
are executed.
to obtain
permit
codes
processed
the second
corrections
be used),
options
vice
of releves
sequence,
can
ni
mirror-image
The
invokes
the first four options
(or an editor
sequence
original
back. The fifth option
releve
sequence
to
The
be
to remember
must
used
can
fourth
delimiters
numbers
also
file
sequence.
can be used.
or using
& 3.3.3).
codename
The
the
alphabetically
abbreviation
or an editor
the value
3.3.2
abbreviations
facilitate
deleting
after
by the
species
when
is executed
either
numerically
names
for species
decreases
the
to
species
species
required
numbers
species,
determined,
The
sequences
releve
updated
match
has been
releves
and processor.
for sequencing
option.
option
the
3.3.1).
code
if any
of
is a per-
sequence,
exclud132
ing mirror
option
images,
should
extremely
on minimum
not be used
total
separation
for more than about
units.
20 releves,
This
as it is
time-consuming.
5) Peripheral
This
based
option
data
input
invokes
ity names,
table
classif ied
codes.
a submenu
title,
for
community
codes
and midpoint
of environmental
habitat
dat.a
Other
codes
for input
data descriptions
which
composition
values
of species
can
be
analysis
used in the matrix
variables
which
commun-
and habitat
data
are
form
input
and
names,
check
growth
lists;
cover
and the actual
can be either
descriptive
values
or num-
erical.
6) Print
options
files
1 to 7 are used to print
checking
or
sequence
of species
frequency
reference
as
alphabetic
species
7) Print
option
full
species
occurrences
for each
names
option
and other
optionally,
This
option
growth
created,
an
alphabetic
forms,
in PC-WRITE
can
also
be
for
species
format
used
if
for
an
is required.
Table
printing
have
separation
total
classification
Final
8 prepares
units per species,
listing
names
and
Option
with,
checklist.
permits
relev8;
8) Print
This
a
Working
This
centage
purposes.
and separation
annotating
files that have been
of a-matrix
been
added.
units
before
The
for each
habitat
output
species;
separation
units
efficiency
(see section
data
includes
total
for all species;
and
total
species
and per-
3.4.1).
Table
permits
peripheral
printing
of a matrix
with
full
species
names
data.
133
9) Process
This
Data
option
invokes
Synoptic
1.
be
table
according
with
number
4. Community
cover
5. Community
and habitat
indicated
u) utility
do
first
three
as
not
which
can
class
"+"
a
3.5).
can be applied
or
scale
the
All
33
options
here.
includes
community
& 3.5.2.2).
(see section
3.5.2.3).
(see section
correlation
3.5.2.4)
with relationships
of a dendrogram
part
options
errors
species
names
being
internal
form
format
(see section
(see section
that
fourth
of
can
the
3.4.3).
3.2.3.7).
invoked
occur
after
the
main
External
program
submenu
are
downloading
menu,
utility
are
pro-
package.
for
The
correcting
matrix
or data bank or to change
data
or
the para-
name file.
(see
for key or other
purposes.
The
converts
option
PHYTOTAB-PC
is used to generate
keys
from
programs.
the
from a mainframe
option
identification
executable
utility
with
for the species
fifth
five
with
section
3.5.2.1
relationships
programs,
to
grams
meters
cover
a
values
programs
utility
referred
(see
assessment
by means
scale;
analysis
analysis
the matrix
indicated
(see sections
phase
6. Species
class
scale
composition
structure
The
five
to the full matrix
2. Community
3. Stand
a
in which
occurrences
plant
available
for the following:
production
to
single
class
The
a submenu
section
species
3.2.2.8)
the matrix
and
sequences
to
split
for plant
matrices
data to the CONDENSED
format
134
for the Cornell
1979b)
or
required
to
the
programs
CANOCO
for
the
(Ter Braak
CANOCO
necessary
available
are those
sixth
option
paired
or
means,
range,
Graphics.
values
The
using
The ninth
output
can
other
used
options
& Istock
include
as well
as
can also be sorted
according
1987)
applications
applications
be
be
et al. 1982).
statistical
variance
data
format
(Scheiner
(Hill
as
input
various
linear
for
according
on
re-
Harvard
to x or y
option
fixed
raster
mapping
according
to
large sequential
files,
such as
for raster mapping.
is used
for inputting
information
point
system.
co-ordinates
This
is
for the
required
for
localities.
option
This
for
is used to merge
geographic
releve
tenth
allows
the printer.
option
ARC/INFO-PC
scale.
The
habitat
loaded,
(Westfall
basic
deviation,
be
editor.
format
for
Should
can
Analysis
variables.
variables
option
could be required
The
an
and DECORANA
ascending.
The eighth
mapping
used
Optional
seventh
grids,
is
1987).
these
using
mainframe
standard
Paired
1986,
for Affinity
unpaired
gressions.
(Hill 1979a)
programs
format,
and for the PHYTOTAB
The
TWINSPAN
does
(C)
not
converts
improve
cover
codes
the precision
to
the
of the
plant
original
number
esti-
mates.
The eleventh
option
(L) is used for generating
and locating
random
135
TABLE 3.7.
- A model of the
primary
options
with
keystroke
sequences
IIHaincutplt
PHYTOTAB-PCprogram package,
square brackets
indicating
II
A. Instructions
on-Line rranual
=11~tion
[D]II
B. Classification
Data i.npJt
Print data files
:l:trh data
=> Cover axles [5-4]
Matrix data [1]
[
~re
<r
fil~
Edit files
i:1Matrhdata
1<
[6-11
[2-HI
leve sequence [6-2-2] ~
Releve sequence [2-1-2]
Sp3cies SEq.lence [6-2-3] <---> Sp3cies SEq.lence [2-1-3]
Nu'rb:r axles
[6-3]
Nurrber axles
[2-1-4]
> Releve sequence [3] 11----'> '---------------'
Sp3cies sequence
Matrix file
Reduce data set
L
,-
Nurrber axles
> ISplit rratr ix [U-4-C]
I[
Classily
I
-> Releves
[4-1]1
<-
.
:]Sp3cies
[4-2]
L::
-
sort; SfECi~------'
dich::Jtcm::usly [U-4-S]
<
--'
,--
Print v..orkingtable
r
136
> User SEq.lence [4-5-2/3-1]
Ascen:li.ngsequence [4-5-2/3-2]
Reverse SEq.lence [4-5-2/3-3]Rarrl:::m
sequence [4-5-2/3-4]
Permutation SEq.lence [4-5-2-7]
Plant '""'"
I
~>IIPrint v..orkingtable
Optional sequences
for speci.es & releves
1
Convert releve nurrbers
ISe:Juencenurt:er to SEq.lencecede [4-5-2-5]
Syn:Jptic rratrix
1<-
[7]1<-
<
;-------c>ISyn:JptiC
files
[9-1]1
1<
- A model of the PHYTOTAB-PC
program,
TABLE 3.7. (continued)
package primary options with square brackets
indicating
keystroke
sequences
C. Final table
Print final table
>I'Final
table
1
PeriIberal data inp.It
------------------------------------------------,
Print checklist
Title, taxa, syntaxa, habitat descriptors, foot.n:Jtes [5-1)
II
II
Habitat data classified cedes or plant characters [5-2)
-> S}::ecieschecklist [6-8)
G=<th form axles
[>I=~cdata
[5-31
.nli..I.~
baM
[Q-PD-l)
D. Envirorrrental aJrrespondence
~
Envirorrrental data inp.It
~I
aJrresporrence
Envirorrrental variables [5-5) 1--------------------------:>
E. G::mfOSition
analyses
II
II
> stan::ifhase analysis [9-3)
======;]
)1 ::t:rorrurli'
ty ccrrpos
i tion
II
Cbmunity &
habitat [9-5 )
[9-2 )
=====================dJ---------------->rr=======================~
Cbmunity oover assesSTel1t9-4]
~II
:37
S}::eciesoover relationshirs
[9-6) II
TABLE 3.7. (continued)
- A model of the PHYTOTAB-PCprogram,
package primary
options
with square brackets
indicating
keystroke
sequences
F. Internal
utility
prc:grcmc;
blanks fran matrix file
(usually wiEn transferring data) (lJ-1]
Convert mY'J:1:BlI.S SPFNAM
to \Pr\DESaU
(for transfer fran data bank)
(lJ-2]
Reclirrensionor rarove nares or blanks
fran file \Pr\DESClU
(lJ-3]
M,
Rarove
IIstatistics
1"=Je
Convert to other forrrats (c:7I,NX)J,
mY'IOTI\B-ma.inrarre
)
(lJ-S]
/ Linear re:;p:-essions (lJ-6] II
I""",_W
Cb--Drdinateconversions or generate
ran::k:mpoints
(U-L]
files
I
!!Sarrple/stand dirrensions
IINurTber
set cxnp:rri9Jns
G. External utility
Cover cx:de conversions
138
(U-q
(U-N]II
prc:grarrs
13-0 onlination
Plotter
Digital ll\3fPing
artp.>t is requized
[C>N3DJ
I
species ccrrpartsons with PREdata
set, search or ad:! autbors
(SP]
I
points
grid.
according
to
Provision
or from decimal
The twelfth
is also made
degrees
option
sequences
same data
set.
stand
ii.
sampling
number
Two
function
number
keys
estimation
are
invokes
a problem
operative
section
solving
files generated
copies
minutes
a 4 mrn
degrees
to
and seconds.
of two sets of group-
two releve
sequences
the following:
stand area and stand
from species
number
for a given
of the
radius;
and area;
and,
area from species
with
for main
of the on-line
PT are saved
tags
are
of primary
items.
manual
to separate
with multiple
are made both data integrity
identification
menu
The
Fl
and the
key
F2 key
guide.
file maintenance
A flow chart
fractional
for determining
from scale,
and
per unit area.
the relevant
simplifies
(S) is used
unit dimensions
invokes
All
and degrees,
dimensions
iii. species
co-ordinates
for converting
such as comparing
option
i.
longitude
(N) is for the comparison
ed number
The thirteenth
latitude,
printed
program
users
and security
automatically
options
This
and if backup
file
are ensured.
File
for
is shown
diskettes.
each
in Table
diskette.
3.7.
3.6.2 Data bank
This
is a separate
functioning
set of programs,
as a floristic
data
on a subdirectory
bank.
The data
bank
C:\PT\PD,
progr.ams are
139
the
C:\PT
directory,
executed
from
the
of the AUTOEXEC. BAT
path
"PO". Four
species
diskettes
menu
file,
files are resident
names,
community
generated
selections
or
if ";C:\PT"
from
any .directory,
on a hard disk,
names
and an index.
the
classification
with
is included
namely,
Data
by typing
matrix
are
in
data,
loaded
from
The
main
programs.
are the following:
1. Load data
This
is from
final
table
grams
are to
used
in the
has been printed
be used
for analysing
2. List
The
diskette
and deemed
for a data
a single
data
bank.
format,
only
satisfactory,
can
splitting
the
if the pro-
The programs
set and
after
also
be
matrices.
data
following
data
can be
listed:
to code or alphabetically;
identifiers,
species
PHYTOTAB-PC
being
sequences
for a particular
names,
an index of data
computer
being
species
codes
the
and
order
according
sets resident;
original
in which
data set and community
both
releve
species
names,
releve
numbers;
should
occur
both numerically
or
alphabetically.
3. Search
Searches
bers,
routines
can be made
data
starting
sets or multiple
subdivision
and merging
ssification
are generated
Searches
names
when
can
also
or truncated
more
species
than
should
one
be
with releve
groups
identifiers,
of releve
numbers.
of data sets. The necessary
name
be present
num-
This permits
files
for cla-
codes,
species
on diskette.
made
species
releve
starting
names.
is
The
entered
in a releve
from
species
Boolean
for
a
"and"
search,
for the releve
logic
applies
i.e.
all
the
to be select140
ed. Apart
from
programs,
a list of species
releve
basis,
4. Correct
This
random
to
and floristic
Species
The
Search
is limited
loading
with selected
community
species,
on a
For
name
files.
The
index
with an editor.
for table
large
is
data
production,
can be
compared
for errors.
by the
time
capacity
of the hard
proportional
sets,
an 80386
to
the
or 80486
disk
amount
drive.
of
processor
data
is an
advantage.
utility
Apart
ASCII
from
an
the
higher),
vegetation
programs
file
editor
following
sb ch
as
programs
PC-WRITE
can
(version
further
3.00
facilitate
analyses:
DECORANA:
The
for ordination
of sample
AA:
the classification
names
3.6.3 External
or
and
on the data base,
and
obvious
species
on diskette,
bank
present.
associated
data can be corrected
names
data
for use with
files
the
species
with those
diskettes
can be obtained.
applies
5. Check
creating
CANOCO
because
and species
Affinity
variation
analysis
of
communities
degrees
of a data
CAN3D:
This
program
DECORANA
output.
(Ter Braak
1986;
of the options
numbers
version
available
in the scatter
(Scheiner
of
1987)
& Istock
compositional
is preferred
and the
inclusion
diagrams.
1987)
for analysing
relatedness
among
the
the
set.
is
A plotter
used
for
three-dimensional
plotting
of
is required.
141
SPECOM:
(Westfall
species
names
in
prep.).
against
list and adding
the
author
This
program
computerized
names
is
used
National
for
checking
Herbarium
synonym
for checklists.
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148
4. RESULTS
CHAPTER
The
methods
applied
based
developed
successfully
on
this
substantiate
Field
and
work
a total
by
study,
this work,
in the
described.
Van
and
AND DISCUSSION
Staden
his
where
in
this
(1992),
results
study
whose
are,
study
area
of 270 sampling
units
have been
is ongoing
and
sampled.
methods
advocated
comprehensively
set
several
plant
number
cover
scale
4. 1 PREPARATORY
Analysis
Bureau,
with
of
on
regression,
where
applied
estimation
techniques
& Panagos
(Westfall
used
to
63 stands
with
of the work
the reconnaissance
were
from
used
the
out-
before
the
1988) was developed.
WORK
computerized
request,
records
were
Much
for example
and
methods
therefore,
in this area has been developmental;
not
been
necessary.
main
were
have
rainfall
for
21
for periods
according
Y
154
y
mean
x
altitude
data
stations
longer
than
to the formula
supplied
within
the
20 years,
by
the
main
gave
Weather
study
the
area,
following
y=ax+c:
(x) + 379
annual
rainfall;
and
(m) x (latitude
(decimal degrees)
- 23,5)
1 000
A
correlation
coefficient
of
r=0,90
between
the
x and
y values,
149
was
obtained
gram
using
package.
A poor
ever,
obtained
where
a rain
annual
when
nique
from
utility
correlation
effect
the
outside
on a stand
the
and
basis,
This
the main
latitude
feasihlity
variables
of the
of using
in studies
where
that
area,
stand.
regressions
how-
included,
implies
study
pro-
was,
the study area were
seems .to occur.
altitude
the
in the PHYTOTAB-PC
between
for any stand within
illustrates
rainfall
statistics
stations
shadow
rainfall,
estimated
the
mean
can be
The
tech-
to predict
topography
can
influ-
ence rainfall.
4.1.1
The
Scale
scale
used
1:250 000 which
ed
by
the
resource
fore,
m
minimum
at
spacing
stratified
minimum
number
relates
to stratified
of
stand
area
mapped
unit
the
area
Correspondence
be
78,5
Van
unit
unit
for
Staden
level.
Stand
included,
in which
stand.
The
is based
on the
same
per
variation
sampling
as
the stratified
there3.1.5).
four stands
314
minimum
argument
of
ha.
The
four
as the
3.1.6)
The stratified
vegetation.
with
of 500 m per
(section
sampled.
the
a
stand
requir-
is,
(section
radius
is
vegetation
is approximately
per
units
for
radius
is 500 m
unit,
(1992)
with the detail
Development
spacing
ha
classified
between
of
to a stand and spacing
sampling
is that
is
Agricultural
can
relates
that
commensurate
a regional
approximately
per
and
area for a stratified
in effect,
stands
of
and minimum
stand
or
study
is considered
inventory
250
stand
this
Department
The smallest
This
in
and
unit
vegetation
of
ze l.eve s ,
unit and vegetation
unit can
releve
or
group
150
assist
in verifying
a classification.
The high
degree
fication
and environment,
buted
mainly
taken
being
be
correspondence
a
stand.
with
(1:10 000 000) the vegetation
sonality
biome,
and summer
rainfall,
rainfall
over
400
mm
factors
can
be
scales
larger
ronmental
small-scale
plicating
per
than
environmental
hand,
in large
unit
area either,
area
that
defined
Scale
also
likely
into
mixed
could
scales
correspondence
cognizance
scale
being
of
work.
similar
in terms
relates
to
be
These
at
envi-
as being
thereby
in
com-
of studies.
appear
can be attributed
to be
to
to stand area or, being
of a stand,
to
especially
result,
not
mm
units
differentiating
adcount,
a
such as
300
vegetation
does
scale
to sea-
1986).
and syntheses
scale
This
about
can be regarded
is not taken
which
1986). Within
Westfall
the
unit
it can be representative
being
are
then
&
hence
for a vegetation
correspond
from
differentiate
scale,
at biome
considerably,
varies
(Rutherford
determines
environmental
& Westfall
can vary
is attri-
community
influence
or biomes,
which
to
Scale
For example,
(Rutherford
annum
If scale
also
units,
Biome,
biome
of scale.
units.
and soils
expected
work,
other
critical
Fynbos
factors
scale-related.
On the
geology
in the
2
aridity
can
classi-
(1992),
unit or potential
Scale
vegetation
stratification,
by Van Staden
taken
area of a vegetation
to
between
achieved
to cognizance
the minimum
is
of correspondence
without
as
sampling
of such an
the
stand
of scale.
to heterogeneity,
relatively
where
homogeneous,
stands
at large
whereas,
as
scales
scale
de151
creases,
heterogeneity
to apply
to the
so that
the
portional
sampling
the
is likely
vegetation
units
heterogeneity
to
scale.
components
of which
cognizance
in vegetation,
of
the
mosaic
should
likely
are
a part,
is inversely
scale
can
scale
be
is also
stands
unit
of
where
This
the
of a vegetation
Thus,
of mosaics
to increase.
also
aid
determines
sampled
prothe
whether
separately
or
jointly.
Scale
can
also
scale
(1:10 000 000) sampling
would
be
considerably
order
to
adequately
biome
scale,
stand
being
10 km,
than
increasing
creasing
4.1.2
The
stand
Stand
sample
radius
the
area
intensity,
intensity
than
the
at
1: 50
scale
50 m required
for
per km2
However,
in
variation
at
is considerably
scale
with decreasing
scale.
area,
for
the
main
area
study
larger,
scale.
compensates
as defined
Thus,
for
in
to a scale of 1: 250 000. The stand
250 m with
stand area being
approximately
for stand definition
to scale.
In other
words
it
identify
a
direct
scale.
vegetation
decreasing
required
The
000
at biome
of stands
at at 1: 50 000
Decisions
is to
example,
in terms
increased
at biome
for
in-
area
corresponds
therefore,
sampling
less
heterogeneity
stand
3.1.2,
affect
association
the smaller
stand
of
are inversely
advocated
in this
at the
study
stand area is the means by which scale is taken
radius
is,
20 ha.
proportional
the scale the more
vegetation
section
difficult
required
between
scale.
scale
and
into account. during
152
sampling
and
is also
Braun-Blanquet
Ellenberg
small
approach
1974).
scale
1975,
7 km-2.
This
is the
and
a particular
area would,
with
which
fraction,
adjacent
scale,
area
study
of the
would
covered
have
by
as defined
for
without
Veld
Types
been
about
Acocks
during
in this
study,
required
stand
at the largest
to scale.
of the
largest
the vegetation
time,
area, between
The minimum
de-
unit.
area
can
two com-
sustainable
given by:
and
scale,
scale
the peri-
This buffer
is, therefore,
(m2)
with
sustain-
of the conserved
be required.
area
However,
to change
or transitional
unit
conserving
The minimum
to the border
related
for
unit.
is likely
sustainable
in metres,
area
area
is the
therefore,
is also
at which
to
vegetation
unit
the ecotone
s=minimum
denominator,
increased
in this
unit can be recognized.
(s) for a vegetation
where
stand
stand,
unit
a conserved
on practices
munities,
&
(Mueller-Dombois
considerably
the
of the minimum
the vegetation
be equated
be
to the
comm.)
of the
for a vegetation
A buffer
is integral
sampling
proposed
the
pers.
application
of such
pending
area
is approximately
determination
at which
that
according
(J.e. Scheepers
area
phery
to note
1988),
sustaining
able
can
stand
large scale work.
(Acocks
A practical
The
to vegetation
by the definition
interesting
sampling
to apply.
Its relevance
work
invalidating
It is
easy
r
is equal
to
the
as a representative
unit can be recognized.
153
4.1.3 Reconnaissance
Approximately
60 structural/floristic
in
study
the
main
naissance
was
the
area
(see section
a deliberate
in
4
species
richness
m2
sample/stand
3.1.3).
dimension
The minimum
requirement
essential
of
for
rion of species
only
necessary
a study will
assessment
ronment
richness
3.4.2.3).
dominant
plant
initial
species
most
likely
at
sampling
of species
the
range
using
program
unit
and
in
the
package
dimensions
sampling
made
specimen
can
also
identification
key
to be encountered
is
crite-
unit dimensions,
model
is
aim of
preliminary
construction
of the
during
purposes,
during
This
on vegetation/envi-
initial
made
The
require
collection
be
is an
approxi-
required.
The particular
observations
for
area
as the
classification.
other variables
However,
Voucher
scale
are subsampled.
be
of a study
expressed
the
to determine
should
species,
plant
units
whether
of variation.
(section
for
in vegetation
stands
determine
relations
richness
obtained
PHYTOTAB-PC
minimum
stratification
where
were
of a reconnaissance
vegetation
both
Species
in
sampling.
of the variation
number
allocation
represent
results
recon-
unit total
by regressions
to
in the
for determining
for stand
mate
These
utility
(sub-plots)
estimate
selected
present
during
colour
3.1.4).
per m2 as determined
variation.
scale
for adequate
(section
quadrats,
and are required
relevant
estimated
The structural/floristic
process
11 species
counts
the
over-estimate
stratification
averaged
at
units were
common
and
reconnaissance,
as these
are
the
sampling.
154
4.1.4
A
stratification
total
of
relevant
The
(section
ecotones,
at
between
two
The
smallest
approximately
Staden
of
The
the
study
unit
zones
should
ensure
by
is
non-contiguity
the
smallest
LANDSAT
MSS
to
ground
data
plant
to
this
scale
are
tural
and
plant
community.
tal
high
basis
such,
cover
zonation
the
imagery,
of
in
a
aboveground
detail
that
can
sense,
be
LANDSAT
fall
place-
unit
between
is,
units,
variations
that
can be offset
over
different
layer
so
phytomass,
is reduced.
time,
that
small-scale
communities
range
of
struc-
shown
classified
on
by means
because
a
horizon-
has
determined
at
within
to the
a
the
of
of
severe
of a particular
layer
by an increase
the
of
for
(1992)
in cover
utilization
data,
locally,
units,
in Van
differences
refers
Staden
vege-
plant
wide
tolerated,
vegetation
and stratified
despite
a fairly
Van
MSS
of plant
structure
vegetation.
It is likely
of vegetation,
borders
is shown
characteristics
contextual
however,
of floristics,
overgrazing.
spectral
In this
of
four
possible
of stand
communities
resolution
variation
correspondence
satellite
mass
affect
and
the
could be
differentiate
to
textural
that
stratified
are
The
at
for
community
79 x 79 m, is such that individual
work.
such
provide
approximately
unlikely
area
314 ha.
corresponding
(1992).
buffer
scale,
and also
use
stratified
The
represented
successful
in
zones equal to the stand radius,
relevant
stands
units
stratified
4.1.1).
the
area
therefore,
tation
were
each with buffer
included
The
units
scale.
stands,
ment.
44
effect
Furthermore,
Van
of
in phytograzing
Staden
on
(1992)
155
has
shown
that
communities
effective
the
relate
in the
factor
limiting
communities
and
in
(section
It
or
used
forms
altitude
this
improvements,
photograph
visually
playa
in
the
for
for small
integrated.
the
to delimit
analysis
its
total
for differthan
either
vegetation
units
i
range.
The
into
stratification
where
than
decision
account.
of
method
and
to
what
could
methods
suggest
of aerial
vegetation
integrate
Topographic
constructing
The
vegetation
topography
visually
as
which
in the vegetation
to the usual
delimitation
prima-
such as altitude,
a discontinuity
of vegetation.
firstly,
that
ad-
Hypothesis
role and scale could be taken
entail,
in
concerned.
factor,
vegetation
Rather
primary
differ
characteristics
study
scale work,
interpretation
secondary
This would
take
of structure
not disproved.
constitutes
study
and vegetation,
to pattern
the
arbitrary
a continuum
can
plant
different,
likely to be responsible
in
the main
Although
leads to the assumption
spectral
alone,
is probably
of phytomass,
of
communities
can be floristically
to an environmental
therefore,
in
This
plant
concerned.
plant
in terms
that the combination
function
is, therefore,
rily according
must,
structure
community
highly
moisture,
which
in the study
is more
cover
1.3)
particular
a
phytomass
is often
mostly
is
differentiating
available
of moisture,
communities.
plant
structure
to
it is suggested
which
aboveground
ences
similar
gradients
implies that adjacent
phytomass,
versa,
plant
This
availability
with
cover,
jacent
primarily
soil depth.
differ
and vice
environmental
topography
be done
features
are
according
would
then
into account.
a transparent
overlay
156
TABLE 4.1. - PHYTOLOC output for random location of stands,
showing random point numbers, x-y co-ordinates for
overlay grid and corresponding latitude and longitude,
in degrees (to the left of the decimal), minutes (first
two decimal places) and seconds(third and fourth decimal
places with decimal fraction following). stand numbers
are consecutive and are not necessarily those of the
random points.
COORD NO. X-AXIS, Y-AXIS
1 X-AX= 53
Y-AX=
123
Lat 24.41390147
Lon
28.00590064
2 X-AX= 83
Y-AX=
89
Lat 24.2313596
Lon
28.1900061
3 X-AX= 73
Y-AX=
52
Lat 24.03106403
Lon
28.13339644
4 X-AX= 25
Y-AX=
90
Lat 24.23461083
Lon 27.44453031
5 X-AX= 89
Y-AX=
25
Lat 23.48328078
Lon
28.2332387
6 X-AX= 30
Y-AX=
107
Lat 24.32588177
Lon
27.47371387
7 X-AX= 101 Y-AX=
102
Lat 24.30162561
Lon
28.29241894
8 X-AX= 13
Y-AX=
111
Lat 24.3508867
Lon
27.37375639
9 X-AX= 65
Y-AX=
78
Lat 24.17159605
Lon
28.08298468
10 X-AX= 75
Y-AX=
3
Lat 23.36375369
Lon
28.15248322
11 X-AX= 52
Y-AX=
106
Lat 24.32263054
Lon
28.0032904
12 X-AX= 75
Y-AX=
118
Lat 24.38564532
Lon 28.13544628
13 X-AX= 98
Y-AX=
20
Lat 23.45502462
Lon
28.2902484
14 X-AX= 55
Y-AX=
64
Lat 24.09407881
Lon
28.02425018
15 X-AX= 37
Y-AX=
43
Lat 23.58180295
Lon
27.52084009
16 X-AX= 52
Y-AX=
106
Lat 24.32263054
Lon
28.0032904
17 X-AX= 90
Y-AX=
63
Lat 24.09082758
Lon
28.23323076
18 X-AX=
9
Y-AX=
111
Lat 24.3508867
Lon
27.3516775
19 X-AX= 63
Y-AX=
26
Lat 23.49053202
Lon
28.07532632
20 X-AX= 55
Y-AX=
52
Lat 24.03106403
Lon
28.02494252
21 X-AX= 17
Y-AX=
98
Lat 24.28062068
Lon
27.40006091
. 22 X-AX= 32
Y-AX=
21
Lat 23.46227586
Lon
27.49163862
157
with
the
dimensions
required
scale,
guide.
Then
single
image.
homogeneous
of
the
adjusted
mount
the
to that
aerial
Observation
pattern,
photographs,
smallest
stratified
of the
aerial
photographs
represented
is no smaller
avoided.
Finally,
trace
the
Vegetation
criterion
primary
primary
units
can
suitable
criteria,
It must
be
units
a
practice,
demarcated
delimitation.
be
based
as deemed
to
by
mapped,
but
on
the
by
required
all
homogeneous
of
onto
the
pattern,
Secondary
topographic
sampling
too
low to
because
unit.
process
and
than
is
is
aerial
thus
photo-
the
delimitation
features
and
sole
of the
or
other
necessary.
that
stands
vegetation
the
borders
distance,
represented
is generally
according
present
observation
emphasized
classification
scales
than or equal to the area of the guide,
as
of
smallest
at the
thus
graphs.
a
guide,
at larger
at the
the
form
than that of the overlay
detail
recognizable
to
aerial
Confusing
greater
wall
as a
on the
distance.
units,
the
detail
same
pattern
a
at which
by textural
at
photographs,
on
distance- is that
unit,
The
which
can
as
intensity
for vegetation
accurately
few
map
as four
stratification
vegetation
be modified
stands
can
re-
process
is,
are
primarily
units
and
vegetation
supported
in
by the
classification.
4.1.5
An
Stand
example
Table
4.1.
location
of
The
output
stand
from
the
location
program
reference,
PHYTOLOC
taken
is
as
given
the
in
stand
158
centre,
is
given
ordinates
refer
transfer
cluded
stand
to
to
the
location
when
(section
4.2.5.2)
simple
relevant
map.
in the
visual
with
overcome
systems
have
also
lator.
current
Precise
position
data
PHYTOLOC
by
scales,
co-
points
program
has
for
been
in-
than
stand
km
habitat
used
in terms of accuracy
will
to below
somewhat
larger
not
show
only
indicate
the
SIOA
the
obtained
were
to
improved
by
in the
field.
improve
stand
should,
in the
The price
of such
R10 000 and the
than
the
An
and range.
(GPS)
of stand location.
considerably
being
systems
be
data
was
to
random
locating
could
position
the
program
in
location
limit,
satellite
of
the
precision
the contour
the problems
can
with
if predicted
2
for classification
because
required,
stand.
a
inadequate
but
x-y
a pocket
co-ordinates
direction
and
size
calcuof
the
distance
to
position.
stand
is made
habitat
with
a system
The
intersection
greater
was
to verify
decreased
the required
seconds.
comparisons
that
of the
decreased,
Such
small
estimation,
positioning
future,
at
field
that
proved
Geographic
overlay
The
However,
rangefinder
location,
and
program. package.
showed
an altimeter
optical
use
grid
stands.
centre
correspond
has
the
working
of
stand
minutes
in the field need not be precise
location
using
degrees,
in the PHYTOTAB-PC
purposes,
with
in
location
will
of Geographic
and
also be required,
Information
incorporating
Systems
vegetation
in the
future,
when
(GIS) for obtaining
sample
sites
in
such
systems.
159
4.1.6.
Sampling
Sampling
unit
species
the
unit
per
area
m2
area,
units)
0,025%
present
in
20
represented
the
by
of the total
estimated
It must
trated
and
for
area
the
the
ion
is
sampled.
in
the
not
of
four
in
stand
50 m2
(four
on
12,5
species
during
richness.
of
the
unit
dimensions
sampled,
of the
count
to be
present,
study
sampling
cover,
is estimated
sampling
m2
Species
canopy
with
main
out of 274)
a
in
about
on the
species
count.
illusscale
in sampling
in
95%
based
for a particular
species
a
however,
dimensions
Increase
12
PHYTOTAB-PC
in the
area.
total
the
of
a
unit
matrix,
but
to affect the classification.
sample
Species
statistical
for a normal
each
species
constancy
unit
ould,
The
species
improve
A minimum
determined
included
based
to the minimum
Sampling
samples.
3.1.6),
(40 species
sampled
that
in area is likely
being
richness
stand
13%
of each
emphasized
area
species
cover of all the species
decrease
s
ha
stand
species
a particular
could
of
20
stand.
cover
here refer
for
about
a
(section
utility
the
ha
canopy
canopy
be
5 m
area,
only
representative
with
a minimum
of
represent
species
x
Sample
therefore,
or
however,
2,5
dimension
package.
is,
commensurate
is
sample/stand
program
area
size
presence
stands
terms,
distribution
be
each
because
is
with
considered
(Freund
the
area
is
not
attribute
four
sampling
insufficient
& Williams
of
1958)
what
the
units,
replica-
within
a
160
TABLE 4.2. - The range of sampling units per stand in the main
study area
Number of sampling units per stand
Number of stands
Percentage of total
4
5
6
7
50
79,4
9
14,3
3
1
4,7
1,6
TABLE 4.3. - PHYTOLOC output for location of sampling units
where the x- co-ordinate represents direction (degrees);
and the y- co-ordinate represents distance from the
stand centre (m)
COORD NO., X-AXIS, Y-AXIS
1 X-AX= 121
Y-AX= 181
2 X-AX= 227
Y-AX= 114
3 X-AX= 168
Y-AX= 176
4 X-AX= 53
Y-AX= 88
5 X-AX= 5
Y-AX= 71
6 X-AX= 166
Y-AX= 138
7 X-AX= 230
Y-AX= 159
8 X-AX= 209
Y-AX= 81
9 X-AX= 149
Y-AX= 118
10 X-AX= 294
Y-AX= 194
11 X-AX= 285
Y-AX= 46
12 X-AX= 80
Y-AX= 21
13 X-AX= 320
Y-AX= 106
14 X-AX= 252
Y-AX= 24
15 X-AX= 69
Y-AX= 96
16 X-AX= 281
Y-AX= 235
17 X-AX= 203
Y-AX= 236
18 X-AX= 242
Y-AX= 102
19 X-AX= 106
Y-AX= 193
20 X-AX= 252
Y-AX= 17
21 X-AX= 196
Y-AX= 120
22 X-AX= 2
Y-AX= 23
161
plant
community.
on normal
This would
distributions.
statistical
comparisons
characterize
the
portion
adequacy
the
and
not
that
sample,
sample
the
and
the
statistical
however,
but to determine
of
necessarily
is meant
The
community
significant
of
often preclude
those
based
is not designed
those
species
responsible
vegetation
tests
cover.
The
for
which
best
the
most
for
test
for
the
should,
therefore,
be
scientific
validity
statistical
validity.
By
scientific
validity
processes
should
be
repeatable. by
independent
observers.
4.2 FIELD
SAMPLING
Because
sampling
sampling
are examples
The number
stands
The
methods
rigorous.
accounts
rather
in Table
used
This
to
of
(1967).
ed
many
In
sound
pertaining
than a synopsis
study
thought
units.
vegetation
results
field
of all the processes.
of the sampling
The
with
such
no
accounts
conclusions
for
field
and
sampling
observation
opposite
extreme,
explicit
units
observations
have
been
are
the best of both approaches.
4.2.1
sampling
location
Table
4.3 shows
of the output
in
is
for all
extremely
the
field
descriptive
such
not
derived.
be to utilize
an example
are
methodology,
would
unit
to
4.2.
in this
sampling
the
of total,
confines
Edwards
and
ongoing
and percentage
is given
chiefly
is
as
restrictThe
from the program
ideal
PHYTOLOC
162
for
random
corner,
sampling
for
each
unit
location.
sampling
to the stand
centre.
stand,
using
quadrats,
record
such
subsamples.
unit
It was
in a single
day.
The
as defined
in this
ties
regarding
described
for
sampling
for small
large
scale
work.
methods
could
also
thereby
permitting
the number
comparable
number
method
sentative
number
of
of
of points
could
scale,
Such
whereby,
species
stand.
the
objective
In this
and
would
the
considerable
minimum
1.3)
case
species
would,
ideal
stand
that
two
stands
could
raises
interesting
Apart
it
from
defined
point
within
a stand
that
of
an
with
field
of sampling
time
area would
therefore,
and
be
also
could
sampling
units
be
fall away.
irrelevant
as
data.
than,
saved.
within
be
the
coupled
achieved.
not be relevant
The
Hypothesis
minimum
plant
say 0,05%
approach
would
A
repre-
of the
could
in a
quadrats.
the species
greater
location
context,
result
with
use
as
point
quadrat
could be recorded
informal
dimensions
location
cover
that
should
make
be
can be used
stand
and
obtained
could
apart
subsampling,
samples
between
and
possibili-
is conceivable
the
a method
40 crown diameters
with
seldom
with
or less than
Thus
mark
also be used to determine
a stand.
the
locate,
within
species
in subsampling
representative
comparison
Obviously,
plotless
used
in relation
to
Furthermore,
be
is
reference
taken
location.
scale work,
stand,
the
time
study
unit
the
of
disadvantage
in the
completed
stand
within
The greatest
is
Position
problem
ii
of
(section
sampling
area
not be applicable.
163
4.2.2
The
Plant
criteria
specimens
The
identification
for
collected
same
characters
cation
have
sented
by the
The
been
initial
are
key
of
the
that
plant
that
cannot
towards
solving
for training
study
in
in plant
vegetation
identification
sively.
use,
camera
with
would
specimens
and
problem.
The
ecology
are
in
given
the
(1992)
area
input.
of
method
goes
also
It
very
criteria
taxonomic
imprinting
advantage
the
Use
first
plant
comprehenuseful
significance.
of the programs.
reference
for numbering
field.
way
useful
for
and
is
of
some
is probably
explicitly
any
for
identification
is
This
study
own
field
the potential
for
the
his
not
benefits
staden
his
that
do
in the study area, although
to be
back,
the
Van
plant
argued
However,
method
where
be
manner
This
identification.
be a decided
stands,
reducing
transferred.
field
repre-
the
in this
to
in
of
could
transfer.
gained
illustrates
a data
It
relevant
thereby
is unlikely
nevertheless,
slides,
the
identifi-
families
construction
required.
key
The key to the families
field
This,
in
the
knowledge
the
the
to
(back pocket).
for plant
of
identification
effort
easily
that
used
according
(Table 4.9, back pocket).
for
knowledge
be
states
time-consuming.
identifications,
plants
A
of
field
4.4 to 4.8
identification
required
is
in the
Tables
collected
direct
portion
unfortunate
for
for
validating
by
in
and character
additional
increased
used
are given
used
input
benefits
warrant
identification
specimens
identification
the
plant
and verification
of
a
numbers
slides
on
of plant
blackboard
with
164
Acacia caffra
community/releve:
1
Recorded cover: 25.8%
Derived cover 25%
Mean crown diameter: 3.05 m
Individual/ha:
353
m sq/individual:
28.31 m sq
Spacing: centre-centre:
6 m
Canopy radius: 1.52 m
Canopy-canopy
gap: 2.96 m
I
spac~ng,
FIGURE
165
6 m
9
Acacia caffra
Community/releve:
2
Recorded cover: 0.91%
Derived cover 1%
Mean crown diameter: 3.05 m
Individual/ha:
12
m sq/individual:
802.87 m sq
'Spacing:centre-centre:
31.97 m
Canopy radius: 1.52 m
Canopy-canopy gap: 28.93 m
P
4.1. - Example ofSPECODA
output
crown and two cover classes.
Spacing:
showing
31.97 m
9
cover data for one
chalked
numbers
for
this
purpose,
is time-consuming
and
cumber-
some.
4.2.3
Species
Examples
for
illustrated
species
cover
selected
species
are
given
in this
program
are
included
used
the
of
cover
PHYTOTAB-PC
cover with
cover
for a stand
a) total
program
species
height
cover
species
estimations
cover
greatly
number
scale
matrix,
cover
(Westfall
class
is demonstrated
However,
community
composition
required
the
allow
periods.
of
to vegetation
facility
in
total
species
height
classes
than the cover
of the
and
limits.
& Panagos
could
enhance
of the plant
by Van Staden
precision
analysis.
species
case,
number
with
these
cover
scale
that
greater
could
This method
the
plant
limits.
on
counting
transect
a
for estimating
is according
would
results
marking
to
for
transect
individuals
refers
be
of the
also has potential
Permanently
by merely
species
the
precision
influence
scale
pattern
(1992). Precision
change.
monitoring
in total
Estimations
1988) are within
intervals
In this
cover-abundance
1974) often resulted
these
less
individual
would
calculations
processing
Domin-Kraj ina
It is unlikely
required.
corners
The
classes.
using
plant.
4.1.
SPECODA
in the following:
the effectiveness
to a whole
program
should be less than the sum of the cover
exceeding
fewer
monitoring
according
& Ellenberg
(Mueller-Dombois
the
comparison
should be greater
cover
for all height
Although
A
class with the least cover;
b) total
Cover
result
Figure
in the
package.
determined
should
in
using
the
at
area
166
required
caused
for
by
other
could
and
be
adopted
sample
moves
species.
of midpoints
Krajina
sampling
tion
The
number
which
potential
these
(Figure
derivatives
mine
plant
well
as
number
in
contrast
4.1)
also
is ideally
number
requirements,
complies
to
the
whether
position
or not
the
diameter
summation
cover.
to informal
on species
stand
selec-
at time of sampling.
illustrations
It
scale
is
environmental
and
species
conceivable
could be used
competition
fully with
that
crown
actual
suited
subjectively
explored.
to
static
necessitates
for observations
was
reactions
Domin-Kraj ina
and
can approximate
spacing
of the plant
or
such as found in the Domin-
species
not
increase
species.
the
the
the
was often obscured
related
doubtful
also allow
the
to
on
in an over-estimation.
transect
scale,
growth
the
using
Changes
where:
resulted
to be made
spatial
species
scale
attributed
scale method
of
plant
for a particular
obtained
It is also
would
to
3.2.3.2).
determining
of large cLa ss intervals,
for monitoring
density
a
cover-abundance
The plant
due
to under-estimation;
scale
along
(section
for cover estimation
often
number
of a plant
are
leading
b) local Clumping
observer
by
area required
by the observer,
by vegetation
plant
detected
scale
a) an adequate
The
increases
under-estimates
cover-abundance
often
or
in transect
over-
numbers
species
defoliation
hand,
decrease
The
counting
between
gradients.
the requirements
that
to exa-
species
The
as
plant
of a scale
167
TABLE
4.10. - PHYTOCAP output for a selected stand showing species
present (collector's number) and cover (symbol) in each subquadrat. Numbers preceded by "0" are line numbers followed
by a number to which the first digit refers to the subquadrat and the last three to the stand. Cover symbols are
those of the plant number scale
001 1047
2149
C
2297
A
2053
3
2157
1
+
2192
2039
3
2131
1
2135
A
002 1047
+
2073
+
2005
2194
1
2165
A
2241
B
5
2167
2077
5
2200
2
003 1047
2012
4
TABLE
001
002
003
004
005
168
4.11.
00472149
00472073
00472012
00472243
00472223
004 2047
2149
1
2240
3
+
2192
2131
2
+
2132
2045
1
+
2043
+
2250
005 2047
2078
7
2077
3
2167
6
2004
2
2241
1
2012
7
2243
2
2074
3
006 2047
2128
1
2144
1
- PHYTOFORM output
showing conversion
Number of species
72297
+2005
42240
12874
+2066
82053
+2194
22132
12128
+2003
007 3047
+
2297
2053
F
2154
1
2131
7
2132
3
2192
1
+
2229
2194
4
008 3047
2039
1
2041
2
2077
2
8
2008
2165
2
2078
5
2116
1
2167
6
009 3047
2223
1
2004
3
010 4047
2297
D
2240
2
2149
8
2066
1
2131
5
2039
2
2132
3
2192
1
011 4047
2241
3
2004
2
2078
5
2165
5
2008
1
+
2003
2167
3
for the stand in the previous Table
of data to PHYTOTAB mainframe format.
= 35. Cumulative cover
62.37%
82157
22165
32045
+2144
+
+2192
62241
+2043
+2154
12039
62167
+2250
+2229
22131
52077
+2078
+2008
42135
32200
52004
42116
5
1
2
+
to Londo
according
Crown
cover
(1976)
estimations
because,
apart
from
approach,
more
factors
cover
which
which
can
be
cover
can
relate
material
similar
within
of
no
species
degree
to
the
crown
more
soil
and
estimations
Braun-Blanquet
cover
than
information
frequency.
of
classes;
unit
4.10.
input.
For
c)
basal
than
that
example,
protection;
reasons
The
b)
crown
utilizable
competitiveness
finger
between
efficiency
on
vat ions ,
can
field
and
be
data to stand
Table
programs
and paper
typing
It
4.11.
It
are
slow
the
in the field
programs
is,
with
data
with the PHYTOTAB-PC
furthermore,
in
the
provision
be
field
for
by
computer
it is generally
recordings
keyboards.
For these
in the PHYTOTAB-PC
that
recording
relevant
on
doubtful
for flori-
and the
suggested
captured
programs,
is
for these
are included
are
data using
is advantageous
on the miniaturized
obtained
sheets,
that
in
E>HYTOCAP program
in the field. Additionally
of these
package.
the
of these
shown
transfer
burden
neither
program
are
using
field data capture
to use pencil
single
data
Conversion
program
computerized
data
loaded
a)
sampling
PHYTOFORM
quicker
data
related
of
cover
data recording
is an additional
than
be
contains
height
in Table
whether
stic
requ~rements
can
from
to basal
size species.
Examples
the
3.2.3).
preferred
the
derived
4.2.4 Floristic
given
are
inherently
to
(section
a
daily after
greater
floristic
casual
laptop
obser-
computer,
fieldwork.
169
If
a classification
only
is
not
the
require
table
is
also
A quick
would
require
classification
values
a
scale
if
plant
are
a
spacing
because
only
tionally
This
because
disparity
of
the
value
plant
in
form/cover
analysis
input
is
the
on
for
synoptic
of
greater
is
the
impor-
required.
per
unit
highly
case,
that
as
be too
is
crown diameter
any
than
such
may not
individuals
in
understand-
used,
This
to
used
height
of
with
should
plant
form categorization
analysis
precision
is,
species
growth
structural
synoptic
Too much detail
data
number scale
such as growth form classes
related
based
dominance only,
terms
zation
being
for
If
area,
convenient
class
is
addi-
necessary
for
length.
of
of
the
techniques
of
plant
Any comparison
the
a
scale.
in
recording
transect
of
imply
and density
the
or
classes.
of
estimation
necessary.
determining
treatment
could
the
PHYTOTAB-PC
does
communities
few cover
assessment
then
required.
presence
dominance from a phytosociological
cover-abundance
visual
required
of
with
numerical
cover
of
species
cover values.
assessment
with
Domin-Krajina
tant
for
Ordinations
Such treatment
possible
then
cover
visual
requires
a study
data
do not require
table
ing.
aim of
minimum floristic
generation.
tables
the
species.
categorization
A simple
can be adequate.
in
this
classes.
vegetation
little
require
to
loss
study
This
be
of
has
categori-
The system of
the
permits
replaced
information.
advantage
a
by
separate
a
growth
Additional
growth form code.
170
Any additional
aims of
a particular
descriptive
to
treat
If
data
to
an
data
informal
observations
A
for
full
set
in
is
descriptive
dependent
is
often
data
sampling
relevant.
A
prior
situations
overcome with
is
are
a
small
can
have
a pre-determined
that
is
far
data
set
numerically.
then
casual
exist
when first
could
It
adopted
danger
the
difficult
lost.
into
stand
recorded
on
must be emphasized
which can only be reduced
be
of
observed
been
list
an
but
overlooked.
of
considera-
observation.
analysis
numerical
programs
be
and sociability
value
phenomenon being
problem
tions
vitality
method of
occurrence
This
as
would
However, it
and their
could
intermittent
its
such
incorporate
a large
recorded
study.
numerically
easier
than
information
requires
crown diameter
species
code
of
data
presence
and growth
using
the
together
with
form code to
PHYTOTAB-PC
cover
be recorded
code,
in
the
field.
4.2.5
In
Habitat
the
main
eleven
study
parameters
parameters
3.2.5.2)
stand
data
derived
were
area,
relating
from
recorded,
eleven
to
parameters
sampling units
1:50
apart
000
Topo
from the
relating
(quadrats)
series
general
to
stands,
and twelve
maps
(section
requirements
of
description.
The general
requirements
for
describing
a stand
sample are:
171
TABLE
4.12.
- HABlMEAN output for stand 47 showing conversion
recorded subquadrat habitat data to stand data
stand number: 47
Aspect vector: 294 degrees
Slope (mean): 0 degrees
Litter cover: 2%
Litter depth: 2 rom
Soil depth (min): 1000 rom
Soil depth (mean): 1200 rom
Soil depth (max): >1200 rom
soil colour (mean): 10YR 4/2
Soil texture (% clay): 4%
Soil form: SP-100%
Surface rock cover: 0%
Surface compaction: 132.38 kPa
Relative herbaceous biomass: 35 rom*
*measured
172
as disc pasture
meter
drop height
of
-a) stand
number,
each stand
the unique
programs.
logical
releve
starting
sampled
number
tables,
of this
on the ground,
Numerical
and
are referred
& Ellenberg
of latitude
being
(section
data
3.1.5)
cause problems
Mappable
the
stand
or
spatial
vegetation
data
include
data
the point
(section
include
structure
include
grazing,
and
4.1.5).
Examples
using the HABIMEAN
program
program
because
is
its
use
averaging
for a selected
not
is
study, whereas,
in a study,
the
can differ
included
restricted
in
of
land
use.
erosion
unit data
sampling
the
PHYTOTAB-PC
habitat
Nonand
recorded
unit
stand are given
to the
the decision
exposure
lithostrati-
browsing,
of
numerical.
record
so that
altitude,
are
used in this
of the
determines
Locating
of soil form, sampling
package
of
in degrees,
of a second
is simple.
could
describing
descriptive
This
reference
reference
to fractions
information
however,
and longitude
fire. With the exception
4.12.
to as
1974);
the temporal
the spatial
PHYTOLOC
geomorphology,
spatial,
in the phytosocio-
data
temperature.
graphy,
is
and
and co-ordinates
Field
numbers
the stand, being
The program
recording
for
by the PHYTOTAB-PC
of communities
(Mueller-Dombois
and seconds,
location
4.2.5.1
for each stand required
co-ordinates
minutes
consecutively
a study area or data set. This
stand or sample
numbers
the stand;
stand.
within
In the abstraction
b) date of sampling
c) sample
at 1 and increasing
data
in Table
program
parameters
as to what parameters
to
widely.
173
The results
metic
of the program
means,
except
is the vector
of aspect
tion
of
are
10°
a mean
of
modified
and
and
of
averaging
slope
south
This
6°.
aspects
for correlation
other
by
the
Soil
forms
rence
soil
form
a
calculated.
value
For
and
soil
chroma
to reflect
fore, a numerical
can be treated
The
argument
that
applies
data
Soil
colour
numerical
Combina-
the
slopes
aspect
to
the
value
with
problem
is required
of slope must be included.
has not been
influence
shown
the
units
Color
a
occur-
stand
are
components
of
1954)
averaged
Charts
for the stand.
of
and
function
but the percentage
individual
according
where
solution
sampling
the
influence
account.
in a north
could
the
colour
are
Soil colour
to the Munsell
as
Descriptive,
non-spatial
in
a
habitat
set
The
and
vegetation
estimation
a low relevance
data
data.
geology
with
coverages
is difficult
large
habitat
such
and
data
a
of
as
fore, have
into
of such vectors
for
to
correlated
fire
a single
The main
example,
are not averaged
manipulated
of
taken
is a possible
descriptive
partially
rence
Aspect
hue,
is, there-
notations,
which
accordingly.
treatment
visually
form and soil colour.
result
latitude
value,
numerical
descriptive
arith-
for
would
(Munsell
an average
are mainly
and the effect
as
appreciably.
of
aspect,
effectiveness
such
soil
is thuS
where
purposes
factors
(Table 4.12)
with magnitude. slopes.
2 ° respectively
slope
However,
for aspect,
of aspects
a north
HABIMEAN
soil
units
information,
include
(section
exception
Geographic
of grazing
to
type
as
is
which
in a
4.2.4)
spatial
can
overlays
Information
be
or
System.
such
as the
occur-
intensity,
would,
there-
in large data sets.
174
(a)
(b)
HEADER
st:an:i
•••
Grid
AltituOa (m)
00:
MAR
Exp:lSure
AsfEct<:---(cBj) M:a.n soil
st:an:i
00:
(cBj)
47
1200
0<---419
(mn)
--RAM
(mn)
00:
Grid
00:
2428AA
607
1801----
200
cEpth (mn)
'I'ofO profile:
(fran drainage to
......atershe:l.
)
I.a.OOscafe slq:e
Top:> profile:-
(cBj)
o
st:an:i slq:e
o
(deg)
scale (in relation
to starrl di.aret:er)
Draillage:
175
500 m
Ge::rrPq:h:>1a;w: Flat (B)
Drainage: !'b fl(Mt -C
Ge::rrPq:h:>1a;w :
FIGURE. 4.2.
scale:
- SIDA output
explanations
for stand 47 showing
(a) and data
(b).
header
form with
TABLE 4.13. - Statistical output for all releves showing correlations
between srDA data (x-axis) and the corresponding field
data (y-axis) for a) altitude
a)
Minimum
X= 890
Y= 890
Linear regression
(y=xb+c)
Maximum
X= 1760
Y= 1775
Slope (b )= 1.005
Range
X= 870
Y= 885
Angle of slope= 45.150 degrees
Total
X= 81260
Y= 81080
Y~axis interception (c )=-9.671
Mean
X= 1289.84 Y= 1286.98
Correlation coefficient (r )= .995
Median
X= 1300
Y= 1280
Regression variance= 463.843
Midrange X= 1325
Y= 1332.5
Standard error of the estimate= 21.537
Harmonic mean X= 1248.31
Y= 1244.65
Mean deviation X= 4.759
Y= 4.714
Variance X= 53495.125
Y= 54518.179
Standard deviation X= 231.290
Y= 233.491
Coefficient of variation X= 17.931% Y= 18.142%
Standard error of the mean X= 29.139 Y= 29.417
Scatter
1775
diagram
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
887
*
1
o
8
1
8
7
6
o
176
TABLE
b)
4.13
(continued). - Statistical output for all releves showing
correlations between srDA data (x-axis) and the
corresponding
field data (y-axis) for b) aspect
Minimum X= 0
y= 0
Linear regression
(y=xb+c)
Maximum X= 444
y= 448
Slope (b )= .801
Range X= 444
y= 448
Angle of slope= 38.724 degrees
Total X= 12875
Y= 14755
Y-axis interception
(c )= 70.33
Mean X= 204.36
Y= 234.20
Correlation coefficient
(r )= .801
Median X= 231
Y= 274
Regression variance= 6706.697
Midrange X= 222
Y= 224
Standard error of the estimate= 81.894
Geometric mean X= 0 Y= 0
Mean deviation X= 3.243
Y= 3.717
Variance X= 18412.912
Y= 18437.716
Standard deviation X= 135.694
Y= 135.785
Coefficient of variation X= 66.397 % Y= 57.976 %
Standard error of the mean X= 17.095
Y= 17.107
Scatter
448
diagram
*
*
*
*
*
*
*
*
*
*
*
*
*
224
*
*
*
*
*
*
*
*
*
*
1
o
177
*
2
2
2
4
4
4
TABLE 4.13 (continued). - Statistical output for all releves showing
correlations between srOA data (x-axis) and the
corresponding field data (y-axis) for c) slope
c)
Minimum
X= 0
y= 0
Linear regression
(y=xb+c)
Maximum X= 180
y= 180
Slope (b)= .707
Range X= 23
y= 29
Angle of slope= 35.281 degrees
Total X= 11127
y= 10993
Y-axis interception (c)= 49.523
Mean X= 176.61
Y= 174.49
Correlation coefficient (r)= .567
Median X= 178
Y= 176
Regression variance= 23.368
Midrange X= 168.5
Y= 165.5
Standard error of the estimate= 4.834
Harmonic mean X= 176.489
Y= 174.284
Mean deviation X= .0006
Y= .0039
Variance X= 21.788
Y= 33.899
Standard deviation X= 4.667 Y= 5.822
Coefficient of variation X= 2.642 % Y= 3.336 %
Standard error of the mean X= .588 Y= .733
Scatter diagram
180
*
*
*
*
*
*
90
1
o
9
1
o
8
o
178
TABLE
4.13
(continued). - Statistical output for all releves showing
correlations between srDA data (x-axis) and the
corresponding
d)
field
data
(y-axis)
for d) exposure
Minimum X= 157
Y= 151
Linear regression
(y=xb+c)
Maximum X= 180
Y= 180
Slope (b)= .707
Range X= 23
Y= 29
Angle of slope= 35.281 degrees
Total X= 11127
Y= 10993
¥-axis interception
(c)= 49.523
Mean X= 176.61
Y= 174.49
Correlation coefficient
(r)= .567
Median X= 178
Y= 176
Regression variance= 23.368
Midrange X= 168.5
Y= 165.5
Standard error of the estimate= 4.834
Harmonic mean X= 176.489
Y= 174.284
Mean deviation X= .0006
Y= .0039
Variance X= 21.788
Y= 33.899
Standard deviation X= 4.667
Y= 5.822
Coefficient of variation X= 2.642 %
Y= 3.336 %
Standard error of the mean X= .588 Y= .733
Scatter
180
diagram
*
*
*
*
*
*
90
1
o
9
1
o
8
o
179
TABLE 4.13 (continued). - statistical output for all releves showing
correlations between srDA data (x-axis) and the
corresponding field data (y-axis) -for e) soil depth
e)
Minimum
X= 75
y= 90
Linear regression
(y=xb+c)
Maximum
X= 1500
y= 1200
Slope (b)= .678
Range
X= 1425
y= 1110
Angle of slope= 34.159 degrees
Total
X= 23915
Y= 34433
Y-axis interception (c)= 288.967
Mean
X= 379.60
Y= 546.55
Correlation coefficient (r)= .381
Median
X= 349
Y= 413
Regression variance= 154199.5
Midrange X= 787.5
Y= 645
Standard error of the estimate= 392.682
Harmonic mean X= 296.425
Y= 261.602
Mean deviation X= 1.942
Y= 8.197
Variance X= 56192.152
Y= 177586.609
Standard deviation X= 237.048
Y= 421.410
Coefficient of variation X= 62.446 % Y= 77.102 %
Standard error of the mean X= 29.865 Y= 53.092
Scatter diagram
1200
*
*
*
*
*
*
180
Based
Van
Staden
unit
and
(1992) the two main
differentiation,
data
are
not
are soil texture
both
the
appears
could
and
It
of
improve
statistical
efficient
method
4.2.5.2
Derived
Examples
of
maps
using
SIDA
field
tion
of
not
very
The
SIDA
output
with
overcome
(section
factor
4.2.5.2),
of the former
methods
(section
using
3.2.5.2)
unreasonable
to
could
be representative
always
Increasing
correlations
but
requirements
is
data
expect
the number
the
number
that
a
of auger
required
impracticable.
determination
and
derived
program
the
the program
to
more
A
is required.
from
1:50
000
are given
in Figure
corresponding
variables
MINISTAT
50 aspect,
dissimilar
following
for vegetation
data
habitat
3550
test"
holes
of soil depth
the
using
"f inger
auger
elsewhere
of a 20 ha stand.
satisfy
responsible
Determination
is," however,
four
of the soil depth
holes
obtainable
and soil depth.
"sausage"
sample
factors
(1981) and
in areas where moisture" is a limiting
readily
adequate.
random
of
in this study as well as Westfall
on observations
in terms
transformations
are given
although
were
4.2.
Comparisons
in Table 4.13.
being
applied,
series
recorded
numerically
of direction,
Topo
for
in the
Correla-
disparate
only
100
are
apart.
comparison
to
this problem:
a) for SIDA values
between
3600 and 900 and releve
values
between
3600 and 900 and SIDA values
between
2700 and 3600 add 3600 to SIDA value;
b) for releve
values
between
2700 and 3600 add 3600 to releve
value;
and
181
c) if SIDA or releve
is taken
The
best
because
0,67;
co-efficient
the
exposure
0,80;
and
0,56
same
soil
and
co-efficients
were
in the
sampling
or
field
incorrect
crepancies
can
be
the
stand
between
attributed
cordings
were
aspect,
for
The
slope
more,
based
Clearly
this
with
rainfall
study
on
is
parameters
360°
field.
and
c)
to the
shortcomings
For
inadequate,
example,
the
data
altitude
for
because
It is also
could
have
4.13).
both
likely
affected
Specific
a) local
b) vegetation
of
correlation
size was
include:
Aspect,
better
location,
(Table
cases.
with
co-efficients
habitat
stands
obtained
subsurface
the
rocks
rethat
the
problems
variation
obscuring
dis-
in
horizon
and
gravel
determinations.
on degree
in the
Staden
using the SIDA program
of slope. This implies
would
the
and altitude
Van
relate
sample
stand
of certain
measurement.
area.
That
field
data measurement
not
in both
from the same source.
summits,
be recorded
this
imprecise
determinations
mainly
not
depth
and
data
determinations;
ground,
where
SIDA
was
correlation
could
in the
for soil depth
used
respectively.
and soil depth;
soil depth
was
location,
of other
exposure
layers
90° and 270° then
(r=O,99)
gave
methods,
to
to field
depth
0,38
location
correlations
source
not obtained,
derived
incorrect
relating
are between
as 0°.
correlation
altitude
slope,
values
be greater
case,
field
The
hence
despite
good
and latitude
(1992)
than
it
is
the
correlation
that
further-
that
all level
1 500 mm
in depth.
suggested
limitations
between
is applicable
found
are,
altitude
that
soil
associated
mean
annual
only to the main
alone
was
suf182
ficient
the
and
SIDA
study
area.
Rainfall
local
basis
for stand
Another
source
namely,
altitude
computing
available
An
this
Although
with
a
for
is
far
orientation.
exposure
in
method
effect
then
Hence,
two
in
insolation
only.
The
parameter
unlikely
to
be
components
its
kloof
on a
a
can
be
of
such
however,
from
they
the
Pieter-
may
account
of
relative
not
are
be
only
the
described
on
to
with
an
because
the
the
available
to
to sun
moisture
of the
values.
The
are
and
separately.
mean
because
be
consider
components
exposure
to
method
yet
to
be considered
taken
east-west
has
reasonable
a kloof
insolation
with
differences
in
exposure.
study,
exposed
is meant
is
significance
in this
detected
should
this
exposure
kloof
appears
components
derive
be
than
components
to
rationale
of Natal,
orientation
would
If, by exposure
of much
used
be done
available
because
was not sampled
furthermore,
these
exposure
SIDA
of
significance
to quantify.
data,
purposes
orientation
of
data
of determining
differences
terms
that
grid basis.
These
It,
for
a similar
University
These
stand
duration
assessed.
of
for
the
data using
Research,
shorter
The
therefore,
are the
1987).
a.situation
described.
wind,
al.
north-south
a
easier
et
of the
such
rainfall
for Water
on a one minute
study
accordingly
interpolation.
precise
advantage
adapted
should,
and topography,
(Dent
sufficiently
correlations
for derived
Centre
maritzburg
was
program
exposure
to
(RAM)
is
large
number
algorithms
are
183
classiTABLE 4.14. - PHYTOTAB-PC
fication of a synthetic data
set using both the heuristic
and permutation methods.
Noise is absent. Total separation units=O. Classification
efficiency=100%.
00 00 00 00
42 81 53 76
Releve
number:
B
B
B
B
B
B
Species
Species
1
2
Species
Species
3
4
Species
Species
5
6
Species
Species
7
8
Species
Species
9
10
Species
Species
11
12
species
species
14
13
++ ++ ++
++ ++ ++
Species
Species
Species
species
Species
Species
15
16
19
18
17
20
++ ++ ++ ++
++ ++ ++ ++
184
++
++
++ ++
++ ++
++
++ ++
+ +
+ +
+ +
+
TABLE 4.15. - TWINSPAN
classification
of the synthetic data set in Table
4.14.Total separation
units=21. Classification
efficiency=74%.
Re1eve
number:
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
Species
species
Species
Species
Species
00000000
35671248
9
20
19
12
11
10
14
13
8
7
18
16
15
6
5
4
3
2
17
1
++
+
+ +
++++
++++++
+++++
+
+++++
+
++ + +
++ + +
+ +
++++++++
++++++++
++++
++++
+ +
+ +
++
+ +
++
complex.
somewhat
wetting
It
is
the
depth,
program.
to record
age
precise
This
field.
would
field,
input
such
and
GIS
program
approach
likely
to
processing,
programs
the
save
otherwise
is often
better
when
such
However,
adapted
in
using
time
and
the
allow
be impracticable
in terms
of drain-
to determine
the potential
habitat
data,
manual
time-consuming.
to digital
becomes
terrain
available.
included
the MINISTAT
under
by
illustrates
is extremely
data
recorded
impracticable
certain
not been
package
work
soil
(1992).
obtained
field
would
maps
effective
parameters
as stand position
be
package.
program
be
to recording
accordingly,
PHYTOTAB-PC
could
SIDA program
have,
is
by Van Staden
the
which
from topographic
is
approach
of
also
which
Although
of an indirect
many
data
to be utilized
in the
approach
simpler
for
line and watershed,
in the
data
that
more
information
far
used with good results
conceivable
field,
SIDA
A
in the
program
data
The
SIDA
PHYTOTAB-PC
is included
the statistics
The
in the
utility.
4.3 CLASSIFICATION
The
PHYTOTAB-PC
are given
in Table
manner
that
absent
if
random.
approach
unit
classification
4.14.
noise,
in
correctly
Identical
which
sequencing
used on small
the
data
form
classified.
results
includes
and
data
The
results
the
sets.
were
of
set was
of
The
permutation
synthetic
constructed
separation
initial
achieved
commonality,
a
using
releve
both
would
sequence
the
and
which
set
in such
units,
similarity
approach
data
a
be
was
heuristic
separation
can
only
be
In the case of the last-mentioned.
185
TABLE
4.16. - Number sequence comparison of the grouped
sequences in Tables 4.14 & 4.15
Correspondence
releve-groups
a)
Table
1 2
Table
4.15
1 0
2
0
3
1
4 1
of
b) Percentage correspondence
rei eve-groups
Table 4.14
1
2
3
4.14
3
4
Table
0
0
1
1
2
0
0
0
0
2
0
0
4.15
1
2
3
4
0
0
50
50
0
0
50
50
4
100 0
0 100
0
0
0
0
Mean correspondence
186
releve
67%
of
mirror-images
approach,
are
available
on the PHYTOTAB-PC
shown
zero
by
separation
excluded.
program
units
and
Both
package.
approaches
Noise
classification
are
is absent,
as
efficiency
is
100%.
These
in
results
the
show that
PHYTOTAB-PC
produce
results
possible
position
cessing
time
similar
tests
results
would
the releve
releves
tion
units
particular
indicate
on
be
set.
the
centage
but
it
the
position
be
the
same
result
data
ranging
reduced
of both
terms,
is
user
should
the
lowest
is
an
each
sets.
Pro-
preclude
that
the
aware
approach
that
does not
total
separa-
obtainable
efficiency
approach
be
can
in
assumed
and that the
of a TWINSPAN
set used
in Table
values,
for
a
however,
improvement
with
(Hill
1979a)
4.14,
with
from 1 to 8 ascending,
as shown by the 21 separation
is
tested
computer,
using the heuristic
Classification
to
are illustrated
sequences
sets
approach)
for small data
on a mainframe
necessarily
heuristic
being
sequencing
on
all
tested.
gives
sequence
releves
However,
obtained
data
releve
methods,
data
similar.
not
of the
efficiency
large
need
cation
is present,
even
for releve
(heuristic
approach)
in each possible
that
4.15
with
(permutation
limitations,
used
package
comparable
classifications
Table
program
sequence
test
the algorithms
74%.
The
units
results
a mean
are compared
correspondence
the
initial
as required.
Noise
and classification
differences,
in Table 4.16 in which
classifi-
between
the
the grouped
in both
absolute
two
number
and
per-
of 67%.
187
TABLE 4.17.- TWINSPAN classification of the synthetic
data set in Table 4.14,
using a random releve input sequence. Total separation units=14. Classification efficiency=82%.
188
Releve
number:
00000000
18243567
Species 3
Species 4
Species 5
species 6
Species 7
Species 8
Species 13
Species 14
Species 15
Species 16
Species 18
species 1
Species 2
Species 17
species 11
species 12
Species 19
Species 20
species 9
Species 10
++
++
++++
++++
++ ++
++ ++
++ ++++
++ ++++
++++++++
++++++++
+
+
++
++
++
++++
++++
+ +
+
++
++
The
TWINSPAN
by releves
results
4i2
are not
and 8i1
SPAN classification.
(Table
same
4.17
data
re leve
gives
set
numbers
have
TWINSPAN
been
result
4.14
retained.
The
4.15)
The
results
in Tables
grouping
of
releves
because
ever,
there
is
releve-groups,
lack
It is noteworthy
SPAN
gives
that
a better
with
a number
the
by noise
present
initial
in this
releve
releve
previous
which
in terms
comparison
the
two releve-groups
a random
result,
in
initial
is
efficiency
correspond
sequence
of the
original
a mean correspondence
first
4.14.
the
from the
correspondence
the
in the
differ
4.14 & 4.17
gives
of
where
as shown
(3i8)
in Table
but
However,
results
TWIN-
classification
4.15,
&
formed
in the
units and a classification
of 82%.
sequences
groups
releves
positioned
order.
by 14 separation
the two releve
the extreme
of a TWINSPAN
a random
(Table
in that,
are contraposed
are centrally
in Tables
in
results
represented
the
used
sequence
4.14)
Furthermore,
TWINS PAN classification
Table
satisfactory
of
between
of 100%. How-
position
of
the
are contraposed.
sequence
for TWIN-
case, than the required
input
sequence.
The
data
set
releve-group
expected
groups
contain
definition
when
using
in
these
pattern
with
adequate
used
for
or
is
TWINSPAN
gaps.
final
contains
consistent.
where
These
Care
to select
Worse
outliers
results
classifications
sequence.
TWINSPAN
examples
should,
species
are
show
in
no
outliers
results
present
that
terms
and
TWINS PAN
of
furthermore,
upon which
could
and
be
releveis
in-
releve-group
be
to group
exercised
releves.
189
> .•....
> •..••.
5
> .•....
3
4
> ..•.•. 2
> ....••
•••••• 7< > •••••• 6
FIGURE
190
1
> •••••• 8
4.3. - DECORANA ordination of releves using data in Table 4.14 and
the CANOCO program. Arrowheads indicate position of each releve.
The horizontal
axis is axis 1 and the vertical axis is axis 2.
On the
tion
other
of
hand,
the
1986),
same
(Figure
grouping
Furthermore,
sequences
result
data
4.3)
releves
correspondence
the
set
of a DECORANA
(Table
shows
complete
with the groups
between
the
classification,
thus
releve
formed,
(Ter
in
4.14,
Braak
terms
of
as the mean
sequences
also occur
(first axis)
ordina-
is 100%.
in similar
and the
PHYTOTAB-PC
latter
in reverse
order.
set, the results
of the DECORANA
ordination,
therefore,correspond
far
the
better
of the
with
TWINSPAN
not affect
because
from cluster
Van Staden
gradient
cation
the
analysing
within
but
the
DECORANA,
is not
species
diagnostic
which
releves,
effectively
can often
reduces
of
was not a species
by community
did
therefore,
supports
the
the
for classification
are
obtained
noise
redundancy
in
Gauch
the
main
This
that
and using
with
in the
of DECORANA
by releve
matrix,
pattern
be from one third
same releve-groups.
releve-groups
the
form
environmental
of
of
(1982) mentions
but
a
is an ordination
is, those
the
synoptic
species
releve-groups
table.
non-diagnostic
releves
appli-
ordination
to a half of the
form
ordina-
the aforementioned
ordination.
in the
releve-group
reduces
either
sequence
in determining
components
species
as
with
data
in large data sets.
the
of ordination
this
input
suitable
his study area. However,
to
and
results.
of the difficulty
diagrams
contributing
which
Changing
For
than
(1992) has also shown the relevance
diagnostic
of
classification
classifications.
classification
by itself,
in
PHYTOTAB-PC
the DECORANA
PHYTOTAB-PC
tion
the
CANOCO
in Table
grouped
the ordination
with
using
correspondence
formed
two
the releve-groups
in both
4.14)
(Hill 1979b)
species
species
belonging
that excessive
This
total
to
the
noise
can
191
of the first data set in the second
TABLE 4.18.- PHYTOTAB-PC classification
study area. Total separation units=127. Classification
efficiency=70%.
Releve-group
Releve
number:
number:
11
11
Chaetacanthus
Sutera
5
11.
1
1
1
congesta
subsp.
congesta
21
sp.
Cymbopogon
1+ 11 1
1111
111
1
11111
11 1
1 11
11
sp.
1111 1
Anthospermum pumilum
Heteropogon contortus
excavatus
Eragrostis chloromelas
Hyparrhenia anamesa
Elionurus muticus
Conyza podocephala
Vernonia oligocephala
Hermannia cf. grandifolia
Crabbea angustifolia
Verbena brasiliensis
Protasparagus
suaveolens
l1elinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
192
4
010 02
341 45
11
minuta
Aristida
3
columbaria
Tephrosia capensis
Salvia runcinata
Tragus berteronianus
Phyllanthus maderaspatensis
Aristida congesta subsp. barbicollis
Eragrostis pseudosclerantha
Helichrysum nudifolium
Tagetes
2
012 0110011100
554 9378212667
CJ
Helichrysum rugulosum
Lippia scaberrima
Convolvulus sagittatus
Scabiosa
1
2122121
3921800
11
22 22
1
22
3
12 1 21
1
1 11
1
I1ll 1 II III
22 1 1
23
133
2 3 33 232 231
3333223 333 3333433233
33333 3 333 3333433333
2222 22 222 22 2222 23
1 lllll
III llllll1ll1
11 II
1 II
1
III
111
11 1
II
11
1 1
324
333
221
1
II
11
1
1
1
+
1
33
33
22
II
11
11
TABLE
4.19.- PHYTOTAB-PC
classification
of the second data
study area. Total separation units=99. Classification
Releve-group
Releve
number:
Chaetacanthus
1
234
5
01100 1121 21 2200 0102101120
62143 734918
2352 7690580451
number:
1 1
sp.
BJ
Hermannia cf. grandifolia
Hibiscus microcarpus
Helichrysum
Phyllanthus
Themeda
[]
11
11 111
11 11
11
barbicollis
triandra
Heteropogon
1 1111 111
11 1
I
contortus
Eragrostis chloromelas
Elionurus muticus
Hyparrhenia anamesa
Conyza podocephala
Trachypogon spicatus
Vernonia oligocephala
Scabiosa columbaria
Crabbea angustifolia
Cymbopogon excavatus
Brachiaria serrata
Anthospermum pumilum
Tephrosia capensis
Melinis repens
Eragrostis capensis
Tagetes minuta
Helichrysum rugulosum
Aristida congesta subsp.
Gazania krebsiana
Dicoma zeyheri
Eragrostis racemosa
Sonchus wilmsii
193
11
nudifolium
maderaspatensis
Aristida congesta subsp.
Indigofera zeyheri
Sutera sp.
set in the second
efficiency=72%.
33323
22222
33333
111 1
111
11111
1111
11111
3333
11
11
11
12
3333
1221
3333
1111
1111
1 1
11
11
32
1111
1
21 2
3323
2222
3333
1111
11 1
111
1111
1
2 223
11
1
1
1
1 1 1 11
11 1
1 1 11
1
1
congesta
33
21
33
11
11
11
11
2 2121 2 1 21
3333233333
2322 22223
333333 333
11111 1111
111111111
11111111 1
111111 1
111
111
1 22
11 1
1
111
11
1
1
1
1
+
II
1
1
1
2
3
3
1
1
1
3
3
3
4
2
3
3
3
3
1
3
4
5
2
4
II
5
FIGURE 4.4. - Position of 2 x 2 m sampling units
in the first 10 x 10 m quadrat showing releve
grouping according to the classification
(Table
4.18), by releve-group
numbers in the position
of each releve. Each of the releve-groups has a
common border with the 10 x 10 m quadrat. Only
one releve-group
(3), exhibits spatial integrity.
3
II
5
II
4
4
2
5
2
5
5
5
5
5
5
1
1
--
--
2
II
3
==
1
1
1
II
5
5
II
4
--
II
2
II
--
II
II
4
FIGURE 4.5. - position of 2 x 2 m sampling units
in the second 10 x 10 m quadrat showing releve
grouping according to the classification
(Table
4.19), by releve-group
numbers in the position
of each releve. Each of the releve-groups has a
common border with t~e 10 x 10 m quadrat. No
rei eve-groups exhibit complete spatial integrity.
194
influence
sites.
DECORANA
The
argument
in vegetation
is no
results
that
relevant
such discontinuities
discontinuities
obtained.
The
a) greater
data
is
options
reciprocal
averaging
classified
option
using
the
influenced
A
combined
sampling
unit positions
In Table
4.18
is
similar
argued
reduce
study
is
shown
tested
4.18
in Figures
increase
classification
without
in the
the total
efficiency
analysis.
The
vegetation
is
purely
according
likely
to
to
be
of the two 10 x 10 m
with
the
diagrams
last
of the
(releve-group
species.
community
2 and
4.
at the
middle
separation
to
variables,
variables.
be sequenced
gap
b) point
analysis,
diagnostic
releve-groups
the
because
4.4 and 4.5 respectively.
except
3,
be
component
also
and 4.19
community
to eliminate
will
ordination
and environmental
is
If no
other
because
classification
3 should
process.
correspondence
of environmental
between
releve-group
this would
the
principal
1982)
determine
preferred
c)
is to classify
releve-group
situated
that
by
and
classification
in Tables
(Gauch
programs
is
oppo-
discontinuities
with axis comparisons;
all the releve-groups
but
releve-groups
However,
given
characterized
species,
not
are
releve-group
DECORANA
as
of the PHYTOTAB-PC
are
one
floristics
combined
quadrats
5) are
of
canonical
by the selection
The results
only
such
was
aim of this
floristics.
PHYTOTAB-PC
scattergrams;
and
pattern
for classification
is possible
the
and
of the classification
version
available
last-mentioned
whereas
on
noise
can determine
the
present,
flexibility
are
need
as part
CANOCO
printed
the
because
are
that
ordination
and hence
longer
and
59%. The
Table
4.19
diagnostic
It
could
end
of the
units
reason
be
of the
matrix.
to 147 and
for this
195
TABLE 4.20. - PHYTOTAB-PC classification
of the combined
sets in the second study area. Total separation
units=375. Classification
efficiency=68%
Releve-group nuaber:
Releve
nuiber:
1 2
3
~ 5
02 121221 010011110 02 U~,~
~5 009218 618237629 54 9'08~6
Helichrysu, rugulosUI
Lippid scaoerr i'd
Convolvulus sagittatus
data
6 7 8
9 10 11
12 13
13 5 3~~ M 33 0014 3~ 223222
58 0 5950 37 23 7142 761 891763
11
11
11
~
Salvia runcinata
Tragus berteronianus
Phyllanthus .a<ieraspatensis
Eragrostis pseudosclerantha
11
11
1111
1111
111
Verbena brssi l iensis
·11
Eragrostis capensis
Hibiscus sicrccsrpos
11
~
Ej
Helin is repens
Irdigoier«
zeyheri
~
Trachypogon spicatus
111 1111 1 1 1111 1 111
Scabi osa col u.bar ia
Heteropogon contortus
Eragrostis chloro.elas
Hyparrhenia enssess
EJ ionurus niticus
Conyza podocephaJa
Vernonia oJigocephala
CyJbopogon excavatus
Anthosper.u. pu.ilu.
Crabbea angustif ol ia
Her.annia cf. grandifoJia
Tagetes .inuta
Tephrosia capensis
Brachiaria serrata
Sutera sp.
TheJeda triandra
Chaetacanthus sp.
Aristida congest. subsp. barbicoJJis
Aristida congest. subsp. congesta
Jlelichrysu. nudifoliu.
Verbena tenuisecta
Athrixia eJata
Protasparagus suaveolens
Gazania krebsiens
Di cosa zeyber i
Eragros ti s rscesoss
Sonchus vilssi)
11111111
111
2 2
12221122
33
33
22
11
11
23
323332
3 3333
22222
11 111
11
3
11
11
11 1
1
11
~332.13
333433333
22222 22
111111111
1 11 1
3 23 33
1 1111 1
1 1
1
1
1
11 111
h 11
122211111
33
33
22
11
1
3323333
3333333
2122122
1111111
111111
2 322
1 1 11
1 1
1
.1 3332
3 333
2 222
1 1111
1111
22
1 1
1 1 1
1
33
33
22
11
1
3
16
2 22 3211~
33
33
22
11
1
22
1
33
33
22
1
11
3&23 331
3333 331
3121 223
1 1 111
11 111
323 331
111
1
1 111
1111
323333
313331
222232
1 1111
11111
33
1 1
111 1
11
111
1
1
1111
1
1
111
1 21
11
~
1~
1
11
1
1
1
11
111
1
1 1
1 111
11
111
111 1 11
11
111
1
1 111
22
\
196
2
2
II 13 II
4
-3
3
3
-3 II 11
II
3
2
2
3
II 11
3
1
II
9
5
5
7
II 11
5
5
8
8
8
2
5
==
12
6
12
12
6
2
13
10
10
--
--
II
II
--
3
-11
FIGURE
197
5
II
8
II
5
==
II
3
II
9
II
1
4
13
13
13
13
4.6. - position of 2 x 2 m sampling units in both
10 x 10 m
quadrats showing releve grouping according to the combined
classification
(Table 4.20), by releve-group numbers in the
position of each releve. Each of the releve-groups
has a
common border with the 10 x 10 m quadrats except for releves
32 & 33 (releve-group
10), which are completely included
within other releve-groups.
Only three releve-groups,
(6,
9 & 11) comprising releves: 15, 38; 3, 47; and 7, 1 , 14 &
42, respectively,
intersect both data sets.
is the distribution
capensis,
Tagetes
releve-group
more
gaps
regarded
minuta
were moved
than
as
of species
those
such as Melinis
and Helichrysum
repens,
rugulosum
which,
to the right of the matrix
obtained.
intermediate,
This
would
releve-group
floristically,
between
Eragrostis
if the
introduce
is,
therefore,
rei eve-groups
2
the
m
and 3.
All
the
releve-groups
quadrats.
The proportion
represented
known
obviously
group
have
the
by
each
proportions,
sented.
Although
common
borders,
releves
in each
especially
The results
a single
classifications
second
shown
As
in
grouped
quadrat.
in Figure
Table
community
most
unknown.
This
to
are
supports
in Table
for the
positions
units
The
of
first
the
could
releve-
borders
the
with
classifica-
into account.
4.20.
for the
un-
releve-group
have common
scale is taken
sequences
69%
10
are repreone
of that
the two 10 x 10 m quadrats
releve
The
which
is restricted
of the releves
are given
x
the 10 x 10 m quadrats,
all the releves
releve-group.
10
of the vegetation
is, therefore,
integrity
in that
classification
bined
with
in area than the proportions
of combining
for the
borders
lie outside
if the detailed
dence
the
releve-group
spatial
4.4),
common
of the variation
which
be larger
(Figure
tions,
have
and producing
Mean
correspon-
separate
quadrat
and
and
classified
35%
units
comfor
are
4.6.
4.19,
diagnostic
in the non-diagnostic
the
position
species
section
of
the
releve-groups
in Table 4.20, are related
of the matrix,
without
to species
such as Hermannia
cf.
198
grandifolia,
Sutera
congesta
subsp.
decrease
Themeda
sp.,
which,
would
classification
sequenced
at the
of the mat.r Lx ,
right
are
the
releve-groups.
considering
the
sequencing
with
influenced
by
diagnostic
all
secondary
be
deemed
be
occurs
in
the
for
This
in
the species
in
releve
and
must
be
species
approach
its
between
of
diagnostic
field
the
importance
grouping
all
were
species
intermediate
into
the
species
is
and
determined
modified
for
hand,
efficiency
by
can be moved,
that
prime
and
a
vicinity
non-
appears
where
mapping
units.
because
it
In
user,
the
to
species
and
not
is
only
be
total
changed
floristic
relationships
species
species
without
for
releve-group
more
been
i.e
the
there-
outliers
where
this
work,
outliers,
can
on
the
as
the
classification
releve-group
Van
of
can,
delimitation,
However,
delimiters
in
is
distribution
minimum
affecting
units
or removed.
species
illustrated
has
sequence
sequences
Releve-group
separation
inserted
classification
shows
allowing
sequences
in
The
only
Tables
sequenced.
moving
importance
releve-groups,
the
species
can
or
of
releve-groups.
necessary.
programmatically
shown
made.
importance
criterion
other
matrix,
Furthermore,
determines
environment
fore
the
can
the
on these
and not merely
grouping
what
Based
Hand
releve-groups
emphasizes
logical
sequence
this
the
of
these
units
species.
releve
over
a
species
correspond
with
This
congesta
separation
floristically
in a matrix,
section
before
diagnostic
because
as
releve-groups.
considered
The
regarded
all species
diagnostic
if
Aristida
and
increase
efficiency
releve-groups
adjacent
triandra
Staden
affect
delimiters
(1992)
has
a community
199
ordination
by reducing
the effect
of explaining
only
three
groups
which
rei eve-group
bined
classification
combined
be
10 x
retained
within
by two
because
unit,
all plants
within
the study
tiguous
sampling
units,
this
tion
was
units,
inadequate
included
vegetation
units
adequately
represent
which
each
two data
be
reliably
terms
the
is
the
done
of
that
a
only
to
but
also
the
represented
to
Thus
the
within
the
respective
vegetation
the
unit
species
that
study
units
can
of variation
degree
the
the
area.
not
vegeta-
words,
the
·vegetation
units
of
syntheses
are
intersected
should
unit
by
synthesized
the study
adequacy
within
are
In practical
reliably
within
the
can only
synthesized
areas.
vegetation
This
of the
the
to
which
sampled
that
small
study
sampling
Al-
too
units
included
in
with the con-
in synthesizing
be
com-
different
concerned.
are
that
vegetation
area
of
of
the
a different
In other
area
suggested
vegetation
proportion
area.
study
(Figure
illustrates
variation
releve-
therefore,
from
sampled
the inconsistency
the
study
the proportion
unknown.
the
variation
where
within
means
borders
study
It is, therefore,
included
this
because
in
is a part. Hence,
sets.
entirely
included
of the
the
unit,
at the scale
nevertheless,
within
has
intact
represents
area were
in terms
form
remained
each
releve-group
the vegetation
to
r-e Lev e s in
vegetation
releve-groups,
each
This
classifications
original
A single
though
sampling
quadrats
and only one species-group
represented
variation
both
its
obtained.
in the data.
in the separate
classification.
10 m quadrat
of SD units
intersect
did not occur
No
could
number
less variation
releve-groups
4.6).
the
the
relates
the
study
variation
area
not
area
is
also be considered
200
=
TABLE
4.21.- Synoptic version of Table 4.18 using
PHYTOTAB-PC.
Total separation units=5.
Classification
efficiency=82%
Community:
1 2 345
Helichrysum
rugulosum
Lippia scaberrima
Convolvulus
sagittatus:
Scabiosa
columbaria
Tephrosia capensis
Salvia runcinata
Tragus berteronianus
Phyllanthus
maderaspatensis
Aristida congesta subsp. barbicollis
Eragrostis pseudosclerantha
Helichrysum
nudifolium
Tagetes
1
minuta
Chaetacanthus
Aristida
Sutera
+ 3
2
2
+ 2
2
+
2
sp.
congesta
subsp.
congesta
sp.
Anthospermum
pumilum
Heteropogon
contortus
~
~
Cymbopogon
Eragrostis chloromelas
Hyparrhenia
anamesa
Elionurus muticus
Conyza podocephala
Vernonia oligocephala
Hermannia cf. grandifolia
Crabbe a angustifolia
Verbena brasiliensis
Protasparagus
suaveolens
Melinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
201
+
excavatus
5 5 5 5 5
5 5 5 5 5
5 5 4 5 5
5 5 5 + 5
2 4 2 4 5
2 + 2 4 5
2
2
2
1
+
+
+
.
+
'
+
TABLE
4.22.- Synoptic version of Table 4.19 using
PHYTOTAB-PC.
Total separation units=4.
Classification
efficiency=75%
1 2 3 4 5
Community:
Chaecacanchus
t]+ +
sp.
Hermannia cf. grandifolia
Hibiscus microcarpus
Helichrysum
Phyllanthus
nudifolium
maderaspatensis
Aristida congesta subsp.
Indigofera zeyheri
Sutera sp.
Themeda
rn
barbicollis
m
[3
triandra
Heteropogon
contortus
Eragrostis chloromelas
Elionurus muticus
Hyparrhenia
anamesa
Conyza podocephala
Trachypogon spicatus
Vernonia oligocephala
Scabiosa columbaria
Crabbea angustifolia
Cymbopogon excavatus
Brachiaria serrata
Anthospermum
pumilum
Tephrosia capensis
Melinis repens
Eragrostis capensis
Tagetes minuta
Helichrysum rugulosum
Aristida congesta subsp.
Gazania krebsiana
Dicoma zeyheri
Eragrostis racemosa
Sonchus wilmsii
202
rn
~
5 5 5 5 5
5 5 5 5 5
5 5 5 5 5
5
congesta
4 5 5 5
3 5 5 4
5 3 5 4
4 3 5 5
+
5 3
4 3 + 4
3
2 5
2 + +
+ +
2
3 + 3
5 +
+ + 3
+
+
+
5
5
4
3
2
2
2
1
+
+
+
+
TABLE
4.23.- Re-classification
of synoptic Tables 4.21 & 4.22 and emphasizing
communi
diagnostic species. Total separation units=32. Classification
efficiency=76%.
Releve numbers for Table 4.22 have been renumbered from 6 to 10
0 0 0 0 1 0 0 0 0 0
5 4 6 8 0 9 3 7 2 1
community
number:
Eragrostis
capensis
Indigofera
zeyheri
G+ +
r
Salvia runcinata
Tragus berteronianus
Eragrostis pseudosclerantha
+
~
Hibiscus
+0
microcarpus
Helichrysum rugulosum
Lippia scaberrima
Convolvulus sagittatus
Eragrostis chloromelas
Hyparrhenia
anamesa
Elionurus muticus
Conyza podocephala
Vernonia oligocephala
Cymbopogon excavatus
Hermannia cf. grandifolia
Anthospermum pumilum
Heteropogon
contortus
Crabbea angustifolia
Scabiosa columbaria
Tephrosia capensis
Trachypogon spicatus
Brachiaria serrata
Sutera sp.
Tagetes minuta
Melinis repens
Aristida congesta subsp. congesta
Verbena brasiliensis
Chaetacanthus
sp.
Themeda triandra
Aristida congesta subsp. barbicollis
Helichrysum nudifolium
Phyllanthus maderaspatensis
Verbena tenuisecta
Athrixia elata
Protasparagus
suaveolens
.Gazania krebsiana
. ,
Dicoma zeyheri
Eragrostis racemosa
Sonchus wilmsii
203
ill
+ 2
2
+
5
5
5
5
5
5
5
5
5
5
+
4
5
4
5
4
5
5
5
4
5
4
5
5
5
5
5
+
2 +
5
5
4 5
2 +
3 5
2
4
+
+
4
5
5
5
5
5
2
5
5
5
5
4
4
5
5
4
5
2
3
2
2
4
4 3 2
3 + 2
4 5
1 + 3
5 4
2 3
1
2
3 3
3
+ 2
1
5
5
5
5
3
3
4
+
5
5
5
5
4
5
5
5
5
2
+
+ 2
+ 2
3 4 3
3
2
3 5
+
5
5
5
2
+
3 +
2
5 4 +
4 3
3
2
3 1
3 2
+
+
+
+
+
+
+
+
+
when
describing
The
classification
sampled
quadrats
can be
be
vegetation
found
with
4.21
synoptic
detailed
produce
a
No
package.
species
the
only
in Table
species
Table
4.23.
The
which
of
groups
and the relationships
tables
vegetation
matrices,
Therefore,
has
not
the
4.23
data
can
are
of
were
4.23
PHYTOTAB-PC
of
unchanged
the
is the
original
remains
between
the
sampling
diagnostic
to
first
but
the
of the
which
The
species-
species-group
the releve-groups
synthesis
with
five.
releve-groups,
unchanged
com-
are reduced
eight
to
re-
to facilitate
species-groups
of
program
the
in
community
shown
a total
is a
objectively
made
only
4.22
Table
shows the result
the
re-classification
improved
unit variation
4.18;
is the last diagnostic
the
tic
using
have been
remains
formed
considerably.
such
and re-classifying
from
integrity
synoptic
and that
of Table
diagnostic
which
4.21
discontinuities
effectiveness
Table
classification
group
that
contiguously
indicating
decisions
In
The community
species-group
m
efficiencies
classification,
process.
combined
data
and Table
and 4.22
non-diagnostic
parisons.
4.19;
subjective
classification
show
10
area.
classification
version
of Table
single
x
10
study
for large scale work.
is a synoptic
4.21
high
for a single
two
.4.19)
&
sampling,
programs
Tables
the
sampled
Furthermore,
version
combining
of
4.18
(Tables
the PHYTOTAB-PC
Table
results
in contiguously
classified.
obtained
unit variation
in
which
species-
has altered
separate
confirms
synopthat
was inadequate.
204
Inadequate
vegetation
above,
also
as
can
suggested
appears
that
to
number
of
is
why
division
a
data
has
by
only
be
Although
arbitrary.
This
data
set
usually
such
is
a
of vegetation
be
at
units
I
J
I
increase
J
number
doubtful
suggested
increasing
vegetation
units
However,
after
a
This
a
they
the
because
common
units
only
and
vegetation
and
could
also
in which
for
classification,
units.
regarded
be
the
highly
original
it can be assumed
classification
some
correlations
so that
classification
can
only
is confirmed
by Van
as
subdivision
possibly
habitat
increase
vegetation
be
on a verified
but
the
could
reason
so that
relationships,
improve
practice.
of
on the manner
floristic
a
so that
can
units;
in which
results
scale
a classification
Such
at
occur more than once.
division
larger
the
by:
in vegetation
vegetation
a
unit.
of subsets
the
are not based
shown
to
by
subdivided.
factor.
set
improve
is dependent
floristic
reliably
data
not
subdivisions
habitat
of
is
This
to improve
confirmed
It
vegetation
as
resulting
units
classifiability,
regarded
obtained
vegetation
effect
subsets,
units,
can
the
the
dividing
units
can
improve
into
of the vegetation
where
this
set
illustrated
into variations
the number
Clearly
to
as
classifications.
set
units
sampling,
of a data
(1982),
vegetation
vegetation
variation
increase
of
scale
variation
Coetzee
improve
particular
a
explain
by
division
unit
be
or
can
is
that
i.e.
other
only
subdividing
a
considered
a
Staden
(1992)
where
and
Mixed
,
the
study
Bushveld
area
(Acocks
could
1975,
be
divided
1988).
into
However,
Arid
his
Bushveld
koppies
which
are
205
TABLE
4.24. - PHYTOTAB-PC
random classification
(1) of the first data
set in the second study area. Total separation units=260.
Classification
efficiency=40%
00111001
82043417
Releve
number:
Eragrostis pseudosclerantha
Aristida congesta subsp. barbicollis
Chaetacanthus
sp.
Convolvulus
12 20 2201 210 200111
81 37 4251 563 069952
~1
~
1
sagittatus
Lippia scaberrima
Scabiosa columbaria
1
1 1
1
~
Helichrysum
rugulosum
1
~
sutera
Eragrostis
chloromelas
Hyparrhenia
anamesa
Conyza podocephala
Elionurus muticus
Anthospermum
pumilum
Vernonia oligocephala
Heteropogon
contortus
Cymbopogon excavatus
Hermannia cf. grandifolia
Tagetes minuta
Aristida congesta subsp. congesta
Tephrosia capensis
Crabbea angustifolia
Phyllanthus maderaspatensis
Salvia runcinata
Tragus berteronianus
Verbena brasiliensis
Helichrysum
nudifolium
Protasparagus
suaveolens
Melinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
206
1
sp.
IG
18
34323343
34333333
111 11 1
2222221
11 11 11
1 11 1 1
22 32
2 3322
11 11
1
1
21
2
1+
1 1 1
1 1
1
1
1
1
23
33
11
2
11
33
33
11
23
1
2
2
3
1
1
1
2
1
1
3333
3333
1111
2222
1
1
2 1
323 233333
333
33333
111 11
2 2 222222
1 1
1
111 111
21 22
3 2 333
3
1 11
1
1-1
11
1
1 1
1
1
1
1 1
1
1
1
1 1
1
1
1 1
1 1
1
1
1
1
+
1
III
TABLE
4.25. - PHYTOTAB-PC random classification
(2) of the first data
set in the second study area. Total separation units=272.
Classification
efficiency=37%
0211112
5404190
Releve
number:
0
Lippia scaberrima
Scabiosa columbaria
Helichrysum
rugulosum
1
11
1
I
1
1 11 11
1
congesta
Eragrostis chloromelas
Hyparrhenia
anamesa
Elionurus muticus
Conyza podocephala
Anthospermum
pumilum
Heteropogon
contortus
Cymbopogon excavatus
vernonia oligocephala
Hermannia cf. grandifolia
Sutera sp.
Tagetes minuta
Tephrosia capensis
Phyllanthus maderaspatensis
Tragus berteronianus
Verbena brasiliensis
Salvia runcinata
Eragrostis pseudosclerantha
Aristida congesta subsp. barbicollis
Helichrysum
nudifolium
Convolvulus
sagittatus
Protasparagus
suaveolens
Melinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
207
1
11
Crabbe a angustifolia
Chaetacanthus
sp.
Aristida congesta subsp.
1010 210 2 21 021 01000
3859 362 5 17 622 48173
2
3332332
333333
2222222
111 1 1
1 1
2 2122
3 3
1 11 1
1 11
11 1
1
1
1
1
3333
3333
2222
1111
11
2 2
3 3
111
324
334
2 2
111
1
2
2
1
1
1
3
3
2
1
33
33
2
11
11
2
3
1
1
1
11
1
1
1
1+
1
1
1
1
1
1
11
1
1
11
1
11
1
1
1
1
1
Q
221
333
333
222
111
1 1
1
3 3
32433
33333
2 132
11 11
1111
23
2 232
1
1
1
1111
1
11
1
11
1
1
1
1
1
1 1
1
1
1
1
1
1
1
+
1
TABLE
4.26. - PHYTOTAB-PC
random classification
(3) of the first data
set in the second study area. Total separation units=263.
Classification
efficiency=39%
200201 101 112 2101 0 02012101
563155 244 804 2619 9 23830771
Releve
number:
Scabiosa
columbaria
I
Sutera
sp.
Lippia
scaberrima
Hermannia
U
cf , grandifolia
1
1 11 1 11
1_1_1
__ 1
1_1_1_1
208
1
1
1_1_1
lJ
1
11
111
1
1
angustifolia
Eragrostis chloromelas
Hyparrhenia
anamesa
Elionurus muticus
Conyza podocephala
Anthospermum pumilum
Vernonia oligocephala
Cymbopogon excavatus
Heteropogon
contortus
Tagetes minuta
Tephrosia capensis
Aristida congesta subsp. congesta
Phyllanthus maderaspatensis
Tragus berteronianus
Verbena brasiliensis
Salvia runcinata
Chaetacanthus
sp.
Helichrysum rugulosum
Convolvulus sagittatus
Protasparagus
suaveolens
Melinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
1
0
1
Eragrostis pseudosclerantha
Aristida congesta subsp. barbicollis
Helichrysum nudifolium
Crabbea
1
333333
333333
222222
111111
332
333
222
11
III
1 1
1 1 11 11
332
323
1
2
2
11
1
1 1
11
1
1
1
1
1
1
1
111
233
333
22
111
1 1
1
3243
3333
2 12
11
1
1
2
2 2
32
11
1
2
1 1
1
1
3
3
2
1
43332333
4333 333
22222 32
11111111
1 11 11
1
1 11
3 2 33 3
2 222 1
1 1 1
1
+ 1
1 2
2
1 1
1
1 1
1 1
1
1
1
1
1
1
1
1
1
1
,
1
+
1
TABLE
4.27. - PHYTOTAB-PC
random classification
(4) of the first data
set in the second study area. Total separation units=296.
Classification
efficiency=32%
Re1eve
number:
11110202
24396430
Scabiosa
I
sagittatus
333 3 23
pumilum
1
3
1
1111 111
2
332
1
1
1 1
1 1
2
111
1
1
1
1
111
1
1
1
columbaria
Eragrostis chloromelas
Hyparrhenia anamesa
Elionurus muticus
Conyza podocephala
Heteropogon contortus
Vernonia oligocephala
Hermannia cf. grandifolia
Tagetes minuta
Sutera sp.
Aristida congesta subsp. congesta
Tephrosia capensis
Verbena brasiliensis
Eragrostis pseudosclerantha
Salvia runcinata
Lippia scaberrima
Aristida congesta subsp. barbicollis
Helichrysum nudifolium
Protasparagus
suaveolens
Melinis repens
Hibiscus microcarpus
Verbena tenuisecta
Athrixia elata
.
209
1
1
Cymbopogon excavatus
Crabbea angustifolia
Tragus berteronianus
Phyllanthus maderaspatensis
Anthospermum
00 21 01 100
28 21 46 551
0
Chaetacanthus
sp.
Helichrysum rugulosum
Convolvulus
12120120
75837019
,
32333332
3333333
22222222
1 1 1111
22212 2
1
11
1
1
11
1
1
11
1 1
33233333
33333333
2 23222
11111111
2 22
11
1 1
11 11
1
1
2
1
1
1
1
1
334
333
221
11
1
2 3
11 11
11 11
1
11
2
+1
1
1
11
11
1
1
1
32
33
2
11
12
1
1
1
33
33
22
11
1 11
1
1
1
1
1
43
43
22
11
1
1
1
1
1
1
1
1
+
1
geographically
be
and floristically
separated
the Arid
from
4.24
with
a random
age.
The
have
been
the
same
and
The
with
number
the
Table
part
of
of 40% or less
tained
with
a random
should
be given
to such
and
are only
not
possible,
so that virtually
sort of pattern.
in a species
indicate
sequence
unique
and
that
any releve
The presence
distribution
over
What
an
adequate
classification
adequate,
is not
Table
diagnostic
that
that
a
are
necessarily
because
4.27
species,
and only
six
classification
which
not
can be ob-
much
but
the
arbitrary
credence
a class i-
solutions
are
could produce
some
in the form of outliers
the
releve
sequence
or
preclude
efficiency.
classification?
efficiencies
user
to show that
sequence
of noise
of 100% classification
considered
and
Pattern
efficiencies
to that
many
the attainment
that
Releve-groups
efficiency
four of many tested
is
is
nine
pack-
classifications.
fication
then
species
is comparable
releve
generated
programmatically.
4.25 has 37% classification
Tables
been
4.18.
efficiency,
efficiency
efficiency
results
are
would
on the PHYTOTAB-PC
and classification
classification
These
have
for Table
of diagnostic
species.
results
available
sequenced
in all four Tables
diagnostic
gaps
Bushveld
floristically
sequences
as that
species
classification
whereas,
These
which
releve
generator
is the
less.
32%
4.27
number
formed
correlated
has
to
data
is evident
or
foots lopes
of the Mixed
Bushveld.
In Tables
40%
the
part
will
It
appears
from
of 60% or higher
not know
if,
for
can
the
be
example,
210
65%
is
better
no
the
obtainable
classification
higher
obtained
the
best
releve-group
These
changes
affecting
could,
was
relates
or
effect
than
sequence
sequence,
both
pattern
a
conducted,
that
It must be remembered
classification
however,
sequencing
obtained
to releve
species
the
further
In all the tests
programs.
efficiency
delimitation
without
through
efficiency
the PHYTOTAB-PC
classification
if
can be obtained.
classification
with
change
or
that
and
not
of which
can
efficiency
in terms
value.
of
species
sequencing.
The
releve
form
sequence
determines
releve-groups
groups.
The
releve
and
also
delimitation
sequence
which
the
of
releve-groups
is influenced
will
this
at
stage,
delimitation
nor
the
affected
the
classification
same.
with
and
The
advantage
correct
species
can be inferred,
group
can
species.
Gauch
This
pattern
is,
is an adequate
(1982)
species
the
to
balance
between
noise
on
to
given
releve-groups
no pattern
releve-group
will
will
remain
be
the
is that
of a classification
strength
minimum
selective
subjective
noise
a
classification,
sequencing.
through
based
to
the
value
to
releve-
releve-groups
adequacy
by pattern
however,
regardS
neither
efficiency
according
strength
between
at which
between
species
and
sequence
increase
what
to an extent,
delimitation
programs,
but
approach
sequencing
grouped
are not sequenced,
relationships
of this
be
according
by the scale
If the species
evident
can
relationships
are to be recognized.
be
releves
through
The
releve-
PHYTOTAB-PC
noise.
The
user
re-sequencing
of
decisions
to
as
and pattern.
(unco-ordinated
occurrences)
and
211
pattern
(co-ordinated
is decreased
then
pattern
as unquantifiable.
Noise,
that
in
however,
which
terms
which
to
can
sequence
outliers.
separation
overall,
which
pattern,
section
1.3. However,
mitted
rix
gradients,
without
3.5.2.1.
The
itation
which
species
sequencing,
a
releve
described
after
is strongest.
species
users
in terms
loss
and
releve-group
This supports
in the
of
of
hence
delimitation
to
terms
of
that produces
also be the
hypothesis
and
(iv) in
is not quancould
be per-
it permits
mat-
species
groups
form
suggested
to
in
section
is releve-group
delim-
scale
affect
and
can
The use of noise
classify
entropy
can relate
because
as
source of noise
shown.
that
delimitation
that
a
data set. The flexibil-
grouping
related
to
iii)
in
noise,
amount
information,
as has been
as a minimum
The releve-group
and
and also
sequence-related
essentially
sequence
related
sequence
is an advantage
last-mentioned
is
is
quantif ied
for a particular
sequencing
simplification,
be
L)
can be quantified
outliers;
the releve
of differences
by different
ity, in species
determine
can
namely,
for a given data set. This should
sequencing,
because
and
delimitation
first-mentioned
noise
is unquantifiable.
which
to
if noise
regards
sources,
sequence
mainly
in that
further
three
Li ) that
to releve-group
species
tifiable
blanks;
He
pattern
attributed· to
relates
units which
in
shown,
to a releve
and
The
noise
sequence
be
as opposites,
is increased.
has been
included
is related
least
As
is related
of
species
occurrences)
the
vegetation
to obtain
can
be
method.
programs
allow
for some
flexibility
,
in releve-group
the scale
delimitation
so that the
of the stratification,
classification
but all the releve-groups
can match
are de212
TABLE
Releve
number
23
19
10
22
4
25
20
11
18
9
21
16
1
6
7
14
15
24
8
3
12
17
13
2
5
213
4.28. - Commonality
index of releves
with species occurrences
in releves
and releve-groups
represented,
from
PHYTOTAB-PC,
for the first data set,
in the second study area
Commonality
Index
125
175
175
200
200
200
200
225
225
225
225
250
250
275
275
275
275
275
275
275
300
300
300
300
300
Species
occurrences
Relevegroup
5
7
7
8
8
8
8
9
9
9
9
10
10
1
1
1
1
5
5
1
3
1
3
1
3
4
3
3
4
2
2
3
4
3
3
3
3
2
11
11
11
11
11
11
11
12
12
12
12
12
limited
user
at
the
same
can adjust
releve-group
for
releve-group
this
reason
according
to
required
that
scale
so
Although
proficiency
field
and
fewer
thereby
processing
Furthermore,
to complete
that
process,
increasing
can
time
increase
increase
redundancy
also
should
150 releves,
sequencing
limits
do
4.28
the
numbers
product
the
of
and
not
have
such
limitations
should
required
repetition
to
be
in the
with
for maximum
releves
with
The other
are
set
data
efficiency,
Automatic
package
of
comp-
data
possible.
program
and
the
increase
where
PHYTOTAB-PC
species
are
with
that
to 1240 for 150 releves.
gives
the
PHYTOTAB-PC
in which
in either
in
with
sampling
It is
186
is also
000
programs
which
in the
dependent
on
hard
space.
Table
using
a
species
package
disk
not exceed
to the
objectivity.
appears
data
to
the
or removing
proportional
exponentially
suggested
limited
present,
the classification.
decisions
It is, therefore,
releve
are
by inserting
and
set size.
sets
scales
is inversely
stratification
cl~ssification
size.
mixed
delimitation
in a classification
of decisions
lexity
Where
delimiters.
Objectivity
number
scale.
commonality
program
each releve
of the extreme
package,
occurs.
central
releve-groups.
extreme
releves
that
Outliers
facilitate
can confuse
It
the
output
together
The first
releve-groups,
the
sequencing.
sequence
is
ze l.eve s ,
releve-group
seven releves
whereas
the
with
for
the
last
14 occur
identification
heuristic
this pattern
approach
occur
of
to
the
releve
in that an extreme
214
TABLE
4.29. - Commonality
index of species with species occurrences and
position in the classified matrix, from PHYTOTAB-PC for the first
data set in the second study area
Species
Verbena tenuisecta
Protasparagus
suaveolens
Melinis repens
Athrixia elata
Hibiscus microcarpus
Convolvulus
sagittatus
Helichrysum
nudifolium
Helichrysum
rugulosum
Lippia scaberrima
Chaetacanthus
sp.
Scabiosa columbaria
Aristida congesta subsp. barbicollis
Verbena brasiliensis
Salvia runcinata
Eragrostis pseudosclerantha
Tragus berteronianus
Crabbea angustifolia
Phyllanthus maderaspatensis
Aristida congesta subsp. congesta
Tephrosia capensis
Tagetes minuta
Sutera sp.
Hermannia cf. grandifolia
Heteropogon
contortus
Vernonia oligocephala
Cymbopogon excavatus
Anthospermum
pumilum
Elionurus muticus
Conyza podocephala
Hyparrhenia
anamesa
Eragrostis chloromelas
215
commonalitx
Index
10
11
11
11
12
18
21
24
27
32
34
36
38
42
44
45
Species
occurrences
1
1
1
1
1
Position
in matrix
single
occurrences
2
2
3
3
3
3
3
mainly
diagnostic
species
4
4
4
47
4
5
57
5
66
68
6
6
82
8
88
97
116
118
123
142
213
216
236
244
10
12
12
12
13
22
22
24
25
8
general
occurrences
TABLE
4.30. - Similarity co-efficients, using the.
PHYTOTAB-PC program package, for the
releves in the first quadrat, in the
second study area
Releve
number
23
11
22
21
6
7
2
8
3
14
1
25
4
10
9
20
13
17
12
16
5
15
24
19
216
Similarity
co-efficient
0,000
77,778
77,273
85,000
71,429
84,615
82,143
76,667
78,571
78,571
80,769
69,231
100,000
83,333
80,000
77,273
71,429
75,000
70,588
73,333
73,333
76,667
78,571
69,231
releve,
cause
with
that
however,
a species
releve
to occupy
is not required
releve
is needed
Table
4.29
using
outlier
the
the PHYTOTAB-PC
as only
program
matrix
not
species
No
as
of processing
pattern
general
for
is
diagnostic.
4.29),
all
are necessarily
in the relevant
Table
4.30
successive
obtained
releves
gives
pair
from
are
Similarity
repeated
study
the
the
and these
the
releve,
The exact
of the
output
together
starting
for
species,
the
position
with
occupy.
but
classified
This
sequence
is included
com-
single
and
to users.
matrix
with
not
in
group
the
as their
is
for
are, therefore,
species
middle
distribution
regarded
(Table
included
area, could be inadequate.
initial
similarity
releves.
The
commonality
identical,
namely,
sequencing,
unlike
co-efficients
starting
sequence
numbers
the
releve
(Table
25 & 4
(23)
4.28).
(100%
commonality
for a data set and is included
releve-group
for
each
is
that
Only
two
similarity).
sequencing,
to save processing
time
is
in
construction.
Classification
efficiencies
as
Van
are
package,
diagnostic,
the
of
sequence
classification
in
could
time.
that the species
species
Not
position.
and could be of benefit
formed
occurrence
releve-group,
an approximation
commonality
in the classified
pleteness
a central
to save processing
gives
required
from a central
follows:
for
Staden
some
(1992)
published
60%;
classifications
scheepers
(1975)
217
Kroonstad
al.
The
48%;
(1985)
Bethlehem
is
efficiency
considered
good
vegetation
units;
vegetation
units;
and
vegetation
units.
These
of
results
unit
borders.
classification
visible
vegetation
and
The
species.
environmental
such data
fied
is shown
and 44%
using
the
were
subdivisions
was
data
and
as
a
reliable
richness
of
overgrazed
weak
in classifying
support
of
the
releves
efficiencies
method
by
and
of
generally
experienced
results
with
obtained
generally
in the classification
These
62%
and
vegetation
sequencing
in
and
area
composition
richness
The difficulty
an
species
floristic
worked
species
set
PHYTOTAB-PC
inserted
in
were possible
had no community
limiters
(1975)
in
of
low
visual
the
environment
distinct
efficiency
in the
of
of 48%
classifica-
assessing
the
of a classification.
last-mentioned
limiters
et
Staden
stratification
achieved
a
Van
integrity
visibly
to
(Bethlehem).
values
no
facilitates
high
gradients.
efficiency
efficiency
The
a
sets
(Kroonstad)
This
Scheepers
with
and
by
between
were
attributed
differences
units.
grassland
tion
is
the
between
correspondence
gradients
clearly
62% and Westfall
obtained
of
correspondence
environmental
(1969)
60%
because
weak
Leistner
(1967)
64%.
classification
(1992)
44%; Leistner
diagnostic
necessary
(Westfall
program
three
1985)
package.
releve-groups
and removed
species.
because
et al.
the
between
Removal
original
was
reclassi-
Releve-group
in
which
de-
obvious
releve-groups
and insertion
which
of de-
stratification,
and
218
N
Sea I~: 1 :45 000
FIGURE
219
4.7. - The spatial relationships
of releve-groups
formed by
there-classification
of releves in Table 4.31. Figures
refer to the releve-group
numbers used in the reclassification.
hence
sampling,
was not according
classification,
species
sequences,
fication
ties
without
efficiency
can
be
represented
4.7
shows
releve-groups
group
on
their
with
the
associated
in Table
4.31
represents
where
are
the mean
between
environment
a much
positions
Table
integrity
better
and
the
4.31
a
species
is 65
in both
on
be
minimum
enhanced
releve-group
original
but
with
number
Sour
as was
sequence
shows a poor
correspond
re-
forest
extreme
better
Bushveld
of Table
the
case
comparison
comparison
Generally,
better
with
Furthermore,
releve-groups
was obtained
most
same
in the classifica-
classification.
of the re-classified
making
on the right
Sourveld
the
is then
classifications.
to
the
in
the
3
The differences
appear
are
thereby
Sourveld
is 36%. This
re-classified
3
4.32)
floristically
two
the
Releve-group
grouped
of the
correspondence
original
of
could
1 and
&
Mountain
correspondence
than
Classi-
the
based
of
are switched
4.31.
by
releve-groups
is
and
25 communi-
number
pattern
Mountain
classification.
releve-groups
re-classified
spatial
(Tables
North-Eastern
illustrated
of
species-groups
Releve-groups
4.32. The extreme
in the original
22
The
relationships
4.31).
geographically
68% and
the
pocket).
releve
with outliers.
spatial
of
to
of the re-
(baok pocket).
64% to
diagnostic
North-Eastern
left
than
tions
the
4.31
re-classification
species
classifications
associated
back
The results
programmatic
from
diagnostic
the
(Table
classification
in Table
community
of more
the
contrast
4.32
but
so that
both
in
in community
by inclusion
for
shown
is increased
(Table
classif ications,
outliers,
altering
identified
classification
Figure
are
to scale.
was
with vegetation
the
the
the
such that
pattern
220
on
the
aerial
between
the
obtained
with
As
with
photograph,
re-classified
species,
the
adjacent
low
constancy
improved
of
although
of the matrix
but rather
A
widely
spaced
have
are
for
for
absence
can be
inferred
be
fundamental
visual
without
species,
could
respective
obtainable
Such
if
constancy.
This
which
dimensions
to sampling,
speciesrefinement
however,
community
of
adequate
species,
the
diag-
which
sampling
of
sampling
sampling
would
to determine
suitable
be
can be as a result
To ensure
a
from the program.
invalid
the
to
probably
of the classification,
pattern
and
community
intermediate
be included.
diagnostic
communities
of
can
Ellenberg
prior
was
unit
require
species
unit
a
are
dimensions
sampling.
releve
is
would
inadequate.
community
classification,
diagnostic
for
than
such as 5 and 8 with
in their
necessarily
a low
species
(quadrats)
dimensions,
not
4.7),
as floristically
species
outliers
relationships
map.
diagnostic
more
is
(Figure
Releve-groups,
to show the basic
species
The
are regarded
more
spatial
releve-groups
was not the purpose
nostic
for their
4.20
community
classification
units
and
including
show
vegetation
releve-groups.
by
groups,
4.19
to
releve-groups
the original
Tables
diagnostic
used
a
regarded
by the
to
1974).
pattern
community
the
The
as
diagnostic
releve-related
other
species
Braun-Blanquet
sequencing
formation,
of
however,
species
in
noise
and
present
can
particular
its
presence
in the releve.
approach
species
a
and
This
(Mueller-Dombois
releves
be greatly
&
based
on
facilitated
by
221
recognition
of
species
a high
the
have
inclusion
diagnostic
of
the
that
not
of
floristics"
case
all species
It is for these
4.31)
is
visual
possible
the
3.2.4)
reasons
this
where
if the total
It
not to
but
a
better
hypothesis
4.32).
(iii)
still
composition
suggested
is clearly
classification
in
must be considered.
classification
more
occurs,
than
the
(Table
original
re-classification
data set. However,
classification,
preclude
community
which
to the
The
that
the
is, therefore,
classification
(Table
not
floristic
sampling,
rather
diagnostic
should
groups,
in the matrix
in a vegetation
"best"
community
that the PHYTOTAB-PC
classification
the
such
inclusion.
present
considered
supports
in
refers
(section
which
However,
are absent,
re Leve s indicates
the
in
constancy.
releves
species
"total
which
rei eve-groups ,
than
one
solution
the problem
hence
also
is
of what
the need
is
for veri-
fication.
4.4 VERIFICATION
The
results
fieldwork
Apart
for
this
is ongoing
from
the
section
in the main
the
units
stratification
factors,
are
classification.
value
of
shown
relationships
the
main
Such
improving
efficiency
between
units
and
criteria
in
relationships
vegetation
in Van
Staden
(1992)
as
study area.
classification
strength,
and
are
map
the
values
classified
differentiating
assessing
can
also
quality
and
the
have
as well
pattern
vegetation
environmental
adequacy
the
of
a
practical
as the. under222
standing
of vegetation
of limiting
It
environmental
is probable
could
cation
tion
that
be linked
has
unit
the
effect
It
on
so
is,
species. distribution,
that
the
ii. gradients
at biome
area,
Classifi-
influences
the
are
correct
influences,
a classification.
hence,
The
follow-
a classification:
for example,
should be present.
study
on a vegeta-
group
such
to be differentiating
applicable
environmental
to
for assessing
should be appropriate,
it is usually
in terms
influence.
environmental
to show
supporting
in a
influences,
necessary
basis
are suggested
not be expected
such
main
therefore,
correlation
ing guidelines
environmental
of averaging
a releve-group
environmental
i. scale
each
implications,
factors.
to one or other
basis,
apparent.
species,
and hence management
seasonality
can
at 1:50 000 scale
as
scale;
It is unlikely
factor will differentiate
that a different
each vegetation
unit
in a study area;
iii. environmental
because
relationships
effect.
Greater
with individual
species
distributions;
iv. environmental
words
particular
Furthermore,
mining
the
emphasized
class
relationships
the relationships
simple,
complexity
should be logical
could
be
in context.
In
and,
should make sense
for the
study area.
the use of the PHYTOTAB-PC
classification
program
and environmental
package
factor
for deter-
correspondence
the following:
i. environmental
ii.
be relatively
of the averaging
expected
other
should
relationships
intervals
are often
for grouping
hierarchical;
environmental
continua
are
not
223
TABLE
4.33. - Alphabetical
listing of species selected from the
PHYTOBAS data bank from undisturbed dune crests with low
rainfall (less than 250 mm) from Leistner (1967)
Acacia erioloba
Acacia haematoxylon
Acrotome inflata
Aristida meridionalis
Boscia albitrunca
Brachiaria glomerata
Bulbostylis hispidula
Centropodia glauca
Chamaesyce inaequilatera
citrullus lanatus
Crotalaria spartioides
Cynanchum orangeanum
Eragrostis lehmanniana
Heliotropium
ciliatum
Hermannia tomentosa
224
Jatropha erythropoda
Lapeirousia littoralis
Limeum arenicolum
Limeum fenestratum
Limeum sulcatum
Oxygonum delagoense
Phyllanthus omahekensis
Plexipus pumilus
Plinthus sericeus
Pollichia campestris
Requienia sphaerosperma
Sesamum sp.
Stipagrostis
amabilis
Stipagrostis
uniplumis
necessarily
iii. vegetation
changes
unit
in
Classification
the
equal;
adequacy
except
units
where
often
be attributed
truth
after
but
to
entire
An
of
the
can
to
factor
adequate
mapped.
occur.
to mixed
low.
These
correspond
same
by the
integrity
should
form
assess
of mapped
diagnostic
the
borders
species
as
points.
of
mappable
of outliers
such as in Figure
is not to
with
at the
The occurrence
scales
reliability
can
4.7. Ground
classification
as well
indicators
as the
for
the
if
its
they represent.
classification
is
values
also be assessed
be
outliers
community
communities
relevancy
need not necessarily
a classification
assess
relevancy
limits
environmental
vegetation
units,
and,
The
is
of
relevancy
little
of
value,
however,
a classification
proportiorial to the uses that can be derived
is directly
from it.
4.5 DERIVATIVES
community
structure,
analyses,
species
growth
cover
the PHYTOTAB-PC
(1992).
These
component
erably.
form
program
stand
assessments,
and
derived
using
and are illustrated
by Van
Staden
the understanding
and the uses
bank
cover
analyses,
programmatically
increase
programs
of the data
community
are
package
derivatives
interactions
composition
analyses,
relationships
No similar
An example
community
of vegetation
for a classification,
consid-
are available.
derivatives
is given
in Table
4.33
in
225
which
species
annual
occurring
rainfall,
required
on dune crests
are listed.
habitat
with
In this case releve
are input.
It is necessary
are required
as the data bank only' contains
A GIS
also
could
ronmental
Where
data
sets
species
required,
be
used
then
can be selected
bank
using
not
Boolean
to determine
listed,
static
dynamic,
ecology.
(1916)
and
relevance.
i
Bews
representing
envi-
than
all
one
vegetation
the
relevant
associated
species
in the field.
is
vegetation
from the data
vegetation
example,
The PHYTOBAS
units,
application
appear
the
for
data
be
bank
data set.
succession
which
potential
approach
to
of
scale
used
have
vegetation
in time requires
be comparable
are
inherently
of
vegetation
suggested
confuse
concepts
dynamics
units
could,
unit
for a stand so that only those
a moment
the vegetation
relevant
species
that
Inferring
information.
common
(1916)
so
where
releves
This
the
and
floristic
the
logic.
can
dynamic
with
"and"
of
temporally
units,
to retrieve
perspective
The
numbers
to know which
releves
representing
for a single
limit
250 mm mean
are available.
more
need be recorded
can also be used
A
select
over
species
units
applied
to
(coverages)
presence
less than
by
Clements
both
spatially
little
practical
units
from
sampling
that:
i.e. sampled
at the same
scale;
ii.
the condition
reference
iii.
the
of the vegetation
be known;
trend
or
units relative
to some
and,
direction
of
change
can
be
inferred,
for
226
..•
TABLE
A
B
C
n
E
227
4.33. - PHYTOTAB-PC applications excluding
transfer, corrections and file listing
data input,
Sequencing
Automatic releve sequence
Automatic species sequence
Alphanumeric
sequence
Ascending sequence
Random sequence
Reverse sequence
User sequence
Classification processing
Checklist compilation
Community composition analysis
Community cover assessment
Community & habitat correlation
Species cover relationships
Stand phase analysis
Synoptic matrix
Internal utilities
co-ordinate processing
Digital mapping
File merging
Format conversion
External utilities
3-D ordination plotting
Species name search, checking
Data bank
Data set splitting
Data sets synthesis
Number set comparisons
Plant identification key
Sample & stand dimensions
Statistics
and author
additions
Information retrieval
Species spelling checker
practical
relevance.
The bench
mark
inferred.
However,
(1978)
concept
in such
units
validity
for
could
is essential
fixed benchmarks,
can result
vegetation
or reference
be
scale
which
questioned.
serve
for
relevant-scale
work
It
far
more
for
from
re Lev e s
benchmark
central
on
tion
representing
could
part
species
be:
of
as
can be
ient, of releves
from
is
be
It is, therefore,
techniques,
only
be
a
releves
as
is
effective
unit.
Such
representing
the
community
to
with
that
veld
for
large
the
PHYTOTAB-PC
composition
and
the
areas,
gradto the
practical
stand
condition
monitoring,
the
vegeta-
relative
scale
a
based
composition
unit,
shown
vegetation
such
constructed
releve
a species
suggested
be
synthetic
the
from
of
ze Lev e s representing
appropriate
determined
including
or
with
to
their
prohibitive.
vegetation
the vegetation
succession
analysis.
adequate
used
representing
Thus
can
the
inferred
can
purposes,
unit;
be
to the
that
number
also
benchmarks
releve;
et al.
differences
reference,
would
is to be
by Foran
required
particular
synoptic
time-span
ment
a
a vegetation
such
Trend
benchmark.
a
composition
unit,
analysis.
feasible
as
The
benchmarks
appears
as suggested
and succession
they
if trend
for
phase
assess-
whatever
if based
on
an
classification.
4.6 PHYTOTAB-PC
The
applications
are summarized
as well
possible
in Table
as online
with
4.33.
The package
fault-finding.
processing
includes
speed
program
online
package
manuals
is dependent
on
228
matrix
size.
consuming
of
processor
speed.
an 80486
processor
The
the
PHYTOTAB-PC
which
can
addition
for
programs
For
research
required
a far simpler
options
are unique,
species
sequencing,
tion
component
cation
ensure
key
The amount
is
represents
growth
information
cessing.
This
flexibility
security
diskettes.
example,
and
to
tested
If
a
is
in
150
assured
offered,
presupposes
fication
theory.
by
data
a
total
bank
To this must
information.
all
of this
knowledge
this background
identififacilities
analyses.
project
400
species
be added
cover,
of
considerable
program
relevant
were
vegeta-
Reduction
requires
nature
of
Data
proand
integrity
and
the
to
separate
flexibility
of vegetation
a researcher
this
package
files
with
are
of the
in a typical
is still maintained.
a fundamental
Without
with
of
automatic
and plant
with the PHYTOTAB-PC
a package
Several
correspondence,
fieldwork
pattern
writing
package
sequencing,
analysis
of 60 000 cells.
is achieved
However,
releve
ze Leve s with
The
program
tool for vegetation
during
meaningful
utility
be developed.
factor
on
tool
analyses.
production
coupled
environmental
research
such as comparison
statistical
phase
These
a
vegetation
results
could
and
time.
essentially
purely
stand
in application
is
been
and comprehensive
a matrix
form,
have
environmental
generation.
For
programs
the
of data collected
vast.
the
such as automatic
analysis,
a powerful
size
and
package
time-
matrix
to test various
purposes.
most
on
objectivity
sequences
the
dependent
package
facilitate
is
is
can halve processing
program
number
sequencing
and
example,
which
of programs
grouped
releve
Automatic
classi-
is unlikely
229
to be able
to apply
menu driven
the programs
and a comprehensive
effectively
even though
online manual
they
are
is available.
4.7 REFERENCES
ACOCKS,
J.P.H.
Memoirs
ACOCKS,
of the Botanical
J.P.H.
Memoirs
BEWS,
1975. Veld types of South Africa.
J.W.
South
of the Botanical
1916. Account
Ecology
with
F.C.
B.J.
DENT,
of
M.C.,
of the Central
Department
Journal
of
vegetation
District,
Kruger
Research
of Agricultural
Washington.
stucture
and
National
Park.
Pretoria.
R.E.1987.
statistics
of the
Institute,
of Pretoria,
rainfall
Catchments
An analysis
Carnegie
S.D. & SCHULZE,
D. 1967. A plant
B.D.,
Mapping
mean
over southern
Unit Report
Engineering,
TAINTON,
Africa.
No. 27.
University
types
Society
survey of the Tugela
Survey
of South Africa
N.M. & BOOYSEN,
of a method
grassveld
Grassland
ecology
of the Botanical
development
three
in
of
Pietermaritzburg.
Memoirs
FORAN,
University
LYNCH,
Agricultural
EDWARDS,
succession:
vegetation.
and other
Natal,
57: 1-146.
of vegetation
notes on plant succession.
1982. Phytosociology,
thesis,
annual
3rd edition.
of South Africa
of the chief types
1916. Plant
landscapes
D.Sc.
Survey
40: 1-128.
4: 129-159.
development
COETZEE,
of South Africa
1988. Veld types of South Africa.
Africa
CLEMENTS,
Survey
2nd edition.
36: 1-285.
P. de V. 1978. The
for assessing
veld condition
in Natal. Proceedings
of southern
Basin.
Africa
in
of the
13: 27-33.
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J.E. & ~~LL~~S,
~REU~D,
Pitman,
GAUCH,
H.G.
M.a.
1982. Multivariate
1979a.
TWINSPAN
Unpublished
report.
Cornell
1979b. DECORANA
report.
O.A.
ecology.
and attributes.
Ithaca,
ecology
of the Botanical
Ithaca,
program
and reciprocal
1967. The plant
for arranging
table by
University,
University,
Memoirs
program
- A FORTRAN
analysis
Cornell
Kalahari.
in community
two-way
of the individuals
correspondence
LEISTNER,
- A FORTRAN
data in an ordered
classification
M.O.
analysis
New York.
multivariate
HILL,
statistics.
London.
Cambridge,
HILL,
~.J. 19~~. Modern business
New York.
for detrended
averaging.
Unpublished
New York.
of the southern
Survey
of South Africa
38: 1-172.
LONDO,
G. 1976. The decimal
quadrats.
D. & ELLENBERG,
vegetation
SOIL
RUTHERFORD,
Africa
Wiley,
CHARTS,
& WESTFALL,
- An objective
Survey
J.C.
Bethlehem
thesis,
TER BRAAK,
COLOR
M.C.
Botanical
SCHEEPERS,
ecology.
C.J.F.
new Eigenvector
analysis.
H. 1974. Aims and methods
1954.
R.H.
Africa
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technique
of
co.,
Baltimore.
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Memoirs
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54: 1-98.
ecology
1986. Canonical
Color
1986. Biomes
of the Highveld
Ecology
Munsel
categorization.
of South
University
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New York.
1975. The plant
areas
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33: 61-64.
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MUELLER-DOMBOIS,
MUNSELL
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D. Sc.
Pretoria.
correspondence
for multivariate
analysis:
direct
A
gradient
67: 1167-1179.
231
VAN
STADEN,
J.M.
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in die noordwes-Transvaal.
University
WESTFALL,
R.H.
Thabazimbi
van die Steenbokpan
1992. Die fitososiologie
of Pretoria,
M. Sc. thesis,
Pretoria.
1981. The plant
ecology
of the farm Groothoek,
District.
thesis,
university
M.Sc.
of Pretoria,
Pretoria.
WESTFALL,
R.H.
an improved
& PANAGOS,
method
belt transects.
WESTFALL,
R.H.,
ecology
M.D.
of cover
Bothalia
VAN ROOYEN,
1988. The plant
estimation
Bothalia
scale
-
using variable-sized
18: 289-291.
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of the farm Groothoek,
Classification.
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G.K.
Thabazimbi
(1985). The plant
District.
II.
15: 655-688.
232
CHAPTER
5. CRITICALEVALUATION
ANDCONCLUSIONS
Scale
The tendency
creasing
scale
unit
of
for
scale
decreases.
This
recognition
and
quirements
at
are
of
larger
hence
uniformity,
a
is
sampling
and
scales
to
unit
would
facilitate
in vegetation
as
vegetation
influence
is
implicitly
1: 8
or
be
be
000
quadrat.
to
For
required.
communication,
ecology
re-
can often
about
unit
scale,
where sampling
scale
from
de-
sampling
unit
of
on a 200 m2 sampling
however,
recommended that
cognizance
with
the
however,
sampling
recognition
smaller
for:
and vegetation
the
then
scale
000 based
scales
that
of
determining
At large-scale,
such
increase
important
mapping;
units;
to
cognizance
particularly
a stand,
Implicit
1:50
is
small-scale.
representative
about
increasing
on vegetation
dimensions
implicit.
heterogeneity
necessitates
environment
unit
vegetation
For
it
is
work be explicit.
Stand area
Definition
of
explicit
is
for
integral
the
its
in
vegetation
Although
to
It
scale,
tested
means by which
sampling
reduced
field.
ecology
not
the
Braun-Blanquet
are
the
be linked
is
implementation.
decisions
delimit
area
in
area
vegetation
to
decisions
these
stand
is,
in
this
work.
method but
relies
By linking
scale
and
the
therefore,
as a simple
scale
stand
is
The
made
stand
on researcher
to
becomes
stand
easier
recommended that
means of expressing
area
to
stand
scale,
in
work.
it
is
hypothesized
that
the
scale-defined
233
can
stand
also
conservation
relate
to
the
of a vegetation
minimum
sustainable
area
for
the
unit.
Reconnaissance
This
work
sampling,
actions
tion
confirms
as
during
naissance
Although
to determine
are
map.
methods
of
a
reconnaissance,
Braun-Blanquet
of both
satellite
other
variation
actions
and will
to
However,
for vegeta-
imagery
and aerial
and produce
recommended
depend
prior
method.
are made more explicit
vegetation
less critical
a pre-
during
to a large
recon-
extent
on
adopted.
it is possible
reconnaissance
field
the
by means
vegetation
particular
in
a reconnaissance
stratification,
liminary
necessity
recommended
photography,
of
the
to obtain
from an analysis
reconnaissance
with
the basic
of aerial
aerial
data associated
photographs
photograph
with
a
a comparison
analysis
was
not
made.
stratification
This
work
suggests
stratified
random
can be used
scale
for the
stratification
detailed.
satellite
and,
vegetation
a vegetation
map which
for
stratification
can also
serve
by the classification.
use
of satellite
although
of
stratifying
vegetation
imagery,
Application
from
purposes,
to be tested
procedures
larger-scale
are
sampling
apart
for a preliminary
as a hypothesis,
Suggested
that,
not
for
these
imagery
tested,
large-scale
computerized
the
for
small-
potential
of
stratification,
procedures
for
234
vegetation
for
stratification
simplifying
scale
i.
the
visual
lead
use
of
to
the
following
aerial
suggestions
photography
for
small
stratification:
that vegetation
tion
be primarily
structure
textual
ii.
has
that
and cover as perceived
vegetation
secondary
on the basis
iii. that
scale
stratified
according
by textural
to vegetaand con-
pattern;
and further
divisions
of the primary
of topography
and other
physical
can be taken
into account
units
factors;
by minimum
be
and,
area
compari-
a visual
pattern
sons.
The
use
of
aerial
photography
in this
analysis
of
aerial
photographs
rather
interpretation
method
fication.
a stratification
based
can
Thus
rather
also
methods
make
have
of a single
stand
than
stereoscopy
been
is
the
adequately
aerial
will
Vegetation
for
but
strati-
be vegetation-
pattern
small-scale
tested
in this way,
photograph
forms the primary
of vegetation
redundant
farm stratified
than
topography
topography-based.
not
analysis
work.
preliminary
appear
These
results
satisfactory.
location
computerized
random
with
seconds,
reduces
location
considerably,
as can
considered
increase
grid
number
together
tion,
whereby
way
generation
co-ordinate
here
conversion
decision-making
for random
also be applied
because
considerably
of
for a 4 mm grid map overlay,
and
to degrees,
facilitates
sampling.
the
experience
with decreasing
scale,
objective
Subjective
in the Braun-Blanquet
minutes
stand
method,
required,
which
and
stand
loca-
is not
could
in its application.
235
-------
Precision
in
proportion
to
information
location
location
the
reliance
systems
for
will
geographic
study,
stand
also
indirect
of
precision
rangefinders
for
in
apparatus,
required
at many
data
stand
the
not tried
in
in
purposes.
data,
stand
input
location,
to
this
However,
necessitated
and
precision
because
and the inability
geographic
is
Altimeters
for
were
on
optical
required.
of the
cost
of
to detect
three
beacons
recommended
for
precise
stands.
Geographic
stand
vegetation
location.
in direct
Precision
environmental
inadequate
techniques
future,
classification
stand
Trigonometrical
increase
data.
Visual
obtaining
proved
time
when
systems.
will
in the
environmental
adequate
methods
placed,
required
information
proved
greater
be
requirements
positioning
location
saving
time
location
and,
where
of stands
of sampling
by
satellite
although
random
not
tested,
stand
has a further
is
shows
location
is
advantage
potential
applied.
for
Precise
of enhancing
the value
data as such data can be used for monitoring
purposes.
Sampling unit area
The
method
study,
is
consuming;
of
sub-sampling
not
recommended
b) the methods
can lead to a low degree
c) sub-quadrat
sampling
has to change
case
plants,
for the
larger'growth
change
in
the
smaller
criteria,
forms.
an
a)
stand,
applied
sub-sampling
are based
of constancy
in the
the
of
because:
applied
plants,
with
a vegetation
is
on minimum
in a classified
forms,
to
It is not certain
adequate
sample
of
this
time-
area which
matrix;
from the criterion
growth
in
and
of rooted
overhanging
that,
the
even
larger
236
-
growth
forms
However,
is obtained.
the
use
flexibility
of
into
a defined
sampling
stand, .as
options"
Stands
suitable
size,
representative
quadrat
but
of representation
be
the
sub-sampled
area
be
limits
of
such
used
of
the
the
these
seems
which
hectare,
with more
for
defines
should
sampled
is often
be known;
large
in this
informal
the
point
sampling
limits
enhance
with
the
can
but
the
methods
can
using , for
lower
for the
a
case,
stands
study,
pre-determined
regression
comparative
for these
limits,
application
repeatability,
and
hence
can be used for areas
up to
purposes.
purposes,
Although
it could
the
probably
regression
be
improved
data.
~amp1ing
unit
Sampling
units,
1ocation
within
the
be located
with
vectors,
distance,
from
the
approximates
that
will
less method
criteria
and
scale with
species-area
adequate
location
as
to be enlarged;
area;
stand
be
introduces
validity.
The use of the
one
The
will
can
as applied
have
number
methods,
scientific
may
stand
plant
applied.
of
quadrats,
quadrats
within
example,
can be
using
recommended,
be that
such
the
centre,
stand,
of the
as a point
for
sampling
repeatability.
In the
stand,
the components
stand
of
defined
case
is circular,
can
of which
are direction
and
unless
the
in which
stand.
method
location
which
of very
case
Where
be
large
the
sampling
or informal
should
sampling
unit
area
sampling
unit
is by a plot-
sampling,
made
stands
known
i.e.
then
to
the
ensure
sampling
at
237
very
small
scales
positioning
recording
bility
of
by
monitoring
The
unit
for scientific
data
so
can
validity,
the
000
be
can
applied
in this
work
have
infra-specific
differences
in plants,
undetected.
criteria
scientific
validity
System
tions.
(S1)
No
Voucher
standard.
plant
plants
of
used
for
of
repeata-
to enhance
be
hence,
made
the value
benchmarks,
which
have
been
serve
as
and
a
by which
tain.
The recommendation
fore,
aimed
work
could
regarding
at the criteria
for
vary
plant
for
observanot
as
a
used
for
on
the
are
identified
the
degree
is very
identification
for identification
physical
depending
repeated,
the
International
and
plants
known
improve
states
information,
be
knowledge
botanical
character
criteria
this
the
of
have
making
can only
reference
the
Without
plant
determined
exist
detection
that
In
know-
otherwise
of
ecology.
Africa
ecology
might
identified
southern
vegetation
applied
are
species
permitted
in
known.
the
it is postulated
character
which
the methods
and
standards
specimens
improved
advantage
vegetation
similar
Furthermore,
used,
the
standards
identification
systems
from
basis,
by which
observations.
should
Apart
on a systematic
Metric
importance
and verification
concerned
the
be
geographic
to ensure,
and secondly,
data
then
The
is firstly,
researchers
gained
less
employed.
the
gone
or
and other purposes.
methods
of
1:500
location
that
identification
ledge
as
satellite
sampling
the
Plant
such
to
uncer-
is, there-
and not necessarily
in this study.
238
species
cover
Species
canopy
cover,
is preferred
to basal
can be derived
dependent
such
cover
the
the
where
The
cover
in
a
plant
are treated
number
cover-abundance
scale.
obtained
ever,
with
care
linear
method
of cover
should
proportionate
cover
a
other
hand,
class
scale
found
which
better
with
many
is preferred
preci-
with visual
for
scales
with
such as the Domin-Krajina
is that which
such as the wheel-point
because
is
scale,
and greater
determination
be exercised
scale
produce
than
can be
classes,
of cover
information
than can be obtained
advantage
a point method,
to
arithmetically,
of cover values,
an intermediate
An alternative
on the
method,
class
minimum
likely
matrix,
scale,
No
is
more
of cover
A simple,
scale,
classified
techniques.
because
Selection
a study.
number
values
sion is required
estimation
of
in the Braun-Blanquet
estimations
former.
Braun-Blanquet
pattern
classes.
aim
is used
cover
from the
on
as
which
such methods
should
be
can be
method.
often
How-
rely
on
converted
to
area
recorded
at
each
cover.
Floristic
The
data
recommended
sampling
species;
far
and
to
are:
growth
a point
of mean
floristic
species
form
determine
specimens.
as with
addition
minimum
unit
easier
herbarium
such
recording
for
canopy
each
than
or the
diameter
be
canopy
species.
precision
method
to
presence;
on-site,
Where
data
The
cover
plant
each
last-mentioned
by means
is used
for
of
literature
in determining
numbe r scale,
for each species,
permits
is
or
cover,
then
the
species
239
densities
No
to be calculated.
time-saving
capture,
advantage
which
was
also entailed
found
with
a greater
computerized
burden
field
data
in the field.
Habitat data
The
recommended
each
stand
minimum
are:
stand
description
stand/releve
ordinates
in degrees,
required
for herbarium
number;
minutes
and
labels,
and farm name
can be included.
It is further
recommended
means
tion
of
environmental
systems,
in the
ation
needs
because
data
as thes~
field.
Where
date;
seconds.
increasing
capture,
become
the criteria
to techniques,
for floristic
required
the
codata
localities
of indirect
as geographic
to
for
centre
and minor
use be made
decrease
informa-
time
are made,
careful
to ensure
adequate
sampling
for environmental
stand
In addition,
such
available,
to be recorded
and
such as, major
field observations
to be given
same as that
that
data
spent
considersamples,
is not necessarily
the
sampling.
Classification
The
aims
of
a classification
to a comprehensible
on floristic
similarity,
releve-groups,
also
can correspond
with
based
between
on
form,
based
are:
through
in
the
field
grouping
to form releve-groups
on floristic
environmental
occurrence
floristic
releves,
to
reduction
of releves,
based
and the grouping
similarity,
gradients;
data
so that
and species
emphasize
the
of
these
grouping,
relationships
releve-groups.
240
The classification
one
solution.
determine
It
is
A classification
classification
recommended
releve-groups,
gradients,
the
of a floristic
where
number
ships
of
should,
species-groups,
sequenced
possible~
to
and
common
correspond
This simplifies
species-groups,
information
therefore,
in more
be verified
than
to
adequacy.
that
be
data set can result
without
provides
with
two
of
or
more
environmental
the matrix,
loss
more
to
by reducing
species-relation-
information
on
gradient
relationships.
Care
should
be
exercized
units
which
are
their
total
floristic
not
ticularly
relevant
units
often
are
synthesizing
be taken
two
Justification
in
could
only
partially
data
included
or
not be shown.
by grouping
variation
tion units.
However,
in
is often
small-scale
or more
inferences
included
variation
for splitting
releve-groups
making
entirely
to
of partially
in
work
included
sets
a
study
unknown.
wher e
in
a
cognizance
vegetation
the
It is suspected
within
vegetation
area
because
This
is
large
vegetation
study
area.
should,
species
classifiability
of
that improvements
is required
When
therefore,
constancy
data
sets
are obtained
larger units to form separate
more work
par-
units.
data sets to improve
improving
about
vegeta-
in this regard.
Verification
It
is
recommended
classification
that
efficiency
classification
values;
degree
verification
of
integrity
include:
of mapping
241
units;
degree
fication;
of correspondence
degree
differentiating
of
between
correspondence
environmental
the constancy
of community
mapping
between
factors;
diagnostic
units
and
strati-
classification
and
pattern
and
strength
in
species.
Derivatives
The
application
potential
tional
to the information
Plant
communities
immense
value
scales,
because
influences
Plant
in
directly
be caused
matrices
gradients
of
as mapping
in
to the
a
in
poor
sampling
can
be
importantly,
can be ascertained
classification
and
planning,
effect
on
propor-
and
at
have
various
environmental
units.
terms
degree
However,
gradients
of
integrating
species,
change.
more
can be derived
practices
definition,
by inadequate
and
therefrom.
their
proportional
Environmental
which
land-use
diagnostic
environmental
is directly
derivatives
and suitability
community
community
are
of a classification
a
of
species
classified
constancy
matrix,
of inter-
and
community
definition
can
in
be
intra-community
can
also
unit area.
derived
directly
natural
discontinuities
from vegetation
unit
from
classified
in
borders,
these
within
the gradients.
Vegetation
data
structure
making
Braun-Blanquet
can be derived
separate
method,
structural
from the recommended
analyses,
as
required
floristic
in
the
redundant.
242
Community
composition
community
composition,
growth
forms
ments
in
both
phase
theory
with
status
of
community
appears
terms
in
cover
within
a
conservation.
of
However,
vegetation
vegetation
types
to
for
before
data
the
is
to
of
more
successional
infer
a
and
with
the
approach
which
for confirmation.
of the
adequacy
cover
are based
input
trend
As
new
minimum
limits
any reliance
for
However,
community.
analyses
and more
improve-
suitability
to
determination
lower
types
and
components
relates
these
frequency,
indicate
change.
this
more
analysing
results.
the
analysis,
community,
in limited
these
to
cover,
results
determined
but requires
approach
species
composition
relation
assessment
new
condition
combines
floristically
a
initial
to confirm
analysis
promising
The
and
composition
Community
cover
cover
stands
is
vegetation
are required
Stand
in
and structure.
assessing
detecting
analyses
analysis
for
soil
on experience
is required
can be placed
of
from other
on the results.
PHYTOTAB-PC
This program
aspects
in
of
other
hence
package
vegetation
programs.
reduce
objectivity,
on
have
classifications
analysis,
The
observer
in
can
the
can
bias,
be
with
put,
suitable
features,
reduce
a
available
the
increasing
and
increase
processing.
enhancing
for many
decision-making,
considerably.
thereby
not
corresponding
data
reduced
for
package,
new
can
analysis
potential
be
with
package
vegetation
classification
programs
is a comprehensive
The
uses
the
Time
in
spent
derivative
to
which
application
243
potential
of
are
documented
fully
programs
and
adequate
Braun-Blanquet
use.
caution
is
community
on-line,
permutations
background
program
be
the
as ·has been
program
Furthermore,
programs
can not be compared
but
smallest
the
efficiency
data
in vegetation
ecology,
and
the
small-scale
work,
use
and
application
and
conservation.
the
conservation
primary
that
In the
natural
case
priority
resources
will
will
releve
on
of
an
in
the
probably
sequencing
approach,
in all
classification
decision-making
certain
classifications,
should
ensure
Africa
for
principles
justify
especially
south
processes
especially
with the basic
of conservation,
in
applying
maximum
spent
The derivatives
vegetation
and training
can reduce
time
of
number
can not be known.
of classifications,
highest
of
the
programs
without
when
automatic
conflicting
method.
methodology
methods
relevancy
the Braun-Blanquet
receive
so
decrease
without
use
with the permutation
of the recommended
increase
preclude
the
as modifications
the
sets,
the
driven,
mentioned,
for such larger matrices,
Application
Although
are menu
possible,
assessment
necessary.
and
in Braun-Blanquet
advised,
cover
approach.
of
expanded
in agriculture
vegetation
because
conservation
should
adequate
of
other
such as soil and soil water.
244
OPSOMMING
Objektiwiteit
in stratifisering,
en klassifisering
monsterneming
van plantegroei
deur
ROBERT
Promotor:
Prof.
Mede-promotor:
Dr.
HOWARD
WESTFALL
G.K. Theron
Dr. N. van Rooyen
in die
Departement
Plantkunde
vir die graad
PHILOSOPHIAE
Die doelwitte
in
die
van
haalbaarheid
om
(PLANTKUNDE)
studie was om waarnemers
stratifiserings-,
prosesse
asook
van hierdie
DOCTOR
monsternemings-
plantegroei-wetenskap
geldigheid
van
is bereik
deur:
en
klassifiserings-
te verminder,
en voorspelbaarheid,
die
bevooroordeling
in hierdie
om sodoende
prosesse
her-
te verbeter,
plantegroei-klassifikasies
te
ver-
beter.
Die
en
doelwitte
klassifiserings-prosesse
middel
van
van
ontwikkeling
bepalings;
bepaling
stratifisering
skaalverwante,
van
monsternemings-
skaal
te
brin9'
die
verbetering
met
skaal-gedefini~erde
klein-skaal
middel
die
van
stratifiserings-,
plantegroeistande;
en dus
verbeterde
plantegroei
plantegroei
plantegroeiversterkte,
metodes
plantegroei-klassifikasie
van
in verband
status
bedekking-tot-frekwensie-verhoudings,
deur
deur minimum
binne
deur
satelliet-beelde;
vir
spesie
kartering
deur
plant
bedekkings-
entropiei
samestelling
en die
volgens
groeivorms.
'n
245
Rekenaarpakket
om objektiwiteit
en om tyd van analises
Die
resultate
visuele
teen
hierdie
van
het
skaal
opsigte
van metodes
gelei.
noodsaaklikheid
verskeie
vir
toegepas
voortspruitende
uit
analise
ter bepaling
basis,
bepaling
veranderings
in
klassifikasie
en
fasiliteite
vir
dataverwerking,
floristiese
klassifikasies
'n
word beskryf.
sleutelspesies
sluit
op
fase-analise
afgeleide
veld
plantgemeenskap
en
word.
veral
in
van
Die plantegroeibuigbaarheid
Aanbevelings
Afgeleide
ten
sluit
die
in,
en
klassifikasie
toepassings,
spesies-samestellingsop
'n plantgemeenskaps
kompe t Ls Leve rmoe
spesies
vir
die
in.
Behalwe
toepassings
indentifikasie
sluit
monitering
die
plantegroei
rekenaarpakket
van plantspesies,
omgewing
van
statistiese
korrelasie
en
'n
in.
beklemtoon
en beveel
die hulpbron
plantegroei
bestuur
bewar ing
die
van
plantegroeistande,
databank
werk
kan word.
klassifikasies
stand
vermindering
'n ooreenstemmende
verseker
van spesiesverhoudings
van
asook
gebaseer
benodig
bevestiging
bevestigingstegnieke
'n
Daar is ook
gedefini~er,
wat
tot
plantegroei-matrikse,
in tyd wat vir klassifikasie
volgens
te verbeter
is ontwikkel.
studie
'n klein-skaal,
stand,
Hierdie
te verminder,
volgorde-bepaling,
opnames
afname
van
by plantegroei-analise
van
die
dat
belangrikheid
die hoogste
toegeken
grond
~an
nasionale
plantegroeiprioriteit
word omdat korrekte
en
grondwater
ook
aan
plantegroeikan
verseker.
246
SUMMARY
in stratification,
Objectivity
and classification
sampling
of vegetation
by
ROBERT
Promoter:
Prof.
Co-promoter:
Dr
HOWARD
WESTFALL
G.K. Theron
Dr N. van Rooyen
in the
Department
of Botany
for the degree
PHILOSOPHlAE
The aims of this
fication,
science
(BOTANY)
study were to reduce
sampling
and
so as to improve
observer
classification
repeatability
as to increase
the relevancy
The
aims
achieved
and
classification
were
DOCTOR
by:
processes
processes
to
the
scale
vegetation
mapping
by the use of scale-related
satellite
imagery;
assessing
to
vegetation
analysis
package
of
was
through
ratios,
developed
vegetation
a method
classification
state
cover-to-frequency
program
small-scale
by means
improving
vegetation
data
and
for
as well
by
sampling
scale-defined
and hence
vegetation-enhanced
improved
minimum
plant
growth
time
forms.
and
according
A
objectivity
spent
cover
entropy;
composition
facilitate
reduce
of
stratification
species
within
to
vegetation
stratification,
stands;
estimations;
in
classifications.
vegetation
developing
in the strati-
and predictability,
of vegetation
relating
bias
on
computer
in
the
analyses.
247
The
results
of
this
sequencing
of
small-scale
work,
for
introduces
can be applied.
ing
a
with
species
composition
species
been
and
basis,
ability,
ing changes
in vegetation
cation
derivatives
and stand phase
stands.
the
treatment
of
community
This
emphasizes
and
work
recommends
national
ensure
that
priority
conservation
the
the
because
Apart
computer
identification
data bank.
of
as determining
for field
floristic
methods
and
importance
vegetation
correct
methods
derivatives
relationships
analysis
resource
vegetation
based
on
for monitorclassifi-
package
species,
includes
statistical
correlations
of vegetation
techinclude
key species
program
habitat
that
verification
species
to
for verify-
from vegetation
of plant
required
according
necessity
for determining
facilities
data,
the
visual
involving
in time
in the sampling
include
in
those
defined
Classification.
as well
reduction
decrease
stand,
several
analysis
a
especially
flexibility
described.
competitive
and
vegetation
Recommendations
have
to
a corresponding
greater
niques
lead
matrices,
The
classification
on a community
have
vegetation
classification.
scale,
study
and
a
classifications
be given
the
management
highest
can
also
of soil and soil water.
248
ACKNOWLEDGEMENTS
The
assistance
organizations
(1) Prof.
and
are gratefully
G.K. Theron
ship, guidance
(2) Dr J.C.
results
suggestions
the plant
for thesis
Herbarium,
on the characters
identification
for the use of
to use the
purposes.
Pretoria
voucher
for
specimens.
and character
particularly
states
and
used
in
aid.
at the Mary Gunn Library,
literature
and the
and Prof. A.E. van Wyk for comments
National
Mrs E. Potgieter
Botanical
for assistance
searches.
(7) Mr J.M. van Staden
many
Council
Institute,
of the herbarium
(6) The Librarians
Institute,
facet
leader-
this work.
and for permission
of the National
(5) Dr O.A. Leistner
and
and encouragement.
Grassland
of an official
persons
for their
throughout
Research
facilities
identification
with
for guidance
Roodeplaat
(4) The staff
following
acknowledged:
Agricultural
excellent
the
and Dr N. van Rooyen
Scheepers
Director,
of
and encouragement
(3) The President,
their
co-operation
for photographic
work and testing
of
hypothesis.
(8) Mr M.D.
Panagos
processing
for capable
and illustrating
technical
assistance,
the characters
data
used in plant
identification.
(9) Mrs J. Schaap
(10) Miss A.P.
for the drawings.
Backer
for accurate
typing
of the first draft.
249
CURRICULUM VITAE
Robert
Howard
Kokstad,
Cape
Cape
Province
the
Westfall
was
Province.
He
completed
in
In
1976
University
1962.
of
born
Pretoria
on
the
he
with
17th
high
December,
school
obtained
Botany
a
and
at
1944
Fish
B.Sc.
Hoek,
degree
Zoology
as
in
at
major
subjects.
In
1977
he
was
appointed
Botanical
Research
Technical
Services)
completion
of
the
Pretoria
in
An
degree
M. Sc.
of
1978
Pretoria
on
as
a
Institute
with
was
(Hons.)
ecology
awarded
submission
He
to
to
taxonomy
him
a
in
of
was
degree,
and
of
Officer
(Department
Pretoria.
in
B.Sc.
Professional
thesis
seconded,
as
for
University
major
by
the
Agricultural
the
1981
at
subjects.
the
entitled
of
University
"The
plant
ecology of the farm Groothoek, Thabazimbi district.
In
1989
which
he
became
Agricultural
post
was
of
service
the
concentrated
in vegetation
has
In
Agricultural
been
African
Grassland
1992,
in
Research
Institute
where
Researcher.
engaged
on South
the
Grassland
Council
he
During
ecological
Centre,
within
the
occupies
the
his period
research
savanna vegetation
and
of
has
and methodology
research.
Mr Westfall
is a member
the
African
South
to
Roodeplaat
Research
Principal
he
transferred
of several
Association
of
scientific
societies
Botanists,
the
such as
Grassland
250
society
of
southern
Ecologists.
He
presenting
Transvaal
He
has
He
also
of
the
Branch
at
on
change.
tables.
for
and
years
committees
Task
for
as
for Natural
on
of
societies,
the
Northern
of
Botanists.
on this
committee.
and
was
the
and
a natural
convenor
evaluating
scientist
Scientists.
The
with
following
papers:
F., MORRIS,
Vegetatio
J.W. & WESTFALL,
R.H. 1978. A
of Braun-Blanquet
38: 129-134.
F. & WESTFALL,
of the western
these
monitoring
aid for the preparation
(2) VAN DER MEULEN,
Institute
Association
several
is registered
of
serving
African
Group
African
member
Departmental
is a list of published
computer
South
active
South
Council
(1) VAN DER MEULEN,
the
congresses
the
He
African
and
an
as treasurer
National
South
been
of
serves
vegetation
the
has
papers
served
Africa
Transvaal
R.H.
1979. A vegetation
Bushveld.
Bothalia
map
12: 731-
735.
(3) VAN DER MEULEN,
analysis
Africa.
(4) WESTFALL,
1982.
of Bushveld
Journal
PHYTOTAB
homogeneity
Bushveld.
vegetation
1980. Structural
in Transvaal,
package
N. & THERON,
G.K.
for Braun-Blanquet
49: 35-37.
VAN ROOYEN,
N. & THERON,
index based on species
Bothalia
South
7: 337-348.
G., VAN ROOYEN,
- A program
Vegetatio
R.H.,
R.H.
of Biogeography
R.H., DEDNAM,
tables.
(5) WESTFALL,
F. & WESTFALL,
G.K. 1983. A
diversity,
in Sour
14: 299-301.
251
(6) WESTFALL,
R.H., VAN ROOYEN,
plant
ecology
District.
(7) WESTFALL,
vegetation
G.K.
of the farm Groothoek,
I. Ordination.
R.H.,
Research
N. & THERON,
EVERSON,
Bothalia
C,S'.&
of the protected
Station.
South
1983. The
Thabazimbi
14: 785-790.
EVERSON,
T.M.
1983. The
plots at Thabamhlope
African
Journal
of Botany
2:
15-25.
(8) WESTFALL,
condition
the
N. & THERON,
R.H., VAN ROOYEN,
assessments
Grassland
(9) WESTFALL,
in Sour Bushveld.
Society
R.H. & PANAGOS,
canopy
and basal
G.K. 1983. Veld
of
southern
Proceedings
Africa
18:
M.D. 1984. A cover meter
cover
estimations.
Bothalia
of
73-76.
for
15: 241-
244.
(10) WESTFALL,
R.H. & DREWES,
Orange
Free State
(11) RUTHERFORD,
Floodplain.
& WESTFALL,
M.C.
Transvaal
R. 1984. Grass root pattern
province
Bothalia
15: 293-294.
R.H. 1984. Sectors
of South Africa.
in an
Bothalia
of the
15: 294-
295.
(12) WESTFALL,
R.H.
1984. Review
Subantarctic
Gremmen.
(13) WESTFALL,
plant
Islands
Junk,
Marion
The Hague.
R.H., VAN ROOYEN,
ecology
District.
(14) WESTFALL,
N. & THERON,
of the farm Groothoek,
1985. PHYTOCAP.
for the PHYTOTAB
of the
and Prince Edward
1982. Bothalia
II. Classification.
R.H.
program
of the vegetation
by N.J.M.
15: 340.
G.K.
1985. The
Thabazimbi
Bothalia
15: 655-688.
A field data capture
program
package.
Bothalia
15:
749-750.
252
(15) WESTFALL,
plant
N. & THERON,
R.H., VAN ROOYEN,
ecology
District.
of the farm Groothoek,
III. An annotated
G.K.
1986. The
Thabazimbi
checklist.
Bothalia
16: 77-
82.
(16) PANAGOS,
R.H. & SCHEEPERS,
M.D., WESTFALL,
Baseline
data
for the vegetation
at the Matimba
Bothalia
(17) WESTFALL,
Power
station,
R.H.
1986. Review
in vegetation
Science.
Part 4) edited
1984.
South
(18) WESTFALL,
R.H.,
E11isras,
GLEN,
plots
NW Transvaal.
methods
(Handbook
by R. Knapp.
Journal
features
key. Botanical
The Hague.
Botany
M.D.
and taxon
of Vegetation
Junk,
of
H.F. & PANAGOS,
aid combining
and an analytical
Society
of Sampling
science
African
identification
52:
192-193.
1986. A new
of a polyclave
Journal
of the Linnean
92: 65-73.
(19) RUTHERFORD,
M.C.
southern
& WESTFALL,
Africa
Survey
R.H. & MALAN,
stratification
satellite
using
imagery.
R.H.
R.H.
1986. The biomes
- an objective
of the botanical
(21) WESTFALL,
of two protected
16: 89-91.
analysis
(20) WESTFALL,
J.C. 1986.
categorization.
of South
O.G.
Africa
1986. A method
scale-related,
Bothalia
1986. PHYTOLOC
and sample
set location
vegetation
sampling.
of
Memoirs
54: 1-98.
for vegetation
vegetation-enhanced
16: 263-268.
- a random
program
Bothalia
number
generator
for stratified
random
16: 270-271.
253
(22) WESTFALL,
R.H.,
Predictive
VAN STADEN,
species
subsample
area relations
size for vegetation
Waterberg.
South
African
preliminary
sequencing
& WESTFALL,
O.G.
vegetation
Advances
(25) WESTFALL,
mapping
in Space
estimation
Research
using
Bothalia
apparatus
pressing.
R.H.,
collecting
specimen
method
of
sized belt transects.
the resolution
of
on phytosociological
1989. Plant
and ecological
collecting
studies
for on-site
1. A
specimen
19: 266-267.
program
collecting
studies
apparatus
2. COLDAT:
for collector's
for
A field-
data and herbarium
19: 267-268.
P.J. & PANAGOS,
BRITZ,
apparatus
for taxonomic
3. A new top-loading
pressing.
Improving
correlations
plant press
and ecological
Bothalia
1989.
R.H.
1989. Plant
capture
studies
97-103.
19: 263-266.
Bothalia
taxonomic
(29) WESTFALL,
pattern
plastic
R.H.
labels.
MSS data.
1988. An improved
variable
for taxonomic
lightweight
data
M.D.
R.H.
& WESTFALL,
M.D.
(28) WESTFALL,
7,11:
for
18: 289-291.
floristic/habitat
(27) PANAGOS,
in
18: 122-123.
with the aid of Landsat
G.B. & WESTFALL,
tables.
for
R.H. 1987. A new strategy
R.H. & PANAGOS,
Bothalia
(26) DEALL,
53: 44-48.
and releves
sets Bothalia
of
in the Transvaal
of Botany
of species
phytosociologicaldata
cover
1987.
and determination
sampling
Journal
M.D.
R.H. & DE WET, B.C. 1988. New programs
(23) WESTFALL,
(24) MALAN,
J.M. & PANAGOS,
Bothalia
plant
M.D.
1989. Plant
and ecological
press
for off-site
19: 268-269.
254
(30) PANAGOS,
& WESTFALL,
R.H. 1989. Plant
for taxonomic
and ecological
M.D.
apparatus
Drier-transporters
for plant presses.
collecting
studies
Bothalia
4.
19: 269-
270.
(31) PANAGOS,
M.D.,
collecting
studies
apparatus
Bothalia
apparatus
Bothalia
(33) WESTFALL,
of plant
M.D. 1989. Plant collecting
and ecological
map cabinet
PANAGOS,
collecting
for vehicle
studies
6. A
and office
M.D. & VAN STADEN,
apparatus
7. A transportable
use. Bothalia
G.B.,
vegetation
use.
for taxonomic
camping
J.M.
1989.
and ecological
kitchen
for vehicle
19: 273-274.
THERON,
G.K. & WESTFALL,
ecology
in the Sabie
Bothalia
and ecological
19: 272-273.
R.H.,
studies
1989. Plant
19: 270-272.
for taxonomic
transportable
R.H.
for field drying
R.H. & PANAGOS,
(32) WESTFALL,
(34) DEALL,
for taxonomic
5. A gas drier
specimens.
Plant
P.J. & WESTFALL,
BRITZ,
area.
of the Eastern
2. Floristic
R.H.
1989. The
Transvaal
Escarpment
classification.
19: 69-89.
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imagery.
R.H.
and basal
1982.
18: 122-123.
1984. A comparison
Johannesburg,
stratification
WESTFALL,
sets.
for
and species
vegetation-enhanced
on Pattern
geophysics,
satellite
G.K.
for Braun-Blanquet
of releves
O.G.
of the farm Groothoek,
WESTFALL,
and
name data
N. & THERON,
data sets. Bothalia
& MALAN,
R.H.
symposium
list with species
G. VAN ROOYEN,
package
for interrogating
& DE WET, B.C. 1988. New programs
R.H.
phytosociological
units
A program
49: 35-37.
preliminary
WESTFALL,
SPECOM:
synonym
- A program
Vegetatio
WESTFALL,
(in prep.).
number
scale
-
using variable-sized
18: 289-291.
267
WESTFALL,
R.H.,
ecology
of the farm Groothoek,
Classification.
WESTFALL,
R.H.,
species-area
vegetation
Journal
WHITE,
Bothalia
G.K. 1985. The plant
Thabazimbi
J.H. & PANAGOS,
relations
and determination
sampling
of Botany
in the Transvaal
53:
District.
II.
15: 655-688.
VAN STADEN,
F. 1981. Vegetation
UNESCO,
N. & THERON,
VAN ROOYEN,
H.D.
1987. Predictive
of subsample
Waterberg.
South
size for
African
241-244.
map of Africa.
Scale
1:5 000 000.
Paris.
268
APPENDIX
I
ILLUSTRATED
MORPHOLOGY
FOR PLANT
The character
and character
are
in
sequenced
stem,
leafless
This
sequence
as
it
was
is
found
that
to
are
187-224)
states
form
used
main groups,
used
for
that
a
set
for
encoding
other
Poaceae
to
the
presence
of
tendrils
plants,
as well
in
pseudo-
particular.
only
as
Poaceae.
include
a group
those
(numbers
character
redundant
they
and
sequences
Certain
are
which
plants,
encoding
characters
exclusive
climbing
plants
or
to
PHYTO00 therefore,
which
of
stemless
facilitates
to
the
identification
and. identifying
applicable
as
field
from general
sequence
dichotomies.
are
IN THE FIELD
namely,
leaves,
both
The program
such
concept
states
and stems·and
identification.
species
three
IDENTIFICATION
are
in
the
implicitly
included.
In
field
identification
allocating
a
inversely
Fewer
character
character
number of
plants
that
character
states
per
example,
provides
acters
to
states
between
a
that
character
the
character
number of
generally
character
and y
states
states
per
It
could
is
the
formula
x
selected
that
to
used.
with
the
number of
number of
unlikely
is
gives
where x
for
of
plant
problems
character
be
a
fewer
The
=
of
states
imply
states.
efficiency
character
can be differentiated
4 character
character
found
to
the
65 536 combinations.
and
was
state
proportional
transitions
For
it
characters.
8 characters
so
few char-
differentiate
269
all
the
species
trates
that
states
could
those
Tables
in
southern
judicious
be far
Africa.
selection
of
more efficient,
Nevertheless,
characters
with
further
this
and
illus-
character
research,
than
used at present.
4.4
character
to
4.9
show the
and character
identification
states,
results
listed
of
the
in this
application
appendix,
of
for
the
field
of plants.
270
* NOT
MUTUALLY EXCLUSIVE
PHOTOSYNTHESIZING ORGANS PRESENT ( REDUNDANT
)
MAIN AXIS SHAPE
1. LEAFLESS
( main axis ph0tosyn~he~ic
3. STEMS AND LEAVES
271
)
(photosynthetic
2. STEMLESS OR PSEUDOSTEH
( main axis (ormed by separate
. photosynthetic
organs)
organs
~eparate
from main axis)
MAIN AXIS
4. ERECT
4. ERECT
6; TWINING
8. DECUMBENT
272
ORIENTATION
S. GENICULATE ( abruptly
7. CLIMBING ( tendrils/hooks
9. TUFTED/CLUMPED
redundant)
bent)
MAIN AXIS OUTLINE IN TRANSVERSE SECTION
10. TRIANGULAR
12. HEXAGONAL
11. QUADRAHGULAR
13. ELLIPTICIfLA TTENED
11.. ROUND/IRREGULAR
273
· *
HAIN AXIS SURfACE fEA lURES
15. SHOOTH
17. STICKY
19. FISSURED ( rectangular)
274
16. RIDGED
18. fiSSURED ( longitudinal
20. STRIPPED/PEELED/fLAKY
)
( bark )
MAIN AXIS
21. KNOBS
APPENDAGES
22. THORNS SINGLE
27. SPINES PAIRED
STRAIGHT
275
)
*
23. THORNS SINGLE
RETRORSE
CURVED
25. THORNS AND SPINES PAIRED OR SINGLE.
( trunk
CURVED ANO STRAIGHT
28. THORNS IN THREES
CURVED
24. SPINES S!NGlE
STRAIGHT
26. THORNS PAIRED
CURVED
29. PRICKLES ( short str·aight
or curved non-woody.
protuberances
)
30. KNOTS
! Poaceae
)
THORN/KNOB ARRANGEMENT
( trunk
31. SCATTERED
)
32. IN RO''';S
BRANCHING FROM MAIN AXIS
34. OPPOSITE
35. REO ANGULAR
33. UNBRANCHED
36. SCATTERED
276
BRANCH/BRANCHlET
37. THORNS SINGLE CURVED
APPENDAGES
38. THORNS SINGLE RETRORSE
40. THORNS AND SPINES PAIRED OR SINGLE. CURVED AND STRAIGHT
41. THORNS PAIRED CURVED
44. PRICKLES ( short straight
non-woody protuberances
277
42. SPINES PAIRED STRAIGHT
or curved,
)
43. THORNS IN THREES CURVED
45. SPINES CENT BRANCHLET
SAP ( at stem,
1,6. CLEAR
1,7. YELLOW/
RED
LEAF ARRANGEMENT
1,9. OPPOSITE
petiole or if succulent,
50. DECUSSATE
51,. CLUSTERED ON ABBREVIATED
·BRANCHLETS/STEMS
278
1,8. MILKY
( separate
52. WHORLED
leaf)
photosynthetic
organs)
51. SPIRAl!
53. CLUSTERED TERMINALLY
BRANCHES
AL TERNATE
ON
55. DISTICHOUS ( tvo rcvs )
LEAF A TT ACHHENT
56. SESSILE ( petiole
less
57. AHPLEXICAUL
than 0,5 mm )
59. DECURRENT (leaf
petiole,
stipule
60, SHEATHlNG
base
of leaf!
runs down stem)
(enlarged base,
embracing stem)
(lower part
encircles stem)
62. PETIOLE GREATER THAN LAMINA LENGTH
63. PETIOLE HALF TO LAHlNA LENGTH
61,. PETIOLE LESS THAN HALf LAHlNA LENGTH
------
- -------
62,
279
----,--
---
,\ :; \ z
\/; ~l
------
63.
------
64,
58. PERFOLIA TE Is tem
appears to pass
leaf basel
throogh
(leaf attached
by lower surface not
marginl
61. PELTATE
PETIOLE
65. CYLINDRICAL
SHAPE
( one
or both sides)
PETIOLE APPENDAGES
69. APaX
SWOLLEN
71. SINGLE BASAL
280
68. CHANNELED ABOVE
66. FLATTENED
GLAND
( grooved )
*
70. BASE SWOLLEN
n. PAIRED
BASAL
GLANDS
L1GULES
73. ABSENT IINCONSPICUOUS
75. MEMBRANE
77. MEMBRANE
281
STRAIGHT
POINTED
i 4. FRINGE OF HAIRS
76. MEMBRANE
7B. MEMBRANE
ROUNDED
NOTCHED
STIPULES ( unmodified
79. EXSTIPULATE
82. PAIRED - fREE
- young gro .••.th or sfipular
80. SINGLE
83. PAIRED - JOINED
85. LOBED
282
scar)
81. INTERPETIOLATE
84. PAIRED-
UNEQUAL
LEAF FORM
petiolules greater than or equal to 0,5 mm
for compound leaves
86. SIMP~E
89. ONCE DIGIT ATE/PALMATE
91. ONCE PINNAT E
87. TWO
88. THREE
90. TWICE DIGITATE/PALMATE
92. TWICE PINNA IE
/
93. PARIPINNATE
283
94. IMPARIPINNA IE
LEAfLET
95. SESSILE
LA TERAL LEAFLET
98. AL TERNA T E LEAflETS
284.
ATTACHMENT+
97. SESSILEITERM!Nt,L
PETIOLATE
96. PETIOLATE
ARRANGEMENT
( excluding
terminal
99. OPPOSITE LEAflETS
leaflet
)
100. ALL INSERTED AT ONE
POINT
LEAF SHAPE
101. BROAD - length
to
I<idth equal to or
less th~n 1
RATIO ( compound leaves
- length
102. ST AtWARD
I<idth greater
to 3
to
103. NARROW - lenoth
than 1
LEAF SHAPE
( folded
- entire
I<ioth greater
to 9
transversely
lamina)
to
than 3
i04.
LINEAR - length to
vidth greater
than 9
.l<ith apex on base - lamina)
<
>
10S. ELLIPTIC
106. LINEAR
( parallel
sided
107. OVATE
( broadly/narrol<ly
- grasslike)
>
<
108. OBOVATE
285
( broadlyinarrol<ly'i
109. ORBICULAR
110. BUTTERFLY
111. ASYMMETRIC
( round )
)
LEAF OR LEAFLET
m.
112. ENTIRE
116. INVOLUTE ( rolled
ENTIRE/UNDULATE
.117. REVOLUTE (rolled
dcvn and bac~ )
invar ds )
120. DENT ATE ( sharp outvar d
pointing teeth - middle of
lamina)
121. TOOTHED ENTIRE
MARGIN
124. LOBED, UP TO HALFWAY
TO MIDRIB
286
MARGINS
*
114. CILIATE
115. CONVOLUTE ( rolled
upon ils elf )
118.- CRENI>.TE (scalloped
/rounded
teeth)
122. TOOTHED APEX
ONLY
125. CUT, GREATER THAN
HALFWA Y TO HIDRIB
119. SERRATE ( sharp
f or v ar d pointing
teeth)
123. TOOTHED BASE
ONLY
NERVATION.
( nerves
no t visible I
126. OPAQUE
LAMINA
( side veins diminish
to form net pattern I
129. PINNA TEiNET
131. TRIPLINERVED
128. PI ~NINERVED ( feather-like
127. PARALLEL
( midrib •.•.ith t v o major
side valns parallel
130. LOOPED ( major side veins
287
joined by loops
near margin I
side veins originating
132. DIGIT ATEL Y NERVED [ midrib •.•.
ith more than
(rom base of blade I
- major
to ••.ar ds margin I
from base of blades I
t ••.o major side veins origina'ting
NERVA TlON RELIEF OF MIDRIB - PRINCIPAL RIB, DISTINCTLY THICKER THAN SIDE VEINS
133. NO DISTINCT MIDRIB
134. UPPER RAISED
135. UPPER LEVEL
136. UPPER DEPRESSED
137. LO\O/ER RAISED ( keeled)
139. LOWER DEPRESSED
288
138. LOWER LEVEL
*
LEAF APEX
140. MUCRONATE
( ENTIRE LAMINA )
141. EMARGINATE
A
~~
143. ACUTE/POINTED
~
144. JUTTING/TWISTED
142. OBTUSE/ROUNDED
AI
/)/J}
145. HOODED ( emarginate
when flattened)
LEAF BASE
.: f
146. TRUNCATE
149. SAGITTATE
289
147. OBTUSE/ROUNDED
150. CORDATE ( heartshaped
148. AURICULATE
) 151. ACUTE/TAPERING
LEAF BLADE
( lamina)
COLOURITEXTURE
"*
c
153. GLAUCOUS ( ••••ax/po •••.
der
bloom on bluish green )
152. FLESHY
c:::
42.ga h""j.$, ,..in;p
155. DISTINCTLY
c::::
=
::::::::
156. WHITE SILVERY
BICOLOROUS
154. GLOSSY
BELOW
157. RUSSET BROWN BELOW
=
158. GLABROUS ( both surfaces)
159. STELLATE/TUFTED
~
.....
...' ..
.,
,
"
:
HAIRS
.
'
160. BRISTLY ISCABRIO
=
162. SPARSELY
'.'
~'.i\'.l'l
163. HAIRS ON SINGLE
HAIRY
SURFACE
161,. STICKY
290
( viscid)
"/I,!J,~
"
.:
c::::
161. WOOLLY IfEL TED
<:
165. SCALY
.'I?;:;=<?>
LEAf
166. SPINES/THORNS
168. SPINES/THORNS
APPENDAGES
ON MARGINS
ON UPPER SURfACE
LEAf
291
169. SPINESITHORNS
ON RACHIS
171. DOMATIA
170. SPOTS/GLANDS
172. NEUTRAL
.•
SMELL
173. AROMATIC
174. fOETID
SHOOT APEX
DS. SHALL
176. COVERED BY SCALES
mCONS~CUQUS
178. COVERED BY LE ..••.
f PRIMORD!A
177. COVERED BY VELVETY/RUSSET
BROWN HAIRS
ROOTS/UNDERGROUND
STRUCTURES
/
I
I
/1
/
(
/
/
179. TUBER ( sbrup t svellinq
- vertical.
st arch I
180. COR:1 ( fibrous
bases
leaf
I
181. BULB ( fleshy
leaf
bases I
J.
///
182. ROOTSTOCK
( horizontal
I
183. SWOLLEN
s .••.elling I
292
ROOT ( gradual
181,. RHIZOMES
ABOVEGROUND
STRUCTURES
'/. I
OTHER CHARACTERS
CULM
*
'I
•
186. ROOTING fROM
185. STOLONS
HODES/KNOTS
fOR GRASS IDENTifiCATION
I
,I,
J!• It
'.
I
II
I
I
.0
• I.\~" •
187. CULMS WITH HAIRS
ABOVE NODES
188. CULMS WITH HAIRS
ON NODES
189. CULMS TUBEROUS
BASE
190. LEAVES
293
HAINLY
ON CULMS [ leafy
culms
)
191. LEAVES
MAINLY
BASALLY
AT
AGGREGATED
*
SHEATH/LAMINA
192. LAMINA fLAT
194. LAMINA
196. LAMINA BASE
BEARDED
193. LAMINA fOLDED - PLICATE
195. LAt11NA ROLLED!
CURVED
197. LAMINA BASE WITH
TWO TUFTS OF HAIRS
TUBULAR
198. HAIRS BELOW
LAMINA BASE
199. BASE GREATER
THAN SHEATH
I
I
1/
200. BASE EQUALS
SHEATH
294
201. BASE LESS
THAN SHEATH
202. SHEATH MARGINS JOINED
( at leas t one quarter
of length)
.
.1
I
I
203. FREE SHEATH
MARGINS
FRESH SHEATH
204. FlABELLATE
AT BASE
( f an shaped)
*
205. KEELED
/'
I
J
-r
l
i"
'
/'
j
206. GLABROUS
207. ENTIRE EXTERIOR
HAIRY
208. BASE HAIRY
)
209. APEX HAIRY
-""Vl
211. BASE PURPLE/RED
295
212. APEX PURPLE/RED
-
OLD LEAF SHEA ni
· 213. GLABROUS
m.
217. INTERIOR TINGED
SHINY ORANGE BROWN.
296'
ENTIRE EXTERIOR
HAIRY
218. INTERIOR TINGED
PURPLE .
215. BASE HAIRY
215. APEX HA!RY
219. INTERIOR VISCID ( sticky
)
OLD DEAD LEAVES
no.
ABSENT
221. LOOSLEY
222. A FEW DISTINCT
CURLS
224. OLD BtADES TWISTED
INTO CORKSCREWS
297
CURLED WAVY
223. TIGHTLY
CURLED
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