1. University of St Andrews:
School of Biology
Module Code: BL4201
Module Title: Experimental Research Project
An Analysis of African Bush Elephant
(Loxodonta africana) Damage to Woody
Plants in Balule Nature Reserve, Limpopo
Province, South Africa
Student ID: 120006448
Word Count: 7,662
Submitted as an integral part of the BSc Honours in Zoology Degree course in the
School of Biology, University of St Andrews, April 18th
2016.
@RR
2. 1
Declarations
I declare that I have read the University’s statement on Academic Misconduct, that the
following work is my own original research, and that any significant academic debts and
borrowings have been properly acknowledged and referenced.
120006448
Matriculation Number .................................................
Monday, 18 April 2016
Date of Declaration ...................................................
I declare that the School of Biology informed me of the Safety Guidelines, which it has
drawn up, and that I signed a Fieldwork Risk Assessment Form, thereby agreeing to abide by
these Guidelines.
120006448
Matriculation Number ...................................................
Monday, 18 April 2016
Date of Declaration ...................................................
I declare that this dissertation is 7,662 words in length excluding the title page,
acknowledgements, contents page, abstract, figures, tables, legends, references, and
supplementary materials, and is within the established ± 10% leeway as defined by the
School of Biology.
120006448
Matriculation Number ...................................................
Monday, 18 April 2016
Date of Declaration ...................................................
3. 2
Acknowledgements
I would like to whole-heartedly extend my gratitude to a number of groups and individuals
who have assisted me throughout the design, fieldwork, analysis, and write-up of this
dissertation.
First and foremost, I would like to thank my supervisor, Will Cresswell, Professor of Biology
at the Centre for Biological Diversity, University of St Andrews. There was never a time,
from start to finish of this report, that I could not seek guidance, advice, or assistance from
Will. Two years ago I entered his statistics module with zero skill or confidence, especially in
the use of R Statistical Software. With him to thank, I now see R not as a terror, but as a very
valuable tool. Through his tremendous and patient supervision of my work over the past year,
I was able to accomplish my research goals and indeed “stay on target.” I am exceedingly
grateful for everything that he has done in shaping me to be a better student, and more
importantly, in shaping me to be a better scientist.
I would also like to thank my co-supervisor Laetitia Cronje, General Manager at Campfire
Academy in Olifants West, Balule Nature Reserve. With her incredible knowledge and
expertise as a field guide, the punishing tasks of establishing transects, identifying species of
woody plants, and scoring elephant damage were made much simpler. Tish pushed me to
keep going with my work, even at times when I felt like quitting. I am very grateful and
privileged to have worked under her guidance in a setting as unique as the South African
bushveld. Baie dankie!
Appreciation also goes out to all of the staff at Campfire Academy and Olifants West Nature
Reserve for their assistance during my time collecting data in the field. I would particularly
like to mention Brass Brassett, the student co-ordinator at Campfire. Brass always made sure
I was able to get out into the field when I needed to, and never once recoiled from an
extraordinary request. Even upon leaving Campfire, Brass promptly responded to all of my
(sometimes frantic) emails, and would continue to support me. For this I am very grateful. I
would also like to thank the Warden of Olifants West Nature Reserve, Craig Spencer, for
allowing me to perform my fieldwork in Balule, and for supplying me with information.
Special thanks also goes out to my family and friends whom, without, it would have been
very difficult to complete this research project. To my parents, for always supporting me and
allowing me to pursue my goals no matter what they may be. To Jack, Matt, and especially
Florence; I am perpetually grateful for all of the caring support over the past year.
4. 3
“Grey as a mouse, big as a house,
Nose like a snake, I make the earth shake,
As I tramp through the grass; trees crack as I pass.
With horns in my mouth I walk in the South,
Flapping big ears. Beyond count of years
I stump round and round, never lie on the ground,
Not even to die. Oliphaunt am I,
Biggest of all, huge, old, and tall.
If ever you'd met me you wouldn't forget me.
If you never do, you won't think I'm true;
But old Oliphaunt am I, and I never lie.”
J. R. R. Tolkien
5. 4
Contents Page
1.0 Abstract 5
2.0 Introduction 6
2.1 Background 6
2.2 Aims / Hypotheses 8
3.0 Materials and Methods 10
3.1 Study Area 10
3.2 Establishing the Transects 12
3.3 Data Collection 13
3.3.1 Scoring Elephant Damage to Woody Plants 13
3.3.2 Collecting Grass Samples 16
3.4 Data Analysis 17
3.4.1 Veld Condition and Elephant Grazing Capacity 17
3.4.2 Statistical Analysis in R 19
4.0 Results 22
4.1 Veld Condition and Elephant Grazing Capacity 22
4.2 Statistical Analysis in R 22
4.2.1 Composition of Woody Plant Species 22
4.2.2 Composition of Elephant Damage Types 24
4.2.3 Elephant Damage Preferences Across Woody Plant Species 27
4.2.4 Rate of Elephant Damage 29
4.2.5 Generalised Linear Mixed Models 31
5.0 Discussion and Conclusions 38
5.1 Veld Condition and Elephant Grazing Capacity 38
5.2 Composition of Woody Plant Species 39
5.3 Composition of Elephant Damage Types 40
5.4 Elephant Damage Preferences Across Woody Plant Species 40
5.5 Rate of Elephant Damage 42
5.6 Generalised Liner Mixed Models 43
5.7 Conclusions 43
6.0 References 45
7.0 Appendices 49
7.1 Appendix 1.1A 49
7.2 Appendix 1.1B 50
7.3 Appendix 1.1C 51
7.4 Appendix 1.1D 52
7.5 Appendix 1.1E 53
7.6 Appendix 1.1F 54
7.7 Appendix 1.1G 55
7.8 Appendix 1.2A 56
7.9 Appendix 1.2B 56
7.10 Appendix 1.2C 57
7.11 Appendix 1.2D 58
7.12 Appendix 1.2E 58
7.13 Appendix 2A 59
7.14 Appendix 2B 60
6. 5
1.0 Abstract
During the winter dry seasons when water becomes scarce and grasses lose their
nutritional value, the African bush elephant, Loxodonta africana, prefers to forage upon
wood and bark. The browsing behaviour of elephants can considerably modify the landscape;
elephants will strip leaves and bark from branches, break branches, snap stems, debark stems,
and even uproot and push over entire trees or shrubs. Although elephants are bulk feeders,
they nonetheless exhibit definitive preferences or avoidances for differing species of woody
plants.
The research conducted for this paper aimed to examine African bush elephant damage
to woody plants across four survey sites during a winter dry season, with a particular
emphasis on determining what varieties of damage elephants prefer to perform on specific
species of woody plants, and how elephant damage increases over time. A series of line
transects were carried out at four specified sites within the Olifants West region of Balule
Nature Reserve. Elephant damage was scored on each woody plant based on a 6-point
damage rank-value scale.
There was significant variation between woody plant species and mean Strauss’ Linear
Electivity by elephant damage type. Elephants were shown to particularly prefer certain
species of woody plants over others for performing specific types of damage. The findings of
this research suggest that African elephants are mega-herbivores that cause significant
amounts of damage to the landscapes in which they forage. During dry season droughts
especially, elephants will aim to satisfy their dietary requirements and damage specific
woody plants over others.
7. 6
2.0 Introduction
2.1 Background
The African bush elephant (Loxodonta africana) is the largest land-dwelling mammal
on the planet; the average adult bull elephant is approximately 4 metres tall and can weigh up
to 7 tonnes (Chapman and Reiss, 1992). Elephants are both grazers and browsers of their
environment, meaning that they equally forage on the ground for grasses, and off the ground
for woody plant materials such as leaves, fruits, stems, bark, and roots. Elephants prefer
different types of vegetation depending on the season of the year (Loarie, van Aarde, and
Pimm, 2009). Herbaceous material (such as grass) is a fundamental part of an elephant’s diet;
between 84 and 95 percent of an elephant’s diet consists of grass during the wet seasons
(Anderson and Walker, 1974). Different species of grass tend to have varying ecological
statuses and thus grazing values. The ecological status of grass refers to the categorizing of
grasses based on their response to varying levels of grazing; they can either increase in
number or decrease in number when presented with the ecological strain of overgrazing, and
this can have an adverse effect on the condition of the veld and its overall grazing capacity
for grazing and mixed-diet herbivores in the savannahs (van Oudtshoorn and van Wyk,
2012).
During the winter dry seasons when water becomes scarce and grasses lose their
nutritional value, elephants prefer to forage upon wood and bark (Carrigy, 2013). Woody
plants tend to contain higher proportions of crude protein and nutrients in their tissues than
species of grass during the dry season, and elephants will therefore escalate their ingestion of
woody plant materials during these dry winter months, leading to higher degrees of woodland
damage (Baxter, 2003; Hayes, 2011). In Southern Africa during the peak of a dry season
drought, the amount of browsing material in the diet can reach peak proportions of up to 86
percent (Anderson and Walker, 1974). The browsing behaviour of elephants can considerably
8. 7
modify the landscape; elephants will strip leaves and bark from branches, break branches,
snap stems, debark stems, and even uproot and push over entire trees or shrubs (Estes, 1991).
In this way, elephants play a special role in their environment as allogenic ecosystem
engineers, altering the environment by transforming living or non-living resources from one
state to another through physical means (Jones, Lawton, and Shachak, 1994; Rutina and Moe,
2014).
There are both negative and positive effects of engineering by large herbivores. While
elephant damage to woody plant regimes can cause a wide-spread loss of vegetation and
alterations in species compositions, many less-substantial organisms in the environment rely
on such changes to survive; several savannah-dwelling faunae such as the big cats and
ungulates rely on the open grasslands remaining relatively free from woody plant
encroachment (Jones, Lawton, and Shachak, 1997). Some animals are also known to make
use of elephant- damaged woody plants, be it herpetofaunal species taking advantage of the
modified complexity of the woodland area at the patch scale (Pringle, 2008; Nasseri,
McBrayer, and Schulte, 2010), or larger mammals, such as the steenbok and impala,
preferring to forage in microhabitats with elephant-induced structural changes for increased
protection from predators (Valeix et al., 2010; Hayes, 2011).
Although elephants are bulk feeders, they nonetheless exhibit definitive preferences
or avoidances for differing species of woody plants; elephant damage tends to not be
dispersed in proportion to the relative abundance of woody plants (Baxter, 2003). A study
conducted by Owen-Smith and Chafota (2012) found that of the 27 commonly found woody
plant species in their study area, only 30-50 percent of them were preferred by elephants, and
most of the foraging damage was done to “1 or 2 common shrub species.” It is also
postulated that these damage preferences facilitate the growth of negatively favoured and
avoided species of woody plants (Owen-Smith and Chafota, 2012; Carrigy, 2013).
9. 8
2.2 Aims and Hypotheses
The research conducted for this paper aims to examine African bush elephant damage to
woody plants across four survey sites during a winter dry season, with a particular emphasis
on determining what varieties of damage elephants prefer to perform on specific species of
woody plants, and how elephant damage increases over time. The six main outstanding
research questions to be explored are:
I. How do the four study sites compare in veld condition and elephant grazing capacity,
and does this information help predict levels of elephant damage?
• Hypotheses: There will be variation in veld condition and elephant
grazing capacity at the four survey sites.
• Good veld condition and grazing capacity should lead to less woody
plant damage.
II. What is the composition of woody plant species and how are they distributed across
the four study sites?
• Hypothesis: Woody plant composition will vary across the four study
sites.
III. What is the composition of elephant damage types and how are they distributed across
the four study sites?
• Hypothesis: Elephant damage will vary across the four study sites.
IV. Do elephants have adverse effects on specific species of woody pants?
• Hypotheses: Elephants have preferences to damage certain woody
plant species over others.
• The relative abundances of woody plants will not be in proportion to
their amounts of damage.
10. 9
V. What are the rates of elephant damage types through time?
• Hypothesis: Elephant damage will increase through time, at differing
rates for distinctive damage types, and this will vary across sites.
VI. What variables best predict elephant damage?
• Null Hypothesis: The models will account for zero percent of the
variation.
The conclusions of this investigation should offer a wider knowledge into how
elephants act as ecosystem engineers in their environment; the types of damage they have a
preference to perform on individual species of woody plants, and what the implications of
elephant damage are in a wider biological context, relating the rate of damage through time
with the conditions and grazing capacities of the veld. This could lead to an understanding of
why elephants perform certain amounts of damage, and how to best predict elephant damage
during a dry winter season or draught in Southern Africa.
11. 10
3.0 Materials and Methods
3.1 Study Area
Balule Nature Reserve is a 42,500 hectare protected expanse of land in the Limpopo
Province of the Republic of South Africa, located between the towns of Hoedspruit and
Phalaborwa. Balule is in the lowveld, a sub-tropical scrubland region with several ecological
zones and a diverse array of flora and fauna; more than 336 documented species of woody
plants and as many as 40 species of mammals can be found in the reserve. Balule was
initially divided between several privately maintained game farms until the early 1990s, when
a large-scale conservation effort was placed into effect that removed all fencing between
neighbouring game reserves and the Kruger National Park. Along with Balule, three other
nature reserves, Klaserie, Umbabat, and Timbavati, comprise what is now known as the
Associated Private Nature Reserves (APNR), which together with Kruger National Park form
the Greater Kruger Park. Balule Nature Reserve is comprised of many smaller game reserves
including York, Parsons, Ukhozi, Grietjie, Olifants North, and Olifants West (van Dongen
and Weergeven, 2011).
The data for this research was collected in the Olifants West Nature Reserve (OWNR)
area of Balule Nature Reserve (see figure 1 on page 11). OWNR covers an area of nearly
one-quarter that of Balule Nature Reserve (approximately 8,500 hectares), where 50
proprietors operate on 62 discrete assets of land (Olifants West Nature Reserve, 2016). One
of those proprietors is a conservation organisation called Campfire Academy, best known for
its FGASA (Field Guiding Association of Southern Africa) accredited training programmes
(Campfire Safaris, 2016). The fieldwork for this project was carried out within the 100
hectares of land owned by Campfire Academy.
13. 12
3.2 Establishing the Transects
A series of line transects were carried out at four specified sites on Campfire
Academy’s 100-hectare plot of land. In order to perform a comparative analysis of elephant
damage to woody plants across different habitat types, the sites were chosen non-randomly
using the Google Maps mapping tool (Rasmussen and Rasmussen, 2016). The four sites are
defined in the following manner: Site 1 (24º13’2.7”S, 30º53’13.5”E) can be identified as a
shrubby woodland with a bisecting vehicular dirt road, Site 2 (24º12’55.5”S, 30º53’19.5”E)
can be identified as having semi-eroded soil with fringes of both grassland and woodland
(adjacent to a mud dam), Site 3 (24º13’10”S, 30º53’36.5”E) can be identified as an open
plains savannah, and Site 4 (24º12’50”S, 30º53’10.4”E) can be identified as a shrubby
woodland (see appendix 1.1A on page 49 and appendix 1.1B on page 50). The transecting
method for this project was a systematic array of zig-zag-lines using random starting points;
this approach is said to be appropriate for large areas (Cresswell and Hammond, 2015). A
GPSmap60CSX Global Positioning System hand-held satellite device (accurate to
approximately ± 3 metres) was used to locate the random starting coordinates of each site as
designated by Google Maps. The directionality of the transect was established based on the
researcher’s discretion to best represent each site’s unique habitat type.
A straight line of approximately 100 metres in length was drawn out at each site using
a crank-handle fibreglass measuring tape. Every 20 metres along the length of the transect
line, a 20 metre by 20 metre sampling quadrat was established, alternating between the left
and right sides of the 100 metre line. 18 metal pegs approximately 20 centimetres in length
were hammered into the ground at each 20 metre mark, and treated with red spray paint; the
colour red contrasts with the brown and green colours of the bushveld which made the pegs
conspicuous. GPS coordinates were taken at the location of each peg in order to further aid in
the pinpointing of them on later visits to the sites. These pegs were used as landmarks to
14. 13
establish the boundaries of the transect quadrats. For each of the five 20 metre by 20 metre
quadrats, a diagonal line was drawn out from the centre of the 100 metre line using
measuring tape. By means of the Pythagorean Theorem:
!"
+ $"
= &"
20"
+ 20"
= 800
800
+
= 28.3
this allowed for approximately 28 metres per quadrat, or 140 metres per transect of possible
woody plant surveying at each of the four sites (see appendix 1.1C on page 51).
3.3 Data Collection
3.3.1 Scoring Elephant Damage to Woody Plants
Data was collected between the dates of the 22nd
of July 2015 and the 24th
of August
2015, during the South African winter dry season. At each site, the researcher would walk
along the established 28 metre diagonal lines of the transect quadrats, sampling all woody
plants that were within the researcher’s extended arm’s length (approximately one metre left
and one metre right of the surveying line); every woody touch (including those plants shorter
than the height of the researcher’s arms) was recorded. When a woody plant fell within the
sampling line, it was identified to the species level (with assistance from a FGASA-certified
guide), and designated as one of three woody plant types: a tree, having 1 main stem; a
shrubby tree, having between 1 and 4 main stems; a shrub, having 5 or more main stems.
Along with this classification, each species of woody plant was also designated a unique 3-
letter code for the purpose of simplification (see appendix 1.1D on page 52). Each woody
plant was analysed for elephant damage; a FGASA accredited guide assisted in the
identification of elephant damage types, and the distinction between elephant damage and
15. 14
damage done by other browsing animals such as buffalo or kudu (only damage identified as
elephant was recorded).
Damage was categorized into the following class types: branch breaking damage,
where branches are broken off from the main stem(s); stem breaking damage, where the
stem(s) of the plant are either damaged or broken off completely; pushing over damage,
where the roots are either damaged or the plant is completely knocked over or uprooted;
branch stripping damage, where branches are stripped of their bark; bark stripping damage,
where the main stem(s) of the plant have bark stripped or peeled off; ring-barking damage,
where bark is completely removed from around the circumference of the main stem(s). Bark-
related vegetation damage was measured in the lower 5 metres of the stem, as the average
African bush elephant is approximately 4 metres tall (Laursen and Bekoff, 1978), and any
damage above this height could be from extraneous sources such as boring beetles or birds.
Branch-related damage was measured in the lower 7 metres of the stem; including the trunk
of an elephant which can grow to be 2 metres long, an African bush elephant can reach as
high as 7 metres. Elephant damage was scored on each woody plant based on a 6-point
damage rank-value scale adapted from Anderson and Walker (1974) and Walker (1976),
where: 0% damage = rank of 0; 1-25% damage = rank of 1; 26-50% damage = rank of 2; 51-
75% damage = rank of 3; 76-99% damage = rank of 4; and 100% damaged = rank of 5 (see
appendix 1.2A on page 56). Table 1 on page 15 shows the correspondence between elephant
damage types and assigned damage rank values, depending on how much damage was done
to the woody plant.
Other woody plant variables recorded include stem circumference, stem height, total
height, and diameter of the canopy (all measured in metres). Stem circumference was
measured at chest height for woody plants with tall stems. Stem height can be defined as the
16. 15
height from the ground to the first main set of branches. Total height was measured from the
ground to the peak height of the canopy. Canopy diameter was measured between the two
Table 1: Table showing classification of elephant damage types corresponding with
damage rank value scores. Branch break and branch strip damage scoring came as a
percentage of damage done to all of the woody plant's branches as a whole. If a woody plant
was observed to have been stepped on where the stem was bent to one side, but still perfectly
healthy, a score of 1 was applied for push-over damage.
furthest points of a woody plant’s canopy. Woody plant measurements were all recorded
using a 2-finger distance estimate method. The index and little fingers (approximately10
Rank
Value
Branch Break
Damage
Stem Break
Damage
Push Over
Damage
Branch Strip
Damage
Bark Strip
Damage
Ring Bark Damage
0 no broken
branches
no damage no push-over
damage
no branch
stripping
damage
no bark
stripping
damage
no bark damage
1 1-25% of branches
broken off from
stem(s)
1-25% cracked
stem(s)
1-25%
uprooted
(roots exposed,
but not
damaged)
1-25% of bark
was stripped
from branches
1-25% of bark
was stripped
from the main
stem(s)
1-25% of the stem
was ringed around
its circumference
2 26-50% of
branches broken
off from stem(s)
26-50% cracked
stem(s)
26-50%
uprooted
(roots exposed
and damaged)
26-50% of bark
was stripped
from branches
26-50% of bark
was stripped
from the main
stem(s)
26-50% of the stem
was ringed around
its circumference
3 51-75% of
branches broken
off from stem(s)
51-75% cracked
stem(s)
51-75%
uprooted
(roots exposed,
damaged, and
plant was
partially
pushed-over)
51-75% of bark
was stripped
from branches
51-75% of bark
was stripped
from the main
stem(s)
51-75% of the stem
was ringed around
its circumference
4 76-99% of
branches broken
off from stem(s)
76-99% cracked
stem(s)
uprooted
(roots exposed,
damaged, and
plant is pushed-
over, but still
alive)
76=99% of bark
was stripped
from branches
76=99% of
bark was
stripped from
the main
stem(s)
76-99% of the stem
was ringed around
its circumference
5 100% of branches
broken off from
stem(s)
100% stem(s)
broken off
100% uprooted
(plant was
pushed over
and dead)
100% of bark
was stripped
from branches
100% of bark
was stripped
from the main
stem(s)
100% of the stem
was ringed around
its circumference
17. 16
centimetres apart) were held out in front of the researcher’s face; closing one eye, 1 metre is
established relative to a metre stick by backing away from the metre stick until it fits within
the fingers. At this distance, the fingers are used as relative increments of 1 metre to measure
a woody plant’s height and diameter of the canopy (see appendix 1.1E on page 53). Stem
circumferences were initially measured with measuring tape, and later estimated based on
size similarities. For woody plants with multiple stems, an average measurement was
recorded across all stems.
Transect sites were visited (and elephant damage re-evaluated) a total of 7 times; the
first set of surveys established the “old” elephant damage at the four sites, and subsequent
surveys scored any changes to the woody plants’ damage regimes over time. The other
woody plant variables (height, circumference, and diameter) were only recorded at the first
and last sets of surveys. The same woody plants recorded during the first survey were
analysed for extra elephant damage throughout the subsequent visits; no novel woody plant
species were added to the records following the first set of surveys.
3.3.2 Collecting Grass Samples
A 1 metre by 1 metre throw-able sampling quadrat was used to collect samples of
grass at each transect site (see appendix 1.1F on page 54). Within each of the 5 woody plant
sampling quadrats per site, the researcher randomly threw the 1 metre by 1 metre square
quadrat behind their back. Wherever the quadrat landed, grass cutters were used to cut all
grass patches 3 centimetres from above the ground. This accounted for the approximate
grazing height of savannah herbivores. All grass within the randomly-thrown square quadrat
was identified to the species (see appendix 1.1D on page 52) and had their patch frequencies
recorded. A total of 5 quadrat-throws were performed per site, one in each woody plant
surveying quadrat. This allowed for a total of 20 random quadrat throws to collect grass
18. 17
samples across all 4 sites. The cut grass was amalgamated into one single plastic bag per site;
a total of four plastic bags were filled with grass. Samples were taken during just one of the
seven visits to each site; grass dry mass would be unchanging during the winter dry season
and therefore it would have been insensible to take grass samples through time. The collected
samples of grass were placed into tied bags and left to air-dry for approximately 30 hours.
This ensured that any moisture that was left in the grasses (practically 0% due to the dry
season) would evaporate, and that an accurate dry mass could be calculated from the cut
samples.
3.4 Data Analysis
3.4.1 Veld Condition and Elephant Grazing Capacity
The ecological status of grass is frequently used to establish veld condition. Grass
species can be classified as one of four status classes: Decreaser (palatable climax species),
Increaser I (unpalatable climax species), Increaser II (pioneer and sub-climax species
abundant in overgrazed veld), or Invader (non-native pioneer species) ecological status
classes (van Oudtshoorn and van Wyk, 2012). Grass species were grouped by their patch
frequencies and divided into respective ecological status groups (see appendix 1.1G on page
55). Using the following equation as outlined in van Rooyen, Bredenkamp, and Theron
(1996), veld condition was calculated for each transect site (see appendix 1.2B on page 56):
(
-.
/
100 1) + (
-"
/
100 3.) + (
-4
/
100 3") + (
-5
/
100 6)
Where:
19. 18
P1= Number of grass patches that are Decreaser species.
P2= Number of grass patches that are Increaser I species.
P3= Number of grass patches that are Increaser II species.
P4= Number of grass patches that are Invader species.
N= Total number of grass patches.
d= Decreaser index value (10).
i1= Increaser I index value (7).
i2= Increaser II index value (4).
I= Invader index value (1).
Veld Condition Index ranges from 100 to 1000. Between 100 and 400 is poor, between 400
and 600 is moderate, and between 600 and 1000 is good condition. Elephant grazing capacity
was estimated first by calculating the dry biomass of grass. After approximately 30 hours of
air-drying the bags of grass collected from the four sites, each bag was weighed on a gram
scale. The 1 metre by 1 metre throwing quadrat was used five times per site, summing to an
area of 5m2
. Grams were converted into kilograms, square metres were converted into
hectares, and grass dry biomass was calculated for each site using the following equation:
789 :3;<!== =
>!== (?@)
A8B! (ℎ!)
Following the calculation of grass dry mass per site, an equation for elephant grazing
capacity (in hectares per Elephant Grazing Unit) can be defined by the following equation
(see appendix 1.2C on page 57):
9 =
!(&)
7>($)
20. 19
Where:
y = Elephant grazing capacity (ha/EGU).
a = Number of days in a year (365).
DM = The dry mass of grass per unit of area (calculated for each site).
b = Utilisation Potential (35%).
c = Elephant daily intake rate of grass dry mass (60 kg/day).
A typical adult African bush elephant will consume an average of 60kg of dry matter in one
day (Chapman and Reiss, 1992). The Utilization Potential of grass is defined as the
percentage of the grass matter that is palatable; a commonly used average of 0.35 is described
by Moore and Odendaal (1987). van Oudtshoorn and van Wyk (2012) define the grazing
capacity as the amount of area (in hectares) that a grazing animal would require in order to be
productive and sustain itself in 1 year.
3.4.2 Statistical Analysis in R
Woody plant data was analysed statistically using R Statistical Software (R Core
Team, 2016). A number of tests were run on the data depending on the outstanding research
question addressed. Pearson’s Chi-Squared test of independence was used to explore the
difference in proportions between woody plant species across study sites (McDonald, 2014).
A Correspondence Analysis (CA) test was used to explore the associations between two
categories of multiple variables (e.g. woody plant species and site, elephant damage types
and site), utilizing the R package ‘ca’ (Nenadic, 2016). A Principal Components Analysis
(PCA) was used to summarise the variation contained in the elephant damage types into a
few novel variables called the Principal Components.
21. 20
In order to assess the preferences that elephants have to perform certain types of
damage to certain species of woody plants, Strauss’ Linear Electivity Index (Strauss, 1979)
was used to calculate those preferences. Strauss’ Linear ranges from -1 to +1, where negative
values correspond to a negative preference, and positive values correspond with a positive
preference. It can be defined by the following equation (see appendix 1.2D on page 58):
D3 = 8E,G − IE
where:
D3 = Measure of Linear Electivity as an unweighted difference in proportions.
8E,G = The relative abundance (JE,G) of woody plant species 3 with elephant damage K (as a
proportion of total woody plant species / with elephant damage K).
8E,G =
JE,G
/G
IE = The relative abundance (JE), of woody plant species 3 (as a proportion of total woody
plant species /).
IE =
JE
/
Rate of elephant damage was calculated by taking the woody plant damage rank
values from the final set of survey visits and subtracting the damage rank values from the
first set of survey visits and dividing by the number of survey days. The following equation
was used to define damage rate (see appendix 1.2E on page 58):
22. 21
L =
ME,G − NE,G
7
Where:
L = Rate of New Damage (rank value / day).
ME,G = Rank value of elephant damage K for species 3 at final assessment of damage.
NE,G = Rank value of elephant damage K for species 3 at initial assessment of damage.
7 = Number of days between initial and final assessments of damage.
Both the Correspondence Analysis and Principal Components Analysis tests were performed
on elephant damage rate. Lastly, a set of Generalized Linear Mixed Models (GLMMs) were
run for each elephant damage type, with age of elephant damage set as the random effect.
Where necessary, predictor variables were log transformed or tested for correlations.
Response variables were plot to check their frequency distributions; appropriate distribution
families were assigned to their models. The R package ‘lme4’ (Bolker, 2016) was used to run
the regression models, and the package ‘MuMIn’ (Bartoń, 2016) was used to dredge a global
model and predict a set of best models for each elephant damage type, based on log
likelihoods and delta Akaike Information Criterion (AICc) weights. When looking at
elephant damage, a subset of the data was used, excluding all but one set (age) of survey
visits. The final age of elephant damage was investigated across all analyses, as it was
sensible for comparing across study sites and woody plant species preferences.
23. 22
4.0 Results
4.1 Veld Condition and Elephant Grazing Capacity
The van Rooyen Veld Condition Index can range from 100 to 1000, where values
below 400 are considered poor, between 400 and 600 are considered moderate, and above
600 are considered good. From the equation used to calculate the van Rooyen Veld Condition
Scores for each of the four sampling sites (refer back to appendix 1.2B on page 56), the
following information can be stated: Site 3 (Veld Condition Score = 685) was in good
condition for gazing, while Sites 1, 2, and 4 (Veld Condition Scores = 557, 529, and 508
respectively) were in moderate condition for grazing, suggesting a moderate amount of
overgrazing occurred at those sites (see figure 2A on page 23).
Elephant Grazing Capacity (EGC, in hectares per Elephant Grazing Unit) is defined
as the amount of area (in hectares) required by one grazing elephant to sustain itself and be
productive in a year. From the equation to calculate EGC (refer to appendix 1.2C on page
57), the following information can be extrapolated: Site 3 had the highest level of grass dry
biomass (kg/ha) and the lowest value of EGC (n = 142 ha/EGU), Site 1 had a moderate level
of grass dry biomass and a corresponding moderate value of EGC (n = 216 ha/EGU), Sites 4
and 2 both had relatively low levels of grass dry biomass and high values of EGC (n = 348
ha/EGU and n = 391 ha/EGU respectively). High values of Elephant Grazing Capacity would
reflect poor veld conditions; it represents the area needed by one grazing elephant to be
productive in a year (see figure 2B on page 23).
4.2 Statistical Analysis in R
4.2.1 Composition of Woody Plant Species
There is a significant association between woody plant species and survey site
(Pearson’s Chi-Squared test of independence: x-squared = 131.73, df = 60, p-value <<0.001).
The three most abundant woody plant species across all 4 sites were the red bushwillow
24. 23
Figure 2: (A) Bar plot showing van Rooyen Veld Condition Scores for Site 1 (n=557), Site 2 (n=529),
Site 3 (n=685), and Site 4 (n=508). Scores below 400 are considered in poor condition, between 400-
600 are considered in moderate condition, and above 600 are considered in good for the veld. (B) Plot
of Elephant Grazing Capacity (ha/EGU) by Grass Dry Mass (kg/ha) for Site 1 (n=216), Site 2
(n=391), Site 3 (n=142), and Site 4 (n=348) (y = 0.003x2
– 2.678x + 738.1, R2
= 0.999). High values
of elephant grazing capacity (in hectares per elephant grazing unit) reflect poor veld conditions; it
represents the area needed by one grazing elephant to be productive in a year.
Site 1
Site 2
Site 3
Site 4
y = 0.003x2 - 2.678x + 738.1
R² = 0.9987
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400 450 500
Elephant Grazing Capacity (ha/EGU)
Grass Dry Mass (kg/ha)
Relationship between Elephant Grazing Capacity and Grass Dry Mass
A
B
25. 24
(Combretum apiculatum), the bushveld shepherd’s tree (Boscia foetida), and the knobthorn
acacia (Senegalia nigrescens) at n = 26, n = 24, and n = 20 respectively (see figure 3A on
page 25). From a Correspondence Analysis with woody plant species and site, the following
principal inertias can be defined: eigenvalue 1 (43.64% of explained variance), eigenvalue 2
(38.36% of explained variance), and eigenvalue 3 (18% of explained variance). A strong
association exists between red bushwillows and Site 4 (see figure 3B on page 25). There is no
significant association between woody plant types and survey site (Pearson’s Chi-Squared
test of independence: x-squared = 9.16, df = 6, p-value = 0.1648). Woody plant type rank
abundance across sites was as follows: shrubby trees (n = 60), trees (n = 47), and shrubs (n =
37) (see figure 3C on page 25). From a Correspondence Analysis with woody plant types and
site, the following principal inertias can be defined: eigenvalue 1 (96.16% of explained
variance) and eigenvalue 2 (3.84% of explained variance) (see figure 3D on page 25).
4.2.2 Composition of Elephant Damage Types
There is significant variation between elephant damage types and mean damage rank
values (averaged across woody plant species) by survey site (see figure 4A on page 26).
Branch break damage (mean = 1.33 ± 0.15 SE), stem break damage (mean = 1.09 ± 0.22 SE),
push over damage (mean = 0.81 ± 0.34 SE), branch strip damage (mean = 0.2 ± 0.05 SE),
bark strip damage (mean = 0.08 ± 0.04 SE), and ring bark damage (mean = 0.07 ± 0.05 SE)
all have relatively small sample sizes (One Sample t-test: df = 23) with large, overlapping
standard errors. From a Correspondence Analysis with elephant damage types and site, the
following principal inertias can be defined: eigenvalue 1 (59.25% of explained variance),
eigenvalue 2 (26.23% of explained variance), and eigenvalue 3 (14.52% of explained
variance). An association exists between push-over damage and Site 4 (see figure 4B on page
26). From a Principal Components Analysis with elephant damage types across both sites and
woody plant types, the following principal inertias can be defined: eigenvalue 1 (24.7% of
26. 25
Figure 3: (A) Bar plot showing woody plant species rank abundance summed across sites, with red bushwillows, bushveld shepherd’s trees, and knobthorn acacias having the
highest abundances across sites (n=26, n=24, and n=20 respectively). (B) Correspondence Analysis scatterplot of the sub-space defined by dimension 1 (43.6% explained
var.) and 2 (38.4% explained var.) with woody plant species and site, with sites in the principal coordinates and species in reconstructions of the standardized residuals.
Additionally, sites are represented by points and species are represented by arrows. Point intensity (shading) corresponds to the absolute contributions for the sites. (C) Bar
plot showing woody plant type rank abundance summed across sites for shrubby trees (n=60), trees (n=47), and shrubs (n=37). (D) Correspondence Analysis scatterplot of the
sub-space defined by dimension 1 (96.2% explained var.) and 2 (3.8% explained var.) with woody plant types and site, with sites in the principal coordinates and species
types in reconstructions of the standardized residuals. Additionally, sites are represented by points and species types are represented by arrows. Point intensity (shading)
corresponds to the absolute contributions for the sites.
rbw bst kbt srb ccw skb wrb vrb mgu mrb fcr fma lwd mar cgu fth lcl bgr tam umt vcw
Site 4
Site 3
Site 2
Site 1
Ranked Woody Plant Species
Abundance
0510152025
Correspondence Analysis with Woody Plant Species and Site
Dimension 1 (43.6%)
Dimension2(38.4%)
-2 -1 0 1 2
-0.50.00.5
Site 1
Site 2
Site 3
Site 4
rbw
bst
kbt
srbccw
skb
wrb
vrb
mgu
mrb
fcr
fma
lwd
mar
cgu
fth
lclbgrtam
umt vcw
Shrubby Trees Trees Shrubs
Ranked Woody Plant Types
Abundance
0102030405060
Site 4
Site 3
Site 2
Site 1
Correspondence Analysis with Woody Plant Types and Site
Dimension 1 (96.2%)
Dimension2(3.8%)
-1 0 1
-0.8-0.40.00.4
Site 1Site 2
Site 3
Site 4
Shrubby Trees
Trees
Shrubs
A B
C D
27. 26
Figure 4: (A) Bar plot showing mean damage rank value (±SE) across damage types for each site. (B) Correspondence
Analysis scatterplot of the sub-space defined by dimension 1 (59.3% explained var.) and 2 (26.2% explained var.)
with damage types and site, with sites in the principal coordinates and damage types in reconstructions of the
standardized residuals. Additionally, sites are represented by points and damage types are represented by arrows. Point
intensity (shading) corresponds to the absolute contributions for the sites. (C) Principal Components Analysis
scatterplot of the sub-space defined by PC1 (24.7% explained var.) and PC2 (20.7% explained var.) with damage
types across sites (left) and woody plant types (right) (1=trees, 2=shrubby trees, 3=shrubs).
BB SB PO BrS BS RB
Site 1
Site 2
Site 3
Site 4
Damage Type
MeanDamageRankValue
0.00.51.01.52.02.5
Correspondence Analysis with
Elephant Damage Types and Site
Dimension 1 (59.3%)
Dimension2(26.2%)
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
-0.6-0.4-0.20.00.20.40.60.8
Site 1
Site 2
Site 3
Site 4
BB
SB
PO
BrS
BS
RB
BB
SB
PO
BrS
BS
RB
-5.0
-2.5
0.0
2.5
-2 0 2 4
PC1 (24.7% explained var.)
PC2(20.7%explainedvar.)
Sites 1 2 3 4
BB
SB
PO
BrS
BS
RB
-5.0
-2.5
0.0
2.5
-2 0 2 4
PC1 (24.7% explained var.)
PC2(20.7%explainedvar.)
Woody Plant Type 1 2 3
Principal Components Analysis of African Elephant Damage Types
across Sites and Woody Plant Type
A B
C
28. 27
explained variance), eigenvalue 2 (20.7% of explained variance), and eigenvalue 3 (16.7% of
explained variance). No significant associations are evident between the principal
components (see figure 4C on page 26).
4.2.3 Elephant Damage Preferences Across Woody Plant Species
There is significant variation between woody plant species and mean Strauss’ Linear
Electivity (Li) by elephant damage type (see figure 5 on page 28, and refer to appendix 1.2D
on page 58). Mean Li values (averaged across survey sites) above zero indicate a positive
preference, whereas values below zero indicate a negative preference. Elephants are shown to
particularly prefer certain species of woody plants over others for performing specific types
of damage. It can be inferred from figure 5 (page 28) that elephants prefer: marulas
(Sclerocarya birrea caffra) and false marulas (Lannea schweinfurthii) for ring barking (mean
= 0.71 ± 0.26 SE, and mean = 0.41 ± 0.4 SE, respectively); knobthorns (Senegalia
nigrescens), false marulas, and marulas for bark stripping (mean = 0.82 ± 0.09 SE, mean =
0.2 ± 0.21 SE, and mean = 0.14 ± 0.16 SE, respectively); white raisin bushes (Grewia
bicolor), sickle bushes (Dichrostachys cinerea africana), velvet raisin bushes (Grewia flava),
and silver raisin bushes (Grewia monticola) for branch stripping (mean = 0.32 ± 0.32 SE,
mean = 0.27 ± 0.11 SE, mean = 0.22 ± 0.14 SE, and mean = 0.12 ± 0.11 SE, respectively);
common corkwoods (Commiphora pyracanthoides) and bushveld shepherd’s trees (Boscia
foetida) for pushing over (mean = 0.22 ± 0.19 SE, and mean = 0.11 ± 0.37 SE, respectively).
Branch breaking and stem breaking are less particular damage types. The relatively small
sample sizes (One Sample t-test: df = 125) coalesced with large, overlapping standard errors
serve as further evidence that a significant variation exists between woody plant species and
mean Li for the elephant damage types (see figure 5).
29. 28
Figure 5: (1) Plot showing mean Strauss’ Linear Electivity (Li±SE) across woody plant species for all
damage type. Values above zero indicate a positive preference, whereas values below zero indicate a
negative preference. (2A) Bark strip damage on a knobthorn (Senegalia nigrescens). (B) Ring bark
damage on a false marula (Lannea schweinfurthii). (C) Branch strip damage on a knobthorn. (D)
Branch break damage on a sickle bush (Dichrostachys cinerea). (E) Stem break damage on a silver
raisin bush (Grewia monticola). (F) Push-over damage on a red bushwillow (Combretum apiculatum).
mar fma kbt wrb umt ccw skb vrb cgu tam bgr lwd lcl srb vcw mrb fth mgu fcr rbw bst
Woody Plant Species
MeanStrauss'LinearElectivity(Li)
-0.50.00.51.0
Branch Break Damage
Stem Break Damage
Push-Over Damage
Branch Strip Damage
Bark Strip Damage
Ring Bark Damage
1
2
D
E
F
C
A
B
30. 29
4.2.4 Rate of Elephant Damage
There is significant variation between elephant damage types and mean rate of new
damage (rank value per day, averaged across woody plant species) by survey site (see figure
6A on page 30, and refer to appendix 1.2E on page 58). Branch break damage rate (mean =
0.009 ± 0.003 SE), stem break damage rate (mean = 0.007 ± 0.005 SE), push over damage
rate (mean = 0.0003 ± 0.0003 SE), branch strip damage rate (mean = 0.005 ± 0.001 SE), and
bark strip damage rate (mean = 0.001 ± 0.001 SE) all have very small sample sizes (One
Sample t-test: df = 19) with large, overlapping standard errors. From a Correspondence
Analysis with elephant damage type rates and survey site, the following principal inertias can
be defined: eigenvalue 1 (77.75% of explained variance), eigenvalue 2 (21.02% of explained
variance), and eigenvalue 3 (1.24% of explained variance). A strong association exists
between rate of stem breaking damage and Site 3 (see figure 6B on page 30).
There is also some significant variation between woody plant species and mean rate
of new elephant damage (averaged across survey sites) by elephant damage type, the most
noteworthy variance emerging from the mean branch breaking rate of the false thorn (Albizia
harveyi), and the mean stem breaking rates of the common guarri (Euclea undulata) and
velvet raisin bush (Grewia flava) (mean = 0.008 ± 0.008 SE, mean = 0.006 ± 0.006 SE, and
mean = 0.011 ± 0.005 SE, respectively) (see figure 6C on page 30). From a Correspondence
Analysis with elephant damage type rates and woody plant species, the following principal
inertias can be defined: eigenvalue 1 (46.03% of explained variance), eigenvalue 2 (33.3% of
explained variance), eigenvalue 3 (16.81% of explained variance), and eigenvalue 4 (3.86%
of explained variance). A strong association exists between rate of bark stripping damage and
knobthorn acacias (see figure 6D on page 30). From a Principal Components Analysis with
elephant damage rates across both sites and woody plant types, the following principal
inertias can be defined: eigenvalue 1 (38.4% of explained variance), eigenvalue 2 (22.2% of
31. 30
Figure 6: (A) Bar plot showing mean rate of new damage (rank value / day, ± SE) across damage types for each site.
(B) Correspondence Analysis scatterplot of the sub-space defined by dimension 1 (77.1% explained var.) and 2 (21%
explained var.) with damage rates and site, with sites in the principal coordinates and damage rates in reconstructions
of the standardized residuals. Additionally, sites are represented by points and damage rates are represented by arrows.
Point intensity (shading) corresponds to the absolute contributions for the sites. (C) Bar plot showing mean rate of
new damage (rank value/day, ±SE) across woody plant species for each damage type. (D) Correspondence Analysis
scatterplot of the sub-space defined by dimension 1 (46% explained var.) and 2 (33.3% explained var.) with damage
rates and woody plant species, with damage rates in the principal coordinates and species in reconstructions of the
standardized residuals. Additionally, damage rates are represented by points and species are represented by arrows.
Point intensity (shading) corresponds to the absolute contributions for the damage rates. (E) Principal Components
Analysis scatterplot of the sub-space defined by PC1 (38.4% explained var.) and PC2 (22.2% explained var.) with
damage type rates across sites (left) and woody plant types (right) (1=trees, 2=shrubby trees, 3=shrubs).
BB SB BrS BS PO RB
Site 1
Site 2
Site 3
Site 4
Damage Type
MeanRateofNewDamage(rankvalue/day)
0.0000.0100.0200.030
Correspondence Analysis with Damage Type Rates and Site
Dimension 1 (77.7%)
Dimension2(21%)
-1 0 1 2
-0.8-0.40.00.4
Site 1
Site 2
Site 3
Site 4
BB
SB
BrS
BS
PO
vrb fth srb skb cgu wrb mgu mar kbt mrb rbw bst
Branch Break
Stem Break
Push-Over
Branch Strip
Bark Strip
Woody Plant Species
MeanRateofNewDamage(rankvalue/day)
0.000.020.040.060.08
Correspondence Analysis with Damage Type Rates
and Woody Plant Species
Dimension 1 (46%)
Dimension2(33.3%) -6 -4 -2 0 2 4
0123
Branch Break Rate
Stem Break Rate
Push-Over RateBranch Strip Rate
Bark Strip Rate
vrb
fth
srb
skb
cgu
wrbmgumar
kbt
mrb
rbw
bst
BBDR
SBDR
PODR
BrSDR
BSDR
-5
0
5
-6 -3 0 3
PC1 (38.4% explained var.)
PC2(22.2%explainedvar.)
Sites 1 2 3 4
BBDR
SBDR
PODR
BrSDR
BSDR
-5
0
5
-6 -3 0
PC1 (38.4% explained var.)
PC2(22.2%explainedvar.)
Woody Plant Type 1 2 3
Principal Components Analysis of African Elephant
Damage Type Rates across Sites and Woody Plant Types
A
B
C D
E
32. 31
explained variance), and eigenvalue 3 (18.7% of explained variance). No significant
associations are evident between the principal components, however there does appear to be
more correspondence among the components of elephant damage rates than found in the
components of elephant damage type composition (see figure 6E on page 30, and refer to
figure 4C on page 26).
4.2.5 Generalized Linear Mixed Models
General Linear Mixed Models (GLMMs) were run for each of the elephant damage
type response variables to infer what independent variables best predict elephant damage,
setting age of elephant damage as the mixed effect of the models. Woody plant variables
(stem circumference (SC), stem height (SH), total height (TH), and canopy diameter (CD))
were tested for correlations; total height was significantly correlated with stem height
(Pearson’s product-moment correlation: t = 36.68, df = 10061
, p<<0.001, cor = 0.756), and
total height was significantly correlated with canopy diameter (t = 28.64, df = 1006,
p<<0.001, cor = 0.67). Correlation tests were also run on the categorical variables of quadrat
and site (Cramer’s V = 0.228), and woody plant species and woody plant type (Cramer’s V =
1). The 6 response variables (branch break, stem break, push-over, branch strip, bark strip,
and ring bark damage) were plot as histograms to extrapolate distribution frequencies. Branch
break, branch strip, and ring barking damage displayed Poisson distribution, stem break and
push over damage displayed Gaussian distribution, and bark strip damage displayed Inverse-
Gaussian distribution (see appendix 2A on page 59).
The best model predicting branch breaking damage (BB) is as follows: “BB ~ Site +
logSC + Species + CD + logSH + Site x Species + CD x Species + (1|Age)” (df = 562, AIC =
1679, R2
= 32% of explained variance). The best model predicting stem breaking damage
1
Degrees of Freedom (df) is equal to 1006 because each survey site was visited 7 times to
score elephant damage, which multiplies the true number of woody plants (144) by 7.
33. 32
(SB) is as follows: “SB ~ Site + logSC + Species + Site x Species + logSC x Species +
(1|Age)” (df = 338, AIC = 796, R2
= 70% of explained variance). The best model predicting
push-over damage (PO) is as follows: “PO ~ Site + logSC + Species + Site x Species + logSC
x Species + (1|Age)” (df = 220, AIC = 461, R2
= 76% of explained variance). Stem break
damage was solely predicted by the intercept of the model (df = 82, AIC = 201, R2
= 0% of
explained variance). The best model predicting bark stripping damage (BS) is as follows:
“BS ~ Site + Species + logSH + (1|Age)” (df = 54, AIC = 104). No R2
could be calculated for
BS, as the Inverse-Gaussian family is currently not supported for mixed models. Ring
barking damage was predicted solely by Site (df = 21, AIC = -1533, R2
= 100% of variance
explained). Table 2 on pages 33-36 and appendix 2B on page 60 provide a nice summary of
the best model predictors for African elephant damage types.
36. 35
(-0.827, 1.471)
Canopy Diameter x magic guarri 0.200
(-0.605, 1.004)
Canopy Diameter x mallow raisin bush 0.236
(-1.195, 1.666)
Canopy Diameter x red bushwillow 0.441
***
(0.189, 0.693)
Canopy Diameter x sickle bush 0.308
**
(0.002, 0.614)
Canopy Diameter x silver raisin bush 0.514
***
(0.206, 0.821)
Canopy Diameter x velvet raisin bush -6.785
***
(-10.933, -2.638)
Log Stem Circumference x common corkwood -9.261
***
-12.963
***
(-12.158, -6.364) (-15.895, -10.032)
Log Stem Circumference x knobthorn -23.770
***
-9.696
***
(-38.444, -9.095) (-12.121, -7.271)
Log Stem Circumference x magic guarri 0.293
(-3.203, 3.789)
Log Stem Circumference x red bushwillow -0.840 -11.150
***
(-3.061, 1.381) (-13.524, -8.776)
Log Stem Circumference x sickle bush 6.935
***
(2.964, 10.905)
Log Stem Circumference x silver raisin bush -0.645
(-3.420, 2.131)
Log Stem Circumference x velvet raisin bush 2.977
*
(-0.226, 6.180)
Log Stem Circumference x white raisin bush -5.036
***
(-7.343, -2.729)
Site 3 x common corkwood 0.879
***
2.028
***
2.455
***
(0.320, 1.438) (1.097, 2.960) (1.447, 3.463)
Site 4 x common corkwood 0.723
**
3.328
***
-1.434
***
37. 36
(0.017, 1.429) (2.308, 4.348) (-2.393, -0.476)
Site 3 x false marula -0.095
(-4.894, 4.704)
Site 4 x knobthorn 0.775
**
0.468
(0.070, 1.480) (-0.239, 1.175)
Site 2 x red bushwillow 0.527 2.681
***
(-0.452, 1.505) (1.675, 3.687)
Site 4 x leadwood -3.000
***
(-3.865, -2.135)
Site 4 x red bushwillow 0.674
**
3.004
***
-0.859
***
(0.086, 1.262) (2.248, 3.761) (-1.503, -0.215)
Site 2 x sickle bush 0.382 -2.578
***
(-0.453, 1.217) (-4.282, -0.875)
Site 3 x sickle bush -0.935
**
(-1.827, -0.044)
Site 4 x sickle bush 0.996
**
(0.149, 1.844)
Site 3 x silver raisin bush 2.075
***
(1.159, 2.991)
Degrees of Freedom 562 338 220 82 54 21
Log Likelihood -797.295 -363.126 -208.299 -98.716 -43.886 770.422
Akaike Information Criterion 1,678.590 796.252 460.597 201.433 103.772 -1,532.843
Bayesian Information Criterion 1,860.513 930.058 535.257 206.246 119.684 -1,528.665
Marginal and Conditional R
2
0.32 0.7 0.76 0 NA 1
Note:
*
p<0.1;
**
p<0.05;
***
p<0.01
Table 2: Summary table of best model predictors for African elephant damage types (including 95% Confidence Intervals). The random effect in these
models is represented by (1|Age). Degrees of Freedom (df), Log Likelihood, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and
the Marginal and Conditional R2
values are reported at the bottom of the table for each model. The Marginal and Conditional R2
represent the percentage of
variance that each model explains for the fixed and random effect variables respectively; in these models the random effect adds no extra variation.
38. 38
5.0 Discussion and Conclusions
5.1 Veld Condition and Elephant Grazing Capacity
The original hypothesis that the four study sites would vary in their veld condition
and elephant grazing capacity is accepted. Site 3 was shown to have a lower value of EGC
(ha/EGU) than the other sites, and was the only site to fall within the “good” veld condition
classification based on the index outlined by van Rooyen, Bredenkamp, and Theron (1996).
One reason why Site 3 may be of a higher quality veld for grazing than the other sites could
be that, eco-morphologically speaking, Site 3 is an open grassland veld, and contains a higher
proportion of grass species that perform well under the stress of overgrazing. Summarised in
van Oudtshoorn and van Wyk (2012), veld condition depends on the ecological status and
grazing values of the grass species. Decreasers are grasses that are abundant in good veld, but
decrease in number when overgrazing occurs; Site 3 did not have any Decreaser species.
Increaser I species are abundant in underutilised veld and tend to be unpalatable climax
species, while Increaser II species are abundant in overgrazed veld and increase due to the
ecologically distressing act of overgrazing; Increaser II species tend to be pioneers of the
landscape. Site 3 was abundant in both Increaser I and Increaser II grass species; this site was
in the best condition for elephants to graze, be productive, and be sustained in the least
amount of hectares.
The hypothesis that good veld condition and grazing capacity would lead to less
elephant damage is rejected, as Site 3 was consistently damaged by elephants. As discussed
by Anderson and Walker (1974), elephants can consume up to 86 percent of their diet in
woody plant materials during the peak of a draught. The winter of 2015 was very dry for
South Africa. In 2012-13, there was severe flooding in Limpopo, which washed away the
seed bank below and along-ground into the rivers. 2014 saw normal levels of rainfall in the
39. 39
summer wet season (approximately 400-600mm of rain between October-March). 2015 faced
a severe drought, where as of July, only 126mm of rain had been recorded (one third that of
normal levels (Laetitia Cronje, personal communication, July 2015)). It is therefore likely
that the elephants in Olifants West damaged woody plants out of necessity to acquire the
valuable nutrients and proteins that the grasses simply did not have during the drought.
5.2 Composition of Woody Plant Species
The hypothesis that woody plant species composition will vary across the four study
sites is accepted. Site 1 was abundant with knobthorns (Senegalia nigrescens) and bushveld
shepherd’s trees (Boscia foetida rehmanniana). Site 2 had the least abundance of woody
plants, likely due to its semi-eroded soils and fringe-quality vegetation. There were many
site-specific woody plants at Site 2 such as the umbrella thorn acacia (Acacia tortilis
heteracantha), false thorn (Albizia harveyi), and flame creepers (Combretum microphyllum).
Site 3 was abundant in shrubby trees and shrubs, having a large abundance of white raisin
bushes (Grewia bicolor) and bushveld shepherd’s trees. Site 3 also had a relatively large
abundance of common corkwoods (Commiphora pyracanthoides) and false marulas (Lanne
schweinfurthii stuhlmannii). Site 4 was dominated by red bushwillows (Combretum
apiculatum). It is not uncommon for red bushwillows to exist in a veld of their own; they are
one of the most common bushveld trees and widely utilised by an extensive array of
browsing plains game such as kudu, giraffe, buffalo, black rhino, and elephants (van der
Walt, 2010).
Elephants can play a large role in the dispersal of woody plants across the savannah
(Hofmeyr and Eckardt, 2005). Along with being an engineer, elephants are also keystone
species; those that have a disproportionally large effect on the environment relative to their
population size (Jones, Lawton, and Shachak, 1994). Elephants are known to be seed
dispersers of fruiting woody plants such as the marula (Sclerocarya birrea caffra), and
40. 40
therefore the presence of elephants in the bush can significantly alter the composition and
dispersal of some woody plant species (Baxter, 2003).
5.3 Composition of Elephant Damage Types
The hypothesis that elephant damage types would vary across the four study sites is
partially accepted. While there are some clear connotations that push-over damage was
dominant at Site 4, and that Sites 3 and 4 generally received more damage than Sites 1 and 2,
the patterns of damage across sites are generally quite similar. Site 4 was dominated by red
bushwillows, and many of them were slightly pushed-over, which explains this
correspondence. Because elephants have been established as having preferences for certain
species of woody plants, the distribution of damage is most probably a consequence of this
occurrence. Sites that received the most damage are likely to have trees that are preferred by
the elephants. Although it must be said that the analysis looking at mean elephant damage
rank across sites, unlike the analysis of preference, did not take species availability into
account, and therefore some woody plant species such as the red bushwillow, while receiving
a relatively large mean quantity of damage, were also quite abundant in their velds.
Nonetheless, elephant damage was unequally distributed across the four survey sites, and
therefore it can be said that the composition of elephant damage varies across sites.
5.4 Elephant Damage Preferences Across Woody Plant Species
The hypothesis that elephants hold preferences to forage upon and damage certain
woody plant species over others is accepted. It was shown that not only do elephants prefer
some species of woody plants over others, but they preferentially perform specific damage
types to certain species. The marula and false marula trees were significantly preferred for
ring barking, the knobthorn was significantly preferred for bark stripping, the white raisin,
velvet raisin (Grewia flava), and sickle bush (Dichrostachys cinerea africana) were
significantly preferred for branch stripping, the common corkwood was significantly
41. 41
preferred for pushing over, the umbrella thorn and red bushwillow were significantly
preferred for stem breaking, and branch breaking was ambiguously distributed.
According to Kerley et al. (2008), common woody plant species preferred by
elephants include those of the Acacia (e.g. knobthorn, umbrella thorn), Combretum (e.g. red
bushwillow), Commiphora (e.g. common corkwood), Dichrostachys (e.g. sickle bush),
Grewia (e.g. raisin bushes), and Sclerocarya (e.g. marula) Genera. A study reviewed by
Hofmeyr and Eckardt (2005) showed that 65 percent of 951 knobthorn trees surveyed had
bark stripping damage. Elephants most likely perform bark stripping damage on thick
acacias, such as the knobthorn, in order to get to the foundation layer of cambium that lies
just beneath the outer bark of the tree’s stem. The cambium layer contains the xylem and
phloem of the plant, of which contains the vital nutrients and minerals that elephants look for
during times of drought (Hofmeyr and Eckardt, 2005).
Elephants are also after the crude protein found in the woody plants; during the dry
season, grass species generally contain only 3-6 percent of crude proteins, while woody
plants contain 8-24 percent (Ulrey, Crissey, and Hintz, 1997). Woody plant species such as
the raisin bushes and corkwoods contain crude protein in their roots, leading many elephants
to push over or even uproot the plants in order to get to the roots. Ring barking occurs only
on the mature tall trees; once a tree such as the marula is ringed about its circumference, it is
very likely to die (although as mentioned in Sheil and Salim (2004), ring barking is not
necessarily fatal if the damage does not damage the internal tissues of the plant). Upon death,
these adult trees can then more easily be pushed over, and subsequently branches effortlessly
reached (Ihwagi et al., 2009). Therefore, certain types of woody plants receive certain kinds
of damage depending on what resource the elephants are attempting to extract from the plant
(e.g. cambium, crude protein, minerals, leaves, etc.) (Owen-Smith and Chafota, 2012).
42. 42
The second hypothesis that woody plant species are not damaged in proportion to
their relative abundances is also accepted. As evidently clear in the case of the red
bushwillow, although the species is very abundant (especially at Site 4 where it holds a veld
regime), its level of damage relative to abundance is not very high; red bushwillows have
positive preference for branch breaking, stem breaking, and pushing over damage, but the
positive preferences are not very significant. As outlined by Baxter (2003), certain species of
woody plant, e.g. Combretum, are positively preferred but not in accordance with their
relative abundances in the environment. Elephants, although described to be general feeders,
can be quite selective when it comes to getting the nutrients and minerals that they need,
especially during the dry season (Loarie, van Aarde, and Pimm, 2009).
5.5 Rate of Elephant Damage
The hypothesis that elephant damage will increase through time, at differing rates,
and varying across sites, is accepted. It is clear from the results that branch break, stem break,
and branch stripping damage occurs much faster than pushing over, bark stripping, or ring
barking damage. This is likely a consequence of the energy expense of each damage type.
Branch breaking and stripping can be done with relative ease from the elephant, whereas
damage types such as stripping bark, ringing bark, and pushing over or uprooting plants
would be energetically costly; elephants must use their tusks and peel the bark away from the
stems (Ferguson, 2014). In the case of ring barking, this type of damage occurs over quite a
long period of time relative to other damage types; the elephant will ring a tree such as a
marula, leave it to slowly die, and then return and push it over days or even months later
(Laetitia Cronje, personal communication, August 2015). Along with a variance in rate
among damage types, rate also varies across survey sites. Sites 2 and 3 received a
significantly higher rate elephant damage (in mean rank value increase per day) than Site 1
and 4. Site 2 was the semi-eroded fringe-land, adjacent to a commonly used mud dam. It is
43. 43
likely that the relatively high rate of damage at Site 2 was a consequence being located near
the mud dam, where the elephants were observed to spend a considerable amount of time.
Site 3 was the open plains. It is likely that, simply due to its accessibility, the elephants were
able to utilise Site 3 more frequently than the closed off woodland areas.
5.6 Generalised Liner Mixed Models
The null hypothesis that the Generalised Linear Mixed Models (GLMMs) would
account for zero percent of the variation in elephant damage prediction is partially accepted,
in some of the cases. Explained variances ranged from 70 to 74% for stem breaking and
pushing over damage, to 0% for branch stripping, and 100% for ring barking. It is probable
that a lack of spatial variables is the cause of such widespread (and likely inaccurate)
explained variance. Hofmeyr and Eckardt (2005) describe how elephant damage was most
significantly explained by spatial data such as ‘distance from road’ and ‘distance from water
source’. Thus the data used was likely inadequate for attaining any biological significances.
5.7 Conclusions
The findings of this research suggest that African bush elephants are mega-herbivores
that cause significant amounts of damage to the landscapes in which they forage. During dry
season droughts especially, elephants will aim to satisfy their dietary requirements and
damage specific woody plants over others. These results are significant in that they further
confirm that findings of previous studies; elephants perform differing types of damage, of
varying amounts, to specific species of woody plants. It was also significant to discover that
quality of veld and capacity of a location to host an elephant was not important in
determining the distribution or rates of elephant damage.
A few drawbacks of this study include: a limited opportunity to collect data between
the months of July and August 2015, a limited ability to assess the true compositions of
woody plants through the line transect method used, and a lack of collected spatial variables
44. 44
(such as distances to roads, lodges, sources of water, etc.) to more accurately predict the
variation in elephant damage. Future studies of this topic may look to other methods of
collecting data, such as analysing satellite imagery of woody plant regimes, observing and
tracking elephant foraging behaviour, or adopting a different transecting technique such as
strip or belt transects.
45. 45
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49. 49
7.0 Appendices
7.1 Appendix 1.11
A
Appendix 1.1A: Map displaying the relative locations of the four sites within Olifants West,
Balule Nature Reserve. Site 1 is referred to as “ROAD,” Site 2 is referred to as “DAM,” Site
3 is referred to as “PLAINS,” and Site 4 is referred to as “WOODLAND.” The yellow pins
represent GPS coordinates that define the four transects at each site.
1
Note: An appendix with the prefix ‘1.1’ refers to the figures and tables that supplement the
methods section.
50. 50
7.2 Appendix 1.1B
Appendix 1.1B: Photographs illustrating the diversity in habitat type of each site. (A) Photo
of Site 1 (shrubby woodland with bisecting road). (B) Photo of Site 2 (semi-eroded dam). (C)
Photo of Site 3 (open plains savannah). (D) Photo of Site 4 (shrubby woodland).
A B
C D
51. 51
7.3 Appendix 1.1C
Appendix 1.1C: Diagram showing transect methodology. A straight line of 100m is drawn
out at each site. Five 20m by 20m sampling quadrats are established on alternating sides of
the transect line, with a diagonal line drawn from the inside of the transect quadrat outward
(or vis-versa). From the Pythagorean Theorem, this gives approximately 28 metres of
diagonal line (coloured blue in the diagram) to walk along and record data in each of the five
sampling quadrats per transect.
52. 52
7.4 Appendix 1.1D
Scientific Name(s) Common Name(s) Type 3-letter Code
Commiphora
pyracanthoides
Common (Firethorn)
Corkwood
Tree ccw
Senegalia nigrescens
Knobthorn Acacia Tree kbt
Terminalia prunioides
Lowveld (Purple-pod)
Cluster Leaf
Tree lcl
Lannea schweinfurthii
stuhlmannii
False Marula Tree fma
Combretum imberbe
Leadwood Tree lwd
Spirostachys africana
Tamboti Tree tam
Sclerocarya birrea caffera
Marula Tree mar
Albizia harveyi
False Thorn (Sickle-
leaved Albizia)
Tree fth
Acacia tortilis heteracantha
Umbrella Thorn Acacia Tree umt
Commiphora mollis
Velvet Corkwood Tree vcw
Boscia foetida rehmanniana
Bushveld (Stink)
Shepherd’s Tree
Shrubby Tree bst
Combretum apiculatum
apiculatum
Red Bushwillow Shrubby Tree rbw
Dichrostachys cinerea
africana
Small-leaved Sickle Bush Shrubby Tree skb
Grewia monticola
Silver Raisin Bush Shrub srb
Grewia bicolor
White Raisin Bush Shrub
wrb
Grewia villosa
Mallow Raisin Bush Shrub mrb
Grewia flava
Velvet Raisin Bush Shrub vrb
Euclea divinorum
Magic Guarri Shrub mgu
Rhoicissus tridentata
cuneifolia
Northern Bushman’s
Grape
Shrub bgr
Euclea undulata
Common Guarri Shrub cgu
Combretum microphyllum
Flame Creeper (Climbing
Bushwillow)
Shrub fcr
Schmidtia pappophoroides
Sand Quick Grass -
53. 53
Aristida congesta congesta
Tassel Three-Awn Grass -
Eragrostis pseudosclerantha
Footpath Love Grass -
Bothriochloa radicans
Stinking Grass -
Eragrostis chloromelas
Narrow Curly Leaf Grass -
Aristida congesta barbicollis
Spreading Three-Awn Grass -
Enneapogon cenchroides
Nine-Awned Grass -
Panicum maximum
Guinea Grass -
Chloris virgata
Feather-Top Chloris Grass -
Appendix 1.1D: Summary table showing the Latin name, common name, and 3-letter code
(where applicable) for all tree, shrubby tree, shrub, and grass species surveyed.
7.5 Appendix 1.1E
Appendix 1.1E: Diagram showing method of estimating woody plant height
and canopy diameter. 1 metre is established using the index and little finger
against a metre stick (backing away from the metre stick until it fits within the
two fingers). That distance is then used to estimate the height and canopy
diameters of a woody plant.
54. 54
7.6 Appendix 1.1F
Appendix1.1F: Photo of the throwing quadrat used for taking samples of grass. Dimensions
of the quadrat are 1 metre by 1 metre in length and width respectively.
55. 55
7.7 Appendix 1.1G
Appendix 1.1G: Summary table showing species of grass at sites 1-4 according to their
relative frequency of patches, mean percent density, ecological status (ES), grazing value
(GV), and type of succession.
Site Species Patch Freq. Mean Den. (%) ES GV Succession
1 Sand Quick 7 18.4 Increaser I Med Climax
1 Tassel Three-Awn 3 7.9 Increaser II Low Pioneer
1 Footpath Love 3 7.9 Increaser II Low Pioneer
1 Stinking 10 26.3 Increaser II Low Sub-Climax
1 Narrow Curly Leaf 2 5.3 Increaser I Med Sub-Climax
1 Spreading Three-Awn 4 10.5 Increaser II Low Pioneer
1 Nine-Awned 8 21.1 Increaser I Med Sub-Climax
1 Guinea 1 2.6 Decreaser High Climax
2 Footpath Love 29 51.8 Increaser II Low Pioneer
2 Sand Quick 10 17.9 Increaser I Med Climax
2 Narrow Curly Leaf 14 25 Increaser I Med Sub-Climax
2 Spreading Three-Awn 3 5.4 Increaser II Low Pioneer
3 Sand Quick 95 71.4 Increaser I Med Climax
3 Narrow Curly Leaf 31 23.3 Increaser I Med Sub-Climax
3 Spreading Three-Awn 7 5.3 Increaser II Low Pioneer
4 Nine-Awned 5 6.2 Increaser I Med Sub-Climax
4 Spreading Three-Awn 52 64.2 Increaser II Low Pioneer
4 Narrow Curly Leaf 10 12.3 Increaser I Med Sub-Climax
4 Sand Quick 11 13.6 Increaser I Med Climax
4 Feather-top Chloris 3 3.4 Increaser I Med Pioneer
56. 56
7.8 Appendix 1.22
A
8-Point Damage Rank-Value Scale (Anderson and Walker, 1974; Walker, 1976):
0: 0% Damage
1: 1-10% Damage
2: 11-25% Damage
3: 26-50% Damage
4: 51-75% Damage
5: 76-90% Damage
6: 91-99% Damage
7: 100% Damage
Adapted 6-Point Damage Rank-Value Scale:
0: 0% Damage
1: 1-25% Damage
2: 26-50% Damage
3: 51-75% Damage
4: 76-99% Damage
5: 100% Damage
7.9 Appendix 1.2B
Veld Condition (van Rooyen et al., 1996)
Ecological Status Index for calculation of veld condition
Decreaser Species = 10
Increaser Species = 7
Increaser II Species = 4
Invader Species = 1
Equation for calculation of veld condition
(
𝑃$
𝑁
100 𝑑) + (
𝑃+
𝑁
100 𝑖$) + (
𝑃-
𝑁
100 𝑖+) + (
𝑃.
𝑁
100 𝐼)
Where:
P1= Number of grass patches that are decreaser species.
P2= Number of grass patches that are increaser I species.
2
Note: An appendix with the prefix ‘1.2’ refers to the indices and equations that supplement the methods
section.
57. 57
P3= Number of grass patches that are increaser II species.
P4= Number of grass patches that are invader species.
N= Total number of grass patches.
d= Decreaser index value (10).
i1= Increaser I index value (7).
i2= Increaser II index value (4).
I= Invader index value (1).
Example:
Site 1 = 38 patches total.
Decreasers = 1/38 = 3% * 10 = 30
Increasers I = 17/38 = 45% * 7 = 315
Increasers II = 20/38 = 53% * 4 = 212
30 + 315 + 212 = 557 = Veld Condition Value.
Veld Condition Index ranges from 100 to 1000. Between 100 and 400 is poor, between 400 and 600 is
moderate, and between 600 and 1000 is good condition.
7.10 Appendix 1.2C
Grazing Capacity Calculation (van Oudtshoorn and van Wyk, 2012)
𝑦 =
𝑎(𝑐)
𝐷𝑀(𝑏)
Where:
y = Elephant grazing capacity (ha/EGU). The area (in hectares) required for one grazing elephant (Elephant
Grazing Unit) to sustain itself and be productive in a year.
a = Days in a year (365).
DM = The dry mass of grass (m, in kilograms) per unit of area (r, in hectares).
𝐷𝑀 =
𝑚
𝑟
b = Utilisation Potential (commonly used average of 35%) (Moore and Odendaal, 1987).
c = Elephant daily intake rate of grass dry mass (average 60kg/day for adult male) (Chapman and Reiss,
1992).
Example:
𝑦 =
365 𝑑𝑎𝑦𝑠(60𝑘𝑔 𝑑𝑎𝑦?$
)
290𝑘𝑔 ℎ𝑎?$ (0.35)
y = 215.76 ha EGU-1
for Site 1.
58. 58
7.11 Appendix 1.2D
Strauss’ Linear Electivity Preference Index (Strauss, 1979)
𝐿𝑖 = 𝑟E,G − 𝑝E
where:
𝐿𝑖 = Measure of Linear Electivity as an unweighted difference in proportions (ranging from -1 to +1).
𝑟E,G = The relative abundance (𝑞E,G) of woody plant species 𝑖 with elephant damage 𝑥 (as a proportion of
total woody plant species 𝑁 with elephant damage 𝑥).
𝑟E,G =
𝑞E,G
𝑁G
𝑝E = The relative abundance (𝑞E), of woody plant species 𝑖 (as a proportion of total woody plant species 𝑁).
𝑝E =
𝑞E
𝑁
7.12 Appendix 1.2E
Rate of New Elephant Damage:
𝑅 =
𝑧E,G − 𝑣E,G
𝐷
Where:
𝑅 = Rate of New Damage (rank value / day).
𝑧E,G = Rank value of elephant damage 𝑥 for species 𝑖 at final assessment of damage.
𝑣E,G = Rank value of elephant damage 𝑥 for species 𝑖 at initial assessment of damage.
𝐷 = Number of days between initial and final assessments of damage. *
∗ For my data, D is dependent on Site (1-4) of initial assessment of damage.
For Site 1: D = (24th
August – 27th
July) = 29 Days.
For Site 2: D = (24th
August – 3rd
August) = 22 Days.
For Site 3: D = (24th
August – 22rd
July) = 34 Days.
For Site 4: D = (24th
August – 26rd
July) = 30 Days.
59. 59
7.13 Appendix 23
A
Appendix 2A: Histogram plots showing the distribution frequency for each damage type.
3
Note: An appendix with the prefix ‘2’ refers to the figures and tables that supplement the results section.
Branch Break Damage
africanaBB$BB
Frequency
1 2 3 4 5
050100150200
Stem Break Damage
africanaSB$SB
Frequency
1 2 3 4 5
020406080100120
Push-Over Damage
africanaPO$PO
Frequency
1 2 3 4 5
010203040506070
Branch Strip Damage
africanaBrS$BrS
Frequency
1.0 1.5 2.0 2.5 3.0
0102030405060
Bark Strip Damage
africanaBS$BS
Frequency
1.0 2.0 3.0 4.0
051015202530
Ring Bark Damage
africanaRB$RB
Frequency
2.0 2.2 2.4 2.6 2.8 3.0
02468101214
60. 60
7.14 Appendix 2B
(Intercept)
CD
logSC
logSH
Site
Species
Type
CD×Species
Site×Species
224
256
220
252
CumulativeAkaikeweight(ω)
1.00.80.60.40.20.0
(Intercept)
CD
logSC
logSH
Site
Species
Type
logSC×Species
Site×Species
219
251
CumulativeAkaikeweight(ω)
1.00.80.60.40.20.0
(Intercept)
CD
logSC
logSH
Site
Species
Type
logSH×Species
Site×Species
1
2
8
9
5
CumulativeAkaikeweight(ω)
1.00.80.60.40.20.0
(Intercept)
logSC
Site
Species
TH
Type
logSC×Species
Site×Species
104
120
CumulativeAkaikeweight(ω)
1.00.80.60.40.20.0
(Intercept)
logSC
logSH
Site
Species
Type
logSH×Species
Site×Species
15
31
47
63
79
95
111
127
16
32
48
64
80
96
112
128
21
CumulativeAkaikeweight(ω)
1.00.80.60.40.20.0
Appendix 2B: Model selection table by Cumulative
Akaike weight (ω) for each of the damage type
response variables (top left = branch break damage, top
right = stem break damage, centre left = push-over
damage, centre right = branch strip damage, and
bottom left = bark strip damage, respectively). The
right axis represents models in ascending order of their
Akaike weight, where a weight of zero indicated the
best model. Predictor variables are listed on the upper
axis, where cell shading represents the relative
inclusion of the predictor variables in the models. A
white shading represents no inclusion, and as the shade
of colour becomes darker, the predictor variable
becomes more significant in the model.
